Java Development Kit 27, an Oracle-driven, planned update to standard Java, is set to reach its initial release candidate (RC) stage on August 6. This update has features ranging from making the Garbage-First (G1) garbage collector the default collector and adding structured concurrency to simplify concurrent programming. JDK 27 is currently in rampdown phase 2, with the feature set now frozen.

Overall, there are nine features listed on what is described as a reference implementation of JDK 27. The first five features include making G1 the default garbage collector, post-quantum hybrid key exchange for TLS 1.3, lazy constants, structured concurrency, and primitive types in patterns, instanceof, and switch. The next four include compact object headers, JFR (JDK Flight Recorder) in-process data redaction, a vector API, and PEM (Privacy-Enhanced Mail) encodings for cryptographic objects.

The final release candidate stage for JDK 27 follows on August 20, with general availability scheduled for September 15. Early-access builds for JDK 27 can be found at jdk.java.net.

With JDK 27, G1 becomes the default garbage collector in all environments, rather than just in server environments. Goals are to ensure that the HotSpot JVM will always select G1 and that performance including throughput, latency, memory footprint, and startup time does not degrade significantly.

With post-quantum hybrid key exchange for TLS 1.3, the goal is to enhance the security of Java applications that require secure network communication by implementing hybrid key exchange algorithms. Such algorithms defend against future quantum computing attacks by combining a quantum-resistant algorithm with a traditional algorithm.

Lazy constants introduces an API for objects that hold unmodifiable data. Lazy constants are treated as true constants by the JVM, enabling the same performance optimizations that are enabled by declaring a field final. Compared to final fields, however, lazy constants offer greater flexibility in the timing of their initialization. This feature is in a third preview.

The goal of primitive types in patterns, instanceof, and switch is to enhance pattern matching by allowing primitive types in all pattern contexts, and to extend instanceof and switch to work with all primitive types. This feature is in a fifth preview.

Structured concurrency simplifies concurrent programming by introducing an API that treats groups of related tasks running in different threads as single units of work. Goals include streamlining error handling and cancellation, improving reliability, and enhancing observability. Structured concurrency is in its seventh preview.

JDK 27 makes compact object headers the default object header layout in the HotSpot JVM. Compact object headers reduce object headers from 96 bits down to 64 bits on 64-bit architectures, reducing heap size, improving deployment density, and increasing data locality.

JFR data in-process redaction allows users to redact command-line arguments and the initial values of environment variables and system properties in JFR recordings. JFR recording files may contain events that include sensitive data, such as secrets in command-line arguments, access tokens in environment variables, and passwords in system properties. This data is (optionally) redacted before it leaves the process, so that sensitive information does not leak.

The vector API introduces an API to express vector computations that reliably compile at run time to optimal vector instructions on supported CPUs, thus achieving better performance than equivalent scalar computations. This is an incubating API in its 12th incubation.

PEM encodings of cryptographic objects introduces an API for encoding objects that represent cryptographic keys, certificates, and certificate revocation lists in the PEM transport format, and for decoding from PEM back into objects. This is a preview API.

JDK 27 is a short-term feature release (non-LTS) that will receive six months of support from Oracle. Its predecessor, JDK 26, also a short-term release, became generally available on March 17.

Historically, financial closes required were tedious, manual-intensive processes, which makes them excellent candidates for agentification. AI agents can handle much of the “dirty work” associated with integrating financial data from various sources, reconciling transactions and so on. That said, there are limits on how far AI agents can go in streamlining and accelerating the closing process. It’s unrealistic for businesses to remove humans from the picture entirely.

With this caveat in mind, here’s a look at practical approaches to driving more efficient financial closings with help from AI agents. To ground the conversation, I’ll focus on what the process might look like within environments based on SAP, although many of these lessons apply to any organization and tech stack.

How AI agents can accelerate financial closes

Although ERP systems like SAP house most or all of an organization’s financial data within a central system, closing out the books still tends to be a highly complex process, hampered by challenges like the following:

• Master Data reconciliation
• Working through huge volumes of journaling
• Identifying and resolving transaction reconciliation errors
• Ensuring compliance with governance and regulatory requirements

These are all areas where AI agents can help, even if SAP’s Advanced Financial Closing is used. For example, instead of requiring humans to assess each irregular transaction manually, businesses can employ agents to review the situation and suggest a resolution. Agents also excel at tasks like integrating multiple data sources, then identifying and addressing redundancies or inconsistencies across them.

Similarly, agents can continuously monitor financial workflows throughout the close cycle, flagging anomalies and potential bottlenecks before they delay reporting deadlines. They can automatically collect supporting documentation, validate data against predefined business rules and route exceptions to the appropriate stakeholders for review.

By reducing the amount of repetitive manual work required during closing, AI agents help finance teams focus on higher-value analysis and decision-making. This can lead to faster close times, improved accuracy and greater confidence in the integrity of financial reporting.

The limitations of agents for closing the books

That said, agents can’t handle every aspect of the closing process entirely on their own. Two key limitations apply. The first is that, as with any LLM-powered technology, agents are at risk of making inaccurate decisions or inferences. Businesses can’t blindly trust agents to interpret financial data accurately all of the time. A second factor is that, due to strict regulatory requirements, it’s essential in most cases for humans to sign off on financial accounts. Telling regulators or auditors that you know your books are accurate because an AI agent told you so is not a recipe for compliance success.

Because of these limitations, a healthy perspective on AI agents in financial closing contexts is to think of them as a way to improve visibility, agility and efficiency, not as a replacement for people. Agents can make recommendations, but humans need to be the ones who review, validate and sign off on any actions before they are final.

Integrating AI agents into the closing process in SAP

How can organizations actually take advantage of AI agents to help with closing?

The answer is complicated because every business’s books and closing process are different. This means that, despite the growing inventory of AI agents now available on platforms like SAP, it’s unrealistic to expect to “drag and drop” agents into existing closing workflows and have them do what they need.

Instead, many businesses will find that they need to build custom agentic solutions. Often, they’ll benefit from implementing multiple agents targeted at different tasks, e.g., accounts receivable, accounts payable and foreign currency exchanges, along with an orchestrator agent that oversees them all. Each agent will need to be tailored for the organization’s data sources, governance and compliance obligations, etc.

In addition, organizations must carefully define how agents interact with financial systems and employees. While some activities can be automated end-to-end, others require human review and approval to satisfy internal controls and regulatory requirements. Establishing clear workflows, escalation paths and audit trails is essential to ensure that agent-driven processes remain transparent and trustworthy. Organizations also need to invest in testing and validation to confirm that agents produce accurate results and can handle exceptions without introducing new risks into the close process.

The fact that SAP itself is a complex platform, with native agentic capabilities fully supported only in the latest versions, further complicates the agentification of the closing process. Enterprises need to assess the agentic support level available within the SAP version they use, then determine the extent to which they can leverage SAP’s own agents versus working with third-party agents.

Another key consideration is data quality. AI agents can only perform effectively when they have access to complete, accurate and timely financial information. Organizations may need to improve data governance practices and address integration challenges before agents can deliver meaningful value. The extent to which they can do this easily depends, in large part, on how healthy their underlying SAP data governance practices are.

All of the above means that taking advantage of agents to accelerate closes and other financial workflows within SAP is no mean feat. It requires deep technical expertise in both agentic technology and the complex SAP software portfolio. But the investment is worth it for organizations seeking to reduce the uncertainty and slowness traditionally associated with closing the books. Over time, well-designed agentic workflows can help finance teams spend less time on manual reconciliation and exception handling while enabling faster, more predictable financial close cycles.

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You’d think keeping tabs on your contacts would be about the simplest and most straightforward task imaginable in our modern connected world — wouldn’t you?

I sure would. But as I’ve learned over the years, that perfectly understandable instinct couldn’t be more inaccurate.

Effectively wrangling your contacts on Android and keeping ’em manageable, organized, and optimized for efficiency really is a fine art. And in a way, it’s no wonder: Most of us have reached a point where our phones’ contacts are a sprawling goulash of earthlings from all different eras of our lives — clients, colleagues, college buddies, and, of course, your cousin Carl from Poughkeepsie.

Making matters even more complex is the fact that what constitutes “Android” is a wildly different experience from one device to the next. And most Android phone-makers don’t exactly make it easy for you to make the most of your messy contacts stew.

The good news, though, is that it doesn’t have to be so difficult. Today, we’ll start from square one and get your contacts in tip-top shape, no matter what type of Android phone you’re using or how many unruly old bosses’ email addresses you’ve got stored away.

By the time we’re done, your Android phone contacts will be as orderly as can be — and you’ll be equipped with all sorts of practical knowledge for harnessing their typically untapped potential.

Part I: Android contacts streamlining

First and foremost, we need to make sure we’re all on the same page — ’cause as we just mentioned a moment ago, the Android contacts situation is anything but standardized across the platform.

Specifically, if you’re using a Samsung phone, we need to get you off of Samsung’s subpar and proprietary contacts service and into Google’s better, smarter, and more platform-agnostic alternative.

Samsung’s main goal with its products, y’see, is to keep you within its own universe. The company wants you to continue using Samsung stuff and buying Samsung stuff, and it makes that more of a priority than giving you an optimal experience.

The company’s Contacts app is the perfect example: The app offers no noteworthy advantages over Google’s standard Android Contacts service, and it’s available only on Samsung-made Android devices. It’s less fully featured and pleasant to use than Google’s version, too, and it makes it much more difficult to access your contact info from a computer or any other type of device.

So why does Samsung insist on making that the default contacts service on its phones instead of sticking with Google’s readily available offering? Simple: because it locks you into Samsung’s self-serving ecosystem.

Let’s break you free, shall we?

• Open up the Contacts app on your phone (the one probably represented by a glaringly bright red icon).
• Tap the three-dot menu icon in its upper-right corner, then tap “Settings” followed by “Sync contact accounts.”
• Make sure your main Google account is present and has its toggle active on the screen that comes up next. If you don’t see it, tap the “Add account” option to add it into the mix.

JR Raphael / Foundry

Got it? Good. Now, go download the Google Contacts app from the Play Store. Open it up and approve the permissions it needs to operate. Then make a point to start using it instead of Samsung’s silliness (which, by the by, Samsung won’t let you uninstall or even disable) from here on out.

If you’re using an older Samsung device and the steps described above don’t quite match what you’re seeing, poke around in the Contacts app until you find a similar set of options. They should be there somewhere; the specifics of the interface have just evolved somewhat over the years, so older versions of the app may not be exactly the same.

If you have a non-Google-made phone from someone other than Samsung, meanwhile, check to see if your contacts app is the actual Google Contacts app or not. If it isn’t — and if your device-maker gave you some other random alternative in its place — poke around in that app and try to find a similar set of options for syncing everything over to your Google account. If that isn’t possible, find the option to export your contacts from that app and then look for the import option within the Google Contacts Android app to get to the same spot.

Part II: Android contacts accounts and labels

Now that we’re all looking at the same place and dealing with the same best-available Android contacts management option, let’s take a few minutes to get the lay of the land, shall we?

When you first open the Google Contacts app on Android, you’ll see a merged view of all contacts from every Google account you have connected to the phone. But take note: If you tap the “All contacts” line toward the top of the screen, you can switch to seeing contacts associated with only one individual Google account at a time — assuming you have multiple Google accounts connected — instead of seeing them combined together all at once.

That could be useful if, say, you have both a work account and a personal account connected to your device — or maybe you’re a freelancer and you have multiple work-related accounts connected for different purposes.

JR Raphael / Foundry

If you tap the triangular three-line icon to the right of the “All contacts” dropdown, meanwhile, you’ll find a few filtering options that could be helpful as alternatives to the large search bar at the top of the screen — if, for instance, you need to find a contact and only know the name of their company but also can’t quite think of that company’s name and need a prompt. Tap that icon, select “Company,” and you’ll see a list of every company name in your contacts that you can scroll through and select to apply as a filter.

Finally, if you tap the outlined arrow-like shape to the left of the filter icon, you’ll see a list of any labels you’ve created for your contacts. Labels in Google Contacts work exactly like labels in Gmail: You can create as many as you like, and you can apply any number of labels onto any given contact. They’re less like folders, in other words, and more like stickers — or, y’know, labels — in that there’s no limit to how many any particular contact can have.

So why would you want to bother with labels, you might be wondering? Well, I’ll tell ya: They’re a splendid way to break that mess of mammals in your life down into specific, meaningful groups instead of always viewing ’em in one gigantic lump.

Maybe, for instance, you’d have a label called “Work” that includes everyone from your current company. And maybe you’d have a separate label called “Team” that’s even more narrow and shows only the people you directly work with. Maybe you’d have another label for clients, another for specific subsets of clients, and another for all the people in your life named Josh.

Once you do that initial organization, you’ll have an easy way to limit your view to only the individuals you need at any given moment — and you’ll gain a couple of other easily overlooked advantages, too, as we’ll explore further in a moment.

First, to apply a label onto a contact once you’ve created it:

• Tap the contact to open it.
• Tap the pencil-shaped editing icon in its upper-right corner.
• Scroll down and look for the “Labels” option.
• Tap it, then select whichever label or labels you want to add onto that contact and tap “OK” to save.

If you want to apply a label onto multiple contacts at the same time:

• Tap the label icon — that arrow-like shape we were just talking about a moment ago, on your main contacts list — then select the label you want to use.
• Tap the icon that looks like an outline of a person with a plus sign next to it, in the upper-right corner of the screen, and then select whichever contacts you want to add into the label by tapping them all once.
• When you’re finished selecting, tap the “Done” option in the upper-right corner of the screen, and all of the contacts you selected will be added in one fell swoop.

JR Raphael / Foundry

Capisce? Capisce. Now, let’s move on to some even more advanced Android contacts goodness.

Part III: Advanced Android contacts enhancements

When you first tap a person’s name within the Google Contacts app on Android, you’ll see a screen with their profile appear.

JR Raphael / Foundry

A smattering of interesting features worth noting here:

• As of a relatively recent addition, the Google Contacts app allows you create a custom calling card that adds a background image into the top of that person’s profile and controls exactly what you see on your screen anytime they call you. If you aren’t seeing a background image in this area already, as illustrated above, look for the option to add a calling card — which should appear in that same general space.
• You can also create your own custom calling card that controls how you show up by default on other people’s devices — provided they’re also using the Google Contacts app on Android, of course — if you’re ever so inspired.
• And if you’ve had any interactions with a contact, you’ll be able to see a quick overview of that activity in the “Recent activity” area beneath that — along with any notes you’ve created for the person within their contact profile.

JR Raphael / Foundry

To edit a profile, as you’d probably guess, you’ll just tap the pencil-shaped editing icon in the upper-right corner of the screen.

And one more advanced Android contacts option worth mentioning: Directly next to that pencil icon, you’ll see a hollow star in the upper-right corner of every contact’s profile. You can tap that to fill the star in and mark that person as a favorite.

Doing so will have some significant effects:

• That person will always appear at the top of your contacts list.
• They’ll also typically show up in a special, more prominent area of your Phone app for extra-easy access (and if they don’t, try downloading the Google-made Phone app and using it in place of whatever alternative your phone’s maker preinstalled in its place).
• And they’ll be granted special privileges to reach you even when your phone is in Do Not Disturb mode, with the specifics depending on your preferences in that area of your system settings.

Part IV: Android contacts optimization

One of the best features of the Google Contacts service is how easy it makes it to clean up and optimize your contacts collection.

From the Contacts app on your phone, tap the “Organize” tab at the bottom of the screen — then:

• Tap the “Merge & Fix” option.
• Look to see what suggestions the app gives you, then tap ’em one by one and follow the steps within.

JR Raphael / Foundry

Google Contacts will identify any instances where it looks like you’ve got two separate contact entries for the same person and then offer to quickly combine them for you. It’ll also let you know when it’s found more up-to-date contact info for anyone in your list. And it’ll offer to add in entries for anyone you email often but haven’t yet added.

Easy peasy, right?

And last but not least, for the virtual icing on your Android contacts cake…

Part V: Android contacts actions

Once you’ve gotten your contacts created, organized, and cleaned up properly, the Google Contacts app on Android has several advanced actions that are all too easy to miss.

• You can use the Contacts app as an efficient way to start a new group email or text message thread with any selection of people you want. Just make sure the people are all in the same label, then tap the label icon in the app’s upper-right corner and select the label. Next, tap the three-dot menu icon in the upper-right corner of the label screen and look for the “Send email” or “Send message” option.
• The Contacts app can also serve as an all-in-one hub for initiating communication with anyone in your collection. Open someone’s profile, and you’ll see one-tap icons for calling them, texting them, emailing them, or starting a Google Meet video call with them — all without ever having to poke around in any other apps.
• If you want even easier access to certain high-profile people, check out the Google Contacts widget options: Long-press on any open area of your home screen, select the option to add a widget, and then look for the Contacts section. There, you should see options for adding square-shaped widgets that show a person’s photo along with one-tap links for calling or texting them as well as simpler icon-like shortcuts for calling or texting a specific contact. In the latter case, you can add as many of those as you want onto your home screen and even drag ’em on top of each other once they’re there to create convenient folders.

JR Raphael / Foundry

• Speaking of calling convenience, if there’s a certain contact who calls you a little too often — an overly eager recruiter or maybe that blasted cousin of yours (come on, Carl!) — the Google Contacts app has an easy way to automatically route all of their calls directly to your voicemail. Just open the person’s profile within the app, then scroll down and look for the “Send to voicemail” option — or look a little lower for “Block numbers,” if you really never want to hear from them again.
• In that same area of a contact profile is a speedy shortcut for setting a custom ringtone for any contact so it’s especially easy to identify them (or hide in the nearest underground bunker) whenever they call.
• And don’t overlook the recently added “Reminders” section, where you can store dates like birthdays and anniversaries and create reminders around ’em, in addition to having ’em appear within the app itself.

Last but not least, the real beauty of the Google Contacts setup on Android: It works equally well no matter what type of device you’re using.

On any phone you move into in the future, you can simply install the Google Contacts app, if it isn’t already in place, and all your stuff will instantly be there, synced, and available to you — no restoring required. And if you ever want to poke around or update your contacts from a computer, all you’ve gotta do is pull up the Google Contacts website in any browser where you’re signed in.

So the Android contacts situation isn’t exactly straightforward, as you’ve seen. But once you get it under control, it absolutely can be easy and effective — and, with a teensy bit of advance planning, an important piece of your mobile productivity puzzle.

This article was originally published in November 2022 and updated in July 2026.

Many organizations have seen real gains in productivity and automation from experimenting with AI. But only 34% are using AI to deeply transform their businesses, according to Deloitte’s 2026 State of Generative AI in the Enterprise report. Meanwhile, 37% are using the technology at a surface level with little or no change to underlying business processes.

That may explain why so many organizations are still waiting for the transformative ROIs they expected.

We’ve seen this before. During the process reengineering movement of the late 1980s and early 1990s, and again during the dot-com era, organizations invested heavily in new technologies and new ways of working. Many failed, not because the technology was flawed, but because they were unwilling to rethink how the business itself operated.

A textile manufacturer learned that lesson the hard way more than 30 years ago. The company implemented software designed to support a fundamentally different way of doing business but insisted on preserving decades-old workflows and management practices. The technology was expected to conform to the business, rather than the business adapting to the technology. The implementation failed.

Many companies are at risk of making the same mistake with AI because they rely on a bottom-up approach, where employees find ways to use the technology to solve the problem of the day: writing emails, summarizing meetings and accelerating familiar workflows.

Top-down transformation starts with a harder question: if AI had existed when we built this company, would we have designed the business this way? The organizations seeing transformative returns are the ones rethinking how their business operates from the ground up, not just streamlining existing workflows.

Four reasons AI transformation stalls

Organizations often assume that providing access to AI tools will naturally lead to transformation. The reality is that people, incentives and mindset are what determine success.

1. Organizations reward the wrong behaviors

One of the fastest ways to derail transformation is to reward people for preserving the status quo.

The textile manufacturer encountered this problem when it redesigned its manufacturing ordering system. Leadership wanted greater visibility across the production process and a more responsive, just-in-time operating model. But shift managers were still compensated based on how many pounds moved through their individual work centers each day. Their incentives rewarded maximizing output within their own area instead of supporting the broader changes leadership wanted to implement.

The lesson applies directly to AI transformation. Organizations often talk about reinventing workflows while continuing to evaluate employees using metrics designed for a pre-AI world.

People optimize for how they’re measured. If compensation, accountability and recognition remain tied to legacy processes, employees will naturally protect those processes. Transformation requires aligning incentives with the future state of the business.

2. Communication breaks down in the middle

Executives may have a clear vision for transformation, but that vision often weakens as it moves through the organization.

At the textile manufacturer, senior leadership understood the goal of becoming a just-in-time manufacturer. The technology team understood it because they were involved in the implementation. Middle management, however, never fully embraced the vision.

The result was that executives talked about doing things differently while managers continued reinforcing existing behaviors and employees received conflicting signals about what success looked like.

Many AI initiatives today face the same challenge. Leaders announce ambitious transformation goals, but managers continue operating under assumptions built around the previous way of working.

AI transformation requires both top-down direction and bottom-up execution. The middle layers of the organization serve as the connective tissue between the two. Without that connection, transformation efforts quickly become technology projects rather than business initiatives.

3. Training focuses on tools instead of transformation

Many organizations approach AI training primarily as a technology exercise. Employees gain access to a new tool, and training focuses on how to write prompts, use copilots or navigate the new application. Those skills are important, but they are only part of the equation.

At the textile manufacturer, technology teams needed a deeper understanding of how the production floor actually operated. At the same time, business leaders needed a better understanding of what the technology could enable. Neither side could successfully redesign the process on its own.

A similar dynamic exists with AI. Technology teams need business context, and business teams need technology context. Organizations that can bring those perspectives together through cross-functional teams focused on solving business problems rather than technology implementation are the ones making the most progress.

4. People need permission to work differently

One of the least discussed barriers to AI adoption is psychological. Many people still associate their value with effort; they take pride in the time, expertise and work required to complete a task. When AI reduces that effort, some employees become uncomfortable acknowledging its role.

For some, admitting AI helped feels like diminishing their contribution, which is why leadership visibility matters. Employees need to see leaders openly using AI, sharing examples and discussing how it is helping them work differently. They need to hear that the goal is not simply working faster but applying judgment, creativity and expertise in higher-value ways.

AI transformation is ultimately a mindset shift. People need permission to redefine what productive work looks like.

Transformation requires more than upskilling

Much of the conversation around AI focuses on upskilling. While new skills are important, they are not the primary obstacle to transformation. The bigger challenge is creating a workforce that wants to participate in it.

Some employees will embrace experimentation, seek new opportunities and help shape the future of the business. Others will continue looking for ways to preserve the processes that made them successful in the past. Leaders need to recognize the difference and create opportunities for the right people to rise to the occasion. Employees with a fixed mindset will resist change regardless of the tools available. 

The organizations that succeed will communicate not just what they’re trying to accomplish, but why. Many employees assume AI initiatives are purely about efficiency. The message from leadership needs to be different: we are rebuilding how this business operates, and you are part of that.

Increasingly, everyone has access to the same AI tools. Two organizations can deploy the same technology and achieve dramatically different outcomes depending on how they align incentives, communicate expectations and rethink long-standing business processes.

Companies that treat AI as a way to make existing work more efficient will continue to see incremental gains, while those willing to question whether that work should be done the same way at all will discover entirely new ways to operate.

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Organizations are facing multiple global challenges, including greater geopolitical volatility than seen in decades, unprecedented shifts in cross-border trade frameworks, and stricter climate change-driven regulation. In turn, these issues have made it difficult to accelerate compliance efforts, optimize supply chain management, and update pricing structures, tax activities, and payroll functions.

To remain competitive, nearly all (98%) organizations are piloting, implementing, or upgrading AI technologies, according to Foundry’s 2026 AI Priorities Study. 

Yet, the study also shows that 97% of IT decision-makers are struggling to deploy AI. That’s where technology partners can help. As trusted advisors with deep expertise into business and technology challenges, they are perfectly placed to guide services firms as they navigate these global AI shifts.

Technology consulting firms work across every sector and geography, so they see firsthand how the right AI deployments can help enterprises get ahead in today’s IT and business environments. For example, AI can automate routine tasks and pivot to autonomous workflows, rapidly tap into structured and unstructured data, and incorporate self-learning functions to help organizations adapt. Tech advisors are helping their customers harness these capabilities to:

• Rapidly respond to regulatory changes worldwide
• Hone processes on the fly
• Bring real-time predictive insights to decision-makers so they can focus on strategy and innovation

“In a challenging macro environment that demands efficiencies and agility to the highest extent, AI helps organizations move towards truly autonomous enterprises with humans in the loop wherever necessary,” says Ramakrishnan Ananthanarayanan, vice president, Professional Services Segment at LTM.

Transforming from within

Leading professional services firms are leveraging Microsoft’s AI capabilities to achieve valuable business outcomes, such as boosting workforce productivity, accelerating client project delivery, uncovering richer data insights, and strengthening risk management capabilities.

By integrating advanced cloud analytics and generative AI assistants like Microsoft 365 Copilot into everyday workflows, services firms are automating routine tasks and streamlining analysis — freeing their consultants to focus on higher-value advisory work. In turn, these firms can execute projects faster and deliver more informed, data-driven decisions for their clients at speed and scale.

In addition, AI-driven data platforms are helping firms identify potential risks earlier and extract deeper insights from complex data to improve the quality and confidence in business decisions. At the same time, Microsoft’s scalable AI solutions empower professional services firms to innovate at scale and create new service offerings, driving growth and enhancing competitive advantage.

The results are visible:

• Up to 20% productivity gain, according to PwC
• Revenue gains of up to 4%, reports EY

Boutique firms have also identified business opportunities. In fact, these challengers are quickly disrupting decades-old models, which is pushing incumbent professional services firms to come up with new ways to leverage AI-driven innovation and deliver greater value to their clients.

The competitive stakes are high, requiring services firms to have an advanced, integrated technology stack that can establish an enterprise-wide backbone capable of supporting AI from end to end.

Activate the AI backbone

To translate this foundation into sustained impact, professional services firms should lean into IT partners that bring vast implementation experience and a deep knowledge of innovative products.

LTM, a global technology services company, is a good example. Thanks to their collaborative partnership with Microsoft, they are well-equipped to utilize the full Microsoft AI stack — from optimal harnessing of foundational data to continuous IT improvements. LTM has expertise across Microsoft Fabric, Copilot, Azure AI, Power Platform, and Dynamics 365.  This experience helps LTM work closely with professional services firms to:

• Rapidly identify use cases that can reimagine entire workflows with AI at their core as opposed to simply implementing AI-assisted tasks. Starting with the right use cases helps clients more easily adopt autonomous workflows and improve business operations faster.
• Design and implement AI systems on behalf of their clients, many of whom lack the technical expertise to build and deploy these solutions.
• Provide training and user education to improve AI adoption.
• Validate and govern the ethical use of AI at all times.

“In addition, LTM and Microsoft work together to model and reimagine future scenarios for firms, as well as their clients, to help them move towards truly autonomous workflows with AI at the center,” Ananthanarayanan says.

Discover how LTM and Microsoft can help your firm build an AI-led delivery model. Ready to dive deeper? Download this white paper to explore AI frameworks, use cases, and an implementation roadmap.

Every organization has data scattered across data warehouses, data lakes, SaaS platforms, cloud drives, and data centers. Data fabrics enable organizations to centralize and control data access, making it easier for users, such as data scientists and citizen data analysts, to find and use trusted and governed data sources. 

Data fabrics, data meshes, and distributed data clouds are all platforms to help IT and data teams put some order to the chaos around the myriad of data sources they support. Large companies need data fabrics due to the volume and variety of their data sources.

“A data fabric can be thought of as the connective tissue that ensures consistent accessibility, availability, and understanding of data across an organization,” says Dominic Wellington, data and AI expert at SnapLogic. “Individual siloed platforms may have their own internal data transfer systems, and particular teams or departments may adopt interchanges that work for that domain, but a data fabric operates at a higher level, ensuring that unified data policies are applied end-to-end across the entire enterprise.”

Types of data fabrics

When reviewing data fabrics, it’s important to consider their primary use cases, supported data types, data processing capabilities, data management structures, and governance functions. Below are some considerations when reviewing data fabrics as features, platforms, and stand-alone products.

• Some data fabrics are optimized for analytics and machine learning use cases and may have limited support for unstructured data.
• Other data fabrics extend the functionality of data governance platforms beyond data cataloging and metadata management and now include persistent data management, data quality, and dataops capabilities.
• Many data integration and API connectivity platforms go beyond proxying, pipelining, and transforming data to include search, governance, and other capabilities from data centralization.
• Some SaaS platforms are extending their connectivity and data integration capabilities, enabling multicloud portability and persistent data.
• The more advanced data fabrics support features needed for AI agents and AI model training. These platforms create a semantic context layer for structured and unstructured data sources, support Model Context Protocol (MCP) integrations, have real-time query capabilities, centralize policy-driven governance, and track data lineage.    

Why data fabrics are needed for AI

Data fabrics are not just for enterprises, and today, even smaller companies need them as part of their AI democratization programs. Here are a few reasons why:

• AI agents in enterprise SaaS solutions need access to broader data sets than those core to their workflows. Platforms such as Adobe, Appian, Oracle, Salesforce, ServiceNow, SAP, and Workday offer data fabric capabilities to bring data outside of the business processes they manage into scope for their AI agents.
• Unstructured data is important for setting the context for AI agents, and data fabrics are now used to provide access to documents, emails, transcripts, and other media formats.
• Data fabrics provide data access standards for the devops teams experimenting with AI code generators, vibe coding tools, and spec-driven development approaches to develop applications and AI agents. 
• As companies use MCP servers to connect AI agents, data fabrics provide a standardized way for the agents to access governed, trusted data sources.

“As AI agents move from generating insights to taking action, the data fabric becomes foundational in the agentic era,” says Irfan Kahn, president and chief product officer of SAP Data & Analytics. “Most enterprises operate across scattered data sources and diverse data landscapes, and what’s needed is shared business context, governed access, and clear accountability for how data is used in decision-making. Without that context, agents can’t fully understand or coordinate across the enterprise to deliver meaningful value.”

Sanjay Koppikar, chief product officer and cofounder of EvoluteIQ, adds, “Multi-agent architectures become untrustworthy when a unifying data fabric architecture is missing, since agents will often work against each other in the service of their own objectives.”

Delivering context to AI agents

AI agents need a combination of real-time data, user information, problem details, and historical context to guide their decision-making. Vishal Sood, president of research and development at Typeface, says, “MCP and data fabrics give agents access, but the harder problem is contextualizing data across multiple sources and ensuring the underlying content, media, and unstructured data are trustworthy.”

Data fabrics are the foundational elements for providing current information and long-term memory to AI agents. They simplify the many-to-many problem of connecting multiple AI models, AI agents, and MCP server integrations to multiple structured and unstructured data sources.

“The data fabric does a beautiful job of encompassing three concepts needed to create applications and processes: the data catalog, the data model, and data access,” says Sanat Joshi, executive vice president of product and innovations at Appian. “But now add business rules, process models, APIs, security groups, the organizational model, and their interrelationships into one unified view of the enterprise, and that becomes your context layer.” 

Integrations with data fabrics

Devops teams just getting started on an AI agent proof of concept may want to connect directly to the optimal data sources and APIs. Michel Tricot, CEO and cofounder at Airbyte, says connecting agents to live APIs is a great start, but it creates two big problems: APIs only return data that an agent already knows to ask for, and every query is an expensive API call chain that, with overhead, can overwhelm infrastructure in production volumes.

Tricot says the data fabric for AI use cases must be dynamic, leveraging discovery of available information from replicated data, fetching live contextual information, and writing the data back to business applications to update records.

Moving data in and out of the data fabric requires an integration strategy. Zero-ETL (extract, transform, load) is one low-cost, efficient approach for connecting to structured data sourced without replicating information. Once information is accessed centrally, it also enables streamlined security and governance.

“The promise of AI agents breaks down when they’re stuck waiting on brittle ETL, dealing with poor data quality, and lacking the right context to perform analysis,” says Preston Wood, chief security and strategy officer at Databahn. “Generating AI-ready data within a data fabric gives agents real-time access to operational data without the latency and drift that undermine decision quality. A well-architected data fabric provides the governance and lineage controls that let you deploy agents confidently, knowing exactly what data they’re touching and why.”

Centralizing AI-ready data

Data fabrics centralize AI-ready data and help data governance teams address data quality issues, biased data concerns, privacy compliance, and other data governance non-negotiables. Data fabrics also help address integration issues, monitor for data pipeline errors, and report on performance latencies. The result is that AI agents, models, and other analytics capabilities can then connect to trusted data sources with consistency.

“As AI agents and MCP architectures increasingly rely on data fabrics as their golden source of truth, data quality stops being a hygiene problem and becomes a trust problem, as we all know that trust is foundational to autonomous decision-making,” says Kellyn Gorman, database and AI advocate and engineer at Redgate Software. “Organizations that invest now in semantic consistency, lineage tracking, and observable data contracts across data fabrics will be the ones whose AI agents can be trusted to act without constant human correction.”

Data fabrics that support zero-ETL and other bidirectional integrations with sources thus become an organizational knowledge base, the data source for training AI models, and a foundation for producing data metrics.

“AI agents are only as reliable as the data they’re built on, and most organizations underestimate how much implicit tribal knowledge lives in their transformation logic rather than their source systems,” says Tobias Ostwald, director of analytics at NMI. “If you’re exposing a data fabric to agents or MCP integrations, you need lineage, testing, and metric definitions baked into the layer itself, not just documented somewhere, because the agent can’t call a colleague to gut-check a number.”

Streamlining security and governance

With a data fabric in place, governance, security, and other risk management leaders have a central location to manage data security, centralize access controls, and fulfill other governance responsibilities. Miles Ward, CTO of AI in Solution Lines at Insight, says, “We have to move past security by isolation to a governance model where the fabric itself enforces the pavement and walls of compliance.”

The data fabric also governs entitlements for AI agents and their users. Centralizing these business rules can help organizations avoid creating AI debt, a risk if controls are implemented directly in data sources or consumers.

“The convergence of AI-generated code sprawl and autonomous MCP connectivity creates a ‘perfect storm’ of architectural drift and toxic permission combinations,” says Karen Cohen, vice president of product at Apiiro. “Effective governance requires a security data fabric that monitors these autonomous connections in real time to enforce intent-based policies and strictly limit agent scope to its specific purpose. By integrating guardrails that align AI-assisted development with secure architecture principles, enterprises can proactively secure their expanding attack surface without sacrificing developer velocity.”

Future considerations for data fabrics

Expect vendors to expand the scope of their data fabrics beyond text and documents. Some will include specialized document processing for common formats such as invoices, contracts, and product documentation. There will be skills and tools to support industry-specific documents such as health records and construction documents. Others will support multimedia file types and provide metadata extraction and search capabilities. 

“Enterprises are asking agents to reason across contracts, images, PDFs, and video, and this is where most data fabrics break,” says Dave Shuman, chief data officer at Precisely. “Multimodal data must be chunked, embedded, and governed with the same rigor as structured data, including lineage and access controls.”

Several other emerging capabilities include:

• Extended support for AI agent interfaces to aid in data discovery, and with greater contextual controls on where and when AI agents can access sensitive data
• Business ontologies, semantic layers, and knowledge graph capabilities, with management tools or integrations with third-party platforms
• Support for data contracts, service-level agreements, centralized data observability, auditing, and other functions that will enhance explainable AI capabilities
• Finops functions to track costs for data owners and consumers

As more companies depend on AI agents in their operations, expect top data fabric platforms to release capabilities to expand scope, scale, use cases, and governance.  

The need for a central data repository for enterprise analytics and gen AI has made the data lakehouse the default choice for enterprise data. Meanwhile, the emergence of open table standards makes the shift easier and reduces vendor lock-in for enterprises while also allowing for better integration between lakehouses and other enterprise systems and service providers.

Data lakehouses combine the structure of data warehouses with the flexibility of data lakes, making them versatile tools to make the most of any data the enterprise collects, whether it’s for business analytics, integration with other systems, or providing relevant context to LLMs.

The idea behind the data lakehouse is to merge together the best of what data lakes and data warehouses have to offer, says Gartner analyst Adam Ronthal.

Data warehouses also enable companies to store large amounts of structured data with well-defined schemas, as they’re designed to support a large number of simultaneous queries and deliver results quickly to many simultaneous users.

Data lakes, on the other hand, enable companies to collect raw, unstructured data in many formats for data analysts to hunt through. These vast pools of data have recently grown in prominence thanks to the flexibility they provide enterprises to store massive data streams without first having to define the purpose of doing so. 

According to Gartner, data lakehouses are the next step in the evolution of data architectures, merging these two capabilities into a single platform to overcome limitations of previous architectures, reducing complexity, streamlining data management, and supporting diverse workloads.

In late 2025, Gartner also released the first market guide for data lakehouse platforms. “The lakehouse is now firmly established as the architecture that most organizations will seek to standardize on,” wrote Ronthal and his co-authors in the report.

Meanwhile, data lakehouses themselves are also standardizing on the Apache Iceberg data table format, first created by Netflix in 2017, and donated to the Apache Software Foundation the following year. It hit the tipping point in 2024 with adoption by companies like Apple, LinkedIn, Adobe, and all the major cloud vendors. Even Databricks, which created the competing Delta Lake standard, now supports Iceberg natively.

Lakehouse vendors are opening their architecture more to allow better access to the data by third parties, says Gerry Szatvanyi, chief AI officer at consulting firm OSF Digital. “That wasn’t the case a few years ago,” he says.

And other enterprise service providers have been taking advantage of this, he says. For example, Salesforce Data Cloud can connect directly to Iceberg-formatted data.

“Salesforce has a Zero Copy access format, so it can connect its own data platform to another data lake without copying data into Salesforce,” says Szatvanyi.

And of course, as with everything else in the enterprise today, gen AI is having a big effect. Data lakehouses are particularly good for LLMs because they can provide critical business context for RAG embeddings and MCP access, the two most common ways to feed data into LLMs.

“Lakehouses are being accessed more by AI agents,” Szatvanyi says. “It’s the main thing I see happening.”

Even traditional business analytics is now increasingly handled via AI interfaces, he adds, democratizing user access to enterprise data.

In a recent IDC report, the leading vendors in the data platform space are Databricks, Google, Oracle, and Snowflake, with other major players including Microsoft, IBM, and Cloudera. IDC also listed Amazon SageMaker as a lakehouse platform to watch, but it only became widely available in early 2025, so wasn’t yet included among the top vendors.

Gartner includes Databricks, Google, Oracle, Snowflake, Microsoft, IBM, Cloudera and Amazon SageMaker on its list of representative vendors for data lakehouse platforms, among other firms.

The business benefits of data lakehouses

Docusign opted to go with Snowflake for the data platform used to train an internal agent for sales, and is training its ML models in order to serve customers more accurately. Information is pulled from Salesforce, and they’re also exploring Atlassian and ServiceNow, as well as other internal custom tools.

The information also goes out to LLMs using RAG embedding pipelines, and MCP connectivity is also being explored as the technology matures.

Other companies use data lakehouses for the flexibility of the data sources it supports and the volume of data they can handle.

Sega Europe, for example, began using the Amazon Redshift data warehouse to collect event data from its Football Manager video game back in 2016. At first this event, data consisted simply of players opening and closing games.

“But there was so much more data we could collect,” says Felix Baker, the company’s head of data services. “Like what teams people were managing, or how much money they were spending.”

Because of the data structures needed for inclusion in the data warehouse, data was coming in batches and it took too much time to analyze.

“We wanted to analyze the data in real-time,” Baker adds, but this functionality wasn’t available in Redshift at the time. “Databricks offered an out-of-the-box managed services solution that did what we needed without us having to develop anything,” he adds. In addition, the data lakehouse architecture enabled Sega Europe to ingest unstructured data, such as social media feeds.

The cost efficiencies enabled by providing a source for all of an organization’s structured and unstructured data is a value driver for data lakehouses, says Steven Karan, AI transformation lead at Capgemini, and it’s helped implement data lakehouses at leading organizations in financial services, telecom, and retail.

Moreover, data lakehouses store data in a way that it’s readily available for use by a wide array of technologies, from traditional business intelligence and reporting systems to ML and AI. “Other benefits include reduced data redundancy, simplified IT operations, a simplified data schema to manage, and easier to enable data governance,” Karan says.

Helping data emerge

One particularly valuable use case for data lakehouses is in helping companies get value from data previously trapped in legacy or siloed systems. For example, one Capgemini enterprise customer, which had grown through acquisitions over a decade, couldn’t access data related to resellers of their products.

“By migrating the siloed data from legacy data warehouses into a centralized data lakehouse, the client could understand at an enterprise level which of their reseller partners were most effective, and how changes such as referral programs and structures drove revenue,” he says.

One company capitalizing on the benefits of data lakehouses is life sciences, analytics, and services company IQVIA, which began using data lakehouses several years ago.

Before the pandemic, pharmaceutical companies running drug trials used to send employees to hospitals and other sites to collect data about things such as adverse effects, says Wendy Morahan, senior director of product management for clinical data analytics at IQVIA. “That’s how they make sure the patient is safe.”

Once the pandemic hit and sites were locked down, however, pharmaceutical companies had to scramble to figure out how to get the data they needed — and to get it in a way that was compliant with regulations, and fast enough to enable them to quickly spot potential problems.

Snowflake and Databricks gave the company the ability to store the raw data in any format, including images and audio, all in a single platform.

Lakehouse adoption growth

In a February report from Research and Markets, the data lakehouse market has been growing exponentially.

In 2025, it totaled $10.3 billion and is predicted to hit $12.6 billion by the end of this year, a compound growth rate of 22%. By 2030, this will be up to $27.3 billion, the research firm projects.

And according to a recent survey from Dremio, a lakehouse vendor, 63% of companies run most analytics on a lakehouse rather than a traditional warehouse, up from 55% in 2024.

Data lakehouses are also increasingly being used for IT and security workloads, says Ed Bailey, field CISO at telemetry vendor Cribl. “Previously, lakehouse providers were the realm of business data with a focus on structured data and SQL.”

Lakehouses can handle IT and security data at a lower cost, and this is a critical issue given the volumes of data in this space. “Even mid-sized companies produce much more IT and security data than business data,” he says. “Lakehouse vendors are finally starting to push into the market.”

But it’s still early, and initial solutions are immature and an awkward fit for this kind of data. “IT and security data are very different from business data,” he adds. For example, business data tends to be more predictable and well-structured. Plus, business users are more familiar with using data analytics tools than IT and security users. “This mismatch has been a serious obstacle to adoption,” he says.

Data lakehouses are also evolving in another way. Gartner says the lakehouse isn’t the ultimate solution but a transitional architecture on the way to more advanced systems, such as data fabric. The difference between the two is data lakehouses contain data from disparate systems, while data fabrics simply contain pointers to where the data is natively located.

An advantage of data fabrics is that the original security access controls and metadata are preserved and used, there’s no duplication of data, and no need to reconcile disparate standards.

“But it comes with some performance issues, and access isn’t that seamless,” says OSF Digital’s Szatvanyi.

A data fabric might be a good place for smaller companies to start, he says, or you can use both. “You can have a big chunk of data in a lakehouse and have a fabric for two or three secondary systems,” he says. “But I’d say the data lakehouse is the gold standard.”

If the supreme goal for software development teams is to get high-quality products to market as quickly, efficiently, and securely as possible, then deploying AI-powered tools for devops might be the way to achieve that objective.

AI-powered tools can help to speed up software delivery, enhance system reliability, and reduce operational costs by automating complex and repetitive tasks. They enable development, operations, and security staffers to resolve incidents more quickly, detect anomalies proactively, optimize cloud resource management, and ultimately accelerate processes throughout the development life cycle.

AI and devops – a natural fit

In many ways, AI and devops seem made for each other. Any automation that teams can add to the software development process is a plus.

“At this point, most of the enterprise teams I work with have moved well beyond experimenting and AI is part of the daily workflow,” says Jackie Swanson, managing partner at research firm Gartner. “The on-ramp for most has been AI-assisted coding. Tools like GitHub Copilot and Amazon Q Developer are showing up everywhere, helping developers knock out boilerplate, write unit tests faster, and scaffold infrastructure-as-code.”

But the more interesting shift is happening further down the pipeline, Swanson says. “Teams are leaning into AIOps platforms for smarter monitoring, anomaly detection, and incident triage that used to eat up hours of an engineer’s week,” she says. “The real story right now is the move from adopting individual AI point solutions to thinking about AI as a layer across the entire delivery chain.”

Teams using AI-assisted coding and automated test generation are compressing cycle times by 20% to 40%, Swanson says. They are also resolving incidents more efficiently, with AI platforms correlating alerts, flagging probable root causes, and suggesting fixes. This means “on-call engineers aren’t spending their nights sifting through dashboards,” Swanson says. “Mean time to resolution drops and so does burnout.”

In addition, developers are spending less time on repetitive work and more time on things that actually move the business forward, such as architecture decisions, customer-facing features, and problem-solving, Swanson says.

“I’m seeing AI used as a layer across the devops workflow rather than a single tool,” says Sonu Kapoor, who has worked as an independent software engineer for more than two decades, architecting front ends for Citigroup’s global trading platform and modernizing enterprise stacks for Sony Music Publishing and Cisco, among other work.

“Teams use it for code assistance, CI/CD support, log and telemetry analysis, incident investigation, and security triage,” Kapoor says. “In practice, this means engineers are using AI to explain failing builds, summarize alerts, investigate production issues faster, and reduce the time spent switching between tools.”

What’s particularly interesting is that AI is starting to sit closer to the actual engineering process, Kapoor says. “It’s not just generating text; it’s helping interpret signals and suggest next steps across code, infrastructure, and operations,” he says.

The biggest benefit of the trend is the reduction of friction, Kapoor says. “AI shortens the path from signal to action,” he says. “That shows up as faster onboarding, quicker incident investigation, less time writing repetitive code, and better interpretation of logs and metrics. It becomes meaningful when the tool is grounded in real context: your codebase, your infrastructure, and your telemetry. Without that, it’s just generating plausible answers.”

The same applies to security workflows, Kapoor says. “If a tool can’t translate findings into something actionable, the value drops quickly, regardless of how advanced the underlying AI is,” he says.

Streamlining engineering workflows

At engineering and construction company MasTec, AI tools are becoming part of the daily workflow, “but not in a flashy way,” says Sid Vangala, senior AI systems engineer.

“In my experience working on production back-end and AI platforms, the adoption has been gradual and very practical,” Vangala says. “On the development side, tools like GitHub Copilot are used pretty heavily for scripting, writing infrastructure configuration, and speeding up repetitive tasks, especially when working with Python services, APIs, or Docker setups. It’s not about letting the tool write entire systems, but about reducing the friction of routine work.”

From an operations standpoint, MasTec relies on Azure-based monitoring tools and AI-assisted observability features to analyze logs and performance metrics. “These tools help identify anomalies or performance bottlenecks earlier than we would catch them manually,” Vangala says. “In practice, AI tools in devops are less about automation for its own sake, and more about making engineers faster and more aware of system behavior.”

The biggest benefit Vangala has seen is faster troubleshooting. “When something breaks in a distributed system, the hard part isn’t usually fixing it; it’s figuring out where the problem actually started,” he says. “AI-assisted log analysis and anomaly detection tools help narrow down the likely root cause much faster than manual inspection alone.”

Another noticeable benefit is reduced time spent on repetitive engineering tasks. “Writing scripts, setting up environments, or generating API documentation templates used to take a lot of time,” Vangala says. “With AI-assisted tooling, those tasks are faster, which gives engineers more time to focus on architecture and reliability.”

MyManager, a business management platform that allows entrepreneurs to better manage operations, has gradually introduced AI into its development workflow to enhance engineering productivity and streamline day-to-day development tasks, says CEO Clinton Oh.

“Across the team, AI is primarily used to accelerate code writing, reduce repetitive implementation work, assist with debugging and issue resolution, and support developers in exploring different approaches during implementation,” Oh says.

This approach has helped standardize how the company leverages AI as part of development, allowing engineers to move faster while maintaining full control over system design and technical decisions. “Overall, AI serves as a productivity layer within our development process, enabling more efficient execution without replacing core engineering judgment,” Oh says.

Evaluating AI-powered devops tools

When evaluating AI tools for devops, one of the most important considerations is context awareness. “The tool needs to understand your actual environment: code, pipelines, infrastructure, and telemetry,” Kapoor says.

This includes being able to fit into existing workflows. “The best tools integrate where engineers already work,” Kapoor says, including integrated development environments (IDEs), CI/CD, and observability platforms.

“When we evaluate AI tools for devops, the first question is always, does this actually fit into how we already work?” Vangala says. “If a tool requires major architectural changes just to adopt it, that’s usually a red flag. The best tools integrate cleanly into existing CI/CD pipelines, logging systems, and cloud environments.”

Regardless of how impressive a product demo from a vendor looks, “if it forces engineers to change the way they work, adoption will stall,” Swanson says.

Another thing Vangala pays close attention to is how transparent the tool is. “If an AI system recommends an action, engineers need to understand why,” he says. “Blind automation is risky in production environments.”

Actionability is something else to explore. “Does the tool just summarize a problem, or does it show what to do next?” Kapoor says. “That’s something I’ve been exploring directly in my own work with a CLI [command-line interface] tool for dependency vulnerability scanning.”

One of the gaps Kapoor kept seeing is that tools will surface common vulnerabilities and exposures (CVEs), “but won’t clearly guide developers on what to fix first,” he says. “The approach I’ve taken there is to separate direct and transitive issues and suggest a concrete remediation path, not just a report.”

That same principle applies to AI in devops. “If the output doesn’t reduce ambiguity or help you take action, it’s not solving the real problem,” Kapoor says.

Overall security is a key factor as well, including the ability to verify that tools are taking the necessary precautions to prevent data leakage. “Engineers must validate the AI’s suggestions, especially in production or security contexts,” Kapoor says.

“Many devops tools interact with sensitive infrastructure data, such as logs, configs, and deployment pipelines, which makes governance and data-handling policies matter just as much as technical capabilities,” Vangala says. “But honestly, the biggest test is how the tool behaves during failure scenarios. Tools always look good during normal operations. The real evaluation happens when something goes wrong.”

In-demand AI tools for devops

AI-powered devops tools come in many shapes and sizes, covering the full spectrum of tasks across the software development life cycle. The eight tools listed below represent a tiny fraction of the large and growing number of tools for AI-assisted development, software testing, security scanning, infrastructure automation, CI/CD, monitoring and observability, cloud optimization, incident response, and much more.

• Amazon Q Developer—An AI-powered coding assistant that combines code completion, code generation, code explanation, and an agent that can autonomously perform a range of tasks across the software development life cycle. You can also chat with Amazon Q Developer about AWS capabilities, and ask it to review your resources, analyze your bill, or architect solutions. It knows about AWS well-architected patterns, documentation, and solution implementation.
• Azure Monitor—A comprehensive observability service from Microsoft that collects, analyzes, and acts on telemetry data from cloud and on-premises environments, in order to maximize application and infrastructure performance. It automatically monitors Azure resource metrics/logs and provides deep insights into virtual machines, containers, and databases, with key features including alerts, dashboards, and automated troubleshooting. 
• Datadog Bits AI—A generative AI-powered devops copilot integrated into the Datadog platform and designed to automate incident investigation, security triage, and remediation workflows. It acts as an “agentic teammate,” assisting engineers by analyzing logs, metrics, and traces; suggesting code fixes; and interacting via chat in the web app or Slack. 
• GitHub Copilot—An AI coding assistant that helps developers write code faster and with less effort. It provides real-time, context-aware code suggestions that range from single lines to entire functions, directly within code editors such as VS Code, Visual Studio, and JetBrains. The tool supports OpenAI, Anthropic, and Google models. Organizations can get Copilot Business for development teams through an enterprise account.
• Google Gemini Cloud Assist—An AI-powered collaborator integrated into Google Cloud, Gemini Cloud Assist is designed to help teams move from reactive troubleshooting to proactive, autonomous cloud operations. The multi-agent system can simplify the end-to-end application life cycle by offering agentic guidance for designing, deploying, troubleshooting, and optimizing cloud workloads.
• Harness AI—An AI-native software delivery platform that accelerates development by automating devops, testing, security, and cloud cost management using specialized AI agents. It enables faster and more secure software releases by providing intelligent, contextual insights for continuous delivery and incident management, reducing the need for manual work by developers.
• IBM Cloud Pak for Watson AIOps—An AI-driven platform that automates IT operations by analyzing logs, metrics, and events in real-time. It helps teams predict incidents, reduce alert noise, and accelerate incident resolution, enhancing hybrid cloud resilience. Key features include anomaly detection, topological mapping, and chatops integration.
• Snyk AI Security Platform (or Snyk AI)—A developer security platform designed to secure AI-driven development by providing visibility, control, and autonomous defense for AI-generated code, AI-native applications, and agentic systems. It enables developers to create products securely using AI coding assistants while automatically identifying, prioritizing, and fixing vulnerabilities in source code, open-source dependencies, and infrastructure as code.

With cloud migration, digital transformation, and now the drive for AI adoption, CIOs face a real imperative to get modernization right.

However, industry research continues to show that modernization projects can fail to deliver the promised benefits and suffer cost overruns, even as the appetite for overhauling legacy technology grows.

CIOs, enterprise technology leaders, and advisors offer advice on some of the most common modernization pitfalls and how to avoid them.

1. Stacking new technologies on top of legacy systems

Experience has taught Bill Pappas, who as EVP and head of global technology and operations has managed IT modernization at 158-year-old company MetLife, that CIOs should avoid “stacking new technologies on top of outdated, overly complex legacy systems”.

“Modernization is not a race to deploy the newest tools; it’s a disciplined effort to create enterprise value,” Pappas says.

In most cases, new technologies, particularly AI, can’t simply be bolted on to the existing infrastructure.

“Instead of driving transformation, organizations end up with expensive solutions that cannot scale or integrate properly. These systems also introduce added security and compliance risks, leaving organizations vulnerable to breaches and regulatory failures,” he says.

CIOs’ starting point should instead be simplification, which includes strengthening data foundations, streamlining legacy systems, and linking IT initiatives with business objectives and customer outcomes.

“By focusing on simplification, security, and strategic alignment, CIOs can unlock transformation without falling into the traps created by outdated technology,” he says.

2. Overlooking cultural and leadership fit

Doug King, CIO of ePlus, cautions CIOs that a siloed approach to modernization risks failure because it overlooks the deeper cultural and leadership shifts needed to move the organization toward a shared vision of transformation.

The risk is that modernization efforts become a series of disconnected projects instead of a cohesive ongoing transformation that benefits the entire organization. “Ignoring alignment risks wasted resources and investments falling short of delivering meaningful business value,” he adds.

His advice to CIOs is to engage cross-functional leaders, clarify decision-making roles, and focus on the business transformation narrative. “CIOs need to make trust-building and organizational alignment core to their strategy, ensuring every team understands the broader vision and is working together toward it,” he says.

Above all, organizations must clearly identify and articulate what they hope to accomplish and remain grounded in why they’re modernizing. “Modernization isn’t a one-time path your organization travels; it’s an ongoing journey,” he says.

3. Treating cloud migration as the finish line

Many organizations declare success once applications are moved to the cloud, but that mindset can stall modernization just as it should accelerate. Andy Tay, global lead for Accenture Cloud First, warns that cloud migration is often mistaken for transformation. “Cloud migration isn’t the finish line; it’s the starting block,” Tay says.

Without ongoing modernization — spanning architecture, data, operating models, and ways of working — cloud platforms can struggle to deliver sustained business value or support AI-driven innovation. “Leading organizations modernize while they migrate and treat cloud as a business enabler, not just an IT project,” he tells CIO.

CIOs should treat cloud as a living platform, continuously improved through automation, security-by-design, cost governance and AI-enabled operations, rather than a one-time migration milestone, says Tay.

4. Repeating cloud mistakes with AI adoption

Organizations — and CIOs — are under intense pressure to move quickly with AI adoption, but speed mustn’t overshadow security, says Blue Mantis CIO Richard Amos.

“The rapid acceleration of AI adoption in the enterprise reminds me of the early days of public cloud transformation,” says Amos.

As with cloud, AI requires a robust approach to safeguarding data, models, and agents.

“The stakes are even higher with agentic AI, which automates complex, knowledge-based workflows, but also significantly expands the attack surface,” he says.

Agentic AI in particular demands rigorous identity and data access management, with agents

treated as first-class digital identities, with least-privilege access that is task-scoped, time-bound, and continuously monitored, according to Amos.

“Anything less creates unnecessary risks. Human validation should also be mandatory for sensitive actions affecting financial, legal, or customer-impacting domains,” he says.

His advice is not to overlook strong data security and privacy controls, especially for regulated industries, such as data obfuscation, encryption, lifecycle management, and clear supplier oversight. Layered prompts, input/output filters, and explicit permission gating before tools or APIs are invoked are recommended to guard against prompt injection and misuse.

Best practice is to align agentic AI with governance and regulatory standards through a cross-functional AI governance office to ensure compliance with existing and emerging regulations.

“Agentic AI delivers transformational potential, but its benefits will only be realized with robust security and governance,” he says.

5. Overlooking a strong foundation of data quality

CIOs often frame modernization as a tech refresh, and focus on new platforms, cloud migrations, and cutting-edge tools, but often overlook the foundation piece: “The quality of your data and how well it’s integrated,” says Conal Gallagher, CIO and CISO of Flexera.

“This becomes a trap because modernization without clean, connected data is bound to fall apart. Poor data governance and fragmented systems create blind spots that undermine analytics, automation, and decision-making,” he says.

The problem is that CIOs assume that upgrading systems automatically improves data integrity, but modernization amplifies complexity when integration isn’t prioritized. “Instead of reducing silos, organizations can end up stacking them higher,” he says.

The uptake of AI, which requires high-quality, integrated data, can escalate the problem, creating flawed insights and eroding trust. “With companies integrating AI at such a rapid pace this year and planning to in the near future, this becomes a much bigger point of contention,” he tells CIO.

Gallagher recommends that CIOs start any modernization initiatives with data governance policies and look at unifying data across vendors and platforms.

Data strategies should be tied to business outcomes to guarantee that usability becomes a core success metric of transformation efforts. “If teams can’t access and trust the data, modernization of ROI will remain elusive,” he adds

Above all, data should be treated as a product that requires a cross-functional approach across core business units, security, and IT.

“Modernization isn’t complete when systems are upgraded; it’s complete when insights are accurate, timely, and actionable,” he says.

6. Disregarding the ‘emotional debt’ of legacy tech

“Everyone loves to talk about technical debt, but they conveniently ignore or avoid the emotional damage that comes with it,” says John Boesen, chief digital information officer at Plan A Technologies.

It’s a script familiar to many: Years of surprise changes, failed projects, and broken promises create a quiet cynicism inside teams.

“We’ve all seen this — leadership announces a big modernization push, but deep down no one believes it, even if no one says it out loud. That doubt is real,” he says.

To lay the groundwork for success, Boesen is a fan of “future postmortems” that may seem counterintuitive as a planning session.

“Bring the team together and write a postmortem dated two years from now, assuming the modernization failed. Then ask, ‘Why did this happen? Who felt the pain? What went wrong?’” he tells CIO. “A future postmortem exposes risks no one brings up in traditional meetings and leads to a far more honest and realistic roadmap.”

7. Not linking modernization to business value

“Even as enterprises pour money into AI, cloud, and automation, the failure rate remains stubbornly high,” says Matthew Guarini, executive director of Technology Business Management Council and former National Grid’s US CIO.

Guarini points to research from McKinsey that 70% of digital transformation initiatives failed to meet their objectives in 2025, despite years of effort and trillions of dollars.

“A major challenge of IT modernization is the difficulty enterprises face in delivering value from their IT investments,” he says.

And with the enterprise technology landscape increasingly complex and the real prospect of failure rates, CEOs and CFOs are wary of making investments, according to Guarini. Instead, CIOs need to connect technology resources to business outcomes such as increased revenue, greater productivity, enhanced innovation, or improved sustainability.

“Pressured to modernize, CIOs must leverage technology to deliver value from their IT investments, but most tech leaders focus disproportionately on the nuts and bolts of their innovations rather than the true goal of modernization: delivering value to customers and employees,” says Guarini.

8. Treating modernization as a big bang replacement

Another path that can lead to failure is assuming modernization must happen all at once. When organizations talk about modernization, they often think in extremes. “When people think about modernization, they often imagine replacing everything at once or maintaining two parallel worlds in conflict,” Boesen says.

Instead, he advocates creating intentional zones where legacy and modern systems work side by side, each with a clear purpose. “This reduces disruption, controls costs, and allows change to happen at a pace the organization can genuinely absorb. It is a more realistic and more human way to handle a process that is usually more complex than it appears,” he says.

Boesen likens IT environments to cities rather than machines. “Some neighborhoods are brand new, others are historic, and there is always some construction happening somewhere,” he says. The challenge for CIOs is not rebuilding everything at once, but deciding which areas to renovate first to create the greatest impact.

“To prioritize this, the path is simple: Listen to the people closest to the problems and give weight to impact. That is how you focus on what really moves the needle,” he says.

Most enterprise AI tools sit outside the system of record. They generate code that compliance teams can’t audit and CIOs can’t deploy with confidence. Tribal works the other way: AI agents that operate inside the customer’s existing system of record.

The metadata graph and every generated artifact live in the customer’s own system of record. Every change runs through the customer’s existing governance layer before it ships.

The company closed a $10 million seed round to scale the approach. Team8 led, with DYDX Capital and angel investors from the Salesforce ecosystem joining. The funding goes into expanding to ServiceNow, SAP, NetSuite, and Workday, plus US hiring.

Two early customers describe what changes when AI builds inside the system.

Nir Rehav, CIO of ADAMA, runs Tribal across the company’s operations in 19 countries:

 “With Tribal, we move significantly faster—iterating quickly and deploying to production with confidence. We’re now able to serve users in 19 countries better while streamlining how they work with data across both new and existing markets.”David Kestenberg, Director of IT at Pro-Driven Brand, brought Tribal in to clear years of accumulated tech debt: “Tribal gave us a self-serve platform to standardize how we evaluate and manage growth, so teams can move faster with a shared, trusted view of what matters. It helped us remove tech debt by replacing brittle workarounds with a clean, maintainable setup, and it gave our team a real speed boost — about a 10x faster.”

Tribal customers see backlogs cleared 10x faster and 80% lower maintenance costs.

Under the hood, Tribal’s Metadata Fabric ingests an enterprise system’s full layer in minutes: objects, automations, permissions, dependencies, business rules. AI agents generate against that graph. Every output is tested against the customer’s metadata before it deploys. As Salesforce rolls out Headless 360 and Agentforce, the same layer keeps those agents working inside the customer’s existing governance.

Tribal was founded in 2024 by Yoav Kolodner (ex-VP Engineering at Salesforce), Yakir Daniel (founder of Spot.io, sold to NetApp, and Swordfish, sold to Huawei), and Lior Sidi (ex-AI lead at Wix). The three hit the same wall from different seats: building AI prototypes is easy, shipping governed change inside a live enterprise system is hard.

“The shift to headless enterprise infrastructure is real and accelerating. But infrastructure without organizational context is just plumbing. This $10M lets us go deeper on the product and build the intelligence that will make Enterprise AI tick,” said Yoav Kolodner, CEO of Tribal.”Enterprises are under growing pressure to turn AI from experiments into real, repeatable impact inside their business applications and workflows,” said Ori Barzilay, Partner at Team8. “The Tribal team brings deep and rare experience building on the trusted platforms of global enterprises and is uniquely positioned to help organizations bring AI safely to fulfill core business needs.”

About Tribal

Tribal is an enterprise-native AI platform founded by Salesforce, Wix & Spot.io veterans who spent careers inside the world’s most complex enterprise systems, and got tired of watching AI fail them. Tribal’s context-aware AI agents learn the complete metadata graph of an enterprise system, then build with full organizational context. The result: backlogs cleared 10x faster, 80% lower maintenance costs, and AI-solutions that compliance and IT leaders can trust from day one. For more information, visit https://gotribal.ai/.

Contact

Orian Tal

thepitch.media

orian@thepitch.media

A wave of AI agents has recently emerged for sales and revenue teams, including Highspot’s Deal Agent, an agent to accelerate pipeline generation and conversion, and Qualified’s Piper for Demandbase, an AI sales development representative (SDR) agent. Salesforce’s AgentForce and many others are also rallying behind using agents for this use case as well, so much so that Gartner estimates for the remainder of the year, 40% of enterprise applications will feature task-specific AI agents. Further analysis in a Workato-sponsored 2025 survey by Harvard Business Review paints a similar picture, where 86% of 600 technology decision-makers plan to increase investment in agentic AI over the next two years, with a growing proportion to empower sales.

“Sellers are preparing faster, showing up with sharper points of view, being more strategic, and spending more meaningful time with customers,” says Kellie Romack, CDIO at ServiceNow. “We’ve already seen amazing early results with sellers cutting prep time from hours to minutes, almost a 95% improvement.”

Executives report that AI agents are accelerating sales teams by conducting prospect and customer research, reducing manual toil across the sales workflow. Yet, while the excitement is evident, it’ll take discipline to know where precisely to deploy AI agents, let alone how to operationalize, secure, and scale multiple agents throughout an entire sales organization.

“AI agents deliver the most value in sales workflows that are well-defined, rule-based, and involve high volumes of repetitive activity,” says Dan Shmitt, CIO at Salesforce. “These are areas where scale, consistency, and speed matter, but ultimately, where teams lack the capacity to engage at scale.”

Conducting customer research

AI agents are poised to accelerate sales teams in a few ways. One area is giving sales engineers better context. “AI agents can help make sales teams the most relevant communicators in front of their prospects or clients,” says Tiago Azevedo, CIO at AI development platform OutSystems.

Or take the use of AI agents in sales at fleet management company Samsara, for instance. CIO Stephen Franchetti, previously CIO at Slack, shares that AI has always been fundamental to Samsara’s platform to help automobile fleet operators optimize routes or check vehicle health. But more recently, they’ve applied AI knowledge agents internally throughout the company.

Samsara has created an internal model fine-tuned on Samsara data. This Samsara GPT, as he calls it, is trained on the company’s product knowledge base, specific customer data, and is connected to Salesforce and other systems, helping sales executives quickly become account-specific experts, answer customer questions faster, and accelerate onboarding processes.

“It’s the most successful rollout we’ve done,” says Franchetti. “It’s universally embraced by our sales organization and we’ve received amazingly positive feedback.” As a result, Samsara account development representatives (ADRs) are experiencing 16% better attainment using this internal GPT.

At ServiceNow, there are AI agents empowering sellers with highly relevant context. “We’re using an AI-powered coaching experience and sales hub to make our sellers more effective at preparing for meetings and navigating deals,” says ServiceNow’s Romack. “Our AI Sales Coach, built on Anthropic’s Claude, pulls together account data, research, and product intelligence into actionable guidance.”

Accelerating lead prospecting

That knowledge is also improving outbound strategies, helping ADRs synthesize prospect data and draft more personalized outreach. For example, Samsara’s pilot group has noticed a 300% increase in callbacks to emails using this strategy, says Franchetti.

In other organizations, internal experiments are still in pilot mode but showing strong signals. For example, Kate Prouty, CIO at Akamai, shares that the cloud, security, and content delivery network company has been piloting an internal AI-powered sales assistant it calls SaiLS Bot within its sales development representative (SDR) team. “SDR teams used SaiLS to accelerate prospect research with quick company understanding and impact analysis,” she says. This helps spin up account-tailored go-to-market plans, and surface cross-sell opportunities.

By using AI agents, SDRs at Akamai are able to quickly understand target companies, existing solutions, and potential cybersecurity impacts, dramatically shortening their prospecting cycles. “Quantitatively, in the first nine months of using SaiLS Bot, the sales team saved the equivalent of three full-time employees in labor,” says Prouty.

At ServiceNow, agents aid in how they prospect new leads, conduct post-sales follow-up, and query the status of deals in the pipeline more easily. For instance, a seller might prompt something about opportunities in a particular territory with a range of probability to close and stage details in order to gain highly targeted information.

Some leads naturally come from inbound sources, too. But human labor has historically struggled to qualify this interest at scale. “Our recent research shows only 25% of inbound sales leads require human outreach or follow-up, leaving roughly 75% of leads historically receiving no engagement at all,” says Salesforce’s Shmitt. “That gap represents a structural limitation of traditional sales models.”

At Salesforce, one remedy has been deploying its website agent, which answers product questions and qualifies inbound interest. Salesforce then uses a sales agent internally to inform sellers with quick access to account information, deal history, pricing, and executive briefing materials, helping shift initial interest into active conversations.

Enriching pipeline and follow-up

Sales teams are experiencing high pipeline growth using AI agents. “We’ve deployed AI agents across our sales operations at Workato,” says Carter Busse, CIO at the automation and integration platform. For instance, they recently generated $2.7 million worth of new sales opportunities using an internal agent that analyzes calls in Gong, an AI operating system for revenue teams, to determine what made closed-deals successful to better cater future outreach.

In total, Workato has built 28 sales agents for various autonomous sales processes, including handling opportunity enrichment, quote generation, approval routing, and meeting follow-ups. Agents also automatically update CRM fields with new data from client interactions, improving their data quality and reliability.

Busse grounds the results in efficiency savings. “Deals with agent-enriched data progress through pipeline stages up to 20% faster,” Busse adds. “We’ve also seen 40% faster quote turnaround, and five to seven hours per week back to sellers to focus on customer conversations.”

Salesforce’s Shmitt also sees big potential in using agents for repetitive workflows related to follow-up communication, expanding what human sellers can realistically respond to. “In sales, this typically includes processes like answering product questions, managing follow-up, re-engaging stalled interest, and determining when to route a lead to a human seller,” he says. The company deploys its tech internally, using its engagement agent to automate some of these lead engagement activities. “The agent delivers 24/7 personalized outreach, product Q&A retrieval, objection handling, stalled-lead re-engagement, meeting booking, and lead information collection, with defined escalation paths to humans,” he says.

OutSystems is similarly seeing gains from deploying agents to automate tedious tasks and accelerate revenue-facing workflows. “Agents are improving pipeline accuracy and reducing manual administrative work that slows deal cycles,” says Azevedo, who estimates they’ve saved 1,700 hours in manual administrative work using their contact agent. “By arming our team with the right proof points at the right moment, we validate value faster and shorten deal cycles.”

Generating sales enablement materials

AI agents can also generate customer journey references and case studies. For example, OutSystems is dogfooding its AI agent building tools to streamline sales operations, most notably using Agent Workbench. “We’ve built a multi-agent system that arms our sales team with key insights on prospects and their buying signals, relevant case studies, and points for successful pitches,” says Azevedo.

“We’ve enrolled all our sales team members in Deal Mate, one of our customer story agents, which has suggested 2,859 relevant stories to the sales team since October,” he adds. They’ve also created hundreds of automated sales decks using a CMS assistant, which has delivered significant time savings as well.

Overall, AI agents are giving sellers more firsthand, cross-industry insights into how customers are using their platform or services, along with business improvements they’re gaining. CIOs report that this automated research is contributing to productivity savings and improving funnel conversions.

Also, financial analysis can be expedited using agents. At Samsara, MCP servers are supercharging financial analysis that would’ve traditionally required considerable manual labor. “Technically, it’s much less of a lift,” says Franchetti. “Our financial language gets to our community in a much more natural way.” For this, they’ve used Workato One to take recipes and workflows, and expose them as MCP servers for their agentic processes.

Governance and controls required

Although early usage signs look promising for innovating sales pipelines, CIOs must implement governance controls for agentic AI in sales workflows, especially when agents have access to customer data or the ability to conduct autonomous actions.

“The guardrails matter just as much as the technology itself,” says Shmitt. To reduce risks, CIOs should clearly define roles for agents, limit permissions, and set human approvals for high-value actions. “Many organizations introduce agents into workflows where they support decisions rather than deploy fully autonomous agents,” he adds.

Access control, especially authentication and authorization, is a common priority for CIOs. “Our top concern is ensuring agents working on behalf of sales employees only access authorized data,” says Workato’s Busse. “Anything that modifies customer records or sends external communications requires approval.”

For Azevedo, it comes down to balancing innovation with safeguarding infrastructure and data. “Agentic AI requires organizations to invest on both ends of that mission, to ensure customer data and deal integrity remain intact while also automating core business processes throughout the enterprise.” To do so, he recommends unifying disparate data sources, and streamlining how agents discover and create that data.

He adds it’ll also require transparency into agentic behaviors. “Full observability into how agents make decisions is critical for CIOs to support or propel their organization into an age of intelligent hyper-automation based on trust.”

ServiceNow’s Romack adds that it’ll also take CIO leadership. “For users, we’re also here to guide and coach,” she says. “A great example is ensuring our sellers get best practices to create prompts like the right account and deal context, specific metrics, breaking down complex requests, and review and validation.”

In effect, Romack sees the CIO as playing a unifying role to build integrated experiences and curb possible tool sprawl. “The CIO should be the great unifier, bringing together sales, IT, legal, finance, and security to create powerful and responsible experiences,” she says. “That balance is what unlocks value across the organization.”

But it’s not just humans that need leadership and unification. Agents need training on institutional knowledge and policies as well. “We treat agents like employees,” says Busse. “They’re onboarded with curated knowledge about the business, trained on data governance policies, and monitored continuously.”

Plus, CIOs have a financial responsibility to consider. “AI agents should be treated as long-term systems, not short-term experiments,” says Akamai’s Prouty. As such, CIOs should collaborate with IT leadership to manage costs for AI agent rollouts. “Without oversight, costs can spin out of control for AI tools that aren’t delivering value to teams.”

Best practices for AI agents in sales

Before diving headfirst into agent-assisted revenue workflows, sales teams will need foundational capabilities in place. “Many organizations still lack a clear roadmap for how to start, scale, and define success,” says Shmitt. Preparing things like data, governance, and operating structures are key to responsible adoption, he says.

Success will lie in a strong data foundation consistently updated to avoid stale or static resources. “Enterprises that thrive will be those prioritizing a centralized foundation and a strong knowledge base of content and data, and collaborating with business champions across their agentic journey,” says Azevedo. The latter will necessitate a cultural shift, he says, which requires viewing agents as extensions of the workforce with clear ownership and operational structure. As such, he recommends assigning select team members to specific agents, monitoring performance, and flagging issues as necessary.

Next, start small with valuable use cases tuned to seller workflows. “Pick one or two high‑impact use cases where the value is obvious, like sales prep or deal coaching,” says Romack. To do so, she recommends working closely with sellers to understand their pain points to create experiences that function inside their existing workflows. “If they’re swiveling between systems with AI that doesn’t help them land their next meeting or close their next deal, it won’t stick.”

So start where conditions are already strong, adds Shmitt. “Agents deliver the most value in sales environments with clear documentation, structured data, and well-defined workflows,” he says. Testing agents in these areas will help validate accuracy and value, which can inform adoption.

Other CIOs agree organizations shouldn’t automate everything at once. Instead, Busse recommends starting small with high-pain, low risk workflows like an agent that proactively manages license optimization. “Pick something where you can measure impact quickly,” he says.

Past the experimentation stage

Executives are attempting to drive ROI from their AI investments, but returns have been murky. A report from PwC at Davos found that 56% of CEOs said they’ve seen no financial benefit from their AI investments, and only 12% reported both cost savings and revenue growth.

On the flip side, agentic AI in sales is showing more promising returns. While it’s still early days, executives report impressive gains from deploying AI agents within revenue workflows. They help sellers automate repetitive work across the sales lifecycle, from qualifying leads, refining messaging, and researching accounts, to gathering relevant customer insights, auto-updating CRM systems, and more.

“All these use cases and investments we’re making deliver ROI and key metrics from a business perspective,” says Samsara’s Franchetti. “We’ve moved past experimentation. Now it’s more about where the value is and how do we apply AI to meet that value.”

And rather than replacing sales teams wholesale, AI agents are complementing humans and allowing them to emphasize soft skills. “The efficiency gains alone justify the investment,” says Busse. “Reps are spending more time on actual customer conversations and less on administrative work.”

But enterprises don’t yet report closing deals entirely with AI agents. Instead, as Romack says, these tools elevate human potential, with AI agents doing the heavy lifting on context, synthesis, and next steps, while sellers bring the strategy, relationships, and outcomes.

“The value is immediate,” she adds. “You can jump right in and feel the results in your first meeting of the day. Over time, that compounds into better deal execution, like fewer stalled deals, stronger mutual plans, and more consistent value.”

Serving in the military requires a precise, tactical mindset, and that’s exactly what Barry Hensley espoused during his 24 years in the US Army, where he rose to the rank of colonel.

The military “is where you earn your stripes, showing your soldiers your willingness to jump into a foxhole and pick up a weapon,” says Hensley, CSO of Brown & Brown, an independent insurance brokerage firm.

As a security leader in an industry that is constantly evolving, Hensley has leaned on that in-the-trenches approach as a key part of his leadership ethos, even as the CSO role has grown increasingly strategic.

“A security leader needs to be close enough to the tactical fight to effectively guide the organization’s strategic direction, align with business goals, manage risk and investments, and influence culture,” he explains. Business units need to have confidence in a security leader’s level of expertise in a specific security domain so the leader can properly represent the risks and investments required. 

“Rolling your sleeves up in the middle of a security event is never a bad thing,” Hensley adds. “It shows your willingness to lead from the front and/or support in the most stressful situations.”

And increasingly stressful those situations have become, as the spotlight on CISOs has never shone more brightly. Now alongside increasing responsibilities that include data protection and privacy, third-party and supply chain risk, and regulatory compliance and reporting, CISOs must confront the rise of AI — both in the hands of bad actors and throughout the enterprise. And as the CISO role evolves and grows, many are rising to embrace the opportunity.

According to Foundry’s latest Security Priorities Survey, 95% of top security leaders regularly engage with the board of directors multiple times a month, up from 85% in 2023.

This is helping advance cybersecurity initiatives. The CISO’s elevated prominence is also leading to new reporting structures, the survey found, with 31% of respondents reporting that the top security leader reports directly into the board of directors. Only one in five respondents said their security chief reports into the corporate CIO, “another sign that cybersecurity commands its own infrastructure and leadership outside of IT.”

CSO spoke with Hensley and other 2026 CSO Hall of Fame inductees, about how they are governing as AI initiatives become firmly rooted in the enterprise.

Implementing an AI security framework

AI is a core component of Brown & Brown’s security strategy: enhancing SOC operations, streamlining vulnerability management, determining the risks/rewards of third- and fourth-party partnerships, and boosting security application development, Hensley says. 

“For 2026, publishing an AI security framework is our top priority to enable the business to move fast — safely,” he says. His staff is partnering with the firm’s AI engineering and enablement teams to perform AI risk assessments and ensure that AI is fit for purpose and used responsibly through the company’s AI Governance Working Group.

“AI is top of mind for our leaders and a prominent topic with the board of directors, serving as a key consideration and differentiator for our business,” Hensley says.

Companies need governance frameworks that require security reviews before any AI capability is deployed, agrees Shaun Khalfan, senior vice president and CISO of PayPal. This ensures use cases are evaluated against security requirements, data sensitivity, operational risk, and business impact.

“This is why I am a strategic business advisor for major AI business decisions at PayPal,” says Khalfan, whose team is applying advanced risk detection technology and oversight, including machine learning models running in real-time that evaluate over a billion transactions per month. 

The work includes maintaining tight risk and business alignment, incorporating new products into existing compliance and risk frameworks, and adapting them to the unique characteristics of each product, he says. 

Move fast, keep risk at bay

Like Hensley, Jeff Trudeau, CSO of Chime, says the role is fundamentally shifting from a control function to a strategic partner in how the business adopts AI responsibly. At Chime, that means being embedded early in how AI is built and deployed, not reviewing it after the fact, Trudeau says.

“We’re focused on three areas: securing AI systems themselves, governing how AI is used across the company, and helping leadership make clear risk/reward decisions as we scale,” he says.

Noting that AI increases both speed and surface area, Trudeau says his role is to ensure the firm can move fast without introducing unacceptable risk. “That requires tighter integration with engineering, product, and data teams, as well as more direct engagement with executive leadership and the board on how AI changes our risk posture.”

Khalfan also characterizes himself as a strategic CISO with a strong operational and engineering foundation. He strongly believes that a well-defined security strategy aligned to business goals is essential for the success of any cybersecurity organization.

“Security cannot operate as a separate function; it must be embedded in how the business grows, innovates, and continues to earn trust,” he says, adding that “strategy without execution is just theory. We operate in a threat landscape that changes daily, and there are moments when tactical action is critical to managing immediate risk.”

Rapid AI adoption is a perfect example, he says. Echoing Trudeau, Khalfan believes the CISO must help the organization move fast while still protecting customers, data, infrastructure, and reputation.

“The best CISOs know how to balance both, thinking long-term while acting decisively in the short term,” he says. 

All roads lead back to trust and strong governance, he notes. “Trust is the foundation of both technology and business. You must build trust in the system across customers, merchants, partners, and infrastructure to ensure AI and agent-driven transactions are reliable, secure, and verifiable.”

AI is creating the greatest security challenges

For Trudeau, the biggest challenge of the burgeoning AI era is the pace of change. AI is accelerating how software is built, how attacks are executed, and how quickly systems evolve. Traditional security models, periodic reviews, and static controls don’t keep up, he says.

“We’re addressing that by shifting to more continuous, embedded security practices. That includes integrating security into development workflows, investing in detection and response capabilities that adapt in real-time, and building stronger data governance around how sensitive information is accessed and used by AI systems,” Trudeau says.

At the same time, the focus is on maintaining trust at scale. “As we introduce more AI-driven experiences, we have to be clear about how systems behave, how decisions are made, and where human oversight remains,” Trudeau says. “That’s as much a product and trust challenge as it is a technical one.”

AI is also impacting what Brown & Brown is seeing with phishing campaigns, notes Hensley. “AI is maturing in its ability to impersonate individuals, both voice and video, while quickly generating supporting documents to further convince teammates that a fraudulent request is genuine.”

A preview of Anthropic’s Mythos release shows that AI can now rapidly discover previously unknown vulnerabilities and automate their exploitation, Hensley says. “This changes the paradigm. Vulnerability management will likely become a higher priority for organizations as they cannot wait weeks to patch hosts based on a perceived risk tolerance of mitigating controls.”

Most organizations will have to empower their IT platform providers to deploy automation for near-real-time patching — while holding them accountable for the contracted service-level availability, he says.

Managing identity, data, and humans

AI is not the only challenge CISOs have to contend with. Khalfan says that identity, data security, and context are his most important challenges to solve for.

“Identity is becoming more complex, as humans, machines, APIs, and autonomous agents all interact with critical systems,” he says. “Knowing who — or what — is requesting access and ensuring the right level of trust and least privilege is fundamental.”

Context is the multiplier, Khalfan adds. “Security decisions without business context create unnecessary friction, and business decisions without security context create unnecessary risk. Security leaders must create systems that make both visible in real-time.”

To execute, his team focuses heavily on getting the fundamentals right: strong data governance, dynamic policy tuning, continuous validation of the control environment, frequent deployment of security improvements, and designing controls that are embedded into workflows rather than added afterward, Khalfan says.

“Security at scale is less about isolated controls and more about building resilient systems that continuously adapt,” he says.

As much as AI has added new trials, Hensley finds that the human element, along with the expanding attack surface, remain the greatest security challenges. This includes the arms race between attackers and defenders. “Sophisticated social engineering is at an all-time high, challenging our teammates to be not only vigilant but also often the first line of defense,” he says.

To stay ahead, “we are tackling from all angles, including security awareness training, enabling new advanced AI features in our security tools, and taking more proactive actions on behalf of our teammates based on risk/reward evaluations,” Hensley says.

Hall of Fame advice on meeting the current CISO moment

Meeting today’s cyber leadership challenges requires CISOs to lead from the front — something both Hensley and Khalfan practice. That means only adopting AI that is secure and trusted. “Security should not be the department of ‘no’; it should help business partners move faster with confidence, Khalfan says.

Leading from the front also means challenging the status quo, and viewing yourself as a business partner/risk advisor, Hensley says.

For Trudeau, it’s about being able to translate risk into business terms.

Stay close to the business. “If you do not understand how your company creates value, you cannot effectively protect it,” Khalfan says. “Security leaders need to speak the language of growth, customer trust, and operational resilience, not just technical risk.”

Trudeau agrees, saying that security leaders must align their work directly to business outcomes. “If security is seen as separate from growth, you’ll always be reacting instead of shaping decisions.”

Be the enabler. “The best CISOs help the business move faster and safer, not slower,” Khalfan says. “Your job is not to create friction everywhere; it is to create friction where the risk is highest and remove it where trust can be increased through better design.”

Engage early. “The earlier security is involved in product and AI development, the more leverage you have to influence outcomes without slowing teams down,” Trudeau notes.

Khalfan echoes that, saying that data security, identity, and observability are the foundations on which trusted AI systems are built. Business and cyber teams must work hand in hand to ensure those outcomes are achieved, he says. 

“Whether it is defending against AI-enabled threats, protecting AI infrastructure, or evaluating the risk and reward of AI innovation, security must be involved early, not after deployment,” he adds.

Stay proactively compliant. Khalfan says that PayPal’s security organization continually monitors and updates its governance and requirements based on the evolving regulatory frameworks.

Solve business problems. This is a sure-fire way to meet the today’s cyber challenges and raise your profile as CISO. “When security becomes a driver of trust, speed, and competitive advantage, your seat at the table becomes permanent,” Khalfan says.

For example, Khalfan drove company-wide bot protection initiatives, a collaborative, multi-team effort that enhanced fraud prevention. It greatly reduced fraudulent traffic at the top of the process, resulting in higher quality customer engagement, he says.

Talk the talk. If you want to understand how to secure AI, you need to actively use AI, Khalfan stresses. “Security leaders cannot govern what they do not understand. Hands-on experience creates credibility and better decision-making,” he says.

This often requires investing in fluency beyond security to understand how AI systems work, how your company builds products, and what leadership cares about, Trudeau says.

Build credibility through consistency. “As the scope of the role expands, especially with AI, leaders are looking for clear, pragmatic guidance, not theoretical risk models,” Trudeau says.

There’s no ‘I’ in team

A core part of raising to today’s challenges and elevating your CISO role requires security leaders to bring your teammates along. They will always be your greatest resource, Hensley says. 

“My military experience is part of my DNA and has shaped every part of my life, especially how I think of teammate development, building highly cohesive functioning teams, and prioritizing what is most important,” he says.

So many things in life will come and go, but your impact on others will impact generations, Hensley adds. They carry your values forward from culture, ethics, and standards.

“My legacy will be the teammates that I have served alongside through my career,” he says. “I encourage security leaders to focus on the impact you can make on your team every day — it will ultimately serve to elevate your profile and leave a lasting mark.”

One of the more dangerous assumptions in the current AI market is that broad adoption means meaningful adoption. It does not. Much of what enterprises call AI transformation is, in fact, AI experimentation focused at the edge of the business, in systems and workflows that support employees but are not central to how the enterprise actually operates. These include calendaring, scheduling, meeting summaries, employee communications, customer messaging, document generation, internal assistants, and similar productivity-oriented use cases.

Those applications may be useful, but they are not core applications that directly run the business and determine whether the company performs well or poorly. Inventory management, sales order entry, logistics execution, supply chain planning, procurement, warehouse management, manufacturing operations, and financial transaction processing belong in this category. If these systems fail, the business feels it immediately through delayed orders, lost revenue, rising costs, poor customer outcomes, and weakened operational control.

McKinsey reports that AI is most often used in IT, marketing and sales, and knowledge management, with common use cases including content support, conversational interfaces, and customer service automation. It also says most organizations are still in experimentation or pilot mode, and only 39% report any enterprise-level earnings impact. This supports the idea that adoption is broad, but deep, core-business transformation is still limited.

That distinction is critical because it exposes that most enterprise AI efforts are not going into the systems that define operational performance. They are going into the systems that are easiest to automate, easiest to pilot, and easiest to talk about in a board presentation. The market is flooded with productivity enhancements that create movement around the business while leaving the business itself mostly untouched.

The problem here is that it’s often much harder to prove the value of AI in meaningful business terms. When AI is directed toward applications that are less strategic and not central to the business, the benefits tend to be indirect, diffuse, and difficult to connect to outcomes that matter. Saving time in drafting emails, summarizing meetings, or streamlining internal collaboration may sound positive, but those gains often remain anecdotal. They rarely translate cleanly into measurable improvements in margin, cycle time, service levels, or revenue generation. In other words, the farther you move from the operational core, the fuzzier the business case tends to become.

Why enterprises avoid the core

If core applications are where the larger value sits, why are enterprises not making them the center of AI strategy? The answer is simple enough: More is at stake, the costs rise quickly, and confidence remains low.

Applying AI to edge applications usually carries limited downside. If a meeting summary is incomplete or an internally generated document needs revision, the business survives. A person steps in, makes corrections, and moves on. The failures are manageable. That is one reason shadow AI has spread so quickly. Employees can experiment with relatively little organizational risk because the blast radius is usually small.

Core systems are entirely different. If AI makes flawed decisions in inventory allocation, order processing, logistics routing, or supply chain forecasting, the impact is immediate and expensive. It can mean stockouts, excess inventory, missed shipments, poor customer service, broken supplier coordination, and measurable financial damage. These systems do not tolerate loosely governed experimentation. A bad result here is not a minor inconvenience.

Enterprises know this. Many are not confident enough in their own ability to design, govern, and support AI systems that can operate safely within business-critical processes. Frankly, that caution is justified. Too many AI projects are still based on generic strategies, templated approaches, and weak data integration. They are rushed in order to demonstrate something flashy rather than something operationally useful. The result is a parade of pilots, proofs of concept, and isolated wins that never make it into the systems that matter most.

The current AI platform market adds to the challenge. Enterprises are being handed powerful pieces of technology, but often without a coherent path to operational value. In many cases, they still need to assemble the models, workflows, governance, data layers, and integration points themselves. That engineering burden is already heavy at the edge. In the core, where systems are older, more customized, and more intertwined with business processes, it becomes even more difficult. It is no surprise that enterprises often choose the safer route and automate around the business before they attempt to automate within it.

Edge use cases are also attractive because they are visible and politically easy to support. Executives can tout progress because employees are using AI tools. Vendors can point to adoption numbers and usage metrics. Consultants can highlight quick wins. But visibility is not the same as impact. In fact, the emphasis on visible but low-risk use cases may be delaying the harder work required to produce real enterprise value.

Finding real AI value

Enterprises are unlikely to find real transformation until AI improves the systems that determine how the enterprise performs. Better demand forecasting. Smarter inventory positioning. Faster and more accurate sales order processing. Improved logistics coordination. More adaptive supply chain decisions. Better procurement timing. Stronger resilience in fulfillment operations. These are not cosmetic improvements. They affect the outcomes that executives, boards, and investors actually care about.

This is also where value becomes easier to measure. In edge applications, benefits are often framed in soft language around convenience, efficiency, or time savings. In core applications, the metrics are tangible. Did order accuracy improve? Did cycle time drop? Did stockouts decline? Did transportation costs fall? Did customer service levels rise? Because core applications sit closer to the economics of the business, AI deployed there has a much clearer chance of proving itself.

None of this means enterprises should ignore edge applications altogether. There is still practical value in reducing low-level manual work and giving employees better tools. But those efforts should be viewed as supporting use cases, not the center of strategy.

Enterprises need to stop confusing easy automation with strategic transformation. The priority should now be a smaller number of AI initiatives aimed directly at business-critical systems, supported by better data, stronger governance, internal expertise, and realistic operational design. Until that happens, much of enterprise AI will remain what it is today: interesting technology circling the edges of the business while the real opportunity sits untouched in the middle because success is harder and risk is greater.

Across industries, CIOs are navigating environments defined by constant change, growing attack surfaces, and increasing expectations for availability. The question is no longer whether disruption might occur, but whether IT organizations are prepared to keep the business running when it does. Coming out of IGEL Now & Next® 2026 in Miami, CIOs discovered a clear direction for how endpoint strategy must evolve to meet today’s operational realities.

Security incidents, operational outages, and infrastructure failures are no longer confined to edge cases. They are conditions that modern IT and OT environments must be designed to withstand. As a result, the conversation around endpoints is shifting. The focus is moving from prevention alone toward preparation, continuity, and recovery as part of day‑to‑day architectural decisions.

AI is raising the stakes: Zero trust must now do the heavy lifting

Forrester Principal Analyst James Plouffe described in his keynote how adversaries used AI for reconnaissance, to enumerate vulnerabilities, write custom exploits, and even do the heavy lifting of the paperwork, with AI guardrails “still in their infancy.” 

The recurring thread is velocity and volume, and AI “exacerbates security issues you already have.” He noted that only 67% of organizations report their sensitive data was breached or compromised at least once in the previous 12 months, averaging roughly 2.8 breaches a year, representing billions of dollars in damages. 

While he notes zero trust suffers from fatigue, with “just over 20% putting it in their top five strategic priorities,” implementing zero trust well helps take a big bite out of 14 of the other 22 priorities, including improving detection and response and preventing social engineering and identity-based attacks.

From secure endpoints to adaptive platforms

A key outcome of Now & Next 2026 was clarity around how endpoint strategy must evolve. IGEL’s move from a Secure Endpoint OS Platform to an Adaptive Secure Endpoint Platform™ reflects a broader shift CIOs are navigating: Endpoints are no longer static control points, but active participants in security, access, and resilience strategies.

For CIOs, immutability and centralized management remain essential, but no longer sufficient. Modern environments require policies that adjust dynamically based on role, location, and trust conditions. As IGEL describes, this approach allows “security and experience to adjust in real time based on who the user is, the device, location, and trust conditions.”

CIOs are changing their architectures to account for variability up front, using adaptive endpoints to preserve secure access and continuity however conditions may change.

Business continuity becomes an endpoint capability

Continuity planning is no longer about post-incident recovery. CIOs increasingly need the ability to maintain secure access while an incident is still unfolding, not after remediation is complete. At Now & Next 2026, this shift was a major focus in Jason Mafera’s keynote, where he described business continuity as part of a broader “constant of change” shaping IT strategy. He shared that it is no longer about VDI or not VDI. It is about enabling all application delivery methods while providing flexibility, security, and easier management regardless of strategy. 

This is the operational reality CIOs are designing for in 2026. Environments now span cloud desktops, published applications, SaaS, enterprise browsers, and locally delivered workloads, and continuity must extend across all of them. When disruption occurs, whether from ransomware, outages, or failed updates, the priority shifts from restoring systems to preserving access. 

IGEL BC&DR™ supports this model by allowing IT teams to instantly move endpoints into a secure, controlled state while maintaining user productivity through alternate delivery paths. The result is a more resilient operating posture, where continuity is defined by the ability to shift execution paths without breaking access, keeping the business running as conditions change. 

Preparing for when attacks happen

The business continuity message continued on the mainstage by General Paul M. Nakasone, former Commander of U.S. Cyber Command and former Director of the National Security Agency. He stated in his keynote that resilience is not simply a technical capability, but an organizational mindset that must be established well in advance of an incident.

Nakasone challenged organizations to move beyond contingency thinking rooted in uncertainty. Rather than asking, “If we get attacked, what will we do?” he argued that leaders must operate from the assumption that attacks are inevitable and plan accordingly, shifting the question to, “When we get attacked, here’s what we do.” The distinction has direct implications for preparedness, decision‑making, and execution.

The time to figure out roles, responsibilities, and decisions is not during the incident.

For CIOs, that message resonated strongly with broader discussions across the conference focused on business continuity at the endpoint. When disruption occurs, organizations need predefined response paths, clear authority, and the technical ability to preserve secure access without hesitation.

Customer leadership in action

During the event, IGEL presented the inaugural Now & Next® Innovation Award to Baptist Health Jacksonville. Baptist Health Jacksonville was recognized for revolutionizing its endpoint security, cutting device costs, streamlining management, and channeling cost savings into patient care. 

The two runners‑up, Eurocell and Texas Children’s Hospital, were also recognized for their efforts to modernize endpoint access and security within their respective environments. These customers tell their story in videos found on the Now & Next sessions page.

A growing ecosystem shaping what comes next

Beyond customer stories, Now & Next 2026 highlighted continued growth across the IGEL ecosystem.

In the keynote sessions, IGEL leaders announced expanding engagement across customers, technology partners, and community members. Recognition programs, including the IGEL Ready Partner Awards, MSP Awards and the inaugural class of IGEL Technology Professionals, recognized key partners and IGEL experts who are paving new paths forward.

CIO takeaways from IGEL Now & Next 2026

For CIOs planning the next phase of their security and resilience strategy, several themes stood out:

• Zero Trust must be executed at the endpoint, not just in identity or network layers.
• OT security is evolving to resilience and predictability. Immutable systems reduce risk without impacting uptime.
• Business continuity is about access, not just recovery. Maintaining secure user access during an incident matters as much as restoring systems
• Preparedness requires active planning. Organizations must assume attacks will occur and define response paths in advance.

IGEL Now & Next 2026 session content is available on demand. To learn more about these and other announcements from IGEL Now & Next 2026, visit igel.com/nowandnext2026. 

If your organization uses Microsoft 365 (a.k.a. Office), chances are you’ve encountered Teams, at least for video meetings. But it’s capable of a lot more, providing an effective way for groups of people to collaborate on work and advance business objectives.

Teams is, at its core, group chat software with videoconferencing capabilities and some interesting features around working with documents and spreadsheets, especially those stored in SharePoint and OneDrive for Business. In other words, it’s a collaboration hub. Teams can be bundled with Microsoft 365 plans, and it’s also available as a standalone subscription for both enterprise and small business customers.

Once you get to know it, Teams is a genuinely helpful tool for teams in companies that use Microsoft 365, since it brings together a bunch of suite components and surfaces them in one convenient place.

It’s available as a web app, desktop application (Windows, macOS, Linux), and mobile app (iOS, Android). Microsoft regularly releases updates to the Teams apps, usually with minor user interface refreshments, but sometimes they’re major updates with significant performance improvements, UI changes, and/or new features.

This guide covers the essentials for getting started with Teams and working efficiently within the platform. We also cover extras such as using Microsoft Loop components in Teams and practical use cases for Microsoft’s ubiquitous generative AI tool, Copilot. In this story we’ll demonstrate in the Mac app, but you’ll find that Teams works similarly in any environment.

In this article:

• Getting around Teams
• Joining and creating teams
• Understanding channels
• Chat: Direct messages and group conversations
• Video and audio calls
• Working with files and OneDrive
• Microsoft Loop within Teams
• Copilot Chat in Teams
• Tips for using Teams effectively

Getting around Teams

The navigation bar on the left side of the Teams window is your primary way to move through the app.

Understanding these core sections will help you navigate Teams effectively. Note that, depending on your organization’s configuration, not all of these options may be available to you:

• Activity: Shows notifications about mentions, replies, and other updates relevant to you.
• Chat: Your hub for one-on-one conversations, group chats, and (with recent updates) access to your teams and channels. The unified interface now combines what were previously separate Chat and Teams/Channels sections.
• Calendar: View and schedule meetings, linked to your Outlook calendar.
• Calls: Make and receive voice calls, access voicemail, and view call history, if your organization has a Teams Phone license assigned to your account. (This is not terribly common.)
• Files: Quick access to recent files, OneDrive files, and downloads.
• Apps: Browse and add applications that integrate with Teams.

The navigation bar is customizable — you can reorder items by dragging them or pin frequently used apps for quick access. You can also add things to the navigation bar by clicking the three dots. A few minutes spent rearranging and adding/removing can make your regular daily Teams use much more snappy, without a lot of friction.

Access your chats, teams, and channels in one place

One of the most significant recent changes to the Teams app is the unified chat and channel interface. In Teams, chats are private conversations (one-on-one or with a small group) that you start on the fly, whereas teams are set up for specific groups of people who need to collaborate on an ongoing basis. A team can be a small group you’re working with on a project, your whole department, or in some cases even the entire company. Each team has one or more channels where members collaborate (more on this later in the story).

Until recently, Chats and Teams/Channels were entirely separate sections in Teams, but an interface update has put all conversations in the same place. Here’s how to navigate:

1. Click Chat in the left navigation bar.
2. Next to the “Chat” header at the top of the left sidebar, click the three-dot menu icon.
3. Select Your teams and channels.
4. From here you can browse and access your individual teams and channels.

Jonathan Hassell / Foundry

This change initially confused many users, but the unified approach aims to reduce switching between chats and channel conversations.

Use search as a navigation shortcut

The search bar at the top of Teams is one of its most powerful features for finding messages, files, people, and more. You can search for messages in chats and channels, files and documents stored in your OneDrive for Business or any linked SharePoint libraries, interactions with people in your organization, and “@mentions” of yourself.

Here are some tips to make search more useful:

•  Use / commands in the search box for quick actions (e.g., /call to start a call, /files to find files). If you use Cmd-space in macOS to launch Spotlight, this is similar, but for Teams.
• Filter results by category using the tab buttons that appear across the top (e.g., Messages, People, Files).
• Use keywords like from: to search for messages from specific people.
• If you perform the same searches over and over again, you will find that recent searches appear as suggestions when you click the search box, so just grab the one you want in a single click.

Jonathan Hassell / Foundry

Joining and creating teams

Your organization probably already has several teams that appear in your Teams client. To join an existing team, click Chat in the left navigation, and then under “Teams and channels,” click See all your teams. You’ll see teams you’ve already joined as well as public teams available in your organization for immediate membership.

Private teams are invite only; to join one, you respond to an invitation that comes from a current manager of the team.

You can also create teams ad-hoc (unless your administrator has disabled this option) to facilitate working together. When you create a team, Microsoft 365 automatically sets up a bunch of supporting apparatuses:

• A Microsoft 365 Group
• A SharePoint site for file storage
• A shared OneNote notebook
• Microsoft Planner integration
• An Exchange mailbox for the group

To create a new team:

1. Click Chat in the left navigation.
2. Under “Teams and channels,” click See all your teams.
3. Click Create team in the top right.
4. By default, you’re building a new team from scratch. You can click the More create team options link to pick an existing team as a template on which Teams can model the new team you are creating.
5. Name your team and add a description.
6. Select Private (invite-only) or Public (anyone in the organization can join).
7. Add a first team channel. If you don’t know what you want, just click the dots on the side of the field and it’ll set the name to General.
8. Click Create.

9. On the next screen that pops up, add members by typing their names or email addresses.

If Microsoft 365 is configured appropriately in your company, you can even invite guests from outside your organization, such as vendors and contractors, simply by typing their email addresses into the team-picking screen. Their “guest” status will be clearly denoted in all of their actions. (If you don’t have permission from your administrator to do this, Teams will report back that you are not authorized.)

To manage your team at any time, click the three-dot icon next to its name in the sidebar on the left. You’ll see a pop-up menu where you can add or remove members, create channels for the team (more on that below), change the team name or description, and more.

Understanding channels

Channels are where you converse and collaborate within a team. The General channel is meant to be a catch-all place where you go to start conversations when you first begin using the Teams product; usually more specific topic-related channels will spring from there.

You can create multiple channels for any given team — the Widget Launch team might want to have sales, production, and marketing channels, for example. To add a channel, click the three-dot icon next to the team name in the navigator bar on the left. You’ll see a pop-up menu, and then click Add channel. On the screen that appears, type in a name and a description for the channel, pick a privacy option if you want to limit the people who can access the channel, and click the Create button. All the channels for a team appear underneath the name of the team in the left pane.

Each channel has different pages that let you bring documents, apps, and websites directly into your channels for quick access. From the channel name, click the down arrow icon underneath, and you can choose from a new page, an existing page, other embedded Teams apps, conversations, files in the related SharePoint library, notes in a OneDrive shared notebook, or other items.

Common uses include pinning a specific Excel spreadsheet that your team updates frequently, a OneNote notebook for meeting notes, or a Power BI dashboard for tracking metrics.

The Posts tab in a channel kind of works like Facebook or LinkedIn in that you can comment to your teammates in an ongoing conversation. Channel managers can choose to have a traditional channel, where you just have posts, or a channel organized by threads where you can follow conversations and related activities. (See more on the new threads layout from Microsoft.)

Composing messages is straightforward: Just click the Start a new conversation text box, or click Reply below an existing conversation and start typing.

You can call teammates’ attention to certain parts of the conversation by tagging them with an @ sign when typing, like this: @Adele Can you share the latest workback grid? Users who have been tagged will see, in their own copies of the Teams clients, those tagged parts of the conversation highlighted in bright red so they can easily see and respond to messages. You can use emoticons, emojis, and GIFs as well — that’s what I mean by thinking of this area like Facebook.

Other useful features:

• Formatting: Use the toolbar to add bold, italics, highlights, bullet points, numbered lists, and more.
• Attachments: Click the paperclip icon to attach files.
• Emojis and GIFs: Click the emoji or GIF icons beneath the compose box. Everyone loves a funny meme at work. OK, perhaps not everyone, but sometimes they’re a needed relief.
• Important/Urgent: Mark messages with priority flags using the ! icon.

Chat: Direct messages and group conversations

While channels are great for team-wide or topic-specific discussions, chat provides space for smaller conversations.

To start a one-on-one or group chat:

1. Click Chat in the left navigation.
2. Click the New chat button (pencil icon).
3. Type the name(s) of the people you want to include.
4. Type your message and press Enter.

Chats persist, so you can return to them anytime. They won’t vanish when you close Teams or log off for the day.

If you have an ongoing conversation and find you might want to add someone to a conversation, you can easily start a group chat: head up to the top right of your chat window, hit the three dots, click Participants, and from the pop-up submenu select Start a group chat.

You might be wondering when it’s appropriate to use a chat as opposed to convening everyone involved in a Teams channel. I suggest using channels for topics that need transparency and team-wide visibility. In contrast, use chat for quick questions, sensitive discussions, or conversations involving people outside your main team.

Video and audio calls

Teams offers robust calling and meeting capabilities, from quick calls to scheduled meetings with dozens of participants.

To make an immediate call, click Calls in the left navigation. On the Calls screen, start typing the name of the person you want to call, then select their name from the list that appears. Then click the Call button.

To schedule a call or meeting in advance, click Calendar in the left nav and click the New button in the top right corner. A “New event” pane pops up, where you can add a title, invite attendees, type a message, and add other information if desired. Alternatively, you can set up the meeting from Outlook. Either way, “Teams meeting” is selected by default as soon as you add attendees. You can also book a conference room or other shared resource here, too.

Jonathan Hassell / Foundry

If you’ve been invited to a meeting or call, you’ll typically receive an email or other notification message with a meeting link or Join button. Click the button or link to join the call. For scheduled meetings, you can also click Calendar in the left navigation bar, click on the meeting, and click Join. If you have been sent a meeting invitation with an ID number, you can use the Join with an ID button at the top of the Calendar screen. Or if someone is just calling you ad-hoc, then you can choose to either accept the call without video, accept the call with video, or decline it on the pop-up that appears while the call is “ringing.”

Note: The first time you start or join a call in Teams, you’ll be prompted with a screen to help you set up your audio and video settings for that call. Also, on some systems, particularly Macs, you may be prompted to give Teams permission to record your screen, camera, or microphone. On Windows, your organization may have already given permission to Teams through its systems management software, and you may not be prompted. But if Teams is asking, click Allow to let Teams get the necessary access to each device.

Once you have accepted a call, you can control it using the buttons on the top of the call window, including a dial pad (for voice calls that need key presses); hold; transfer; chat; turn the camera and mic on or off; share content like an app, window, or screen; and then of course hang up the call.

Jonathan Hassell / Foundry

If you have a webcam connected, Teams will grab that camera feed automatically. You can adjust cameras and backgrounds using the Camera button on the top bar. Blurring your messy room or switching to a solid color can often help reduce distractions in meetings. If you want to start the meeting with your camera off, just click the Camera button.

Using the Mic button and dropdown, you can select your audio devices, including your speaker volume. In most cases, Teams selects this correctly, but if you want to switch to a Bluetooth or USB headset, for instance, choose the right device from the Speaker and Microphone section of the right pane. (For headsets, you’ll most likely choose the same device for speaker and microphone.) If you want to start the meeting with your mic off, just click the button.

Under the More control, you can opt to record and transcribe a call, insert video effects, change audio, ask for translation options, and other settings.

To share other content with participants, click Share. You can then elect to share your screen, collaborate on a shared digital whiteboard, present a PowerPoint slide deck, or share individual files to your meeting participants without broadcasting them on the screen.

Working with files and OneDrive

Teams integrates deeply with SharePoint and OneDrive, making file collaboration seamless throughout the platform. When you share files in a channel, those files are automatically stored in the team’s SharePoint site. Similarly, files that are shared in a chat are stored in your OneDrive for Business account.

The Files menu in a Teams channel offers several useful features for document management. It displays all documents that have been shared in that particular channel, allowing you to create new folders to organize your content, upload files from various sources, and open relevant libraries in SharePoint when you need to do so.

To upload files to Teams, you can navigate to the Files menu within a channel (remember, that’s the dropdown menu under the channel name). You can upload a file with the three-dot menu or create a new file of a typical type, like a Word document or Excel spreadsheet, using the + New menu. Alternatively, you can drag and drop files directly into the Files tab or even into a conversation thread for quick sharing.

When you need to work with files in Teams, you can edit them directly within the application. Office files can be edited right in Teams by simply clicking on a file to open it. If you prefer the full desktop experience, you can choose to open files in Word, Excel, or PowerPoint by selecting the Open in Desktop App option. Teams also supports co-authoring, which means multiple people can edit the same document simultaneously. Additionally, you can access the version history to review previous versions of documents through the three-dot menu.

Teams recently updated its interface to bring OneDrive functionality directly into the Teams environment. This integration allows you to access your personal OneDrive files without having to leave the Teams application, streamlining your workflow.

To access OneDrive within Teams, you can click on the OneDrive option in the left navigation panel. This integration makes it significantly easier to share personal files in Teams conversations without the inconvenience of switching between different applications.

Microsoft Loop within Teams

Microsoft Loop components are collaborative blocks of content that can be shared and edited across Microsoft 365 apps, including Teams. These components function as living, shared content blocks that operate similarly to a task list, table, or paragraph, but with a key difference: they stay in sync across applications. When you create a Loop component in Teams and share it in Outlook, any edits made in either location will update everywhere the component appears.

There are several common types of Loop components available for different collaboration needs, such as:

• Lists allow you to create shared to-do lists with assignments and due dates, making task management more efficient.
• Tables serve as collaborative data grids where team members can input and organize information together.
• Paragraphs function as shared notes and content that multiple people can edit simultaneously, enabling real-time collaborative writing and brainstorming.

To create a Loop component within your workflow, you begin by typing /loop directly into the compose box of a chat or conversation to access the component creation menu. Next, you choose the specific type of component you want to create based on your needs, whether that’s a list, table, paragraph, or another option. After selecting your component type, you add your content and click Send to share it with your team members.

Howard Wen

Once the component has been shared, team members can click on the component to edit it collaboratively, with all changes syncing in real time across all instances of that component throughout Microsoft 365.

For a deep dive on Loop components, see “How to use Loop components in Microsoft 365 apps.” (There’s also a standalone Loop web app — see “Microsoft Loop cheat sheet” for details.)

Copilot Chat in Teams

Copilot Chat offers several powerful generative AI capabilities to enhance your productivity within Teams using Microsoft’s favorite new child, Copilot.

It can summarize recent chats and channel conversations, helping you quickly catch up on discussions you may have missed. The tool can also answer questions about your Teams activity, providing insights into your communication patterns and interactions. Additionally, Copilot Chat can help draft messages for you, saving time when composing communications. Beyond these specific functions, it provides general information and assistance across a wide range of topics and tasks.

To access Copilot Chat within Teams, you need to look for the Copilot icon, which can be found either in the left navigation panel or at the top of the Teams interface. Once you locate the icon, click it to open a chat window with Copilot. From there, you can ask questions or request assistance with whatever you need help with, and Copilot will respond to your queries.

Jonathan Hassell / Foundry

There are many practical ways to use Copilot Chat in your daily work.

• For example, you might ask it to “Summarize the Project Alpha channel from the last week” to get a quick overview of all the important discussions and decisions made in that channel.
• You could inquire “What did Sarah say about the budget?” to find specific information from past conversations without scrolling through lengthy message threads.
• Another useful application is asking “Help me write a message asking the team for status updates” when you need assistance crafting clear and effective communications to your colleagues.

This basic Copilot functionality is distinct from the more advanced Microsoft 365 Copilot integration which happens only with an additive license beyond the standard Microsoft 365 subscription. For organizations with Microsoft 365 Copilot licenses, Teams offers more advanced AI capabilities:

• Meeting-related features: Real-time meeting summaries and transcription, automated action items and follow-ups, and chat-like answers to questions about what was discussed during the meeting (even if you joined late).
• Chat and channel features: Comprehensive conversation summaries across multiple channels, suggested replies and message composition, and key highlights extraction from long conversation threads.
• Productivity features: Meeting preparation briefs based on related emails and documents, intelligent recaps for meetings you missed, and follow-up task identification and tracking.

You might not have this level of license, as they’re relatively expensive — $30 per user per month at the enterprise level or $21 per user per month for companies with fewer than 300 employees. That’s on top of what your organization is already paying for Microsoft 365. Microsoft recently reported 15 million paid M365 Copilot seats — just 3.3% of the Microsoft 365 user base.

Tips for using Teams effectively

Here are some battle-tested tips for getting the most out of Teams and, perhaps more importantly, not letting Teams eat your workday and become the place where knowledge goes to die.

Tamp down notifications and activity settings

Managing notifications prevents Teams from becoming overwhelming. If you go with the defaults, Teams will buzz you — and your phone, too, if you have the companion app installed and set up — every time you get a call, message, or chat with an @mention to you. In a busy organization, this can drive you up the wall or result in your entire day being swallowed by Teams notifications.

To customize notifications:

1. Click your three-dot menu in the upper right and select Settings.
2. Go down to Notifications and Activity on the left side.

Adjust everything to your preference. Teams is a very chatty app, so you may want to play around with these settings over time to find your particular sweet spot.

Add apps to Teams

In addition to its built-in integrations with Microsoft 365 tools like OneDrive, Loop, and Copilot, Teams lets you bring a variety of Microsoft and third-party apps right into a channel or group chat. You can add apps for brainstorming, project management, polling, training, data visualization, sales, customer support, and countless other uses, allowing you to work in them without having to leave the Teams environment.

See “18 Microsoft Teams apps for content collaboration and management” for complete instructions on finding and installing apps, but in a nutshell, you click the Apps button in the left navigation bar, browse or search the available apps, and then install the one you want. (Note that your organization may restrict which apps you can install in Teams.)

5 more quick tips

• Keep channels focused: Create channels for specific topics, projects, or workstreams rather than having everything in General.
• Use @mentions strategically: Mention individuals when you need their input, @channel when everyone in the channel should see it, and @team sparingly for truly urgent, team-wide announcements.
• Pin important messages: Hover over any message and click the three dots (…) to pin it to the top of the channel for easy reference.
• Save messages: Click the bookmark icon on messages you want to find later, then access them through the Saved item on the left navigation bar, toward the top.
• Use status messages: Update your status with custom messages like “In a meeting until 3pm” or “Working from home today” to keep teammates informed.
• Use keyboard shortcuts: Press Ctrl + / (Windows) or Cmd + / (Mac) to see a list of keyboard shortcuts that can speed up your work.

What to avoid

Teams is definitely a big step up over endless email chains, but that doesn’t mean it’s suitable for everything. Here are two things to avoid:

Trying to replace all emails with Teams conversations and links. Sometimes we humans have a tendency to gravitate to whatever new features and tools there are, proclaiming them the “killer” of whatever came before and trying to force old square pegs into shiny new round holes. Teams is no different.

As an instant messaging platform, Teams is ideal for back-and-forth quick hits. If your message is longer than a paragraph, chances are, it should go back to email. Longer conversations, project planning, longer term development, all of those types of deep thinking and analysis are best suited for email. In email, you can sort, filter, set up rules, and do other automated things to manage how you see and find information. It’s not impossible to use Teams for longer conversations, but it’s suboptimal.

Trying to send emails to external folks. Unfortunately, there is no way for Teams to send email out to the internet, so unless you want to invite external users as guests into your team (assuming you have permission to do that), you will need to handle some subjects that involve people outside of your organization via old-fashioned email messages. That, of course, limits the utility of using Teams in projects or environments with a lot of collaboration with external users when they’re not a part of your Teams environment.

Your organization may restrict how external users can interact with your Microsoft 365 setup, usually via data loss prevention policies and prohibitions on folks outside your organization accessing Teams chats and channels. Ask your IT department if you have questions.

This article was originally published in March 2018 and most recently updated in April 2026.

The rise of AI has created significant challenges for modern data center infrastructure in terms of power management. Traditional enterprise racks that once consumed an average of 7-10 kW, require close to 30-100 kW today. This significant increase in computational requirements has revealed a fundamental bottleneck: The traditional infrastructure isn’t enough to sustain ‌AI growth.

However, AI can also prove to be a savior: By embedding it into hardware design and automated construction workflows, data centers can evolve into intelligent, adaptive systems instead of the passive hubs that they are.

Revolutionizing hardware architecture through AI

Due to the rapid development of AI models, a significant innovation is required to reshape hardware design into a streamlined AI-driven innovation cycle. This is required at the microarchitecture design and macro-level system management.

AI-driven chip design processes

Modern-day AI accelerator chips integrate chiplets, high bandwidth memory (HBM) stacks and dense interconnect structures; hence, manual design isn’t scalable. AI-driven EDA tools are essentially the future.

AI-driven EDA tools can be pivotal in multiple avenues. In one such instance, Google has shown that something as complex as chip floorplanning can be done in hours that rival or surpass human efforts in terms of quality. These optimizations can reduce parasitic energy losses and prevent thermal hotspots in the physical design (PD).

AI models can also be used to evaluate thousands of multi-die configurations to predict hotspots, “through silicon via” (TSV) density issues and power-delivery constraints. This enables far more thermally balanced 2.5D/3D layouts than traditional heuristics.

Apart from PD floorplanning and prevention of hotspots, verification is another avenue where AI-driven EDA can be useful. Verification is significantly important because it consumes up to 70% of chip development time. AI tools such as Synopsys ML verification and Cadence Cerberus will prove to be useful tools to reduce this development time. Once the development time of a chip is reduced, meeting the growing performance needs of AI models will be feasible.

Another avenue where AI can be useful is reducing the power consumption in frontend design. Researchers have successfully demonstrated that ML-driven dynamic voltage frequency scaling (DVFS) strategies reduce the power without significant performance loss. AI can also be used to predict power consumption of the RTL design and post-layout snapshot in seconds, allowing designers to iterate rapidly.

Thermal and power management

Since modern AI chips generate vast amounts of heat, which can lead to hardware failure, modern AI chips require algorithms that can analyze data from multiple thermal sensors. AI algorithms can play pivotal roles here. Modern datacenters have used AI to reduce energy consumption in facilities, achieving significant amounts of energy savings. These AI-driven systems improve hardware longevity and reliability while significantly reducing operational costs.

AI can also be used to analyze the operational data and identify energy-intensive processes. It also then goes on to allocate computational tasks to efficient resources. This leads to less idle time and hence avoids power waste.

This creates a “self-sustainable cycle”: Power-optimized hardware enables the training of even more powerful AI models, which in turn are used to design the next generation of hardware.

AI in data center design and construction

To meet the demand for “speed-to-market,” AI can be integrated into procurement and design phases of data centers. These segments were historically slowed by manual reviews and complex specifications, something that AI can help with.

Streamlining procurement and design

AI tools can be particularly useful in automating tasks that otherwise require a substantial amount of manual work. For example, LLM-based assistants trained on design standards and Request for Information (RFI) history can now respond to vendor queries in minutes – a task that would have taken a control engineer 2 to 4 hours. Similarly, machine learning systems can be used to extract control point requirements (temperature setpoints and pressure limits, for example)‌ from 100% design drawings. This can help reduce human errors while transitioning from blueprint to physical installation.

In addition to identifying control requirements, generative AI tools can organize information scattered across multiple documents and convert it into structured outputs. For example, AI can automatically generate equipment schedules that list all major components, their capacities, control parameters and operating limits. Activities that once took design teams several weeks—such as cross-checking documents, extracting control data and preparing schedules—can potentially be completed in hours.

Automated commissioning and configuration

The commissioning process of the datacenter spans across five levels: Originating with factory tests and ending with integrated system testing. This is a final hurdle before the data center goes live in operation. Consequently, it’s a key step in the process, but can become tedious as it requires validating complex interconnected electrical and mechanical systems to ensure zero-downtime reliability, often under tight timelines. AI scripts can be helpful in reducing the burden here by automatically checking software configurations and interconnected systems to reduce rework during final testing. Generative AI can also be used to simulate system behavior under various operating conditions before physical commissioning can start. This allows the system to achieve optimal performance upon handover.

Predictive operations and AIOps

AI can also be used to make the management of data centers predictive and proactive instead of reactive. For example, AI can be used to predict the maintenance schedule. This is possible after the initial model is trained on vibration and voltage sensors. After that, the AI model can forecast failures. This will lead to an increase in reliability and a reduction in unplanned downtime.

Similarly, AI can also be used to place high-intensity workloads in cooler areas of a datacenter, preventing “thermal hotspots”. This will reduce the energy required for ‌cooling.

Since security is of paramount importance in data centers, AI can also be used to enhance physical and digital defense by tracing network anomalies, such as suspicious traffic patterns or unauthorized access attempts. This will lead to the neutralization of threats in real-time, instead of reacting to the threats.

Sustainability and the circular hardware economy: Beyond the linear lifecycle

Traditionally, enterprise servers had a lifecycle of 3-5 years. Now, with AI models being developed rapidly, AI hardware is being refreshed in 12-18 months. This is leading to large amounts of “embodied carbon waste”, which isn’t environmentally sustainable.

Consequently, hardware and infrastructure engineers need to pivot to a circular hardware economy framework, where hardware is an “evolving asset”.

At the hardware level, modularity is paramount, so that operators can upgrade to high-performance accelerators while retaining the chassis, power delivery units and cooling manifolds. This will significantly reduce the embodied carbon waste during raw material extraction and fabrication of the non-compute components.

To further solve this issue, AI can be used to decommission the hardware. Intelligent AI systems can analyze the telemetry data points from the server rack’s operational history to predict the remaining useful life of the chip or components around it (such as power delivery units or cooling manifolds). Healthy units can then be redeployed to “edge” data centers for less intense inference tasks, whereas failing units can be routed to specialized facilities for recovery of important materials.

This way, we can address the AI paradox: Use ‌AI to mitigate the environmental footprint of the machines. This ensures that the next generation of infrastructure is not produced for better speed and performance, but is also sustainable.

Modern approaches, not conventional engineering

With the exponential growth of performance-critical AI models, AI has become a foundational requirement for datacenter infrastructure. Even though modern AI models lead to an increase in total power/energy consumption, they can also act as a critical driving force to mitigate the same. To keep up with the growing rise of AI models and the subsequent rise of power/energy consumption, we need to switch to modern approaches rather than relying on conventional engineering. The next generation of datacenter infrastructure will be defined by how well we manage the evolution of hardware design and automated construction to build AI-capable data centers.

By integrating AI at the silicon level and the structural level, we are not just building faster computers; we are building a more intelligent foundation for the future of technology.

Disclaimer: The views expressed in this article are solely those of the authors in a personal capacity and do not represent the views of their employers.

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Every business process reflects the constraints that existed when it was first devised, says IT exec Maria Cardow.

Those constraints, Cardow explains, typically derived from technology limitations at the time of the process’s initial implementation. As a result, many business processes to this day involve workflows that still require manual actions or cumbersome jumps between multiple computer applications to accomplish essential tasks.

But artificial intelligence and other modern technologies can knock down those old constraints, “which is why it’s incredibly important to reassess processes” before attempting to automate what may likely be an ineffective, outdated process, says Cardow, CIO of managed security services provider LevelBlue.

“You have to check the assumptions in processes before you kick off a transformation if you want to allow for greater innovation,” she emphasizes.

Executives are feeling the pressure to transform or optimize their organizational workflows with AI. J.P. Morgan’s 2026 Business Leader Outlook found process automation to be the most common AI application that midsize business use or plan to use, cited by 62% of executives surveyed. And EY’s CEO Outlook 2026 found that 43% of CEOs identified optimizing operations and improving productivity as a transformation priority, making it the top cited priority, with enhancing product and process innovation coming in at No. 3.

Cardow is in the thick of leading such efforts, and she’s finding that IT is well positioned to do the work.

For example, when examining business workflows, her IT team often finds that processes span “at least a dozen different systems and that the number of assumptions baked into workflows were dependencies on technologies two generations back,” she says.

Such instances show why CIOs should lead their organizations through process optimization before automating with AI, Cardow says, citing the longstanding admonition against automating bad processes.

A natural fit for optimization

Anytime a technology is up for license renewal, LevelBlue endeavors to review any business process that depends on it, Cardow says. The goal is to determine whether the process is ripe for optimization and transformation before renewing or replacing the existing technology — and before applying further automation or AI.

That timing means Cardow, as CIO, takes a lead role in optimizing enterprise processes. Moreover, she brings more in-depth knowledge than her business colleagues of what technology capabilities can optimize and transform a process. And as CIO, she has a remit to improve productivity and efficiency while driving down costs.

The CIO is a natural fit for such work, Cardow adds, pointing out that CIOs have had responsibility for optimization for most of the role’s existence.

IT also brings objectivity to process optimization, she adds. Workers are often comfortable with the status quo and the tools they’re using, whereas CIOs and IT teams don’t have such attachments.

“Being able to critically challenge how processes are executed is absolutely essential to my role now,” she says. “[The business] will still say, ‘This is how it gets done,’ but CIOs are well positioned to ask about baked-in assumptions and whether certain steps are still appropriate as they partner [with the business] to streamline processes and develop solutions.”

AI heightens the significance of process optimization

As Cardow notes, CIOs have been involved in process optimization since the role’s inception, implementing technologies to automate tasks. But the CIO’s role in process optimization has taken on heightened significance in the AI era.

“It’s vastly different now,” says Doug Gilbert, CIO and chief digital officer at Sutherland, a digital transformation services company. “There’s been an evolutionary shift.”

Gilbert says CIOs focused on improving individual tasks during prior waves of automation, such as when robotic process automation (RPA) was first rolled out. Now CIOs must optimize entire processes and workflows to transform larger swaths of their organization’s operations.

“If you’re looking at AI to be a more complex RPA to solve minute tasks and activities, you’re going to fail,” he says. “You’ve got to look at how humans work across systems and across tasks; automation today has to go across an entire process flow.”

He cites as case in point his company’s Insurance AI Hub, a project launched and completed in 2025.

“As CIO, I was responsible for the overall technology direction and architecture. This meant overseeing the implementation of a full ecosystem of domain-trained AI agents for underwriting, claims adjudication, and policyholder servicing across multiple lines of business, while ensuring clean master data foundations, embedded governance, observability, and human-in-the-loop controls were built in from the start,” he says. “The result is production-scale agentic AI that delivers up to 30% faster claims cycle times, lower leakage, higher satisfaction, and stronger compliance.”

Gilbert cites as a second example his company’s agentic AI platform for healthcare provider credentialing, saying this multiagent system “reimagines a highly complex, regulated, cross-functional process at the macro level — automating and contextualizing information across many previously siloed processes, internal systems, and external sources.”

These agents “work in tight coordination to tackle a much larger, macro-level complex process — sharing contextualized data in real-time, maintaining quality and compliance throughout, and enabling a far more comprehensive level of automation and problem-solving across the entire end-to-end credentialing workflow,” he says. “This turns what used to be a slow, manual, multiweek process into a fast, accurate, governed operation.”

A point of inflection for CIOs

IT execs say process optimization today requires more from CIOs than in the past. CIOs must now understand how work happens within the organization in more detail, as well as how those processes sit within broader workflows, how processes and workflows arrive at decisions, and what defines a good or accurate decision.

“Agentic AI — the ability for LLMs to actually take action — is raising the bar significantly,” Gilbert says. “When designed properly, these systems don’t just follow rules; they plan, decide, act across systems, and learn. That means CIOs can no longer optimize in silos or bolt intelligence onto broken processes. We must now lead the complete reimagination of workflows so they are natively designed for autonomous agents while staying inside clear guardrails.”

Those are more complex — and more significant — requirements than CIOs have faced to date, Gilbert adds.

“In the past we would receive a process from the business, map it, automate the obvious parts with RPA or traditional tools, and hand it back. Today, because AI brings reasoning, contextualization, and the ability to work across systems, the CIO must lead the charge in fundamentally reimagining how work gets done,” Gilbert says.

“We now own end-to-end process intelligence, ensuring the underlying data is clean and contextualized, governance is embedded, and the optimized process actually delivers trustworthy business outcomes,” he adds. “The CIO must sit at the strategy table and, in large part, drive it going forward.”

Organizations across industries are indeed turning to their CIOs to reimagine workflows, says Catherine Malkova, senior vice president of Kyndryl Consult and Practices US.

“There is a focus on business workflow, not just working with old processes and making them more efficient but instead remaking them from the ground up into an AI agentic workflow. It’s about building an AI-native enterprise,” she says, noting that “the ‘agentification’ of workflows is what will drive the next wave of AI adoption.”

Kyndryl’s 2025 Readiness Report highlights the amount of transformation executives are expecting, with 87% of the 3,700 business leaders across 21 countries saying that AI will completely transform job roles and responsibilities at their organizations this year.

Challenges abound

Expectations of transformation are high, but so too are the challenges that CIOs and their C-suite colleagues face.

Kyndryl’s research identifies “five specific readiness challenges ahead of them: getting solid tech foundations, managing global data, evolving workforces, pressure to scale AI pilots, and aligning leadership.”

Rigid workflows, fragmented systems, lack of trust in AI, lack of required skills, and resistance to change also present challenges to CIOs, their colleagues, and their teams as they seek to optimize and transform processes and workflows, Kyndryl’s Malkova adds.

Others list the lack of good documentation for many processes as another barrier, along with concerns about data security, privacy, and AI hallucinations.

Dom Profico, who as CTO of consultancy Bridgenext advises CIOs, says enterprise ambition itself can be problematic, as executives and teams may rush to adopt AI without putting in the work required to transform the processes AI is meant to help optimize.

“AI makes it so much easier to do that automation, so you have a higher risk of automating a bad process. And there’s a lot of pressure in the age of AI. Everyone feels like they’re behind. That adds to the likelihood and risk of going too fast,” he says.

Profico stresses that while automating existing processes may create some efficiencies, it may not lead to the optimization and transformation that delivers the significant returns that CEOs and boards now want from AI investments.

CIO as chief instigator

CIOs must also bring rigor to the discipline of process optimization “to make sure the teams are automating the right things and using the technology in the right way to do that,” Profico says.

He lists systems thinking as a must-have skill for CIOs, as it allows them to take a holistic problem-solving approach where they see how the different parts interact within a larger whole.

That, he notes, requires CIOs to deepen and broaden their knowledge of the business, how work gets done, and what adds value.

Sutherland’s Gilbert agrees, saying needed skills include deep fluency in process intelligence platforms and mining tools; strong data strategy expertise (especially master data management, lineage, and context engineering); AI governance and risk management (particularly around autonomous agents and accountability); advanced change leadership (i.e. designing new operating models where humans and agents collaborate effectively); and business translation skills — “the ability to link technical decisions directly to P&L and customer outcomes.”

“Technical depth is still essential, but the real differentiator today is the ability to think systemically about human-plus-agent workflows and to lead at the intersection of strategy, technology, and risk,” Gilbert adds.

Merim Becirovic, CIO, managing director, and partner at Boston Consulting Group, has a similar take on the process optimization work that CIOs and their teams are leading.

Like others, Becirovic says CIOs in the past focused on optimizing processes within siloes. “But what we’re doing now is not about sending people across the different systems. It’s about transforming the journey and the experiences within that journey. It’s about connecting the dots and connecting the systems that create those experiences and that journey,” he says.

That requires CIOs to focus on how processes and workflows connect to deliver outcomes.

“That’s a natural evolution for me as a CIO,” he adds. “Technology gives you a lens into that work and gives you access to products that allow you to break siloes down.”

Becirovic points to his IT department’s delivery of Deckster, a homegrown AI tool for slide deck creation, as an example of successful process optimization and transformation.

BCG workers create more than 30 million slides annually, making it a process that takes up significant work time. Rather than focus on incremental gains by using generative AI for specific tasks, such as crafting the language on individual slides, BCG teams reimagined the entire slide-creation lifecycle. Using OpenAI’s API alongside a curated library of BCG templates, Deckster produces fully formatted, client-ready slides in just three seconds instead of the 15 minutes it had taken.

BCG is now bringing agentic AI capabilities to Deckster to further optimize and transform the process.

“Those are the opportunities where CIOs can step in and say, ‘Is there a better way to work?’” Becirovic says. “I really think CIOs today and tomorrow need to be far more aggressive in looking for change and driving change. The CIO needs to be the instigator, influencer, enabler, collaborator, and the innovator to make that happen.”

ClickFix malware campaigns are evolving again, with threat actors removing one of their most obvious and user‑dependent steps: convincing victims to paste malicious commands into Terminal. Instead, the latest variant uses a single browser click to trigger script execution, streamlining the infection chain and reducing user hesitation.

Researchers at Jamf Threat Labs have identified a new macOS campaign that launches Apple’s native Script Editor directly from the browser, preloaded with malicious code. The technique abuses the applescript:// URL scheme to open Script Editor automatically, sidestepping Terminal entirely and delivering Atomic Stealer payloads with far less friction.

“Script Editor has a well-documented history as a malware delivery mechanism, so its presence here isn’t
surprising,” the researchers said in a blog post. “What is notable is its role in this ClickFix campaign and the fact that it was invoked via a URL scheme.”

The payload isn’t new. It’s Atomic Stealer, a credential-harvesting strain commonly deployed in macOS-focused campaigns.

Apple drops protection, attackers go around it

Conventionally, ClickFix chains relied on social engineering to get users to paste obfuscated commands into Terminal. Apple’s recent protections introduced scanning and prompts around pasted commands, adding restrictions to disrupt that flow.

This campaign routes around it.

Victims are directed to an Apple-themed page posing as a system fix or cleanup guide. Instead of copying anything, they click a button that invokes an applescript:// URL. That action opens Script Editor with a pre-populated script, ready to execute.

By not directing the user to interact with the Terminal, the attacker has removed a decision point that Apple enforced with macOS Tahoe 26.4. “Apple took direct aim at this in macOS 26.4, introducing a security feature that scans commands pasted into Terminal before they’re executed,” the researchers added. “It’s a meaningful friction point, but as this campaign illustrates, when one door closes, attackers find another.”

Script Editor is a native macOS utility and doesn’t carry the same immediate suspicion as Terminal for non-experienced users. However, there is still some non-targeted resistance to this technique.

The researchers pointed out that the behavior of the Script Editor may vary depending on the macOS version. “On recent versions of macOS Tahoe, an additional warning prompt is presented, requiring the user to allow the script to be saved to disk before execution,” they said.

Lightweight staging for Atomic Stealer

Once executed, the AppleScript resolves to an obfuscated shell command. That command decodes a hidden URL, retrieves a remote payload using ‘curl’, and executes it via ‘zsh’. From here, standard info-stealing takes over with a ‘Mach-O’ binary written to a temporary location, its attributes adjusted, permissions set, and execution triggered.

This binary is a new variant of the Atomic Stealer.

The researchers noted that the staging approach keeps the initial script minimal and less detectable, while the actual malicious logic arrives separately. It is modular, quick to update, and harder to catch at the first stage.

Atomic Stealer’s objectives are consistent with earlier macOS infostealer campaigns, which focused on harvesting browser credentials, saved passwords, crypto wallet data, and developer artifacts. Previous reporting has shown that such stealers rarely operate in isolation, as exfiltrated data is almost always funneled into credential reuse attacks and account takeovers.

The pursuit of artificial intelligence is filled with speculation and frenzy, akin to the Gold Rush in 1848. Organizations are taking large risks and heavily investing to overhaul culture and technology with the hope of accepting failure risks in exchange for potential market-redefining advantages.

But as the initial dust settles, the reality is stark. Prospectors —  companies pouring millions into unproven AI initiatives — are finding that the easily accessible gold is gone. Meanwhile, the ‘shovel sellers’ — the infrastructure and tool providers — are the only ones consistently thriving.

It is time for a complementary viewpoint. For the implementers and consumers of AI, we must shift our mindset from the exhaustion of a gold rush to the agility of a digital egg hunt.

Why the ‘egg hunt’ beats the ‘gold rush’

A gold rush is about urgency, scarcity and the risk of total failure. An egg hunt is about adventure, discovery and the thrill of the find. In an organization, the ‘eggs’ aren’t hidden in some far-off mountain; they are already tucked away in your operating procedures, technical limitations and daily decision-making.

By shifting to an egg hunt framework, we balance the frenzy of the AI era with disciplined discovery. We stop looking for one massive, mythical nugget of gold and start collecting dozens of high-value, low-risk eggs that, when gathered, drive massive cumulative value.

As a child, I had an approach to an egg hunt.  I would run through the yard as fast as possible, looking for eggs easy to find and placing them into my basket. As I found each egg, I would turn to my parents, sharing a moment of excitement before continuing the race for the next egg. Once I found all the easy-to-find eggs, I followed with a second pass seeking more difficult-to-find eggs. I would quickly expand my ability to discover as I learned new approaches my parents had for hiding the eggs behind a bush, on a tree limb or in a downspout. After confirming all the eggs are found, we would return inside to open the eggs.

Training the hunters: Identifying cognitive friction

We want to be a smart, disciplined egg hunter focused on creating maximum value by quickly learning approaches, accelerating our processes and generating value. The collective opportunities in the basket ensure our organization is running as efficiently as possible and all effort is translated into value with tangible, measurable, business outcomes with reduced risk of prospecting.

As AI takes on more technical execution, human-centered skills become a competitive advantage.  We must train our facilitators to look past traditional waste reduction and toward intelligence orchestration and cognitive behaviors.  Creativity, communication, emotional intelligence, resilience and lifelong learning are no longer soft skills. They are core skills in an AI-centric organization.

Research in the Harvard Business Review suggests that more than four in ten working hours are spent on manual transactions, the friction of coordinating people and aligning complex work. Researchers Sharma, Guan and Wilson posit that roughly one-third of these tasks can be fundamentally reinvented through AI agents.

The eggs in your environment are hidden in cognitive friction points. These include:

• Cognitive processing time: The thinking gap in a manual process.
• Cross-referencing: Switching between multiple systems to resolve ambiguity.
• Data scavenging: Gathering information from disparate, unorganized sources.
• Resolving ambiguity: resolving ambiguity or finding clarity on information as part of a process.

View these cognitive friction points as an opportunity to highlight non-value-added work on value stream maps. These maps are a lean management technique for analyzing the flow of materials, requirements and data associated with a given process, system or product. By setting performance expectations on cognitive steps, performance defects can be found, surfacing data quality issues or process ambiguity that can be resolved via governance.

Once processes are mapped, organizations should consider translating these to digital twins of the process. Sharma, Guan and Wilson state that digital twins of the process provide radical visibility to employees of the process. This will challenge legacy mental models of the process and provide complete visibility. Establishing this as a foundation enables users to simulate process changes and foster continuous experimentation.

To aid adoption, consider a training focus that incorporates:

• Practices to identify cognitive load and data friction in tasks that require judgment and logic.
• Pattern recognition for individuals to identify content creation that can be automated. This can leverage a prompt-as-a-process pattern.
• Shift from linear flow of process to incorporate feedback loops. This ensures that when transitioning to agentic AI, human-in-the-loop processes can be generated to govern agents.

Deliver a modern egg hunt through digital learning labs and hackathons

A digital egg hunt is best executed through curated learning labs and hackathons. These are not traditional training sessions; they are high-energy, two-to-three-day sprints designed to move participants from passive observers to active facilitators.

These are not merely a replacement for traditional training. Learning labs promote experimental resilience, providing safety to fail and shifts from what is learned to how participants think.

To run a successful lab:

• Partner across organizations to host the learning lab. It is important to ensure a critical mass of leadership is engaged and there is broad support across technological domains.
• Focus on generating ideas that generate direct, demonstrable business outcomes. Opportunities should be prioritized for adoption, focus on eggs that can produce provable ROI by streamlining a specific department or eliminating and reducing stuck costs.
• Curate the agenda to ensure success of the outcomes. Aligning like opportunities enables participants to focus on key learning points and maximize learning.
• Pre-stage and configure long-lead items to achieve ample speed to value. Basic things like account setups, security access, software installations should all be completed in advance. In addition, it would be helpful if a senior resource reviews and resolves risks.
• Communicate results of the workshop to provide participants with a chance to discuss learnings.
• Create a case study of activities to further communicate and expand future learnings.

This rapid generation of ideas with focus on business outcome lowers the total costs of development by driving to rapid prototyping and evaluation. Organizations can quickly identify what opportunities to take forward or kill.

Avoiding the post-event slump

The greatest risk to any innovation initiative is the post-event slump, where participants return to their desks and lose momentum. To realize full business outcomes, high-value eggs found during the lab must be fast-tracked into a 30-60-90-day roadmap.

To maximize ROI, consider these steps:

• Create an evaluation that measures participant work products for the ease of maturation, change effort and business value. Fast-track high-value items for completion in a near-term roadmap.
• Demo solutions to stakeholders to highlight learning and business outcomes.
• Have executives sponsor high-value ideas and partner with participants to bring them forward beyond the learning lab.
• Maintain a learning library that is available to other participants or facilitators.
• Extract reference patterns to be used as accelerators.

The bottom line

In the AI era, you can either mobilize your employees as desperate prospectors or empower them as energized hunters. By fostering a culture of the digital egg hunt — built on cognitive mapping, digital twins and rapid prototyping — you don’t just find value; you build an organization that is faster, smarter and far more resilient than those still digging for gold in empty hills.

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Over the past few years, AI has changed the way we work and has the potential to improve enterprise productivity. 

Yet most organizations have not yet realised a meaningful return on their AI investments. 

Across enterprises, we’re seeing a pattern of AI being used to improve task speed, but this doesn’t necessarily translate into organizational priorities being achieved sooner. Improving productivity at scale requires clarity on goals, deliberate system design for execution, and the effective use of company knowledge. 

This article introduces a framework built around four flows — purpose, work, knowledge and intelligence — that determine how effectively organizations convert AI investment into performance. 

These flows apply across all industries and roles, not just software teams. 

When these flows are healthy, teams move faster, make better decisions and deliver higher-quality outcomes. 

Purpose flow: How clarity moves 

Purpose flow is the movement of clarity through a company. 

It answers the three questions that teams and leadership need to operate effectively: 

• What’s important right now? 

• Why is it important? 

• How does our work contribute to organizational outcomes? 

When purpose flow is strong, teams don’t need constant direction; they make high-quality decisions autonomously, and teams align more naturally during execution. 

When purpose flow is weak, AI accelerates activity but not impact. Teams might produce more output, but not necessarily outcomes aligned to strategy. 

Research shows that teams who experience strong purpose flow are 6.4 times more likely to produce high-quality work, 2.2 times more likely to focus on the work that matters most, and 4.9 times more likely to meet deadlines. 

Given the outsized performance boost purpose flow can deliver, it’s astonishing how many organizations share priorities and strategy annually, then bury it in slides never to be seen again. 

Conversely, when purpose flow is unhealthy, it can negatively impact company culture and overall achievement of the company’s strategy.  

Leaders see slow progress made against goals and are unclear about why their teams can’t deliver faster. Lack of purpose flow could be a contributing factor. Teams that hesitate or overanalyze decisions usually aren’t simply slow; they’re not sure which direction is safe. Strong purpose flow removes that hesitation by embedding clarity and goals into the system of work itself. 

Purpose flow is healthy when goals are continuously reinforced, teams deeply understand context, and can clearly articulate how their work contributes to a measurable company outcome. 

Once purpose flow is healthy, it directly supports work flow. 

Work flow: How execution actually happens 

Work flow is how work moves from idea to outcome, in practice, not on paper. 

In high-performing teams, work progresses with minimal friction and replanning. They dedicate time upfront to understand dependencies, handoffs are clear, and work is made visible. That visibility has a compounding effect; it increases trust across the organization whilst reducing the coordination overhead that slows most organizations down. 

Engineering teams are a useful example to study because, over time, the engineering craft has deliberately designed work to flow. 

The DevOps movement is often misunderstood as primarily about automation or tools. In reality, its greatest impact comes from cultural and organizational shifts that reduce handoffs, encourage cross-functional collaboration, and design work so teams can move together toward an outcome. 

Those same principles apply beyond software teams. 

I recently helped a product marketing team that was launching a campaign spanning product, brand, creative, and digital functions. The work itself wasn’t complex, but the coordination was. The flow of work was painful to watch; progress depended on meetings, email threads and constant status updates as teams waited on each other for context and alignment. 

We mapped dependencies, clarified team responsibilities, aligned on priorities and made all work visible in the planning system. Communication moved to structured async updates instead of meetings. 

Spending a few hours streamlining work flow significantly improved throughput. 

Designing how the work happens had a lasting positive effect for these teams. The number of meetings dropped significantly, as did the coordination overhead through meetings and emails. The friction between teams evaporated, enabling sustained productivity gains across groups. 

“The machine is working right” is the best way to describe a healthy work flow. AI can’t compensate for a poorly designed machine. 

Knowledge flow: How information and learning move 

Knowledge flow is how information, context and learning move through a company. 

Knowledge is one of a company’s most valuable, and most underutilized assets. Organizations invest heavily in cybersecurity and data loss prevention to ensure outsiders can’t access their secret sauce. Ironically, little effort is typically put toward helping internal teams use that precious knowledge. 

Knowledge flow is foundational to productivity. It’s the ability for teams to find accurate, up-to-date information in the moment they need it, without having to ask someone. 

When knowledge flow is weak, companies accumulate information debt. The symptoms include knowledge hidden in inboxes, lessons being repeatedly learned and meetings as the primary vehicle for sharing information. 

Studies consistently show that knowledge workers spend 20-30% of their time searching for information. That’s the equivalent of 1.5 days per week spent looking for information, rather than making progress on company priorities. Even modest improvements in knowledge flow unlock disproportionate productivity gains. 

When knowledge flows well, teams innovate by building on the company’s prior learning. Collaboration between teams shifts from low-value information discovery to high-value problem-solving. 

Knowledge flow is the cornerstone of the transformation I’m leading at Atlassian Williams F1. Centralizing and unlocking knowledge across the organization means teams are able to find the right information at the moment they need it. It supports accelerated innovation and compounds into even more company knowledge. 

Knowledge flow is a prerequisite for effective AI. When knowledge flow is fragmented or outdated, AI operates on incomplete context, producing outputs that require heavy correction and erode trust in the technology. 

Intelligence flow: AI as an amplifier 

Intelligence flow empowers teams with insight, helping them focus on high-value work. 

It’s a powerful flow that comes with a warning: It will amplify the strengths and weaknesses of purpose, knowledge and work flows. 

Organizations with healthy flows can use AI to operate at a different level. Instead of collecting information across multiple systems and people, AI synthesizes organizational context into structured, decision-ready knowledge. 

I was recently preparing for a customer executive engagement. In the past, the account executive would manually prepare a briefing document for me by pulling information from various systems and people working on the account. 

Instead, I used a Rovo Customer 360 AI agent. Within minutes, I had a structured briefing covering our footprint, active delivery work, risks and usage trends. 

What previously required days of coordination was reduced to minutes, with higher quality insight. 

This outcome was only possible because purpose, work and knowledge flows were already strong. The goals were clear, the work was visible, the knowledge was structured and available. 

In contrast, accelerating work flow in an organization with weak purpose flow simply accelerates work misaligned with company goals. In an organization with fragmented knowledge, AI produces outputs that require extensive correction and may not translate into time or other efficiency gains. 

AI doesn’t fix broken systems; it scales them. 

Companies that don’t design for intelligence flow will be left behind. But intelligence flow is only as strong as the purpose, work and knowledge systems beneath it. 

Designing for flow is the real productivity strategy 

Most enterprises try to improve productivity one initiative at a time. 

They introduce a new tool, adopt a new operating model or launch a new transformation program. These efforts fail when the underlying flows are broken. 

The four flows are systemic; they cut across functions, roles and technologies, reinforcing or undermining one another. 

Better tools don’t compensate for weak purpose flow. Poor knowledge flow won’t be fixed with more meetings, and you can’t automate your way out of unclear work flow. 

High-performing teams operate inside systems of work intentionally designed for flow. 

This is the lesson we learned through developer experience, and it’s the lesson enterprises now need to apply at scale. 

Productivity is not about effort. It’s about how purpose, work, knowledge and intelligence move through the organization. 

Enterprise performance is the byproduct of deliberately designed flows. 

Over the next few years, agentic AI is expected to bring not only rapid technological breakthroughs, but a societal transformation, redefining how we live, work and interact with the world. And this shift is happening quickly. “By 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, enabling 15% of day-to-day work decisions to be made autonomously,” according to research firm Gartner.

Unlike traditional AI, which typically follows preset rules or algorithms, agentic AI adapts to new situations, learns from experiences, and operates independently to pursue goals without human intervention. In short, agentic AI empowers systems to act autonomously, making decisions and executing tasks — even communicating directly with other AI agents — with little or no human involvement.

Agentic AI will enable machines to interact with the physical world with unprecedented intelligence, allowing them to perform complex tasks in dynamic environments, which could be especially useful for industries facing labor shortages or hazardous conditions.However, the rise of agentic AI also brings security and ethical concerns. Ensuring these autonomous systems operate safely, transparently and responsibly will require governance frameworks and testing.

Follow this page for ongoing agentic AI coverage from Computerworld and Foundry’s other publications.

Agentic AI news and insights

Cisco goes all in on agentic AI security

March 26, 2026: Cisco is rolling out identity and access management capabilities, a toolkit customers can use to embed security controls in AI agents, and automation features that will allow security operations teams to quickly see and respond to problems.

AI agents still need humans to teach them

February 20, 2026: AI agents need skills — specific procedural knowledge — to perform tasks well, but they can’t teach themselves, a new research suggests.

Why most agentic AI projects stall before they scale

February 18, 2026: As enterprises race from pilots to autonomous systems, rising costs, fragile governance, and unrealistic expectations are forcing a reckoning. So what separates agentic AI initiatives that survive from those that quietly shut down?

How agentic AI helps prospective and existing students at DeVry

February 18, 2026: DeVry is no stranger to AI. It’s used the technology in its classrooms for 10 years and started experimenting with NLP bots and gen AI use cases for internal use as soon as it became widely available. So in April 2025, Devry University deployed its first AI agent.

Task management software gets an agentic boost

February 11, 2026: Task management apps aren’t just for storing and tracking data — they act on it. Explore tools that tap AI to auto-generate workflows, balance team capacity, and eliminate administrative overhead.

OpenClaw: The AI agent that’s got humans taking orders from bots

February 6, 2026: How one man’s vibe-coding session evolved into a reckless global AI experiment where nobody’s accountable.

Forward Networks launches agentic AI system built on network digital twin

January 30, 2026: The new Forward AI capability builds on the vendor’s digital twin and is designed to allow network teams to ask complex questions, understand network behavior, validate outcomes and safely automate workflows.

Agentic AI exposes what we’re doing wrong

January 23, 2026: Agentic AI has changed cloud computing, but not in the way the hype machine wants you to believe. It hasn’t magically replaced engineering, nor has it made architecture irrelevant. 

How to get your enterprise architecture ready for agentic AI

January 22, 2026: While C-suite leaders say they’re investing in agentic AI, the complex enterprise architectures of large organizations often struggle with the tech’s demands.

IBM targets agentic AI scale-up with new Enterprise Advantage consulting service

January 20, 2026: IBM has launched a new consulting service named Enterprise Advantage, designed to help CIOs take their agentic and other AI applications from experimentation to large-scale production.

EY exec: If you think agentic AI is a challenge, you’re not ready for what’s coming

January 15, 2026: Companies struggling to keep up with the arrival of AI agents should buckle up: Even more complicated agentic AI technologies are quickly coming down the pike. That includes physical AI, which includes robots and quantum computing.

Managing agentic AI risk: Lessons from the OWASP Top 10

December 19, 2025: LLM-powered chatbots have risks that we see playing out in the headlines on a nearly daily basis. But chatbots are limited to answering questions. AI agents, however, access data and tools and carry out tasks, making them infinitely more capable – and more dangerous to enterprises.

Agentic AI in 2026: More mixed than mainstream

December 18, 2025: Agentic AI is having its everything, everywhere, all at once moment. Or is it? Data clarifies. While 39% of organizations surveyed by McKinsey say they are experimenting with agents, only 23% have begun scaling AI agents within one business function

Overcome governance and trust issues to drive agentic AI

December 18, 2025: Fully autonomous agentic AI is still way off but AI agents are making inroads within enterprise software and workflows. Gartner predicts 40% of enterprise software will feature task-specific AI agents by the end of 2026 as the current trend for embedded AI assistants evolves.

Nvidia bets on open infrastructure for the agentic AI era with Nemotron 3

Decenber 15, 2025: AI agents must be able to cooperate, coordinate, and execute across large contexts and long time periods, and this, says Nvidia, demands a new type of infrastructure, one that is open. The company says it has the answer with its new Nemotron 3 family of open models.

Microsoft drops M365 Copilot price for SMBs, upgrades free Copilot Chat

November 19, 2025: Microsoft announced that it reduce the price of Microsoft 365 Copilot for small and mid-sized firms beginning next month. Microsoft 365 Copilot for Business will cost $21 per user, per month for customers with any Microsoft 365 Business plan. That’s down from the current $30 monthly price.

Microsoft Fabric IQ adds ‘semantic intelligence’ layer to Fabric

November 19, 2025: Microsoft promises enterprises better understanding of their data for workers and autonomous agents alike, but analysts fear deployment hurdles and vendor lock-in.

Microsoft unveils Agent 365 to help IT manage AI ‘agent sprawl’

November 18, 2025: As businesses begin deploying AI agents in greater numbers, IT teams will need to manage and secure those AI systems as they connect to corporate data. That’s the idea behind Microsoft’s Agent 365 (A365), a new “control plane” that lets customers deploy and govern the use of agents. 

From chatbots to colleagues: How agentic AI is redefining enterprise automation

November 17, 2025: A new wave of agentic AI is taking shape: systems that not only converse but also reason, plan, and act within enterprise workflows. These agents are not assistants that talk; they are digital colleagues that think.

The enterprise IT overhaul: Architecting your stack for the agentic AI era

November 10, 2025: For the CIO, the conversation has officially moved past the large language model (LLM). The next critical chapter is agentic AI — autonomous systems capable of reasoning, planning and executing multi-step tasks across your enterprise. Agentic AI is here. Now, CIOs must orchestrate

October 23, 2025: Agentic AI is about to change how companies create value. Yet, most enterprises aren’t ready. The problem isn’t the technology — it’s the planning and execution. Too many pilots stall out because CIOs haven’t built the AI systems, guardrails and culture to move beyond experiments.

AI agents might smooth some of retail’s worst data problems

October 21, 2025: So many retail challenges hinge on unreliable product data. Can agentic AI clean up that data enough to make a difference? Can it do the same for other verticals?

The impact of agentic AI on SaaS and partner ecosystems

October 16, 2025: The enterprise technology landscape is entering a critical pivot point as agentic AI transforms partner ecosystems from human-mediated, application integration networks into autonomous, self-orchestrating and intelligent ecosystems.

Salesforce updates its agentic AI pitch with Agentforce 360

October 13 2025: Salesforce announced a new release of Agentforce that, it says, “gives teams the fastest path from AI prototypes to production-scale agents” — although with many of the new release’s features still to come, or yet to enter pilot phases or beta testing, some parts of that path will be much slower than others.

Gemini Enterprise is Google’s new ‘front door’ for agentic AI access at work

October 9, 2025: Google introduced an AI assistant to serve as a platform so users can access and coordinate AI agents that automate work tasks. Gemini Enterprise, which replaces the Agentspace app launched last year, also features new enterprise search functions to help customers tap into data from across an organization’s business apps. 

Oracle’s agentic AI push in Fusion Cloud CX offers embedded automation for CX leaders

October 7, 2025: Oracle is adding new pre-built agents to its Advertising and Customer Experience Cloud (Fusion Cloud CX) to help enterprises increase operational efficiency by automating sales, service, and marketing processes.

IBM touts agentic AI orchestration, cryptographic risk controls

October 7, 2025: IBM watsonx Orchestrate offers more than 500 tools and customizable, domain-specific agents from IBM and third-party contributors. Among the additions to watsonx Orchestrate are AgentOps capabilities that offer real-time monitoring and policy-based controls for observability and governance.

How self-learning AI agents will reshape operational workflows

October 6, 2025: Google’s recent whitepaper, “Welcome to the Era of Experience,” signals a shift in the way AI agents are trained. Google hypothesizes that allowing AI agents to learn from the experience of agents rather than solely from human-generated training data will enable autonomous AI to surpass its current capabilities.

Are your agentic AI projects driving toward success?

October 3, 2025: Anushree Verma, Gartner senior director analyst, says most agentic AI projects today are early-stage experiments or proofs of concept, fueled primarily by hype and often misapplied.

Microsoft unveils framework for building agentic AI apps

October 3. 2025: Microsoft has introduced the Microsoft Agent Framework, an open-source SDK and runtime for building, orchestrating, and deploying AI agents and multi-agent workflows, with full framework support for .NET and Python.

Salesforce Trusted AI Foundation seeks to power the agentic enterprise

October 2, 2025: As Salesforce pushes further into agentic AI, its aim is to evolve Salesforce Platform from an application for building AI to a foundational operating system for enterprise AI ecosystems. 

ServiceNow’s AI Experience is an agentic AI UI for the Now Platform

September 30, 2025: ServiceNow today launched the AI Experience (AIx), a contextually aware multimodal AI-driven use UI for its Now platform. Building on the ServiceNow AI Platform and with a foundation in Now Assist, the company describes it as “a unified, conversational front door to enterprise AI.”

How MCP is making AI agents actually do things in the real world

September 29, 2025: You’ve seen them: Those incredible large language models (LLMs) that can chat, write and even generate code. They’ve revolutionized how we interact with technology, but there’s a new, even more exciting chapter unfolding. Discover how MCP is turning chatbots into doers, and the future of work may never look the same.

Agentic AI in IT security: Where expectations meet reality

September 29, 2025: Agentic AI has shifted from lab demos to real-world SOC deployments. Unlike traditional automation scripts, software agents are designed to act on signals and execute security workflows intelligently, correlating logs, enriching alerts, and even take first-line containment actions.

Walmart looks to cash in on agentic AI

September 19, 2025: Walmart doesn’t intend to lose its retail crown anytime soon. And, according to US EVP and CTO Hari Vasudev, the $815B company’s artificial intelligence strategy will play a key role in preventing that from happening.

5 steps for deploying agentic AI red teaming

September 17, 2025: As more enterprises deploy agentic AI applications, the potential attack surface increases in complexity and reach. But there is still hope that AI agents can be harnessed for defensive purposes too, including using traditional red teaming and penetration testing techniques but updated for the AI world.

Google unveils payments protocol for AI agents with major financial firms

September 17. 2025: Google has introduced the Agent Payments Protocol (AP2), an open framework developed with more than 60 payments and technology companies to support secure, agent-led transactions across platforms and payment methods.

CrowdStrike bets big on agentic AI with new offerings after $290M Onum buy

September 16, 2025: At its Fal.Con conference, the cybersecurity giant launched its Agentic Security Platform and Agentic Security Workforce, aiming to outpace AI-driven adversaries with real-time intelligence, automation, and a common language for defense.

Adobe makes Agent Orchestrator and AI agents generally available

September 10, 2025: Adobe Experience Platform (AEP) Agent Orchestrator and six new AI agents are designed to build, deliver, and optimize customer experience and marketing campaigns. The company also announced Experience Platform Agent Composer for customizing and configuring AI agents based on brand guidelines and organizational policy.

Rethinking the IT organization for the agentic AI era

September 2, 2025: With the advent of agentic AI, CIOs must be poised to adjust strategic IT priorities, mitigate new security risks, and reskill staff for a new era.

How to build a production-grade agentic AI platform

September 2, 2025: Modular orchestration, fail-safe design, hybrid memory management, and LLM integration with domain knowledge are essential to agentic AI systems that reason, act, and adapt at scale.

Agentic AI: A CISO’s security nightmare in the making?

September 2, 2025: Enterprises will no doubt be using agentic AI for a growing number of workflows and processes, including software development, customer support automation, and more. But what are the cybersecurity risks of agentic AI, and how much more work will it take for them to support their organizations’ agentic AI dreams?

Microsoft researchers develop new tech for video AI agents

September 2, 2025: Microsoft researchers are developing technologies for a new class of video AI agents to explore three-dimensional spaces before making decisions.The technology framework, called MindJourney, uses a range of AI technologies to understand and analyze 3D spaces, reason about the surroundings, and predict movement

Salesforce AI Research unveils new tools for AI agents

August 27, 2025: Salesforce announced a simulated enterprise environment, benchmark, and account data unification tool that are designed to help customers transform into agentic AI enterprises.

Agentic AI promises a cybersecurity revolution — with asterisks

August 18, 2025: The hottest topic at this year’s Black Hat conference was the meteoric emergence of AI tools for both cyber adversaries and defenders, particularly the use of agentic AI to strengthen cybersecurity programs.

4 thoughts on who should manage AI agents

August 11, 2025: As AI agents proliferate, we need to turn our attention beyond AI agent builder platforms to AI orchestration and AI GRC platforms. It also raises questions about which groups within the enterprise should manage AI agents and how they should be treated.

How bright are AI agents? Not very, recent reports suggest

July 31, 2025: Security researchers are adding more weight to a truth that infosec pros had already grasped: AI agents are not very bright, and are easily tricked into doing stupid or dangerous things

Will AI agents eat the SaaS market? Experts are split

July 31,2025: As hype about AI agents reaches new heights, an emerging theory suggests that the groundbreaking AI tools will kill the SaaS business model. The claim isn’t particularly new, but is resurfacing, with people like Microsoft CEO Satya Nadella voicing this position. 

How agentic AI will change database management

July 28, 2025: Generative AI has already had a profound impact on the world of database management. And now, thanks to AI’s knack for pattern-recognition, teams can use generative AI to analyze data sets, detect anomalies, and access invaluable insights with record speed and precision. 

As AI agents go mainstream, companies lean into confidential computing for data security

July 21, 2025: Companies need to stop ignoring data security as AI agents take over internal data movement in IT environments, analysts and IT execs warn. To address that issue, some tech players are embracing the concept of “confidential computing.” While it’s existed for years, it;s now finding new life with the rise of genAI.

How agentic AI will transform mobile apps and field operations

July 15, 2015: Agentic AI will usher in new mobile AI experiences. Construction, manufacturing, healthcare, and other industries with significant field operations will benefit from mobile AI agents and the resulting operational agility. 

MCP is fueling agentic AI — and introducing new security risks

July 10, 2025: Model Context Protocol (MCP) has caught fire, with several thousand MCP servers now available from a wide range of vendors enabling AI assistants to connect to their data and services. And with agentic AI increasingly seen as the future of IT, MCP will only grow in use in the enterprise. But innovations like MCP also come with significant security risks.

3 industries where agentic AI is poised to make its mark

July 4, 2024:  IT leaders from finance, retail, and healthcare lend insights into what organizations are doing with AI agents today — and where they see the technology taking their organizations and industries in the future.

IFS rolls TheLoops agentic AI into industrial ERP

June 27, 2025: IFS is adding AI agent development and management capabilities to its ERP platform with the acquisition of software startup The acquisition brings TheLoops’ full Agent Development life cycle (ADLC) platform into IFS, enabling enterprises to design, test, deploy, monitor, and fine-tune AI agents with built-in support for versioning, compliance, and performance optimization.

How AI agents and agentic AI differ from each other

June 12, 2025: With agentic AI in its infancy and organizations rushing to adopt AI agents, there seems to be confusion about the difference between “agentic AI” and “AI agents” technologies, but experts say there’s growing understanding that the two are separate, but related, tools.

The future of RPA ties to AI agents

June 10, 2025: RPA is accelerating toward a crossroads, with IT leaders and experts debating its future. Some IT leaders say that more powerful and autonomous AI agents will replace the two-decade-old AI precursor technology, while others predict that AI agents and RPA will work hand-in-hand.

MCP is enabling agentic AI, but how secure is it?

June 2, 2025: Model context protocol (MCP) is becoming the plug-and-play standard for agentic AI apps to pull in data in real time from multiple sources. However, this also makes it more attractive for malicious actors looking to exploit weaknesses in how MCP has been deployed. 

The agentic AI assist Stanford University cancer care staff needed

May 30, 2025: At Microsoft Build 2025 earlier this month, Nigam Shah, CDO for Stanford Health Care, discussed agentic AI’s ability to redefine healthcare, especially in oncology, as physicians get overloaded with the administrative tasks of medicine, he said, which lead to burnout.

Agentic AI, LLMs and standards big focus of Red Hat Summit

May 26, 2025: Red Hat, announced a number of improvements in its core enterprise Linux product, including better security, better support for containers, better support for edge devices. But the one topic that dominated the conversation was AI.

Putting agentic AI to work in Firebase Studio

May 21, 2025: Putting agentic AI to work in software engineering can be done in a variety of ways. Some agents work independently of the developer’s environment, working essentially like a remote developer. Other agents directly within a developer’s own environment. Google’s Firebase Studio is an example of the latter, drawing on Google’s Gemini LLM o help developers prototype and build applications .

Why is Microsoft offering to turn websites into AI apps with NLWeb?

May 20. 2025: NLWeb, short for Natural Language Web, is designed to help enterprises build a natural language interface for their websites using the model of their choice and data to answer user queries about the contents of the website. Microsoft hopes to stake its claim on the agentic web before rivals Google and Amazon do.

Databricks to acquire open-source database startup Neon to build the next wave of AI agents

May 14, 2025: Agentic AI requires a new type of architecture because traditional workflows create gridlock, dragging down speed and performance. To get ahead in this next generation of app building, Databricks announced it will purchase Neon, an open-source serverless Postgres company.

Agentic mesh: The future of enterprise agent ecosystems

May 13, 2025: Nvidia CEO Jensen Huang predicts we’ll soon see “a couple of hundred million digital agents” inside the enterprise. Microsoft CEO Satya Nadella takes it even further: “Agents will replace all software.”

Google to unveil AI agent for developers at I/O, expand Gemini integration

May 13, 2025: Google is expected to unveil a new AI agent aimed at helping software developers manage tasks across the coding lifecycle, including task execution and documentation. The tool has reportedly been demonstrated to employees and select external developers ahead of the company’s annual I/O conference.

Nvidia, ServiceNow engineer open-source model to create AI agents

May 6, 2025: Nvidia and ServiceNow have created an AI model that can help companies create learning AI agents to automate corporate workloads. The open-source Apriel model, available generally in the second quarter on HuggingFace, will help create AI agents that can make decisions around IT, human resources and customer-service functions.

How IT leaders use agentic AI for business workflows

April 30, 2025: Jay Upchurch, CIO at SAS, backs agentic AI to enhance sales, marketing, IT, and HR motions. “Agentic AI can make sales more effective by handling lead scoring, assisting with customer segmentation, and optimizing targeted outreach,” he says.

Microsoft sees AI agents shaking up org charts, eliminating traditional functions

April 28, 2025: As companies increasingly automate work processes using agents, traditional functions such as finance, marketing, and engineering may fall away, giving rise to an ‘agent boss’ era of delegation and orchestration of myriad bots.

Cisco automates AI-driven security across enterprise networks

April 28, 2025: Cisco announced a range of AI-driven security enhancements, including improved threat detection and response capabilities in Cisco XDR and Splunk Security, new AI agents, and integration between Cisco’s AI Defense platform and ServiceNow SecOps.

Hype versus execution in agentic AI

April 25, 2025: Agentic AI promises autonomous systems capable of reasoning, making decisions, and dynamically adapting to changing conditions. The allure lies in machines operating independently, free of human intervention, streamlining processes and enhancing efficiency at unprecedented scales. But David Linthicum writes, don’t be swept up by ambitious promises. 

Agents are here — but can you see what they’re doing?

April 23, 2025: As the agentic AI models powering individual agents get smarter, the use cases for agentic AI systems get more ambitious — and the risks posed by these systems increase exponentially.A multicloud experiment in agentic AI: Lessons learned

Agentic AI might soon get into cryptocurrency trading — what could possibly go wron

April 15, 2025: Agentic AI promises to simplify complex tasks such as crypto trading or managing digital assets by automating decisions, enhancing accessibility, and masking technical complexity.

Agentic AI is both boon and bane for security pros

April 15, 2025: Cybersecurity is at a crossroads with agentic AI. It’s a powerful tool that can create reams of code in a blink of an eye, find and defuse threats, and be used so decisively and defensively. This has proved to be a huge force multiplier and productivity boon. But while powerful, agentic AI isn’t dependable, and that is the conundrum. 

AI agents vs. agentic AI: What do enterprises want?

April 15, 2025:  Now that this AI agent story has morphed into “agentic AI,” it seems to have taken on the same big-cloud-AI flavor that enteriprise already rejected. What do they want from AI agents, why is “agentic” thinking wrong, and where is this all headed?

A multicloud experiment in agentic AI: Lessons learned

April 11, 2025: Turns out you really can build a decentralized AI system that operates successfully across multiple public cloud providers. It’s both challenging and costly.

Google adds open source framework for building agents to Vertex AI

April 9, 2025: Google is adding a new open source framework for building agents to its AI and machine learning platform Vertex AI, along with other updates to help deploy and maintain these agents. The open source Agent Development Kit (ADK) will make it possible to build an AI agent in under 100 lines of Python code. It expects to add support for more languages later this year.

Google’s Agent2Agent open protocol aims to connect disparate agents

April 9, 2025: Google has taken the covers off a new open protocol — Agent2Agent (A2A) — that aims to connect agents across disparate ecosystems.. At its annual Cloud Next conference, Google said that the A2A protocol will enable enterprises to adopt agents more readily as it bypasses the challenge of agents that are built on different vendor ecosystems not being able to communicate with each other.

Riverbed bolsters AIOps platform with predictive and agentic AI

April 8, 2025: Riverbed unveiled updates to its AIOps and observability platform that the company says will transform how IT organizations manage complex distributed infrastructure and data more efficiently. Expanded AI capabilities are aimed at making it easier to manage AIOps and enabling IT organizations to transition from reactive to predictive IT operations.

Microsoft’s newest AI agents can detail how they reason

March 26, 2025: If you’re wondering how AI agents work, Microsoft’s new Copilot AI agents provide real-time answers on how data is being analyzed and sourced to reach results. The Researcher and Analyst agents take a deeper look at data sources such as email, chat or databases within an organization to produce research reports, analyze strategies, or convert raw information into meaningful data.

Microsoft launches AI agents to automate cybersecurity amid rising threats

March 26, 2025: Microsoft has introduced a new set of AI agents for its Security Copilot platform, designed to automate key cybersecurity functions as organizations face increasingly complex and fast-moving digital threats. The new tools focus on tasks such as phishing detection, data protection, and identity management.

How AI agents work

March 24, 2025: By leveraging technologies such as machine learning, natural language processing (NLP), and contextual understanding, AI agents can operate independently, even partnering with other agents to perform complex tasks.

5 top business use cases for AI agents

March 19, 2025: AI agents are poised to transform the enterprise, from automating mundane tasks to driving customer service and innovation. But having strong guardrails in place will be key to success.

Nvidia launches AgentIQ toolkit to connect disparate AI agents

March 21, 2025: As enterprises look to adopt agents and agentic AI to boost the efficiency of their applications, Nvidia this week introduced a new open-source software library — AgentIQ toolkit — to help developers connect disparate agents and agent frameworks..

Deloitte unveils agentic AI platform

March 18, 2025: At Nvidia GTC 2025 in San Jose, Deloitte announced Zora AI, a new agentic AI platform that offers a portfolio of AI agents for finance, human capital, supply chain, procurement, sales and marketing, and customer service.The platform draws on Deloitte’s experience from its technology, risk, tax, and audit businesses, and is integrated with all major enterprise software platforms. 

The dawn of agentic AI: Are we ready for autonomous technology?

March 15, 2025: Much of the AI work prior has focused on large language models (LLMs) with a goal to give prompts to get knowledge out of the unstructured data. So it’s a question-and-answer process. Agentic AI goes beyond that. You can give it a task that might involve a complex set of steps that can change each time.

How to know a business process is ripe for agentic AI

March 11, 2025: Deloitte predicts that in 2025, 25% of companies that use generative AI will launch agentic AI pilots or proofs of concept, growing to 50% in 2027. The firm says some agentic AI applications, in some industries and for some use cases, could see actual adoption into existing workflows this year.

With new division, AWS bets big on agentic AI automation

March 6, 2025: Amazon Web Services customers can expect to hear a lot more about agentic AI from AWS in future with the news that the company is setting up a dedicated unit to promote the technology on its platform.

How agentic AI makes decisions and solves problems

March 6, 2025: GenAI’s latest big step forward has been the arrival of autonomous AI agents. Agentic AI is based on AI-enabled applications capable of perceiving their environment, making decisions, and taking actions to achieve specific goals. 

CIOs are bullish on AI agents. IT employees? Not so much

Feb. 4, 2025: Most CIOs and CTOs are bullish on agentic AI, believing the emerging technology will soon become essential to their enterprises, but lower-level IT pros who will be tasked with implementing agents have serious doubts.

The next AI wave — agents — should come with warning labels. Is now the right time to invest in them?

Jan.13, 2025: The next wave of artificial intelligence (AI) adoption is already under way, as AI agents — AI applications that can function independently and execute complex workflows with minimal or limited direct human oversight — are being rolled out across the tech industry.

AI agents are unlike any technology ever

Dec. 1, 2024: The agents are coming, and they represent a fundamental shift in the role artificial intelligence plays in businesses, governments, and our lives.

AI agents are coming to work — here’s what businesses need to know

Nov. 21, 2024: AI agents will soon be everywhere, automating complex business processes and taking care of mundane tasks for workers — at least that’s the claim of various software vendors that are quickly adding intelligent bots to a wide range of work apps.

Agentic AI swarms are headed your way

November 1, 2024: OpenAI launched an experimental framework called Swarm. It’s a “lightweight” system for the development of agentic AI swarms, which are networks of autonomous AI agents able to work together to handle complex tasks without human intervention, according to OpenAI. 

Is now the right time to invest in implementing agentic AI?

October 31, 2024: While software vendors say their current agentic AI-based offerings are easy to implement, analysts say that’s far from the truth.

Java Development Kit (JDK) 26, the latest standard Java release from Oracle, moves to general production availability on March 17.

The following 10 Jave Enhancement Proposal (JEP) features are officially targeted to JDK 26: A fourth preview of primitive types in patterns, instanceof, and switch; ahead-of-time object caching; an eleventh incubation of the Vector API; second previews of lazy constants and PEM (privacy-enhanced mail) encodings of cryptographic objects; a sixth preview of structured concurrency; warnings about uses of deep reflection to mutate final fields; improving throughput by reducing synchronization in the G1 garbage collector (GC); HTTP/3 for the Client API; and removal of the Java Applet API.

For AI accommodations, Oracle has cited five of these features: the primitive types in patterns capability, Vector API, structured concurrency, lazy constants, and AOT object caching.

JDK 26 is downloadable from Oracle.com. A short-term release of Java backed by six months of Premier-level support, JDK 26 follows the September 16 release of JDK 25, which is a Long-Term Support (LTS) release backed by several years of Premier-level support. General availability of JDK 26 follows two rampdown releases and two release candidates.

The latest JEP feature to be added, primitive types in patterns, instanceof, and switch, is intended to enhance pattern matching by allowing primitive types in all pattern contexts, and to extend instanceof and switch to work with all primitive types. Now in its fourth preview, this feature was previously previewed in JDK 23, JDK 24, and JDK 25. Goals for this feature include enabling uniform data exploration by allowing type patterns for all types, aligning type patterns with instanceof and aligning instanceof with safe casting, and allowing pattern matching to use primitive types in both nested and top-level pattern contexts. Other goals include providing easy-to-use constructs that eliminate the risk of losing information due to unsafe casts, and—following the enhancements to switch in Java 5 (enum switch) and Java 7 (string switch)—allowing switch to process values of any primitive type. Changes in this fourth preview include enhancing the definition of unconditional exactness and applying tighter dominance checks in switch constructs. The changes enable the compiler to identify a wider range of coding errors. For AI, this feature simplifies integration of AI with business logic.

With ahead-of-time object caching, the HotSpot JVM would gain improved startup and warmup times, so it can be used with any garbage collector including the low-latency Z Garbage Collector (ZGC). This would be done by making it possible to load cached Java objects sequentially into memory from a neutral, GC-agnostic format, rather than mapping them directly into memory in a GC-specific format. Goals of this feature include allowing all garbage collectors to work smoothly with the AOT (ahead of time) cache introduced by Project Leyden, separating AOT cache from GC implementation details, and ensuring that use of the AOT cache does not materially impact startup time, relative to previous releases. AI applications also get improved startup via AOT caching with any GC.

The eleventh incubation of the Vector API introduces an API to express vector computations that reliably compile at runtime to optimal vector instructions on supported CPUs. This achieves performance superior to equivalent scalar computations. The incubating Vector API dates back to JDK 16, which arrived in March 2021. The API is intended to be clear and concise, to be platform-agnostic, to have reliable compilation and performance on x64 and AArch64 CPUs, and to offer graceful degradation. The long-term goal of the Vector API is to leverage Project Valhalla enhancements to the Java object model. The performance of AI computation is also improved with the Vector API.

Also on the docket for JDK 26 is another preview of an API for lazy constants, which had been previewed in JDK 25 via a stable values capability. Lazy constants are objects that hold unmodifiable data and are treated as true constants by the JVM, enabling the same performance optimizations enabled by declaring a field final. Lazy constants offer greater flexibility as to the timing of initialization, as well as efficient data sharing in AI applications.

The second preview of PEM (privacy-enhanced mail) encodings calls for an API for encoding objects that represent cryptographic keys, certificates, and certificate revocation lists into the PEM transport format, and for decoding from that format back into objects. The PEM API was proposed as a preview feature in JDK 25. The second preview features a number of changes, including changing the name of the PEMRecord class to PEM. This class also now includes a decode() method that returns the decoded Base64 content. Also, the encryptKey methods of the EncryptedPrivateKeyInfo class now are named encrypt and accept DEREncodable objects rather than PrivateKey objects, enabling the encryption of KeyPair and PKCS8EncodedKeySpec objects.

The structured concurrency API simplifies concurrent programming by treating groups of related tasks running in different threads as single units of work, thereby streamlining error handling and cancellation, improving reliability, and enhancing observability. Goals include promoting a style of concurrent programming that can eliminate common risks arising from cancellation and shutdown, such as thread leaks and cancellation delays, and improving the observability of concurrent code. This feature in Java 26 also brings enhanced concurrency for AI.

New warnings about uses of deep reflection to mutate final fields are intended to prepare developers for a future release that ensures integrity by default by restricting final field mutation; in other words, making final mean final, which will make Java programs safer and potentially faster. Application developers can avoid both current warnings and future restrictions by selectively enabling the ability to mutate final fields where essential.

The G1 GC proposal is intended to improve application throughput when using the G1 garbage collector by reducing the amount of synchronization required between application threads and GC threads. Goals include reducing the G1 garbage collector’s synchronization overhead, reducing the size of the injected code for G1’s write barriers, and maintaining the overall architecture of G1, with no changes to user interaction. Although G1, which is the default garbage collector of the HotSpot JVM, is designed to balance latency and throughput, achieving this balance sometimes impacts application performance adversely compared to throughput-oriented garbage collectors such as the Parallel and Serial collectors. The G1 GC proposal further notes:

Relative to Parallel, G1 performs more of its work concurrently with the application, reducing the duration of GC pauses and thus improving latency. Unavoidably, this means that application threads must share the CPU with GC threads, and coordinate with them. This synchronization both lowers throughput and increases latency.

The HTTP/3 proposal calls for allowing Java libraries and applications to interact with HTTP/3 servers with minimal code changes. Goals include updating the HTTP Client API to send and receive HTTP/3 requests and responses; requiring only minor changes to the HTTP Client API and Java application code; and allowing developers to opt in to HTTP/3 as opposed to changing the default protocol version from HTTP/2 to HTTP/3.

HTTP/3 is considered a major version of the HTTP data communications protocol for the web. Version 3 was built on the IETF QUIC (Quick UDP Internet Connections) transport protocol, which emphasizes flow-controlled streams, low-latency connection establishment, network path migration, and security among its capabilities.

Removal of the Java Applet API, now considered obsolete, is also targeted for JDK 26. The Applet API was deprecated for removal in JDK 17 in 2021. The API is obsolete because neither recent JDK releases nor current web browsers support applets, according to the proposal. There is no reason to keep the unused and unusable API, the proposal states.

In addition to its 10 major JEP features, JDK 26 also has a variety of additional, smaller features that did not warrant an official JEP, according to Oracle. Among these are hybrid public key encryption, stricter version checking when using the jlink tool to cross link, extending the HTTP client request timeout to cover the response body, and virtual threads now unmounting from the carrier when waiting for another thread to execute a class initializer.

Gen AI is a form of AI that outputs content — text, code, voice, images, and videos — from prompts. Since the release of DALL-E for images in 2021, and then, the following year, ChatGPT for text, and Midjourney and Stable Diffusion for images, companies have been eager to find out how gen AI can transform their services, workflows, and processes by better answering customer questions, speeding up employee tasks, writing code, or even helping scientists model the 3D structure of proteins.

Research firm MarketsandMarkets predicts the gen AI software market will grow from $71 billion in 2025 to $891billion by 2032, a CAGR of just over 43% throughout that period.

Here are four examples of how organizations are using and succeeding with gen AI today.

Husqvarna brings gen AI to the factory floor

Swedish industrial manufacturer Husqvarna is leveraging gen AI to reduce unplanned downtime on the factory floor.

The company, which makes outdoor power products including robotic lawn mowers, chainsaws, and trimmers, has created the AI Factory Companion, a gen AI copilot that helps technicians and operators diagnose and resolve issues with machinery. They can describe the symptoms a machine is experiencing to the copilot and it’ll suggest tests to validate the issue along with possible solutions.

“We can’t answer every question yet, but we can address the simple ones, like 20% of the full bucket, and in some cases, we can speed up the time to resolution very quickly,” says Jonathan Wickström, the company’s manufacturing digitalization lead. “If we don’t have the data, we can’t do anything, and it’ll be like that for some time. But now we can improve the common knowledge base instead of just filling in another PDF that no one will look at.”

Wickström’s advice: Master traditional search technologies and combine them with gen AI to get the most powerful results. The magic behind a powerful gen AI companion is good search — finding the documents most relevant to a user’s questions.

Honeywell transforms with gen AI

Multinational conglomerate Honeywell is streamlining its end-to-end processes and reducing employee admin tasks using gen AI.

The Fortune 500 company, which primarily operates in aerospace, building automation, industrial automation, and energy and sustainability solutions, uses Microsoft 365 Copilot to aggregate and summarize content, GitHub Copilot for code generation and software modernization, and is testing Microsoft 365 Copilot for sales and finance with Salesforce and SAP integrations.

“This is a disruptive technology that’s fundamentally going to change how we work, live, and play,” says Sheila Jordan, the company’s SVP and chief digital technology officer.

Jordan’s advice: Success with gen AI is all about having the right data strategy in place. “You can’t have a gen AI strategy without a data strategy,” she says. “The good news for us is that while our data isn’t perfect, it’s in such great shape that we really do run the company with data-driven insights. Now we can layer gen AI on top of that and unleash new knowledge, insights, and opportunities.”

Ally Financial finds gen AI success with three guiding principles

Bank holding company Ally Financial started its gen AI transformation with two use cases: call summarization and marketing.

The call summarization capability was developed to support Ally’s customer care and experience group, and leverages Microsoft Azure and Azure OpenAI Service to produce detailed documentation for each call taken by call center associates. It provides real-time access to summaries of tens of thousands of customer service calls per week.

The marketing team capability, on the other hand, uses the LLM chat and prompt functionality on Ally.ai, a proprietary platform for all the company’s AI applications, to help marketers produce creative campaigns. Ally says it’s helped reduce the time needed to produce creative campaigns and content by up to three weeks, primarily with early-stage tasks like research, first drafts, and naming exercises, resulting in an average time savings of 34% compared to typical processes without AI.

Both use cases leverage Ally.ai, along with a gen AI playbook to allow the company to incorporate gen AI into its highly regulated business without sacrificing security or governance measures.

“What I was very clear on from day one was that while you might be seen as an industry leader and get recognition for adopting gen AI early, even a small misstep would put us back 100 steps from the half step we’ve taken forward,” says Sathish Muthukrishnan, Ally’s chief information, data, and digital officer. “We didn’t want to be in that position, so we needed to protect ourselves.”

Muthukrishnan’s advice: Follow three guiding principles when it comes to gen AI: start with focused, intent-based investment and build; have the courage to experiment with new technologies, but with the intent to showcase value in order to bring the entire organization together; have the patience and thoughtfulness to educate the entire company.

“Don’t just experiment and invent within the four walls of tech,” he says. “Showcase the value of tech to the entire company, speak their language, and bring them along by educating and empowering the entire organization.”

Henkel embraces gen AI as enabler and strategic disruptor

German consumer packaged goods (CPG) company Henkel is using gen AI for trade promotion management (TPM) and trade promotion optimization (TPO).

TPM and TPO are key disciplines in the CPG space that involve managing and optimizing all promotional activities conducted with retailers, from discounts to deductions and payments. Henkel worked with the SAP co-innovation team to build a tool that leveraged the Just Ask gen AI feature of SAP Analytics Cloud.

“The whole use of trade promotion, which is still a complicated animal, has become much more intuitive for the key account managers,” says Henkel CDIO Michael Nilles. “The key account manager or the salesperson is looking at the trade promotion data and it’s giving really great hints. This just wasn’t possible with traditional machine learning. With gen AI, the AI capabilities have become much more widely usable by people who aren’t PhDs in data science.”

Now account managers can walk into a meeting with retailers about the proper discounts to offer during a campaign, and use the tool with natural language to explore options in real-time. The account managers can have a finished plan ready to share by the next day, instead of the months it used to take.

Nilles’s advice: Think about building vertical LLMs specific to your business to really take advantage of gen AI.

“We believe there’ll be new vertical LLMs emerging, even micro-vertical ones that are domain-specific,” he says. “Having LLMs for the right things will be a huge competitive advantage, and if we don’t do it, we’ll be threatened and jeopardized by others.”

IT is moving from managing infrastructure to orchestrating digital services across applications, data, and business processes. In that model, reliability and performance aren’t attributes of a single platform. They’re properties of the whole system. Now, AI agents are starting to do real operational work, not just route it. That raises the bar: operational context and governance become prerequisites for scale, safety, and measurable outcomes.

AI introduces a new layer of automation into environments that are already complex. Infrastructure, applications, data pipelines, and services interact in ways that are difficult to map manually. As AI agents begin operating within those environments, the need for reliable operational context becomes even more critical. 

In many organizations, the ability to prevent issues depends on operational controls that are fragmented across systems, manual rules, and human effort. For example, one financial services company maintains five systems and more than 25,000 manually created rules. A leading provider of communications solutions has a 5TB CMDB consisting of more than 200,000 assets with 17 million relationships amongst those assets—a system that’s fragmented, but hugely intertwined.

Fragmented systems lead to incomplete data, fragile change processes, and operating models that prioritize reaction over prevention. Enterprise software in the AI age must now also provide the context and governance that AI requires to operate safely.

The opportunity: Reset the economics of prevention

While much of IT operations focuses on improving ticket resolution and outage response, one of the biggest financial impacts, and potentially the most overlooked, is the cost not just of resolving incidents, but of preventing them before they occur or recur. 

For a 10,000-employee enterprise, resetting the economics of prevention can represent $12–30 million per year in recurring economic impact—costs related to observability tools and data that could be streamlined, and to the skilled manual work involved in prevention that could be reduced with Agentic AI. This also represents significant time that could otherwise be spent on root cause and post-mortem analysis to ensure incidents aren’t repeated.

To capture this value, IT needs to shift from manual control to automated and governed speed, operating with the rigor required for reliability at the pace demanded by DevOps and AI-driven innovation.

Why traditional change management falls short

AI agents cannot operate safely in enterprise environments without understanding how systems depend on one another. When service models are maintained as a separate manual effort, change remains risky, compliance becomes a documentation burden, and teams lack the insights to resolve or prevent issues effectively.

A new approach is needed: A dynamic service model provides the necessary understanding, mapping assets, changes, and service relationships so automation can act with the same context experienced engineers use today. By continuously populating and interpreting operational data, agentic AI makes a dynamic service model practical, transforming it from documentation into a living system of context. Public models are trained on language-based data, but use cases like prevention require fine-tuning with a model that runs on operational telemetry and increases platform gravity, not fragmentation. 

With reliable operational context informing a roster of AI Agents, IT leaders can move beyond optimizing incident response to working on the conditions that create incidents in the first place.

ServiceOps: Integrating configuration into the flow of work

A new operating model is emerging that integrates service management and operations into a single flow of work: ServiceOps provides a path forward by embedding change and configuration management directly into the processes of delivery, incident response, and prevention.

• AI-led discovery keeps service and dependency data continuously current.
• AI governance enforces data quality at scale.
• AI risk analysis flags unsafe changes and surfaces potential issues before they become incidents.
• Automated impact assessment helps teams understand the scope of potential damage instantly.

With accurate data in place, teams can identify emerging problems from clusters of incidents, accelerate root cause analysis, and prevent repeat failures.

The result: Reliability and possibility without compromise

By streamlining the tools of prevention, adopting Agentic AI for the manual work of prevention, and integrating configuration management into everyday operations, CIOs can reduce operational costs, cut unnecessary tooling and compute spend, automatically prevent repeated incidents, and free critical engineering resources from firefighting. Organizations that can automate prevention will spend less time responding to outages and more time building the digital capabilities that drive growth. 

Most importantly, AI-assisted operations allows IT teams to reset the limits of what they can deliver so they can achieve both reliability (keeping systems running) and possibility (unlocking AI-driven transformation). For CIOs, the economics of prevention represents one of the largest financial and strategic opportunities in the current technology landscape. 

To learn more, visit https://www.helixops.ai/

The newspaper of tomorrow costs a million dollars, but yesterday’s newspaper cost less than a dollar. It is always a human endeavor to know or predict the future as accurately as possible, but it remains elusive due to technological limitations, static models, outdated data or gut feeling. In the world, torn by wars, tariffs, political fragmentation, geopolitical risk, rapid AI adaptation and weather catastrophes, uncertainty is just the norm rather than an exception.

During COVID-19, the world was exposed to huge supply chain disruption, essential commodities became scarce, and the world started understanding the challenge of the long supply chain. You will be surprised to know the iPhone travels 240k miles, the distance between Earth and the Moon, before you turn it on. Apple depends on suppliers from 50 countries to manufacture the iPhone; any small disruption has huge implications for the iPhone supply chain.

As a supply chain expert, I have seen how C-suite executives of every company struggled during the tariff war introduced last year by the US. Today, most supply chains are global, meaning raw materials are produced in one country, manufacturing in another and products are used in another. To increase efficiency and cost reduction, the chain of the supply chain has only been elongated. With technological advancement and communication improvement in land, sea and water, dependencies of one country on another have only increased. It is non-negotiable that the critical supply chain must continue in the face of any uncertainty. No company ever considered the tariff in the landed cost (total cost to bring in an item from outside), which increased the price of the final product that consumers pay. Companies are planning to move their manufacturing to the Friend Shore rather than onshore or offshore.

Launching a first-of-its-kind product is a forecasting nightmare. Without a historical roadmap, executives often fly blind, making it nearly impossible to scale production or manage suppliers accurately. The stakes are high: One wrong guess can lead to a mountain of unsold stock or — just as bad — turning away eager customers.   

In manufacturing, the failure of a single critical machine can stall operations for weeks. When repairs are delayed by long lead times for parts or specialized expertise, the resulting production bottlenecks lead to significant revenue loss and damaged customer relationships.

In a traditional product design approach, designers and engineers wait for customer feedback to be incorporated in the next iteration. Frequent changes in product safety regulations could derail the entire design.   

In an era where decision speed is the ultimate currency, volatility has become the new norm. Moving from annual to monthly product development cycles provides a vital competitive edge. By shifting from reactive to proactive strategies, AI-powered digital twins act as a “crystal ball,” providing the accurate forecasts needed to unlock new operational possibilities.

Digital twins in the supply chain

What is a digital twin in the context of the supply chain? Generally speaking, a digital twin is a digital replica of a physical object. It could be anything from an electric iron to a multistory building. Having a digital twin helps organizations simulate real-life applications and user-friendliness. Similarly, organizations using digital twins in their supply chain are better suited to deploy AI technology to drive and extract the best possible operational outcomes.

Digital twins in the supply chain allow an organization to extract its own captive data from manufacturing, inventory, suppliers, ESG, compliance and more into a single point of truth so proactive decisions can be made from real-time visibility. In this way, digital twins can speed decision-making by up to 90%, according to a McKinsey study.

Real-life use cases

According to a study by SPARQ360, a supply chain optimization, sustainability and compliance firm, some of the most effective digital twins in supply chain are:

• Unilever: Uses digital twin technology to model factory operations, optimize production schedules and enhance sustainability initiatives, leading to greater efficiency and cost savings.
• Boeing: Employs digital twins to improve supply chain transparency, reducing delays in aircraft production by modeling real-time supply constraints.
• DHL: Leverages digital twins in logistics operations to simulate and optimize warehouse efficiency, improving order fulfillment speed and reducing downtime. (
• Siemens: Uses digital twin simulations to design and manage supply chains for its industrial manufacturing clients, ensuring optimized production and distribution. (https://www.siemens.com/global/en/company/stories/industry/2025/digital-twin-for-industry.html)

From personal experience, the Kion Group partnered with Accenture and NVIDIA to leverage this innovation to address inefficiencies in warehouse operations. We enhanced supply chain efficiency through AI-driven solutions and digital twins, which were created to simulate warehouse dynamics. The digital twins facilitated real-time testing and optimization of layouts, robotic fleet management and operational processes.

This collaboration led to more autonomous and efficient warehouse operations, reducing manual interventions and allowing quicker adaptations to operational changes, thereby streamlining warehouse management and increasing overall efficiency.

Overcoming implementation challenges

Digital twins can be a part of the overall digital transformation strategies of an organization’s supply chain. However, as in all digital transformations, the following challenges will need to be considered:

Data integration and accuracy issues

One of the biggest challenges of digital supply chain twins is the mismatch of data. Often, inaccurate or conflicting data is sourced from logistics management software, ERP systems, inventory management systems and IoT sensors. This makes coordination with multiple stakeholders extremely difficult.

One approach we’ve often implemented to fix this issue is to first understand the data maturity model of the architecture you’re implementing the digital twin in, which includes mapping all the data sources and calling out the potential data silos. Secondly, we look at employing a myriad of different additional data strategies, like a data mesh or data fabric, to essentially call some of the data, as opposed to simply leveraging data lakes and data warehouses. 

When building a data lake or migrating data to a centralized source, leveraging a solution like Snowflake or AWS can be effective. With regards to data mesh and data fabrics, there are some commercial off-the-shelf (COTS) software solutions that install within existing topologies to map all data points into a meshed architecture. Conversely, we have also developed custom mesh solutions for digital twins in the supply chain by partnering with firms like Palantir or Amazon to achieve similar results.  

High upfront and maintenance costs

Digital Transformation initiatives are seldom easy or without high costs. Integrating supply chain digital twins requires investing in hardware, software, AI-driven analytics tools and cloud infrastructures, along with ongoing maintenance costs. Here are some tips as platitudes we have tried in the past that seem to help: 

• Don’t boil the ocean. Digital transformation in the supply chain is about showing value as quickly as possible. Nothing gets to value quicker than starting with a small pilot project the scale up. The team needs to focus on the most critical constraint, deliver value quickly and build momentum.
• Don’t reinvent the wheel. Utilize existing data sources and integrate with current infrastructure to reduce the need for new data collection and technology and collaborate with industry partners. Collaborative models can spread financial risk and leverage shared expertise, leading to cost savings and enhanced capabilities.

By employing these strategies, organizations can effectively manage the financial burdens associated with building and maintaining Digital Twins by minimizing upfront investment while continuously improving the system’s value, allowing organizations to allocate resources based on proven benefits.

These steps also allow organizations to test the Digital Twin concept, refine strategies and demonstrate value without significant investment.

Resistance to change

In many ways, we have noticed that the best approach towards removing resistance to change starts by implementing a culture of change. Employee resistance is very common when it comes to the implementation of digital twins, especially in supply chain management. This often stems from inadequate training in the Internet of Things (IoT), data analytics, artificial intelligence (AI) and working with the digital twins themselves.

From our own experiences, the best approach is to implement such changes in a phased manner and then scale up. This includes the following steps:

• Ensuring leadership buy-in. It starts at the top with strategies where leadership champions experimentation, setting the tone and providing necessary resources.
• Creating a safe space for failure. Communicate and champion a message that resonates with how users are in a safe environment where failures are seen as learning opportunities.
• Measuring success beyond traditional metrics. Leverage alternative metrics to promote experimentation and innovation.

By taking these steps, integration and adoption of digital twins become seamless and focused on value, not mandates.  

The future of digital twins in the supply chain

Once you’ve implemented digital twins in your supply chain and successfully garnered employee confidence, the story doesn’t just end there. Scaling up your supply chain digital twins is the next logical step forward. And this is where AI integration with digital twin applications comes into the picture.

Imagine the possibilities a well-integrated supply chain digital twin system with AI can bring to the table. With AI enabling the processing, analyzing and interpreting of massive multidimensional datasets, your supply chain can remain agile and resilient while navigating the turbulence of geopolitical or macroeconomic disruptions.

This article is published as part of the Foundry Expert Contributor Network.
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Anthropic’s assertion that its agentic coding tool Claude Code can rapidly refactor legacy COBOL code has unsettled IBM investors, triggering a sell-off in shares, which resulted in the stock registering a 13% drop on Monday — its biggest single-day drop since October 2000.

The assertion came in the form of a blog post from Anthropic that suggested that AI could take away the cost and complexity associated with modernizing a mainframe.

“AI excels at streamlining the tasks that once made COBOL modernization cost-prohibitive. With it, your team can focus on strategy, risk assessment, and business logic while AI automates the code analysis and implementation,” the company wrote.

Investors were concerned that Claude Code would erode the value of IBM’s strong and growing infrastructure services and consulting business, which depends on high-margin COBOL modernization on mainframes.

However, modernizing COBOL applications to counter the dwindling numbers of developers proficient in the programming language is not a novel idea.

IBM itself, in 2023, expanded the abilities of its generative AI-based Watson Code Assistant to include COBOL code translation into Java.

Hyperscalers, such as AWS, Microsoft, and Google, too, have been offering mainframe modernization tools, including AI services. IBM-spin off Kyndryl has also been partnering with hyperscalers to offer similar services.

Yet competition from hyperscalers and partners has so far failed to meaningfully slow IBM’s mainframe business.

During its fourth-quarter earnings calls last month, IBM CEO Arvind Krishna, in his prepared remarks, said that its Z family of mainframes had seen a 48% increase in yearly revenue — its highest in 20 years.

Krishna further pointed out that the increase in revenue from its Z line could be attributed to the efficiency of the Watson Code Assistant in modernizing COBOL applications, in turn making it easy for enterprise development teams to run the mainframes.

Moreover, IBM, in response to Anthropic’s assertions and to allay investors’ fears, put out a blog post of its own arguing that translating COBOL code is not equivalent to modernizing the underlying systems in a mainframe.

“Translation captures almost none of the actual complexity. The modernization challenge is not a COBOL language problem. It is everything the application runs on and integrates with,” Rob Thomas, SVP of IBM software, wrote in the post.

Despite the buzz surrounding AI, many organizations struggle to move beyond experimentation and pilot programs to fully scale AI across their enterprises. According to recent industry research conducted by EXL, while 96% of organizations believe scaling their AI initiatives over the next year is very important, companies have successfully integrated AI into less than half their workflows. Less than half report achieving their desired outcomes with AI as well.

This gap between ambition and execution is precisely what EXL, a global data and AI company, is addressing with its flagship virtual event, AI in Action 2026. Scheduled to take place across three regions, March 11 for the Americas, March 18 for EMEA, and March 24 for APAC, this event will dive into how agentic AI is reimagining data, intelligence, and enterprise workflows.

The challenges of scaling AI

The promise of AI is undeniable. From streamlining operations to enhancing customer experiences, AI has the potential to transform how businesses operate. However, the journey from proof of concept to enterprise-wide implementation is fraught with challenges.

One of the most significant hurdles is embedding AI into existing workflows, many of which need to be redesigned in order to fully take advantage of AI, particularly as agentic AI systems increasingly operate business processes with greater autonomy. According to EXL’s upcoming 2026 Enterprise AI Study, only 45% of organizational workflows currently have AI embedded.

Many organizations also find themselves grappling with siloed data and a lack of AI-ready infrastructure, enabling them to embed intelligence into their workflows. This underscores the need for a more strategic approach to AI adoption, one that goes beyond isolated use cases and focuses on scaling AI to deliver consistent, measurable outcomes and to support agentic AI that can continuously learn, adapt, and enhance performance.

Another challenge is orchestrating multiple AI agents, autonomous systems capable of making decisions and taking actions without human intervention. While these agents hold immense potential, their deployment requires robust governance frameworks to ensure ethical and responsible use, which is strengthened through shared standards and best practices across our partner ecosystem.

From ambition to execution

While pilots and proofs of concepts are an important first step, AI’s true potential is unlocked when it becomes part of how work gets done every day, not as a bolt-on or point solution, with agentic AI embedded into core workflows to connect data, intelligence, and action in real-time, supported by integrated partner technologies and co-innovation models.

It’s essential to prioritize the most critical workflows and adapt them for the AI era by embedding intelligence at scale, governing AI responsibly, and transforming innovation into lasting business value.

AI in Action 2026 is designed to help organizations navigate this complex journey. The event will feature a keynote on innovation, disruption, and business value, delivered by EXL Chairman and CEO Rohit Kapoor and Jon McNeill, former president of Tesla, Inc. and COO of Lyft. McNeill, now the CEO and co-founder of DVx Ventures, brings a wealth of experience in scaling technology-driven businesses.

Practical insights and real-world use cases

One of the standout features of AI in Action 2026 is its focus on practicality. The event is tailored for CXOs, CIOs, CTOs, and technology and data executives who are looking for actionable strategies to scale AI within their organizations, on how to deploy agentic AI to orchestrate data, AI, and workflows at enterprise scale, leveraging solutions developed jointly with leading partners.

The agenda includes specialized sessions on:

• Reimagining data science and workflows with agentic AI: Learn how to identify high-impact workflows and reimagine them for the AI era.
• Getting your data house in order: Discover how to make data AI-ready and overcome common challenges related to data silos and legacy systems.

In addition to these sessions, attendees will have the opportunity to see real-world demonstrations. Together, these sessions will provide valuable insights into the strategies, tools, and frameworks that have proven effective in driving ROI from AI investments.

Why AI in Action matters

The timing of AI in Action 2026 couldn’t be more critical. As businesses face increasing pressure to innovate and stay competitive, the ability to scale AI has become a key differentiator. However, achieving this requires more than just technology; it demands a holistic approach that orchestrates AI with domain expertise, robust data management, and the ability to execute at scale.

By bringing together industry leaders, hyperscalers, technology experts, and real-world practitioners, AI in Action 2026 aims to bridge the gap between ambition and execution. The event is about showcasing the potential of AI and equipping organizations with the tools and knowledge they need to turn that potential into reality, with partners playing a critical role in turning vision into execution.

Looking ahead

As the world becomes increasingly data-driven, the role of agentic AI in shaping the future of business cannot be overstated. However, unlocking the full value of AI requires a shift in mindset, from viewing AI as a standalone solution to embedding it into the very fabric of how work gets done. Agentic AI will serve as the connective layer between data, intelligence, and orchestration—with a strong partner ecosystem ensuring these capabilities scale with confidence.

AI in Action 2026 offers a unique opportunity for business and technology leaders to gain practical insights, learn from real-world examples, and connect with peers who are navigating similar challenges. With a focus on moving from experimentation to execution, this event is poised to be a game-changer for organizations looking to scale AI and drive measurable business value.

To learn more about AI in Action 2026 and register for the event in the region of your choice, visit here.

About the author: Vishal Chhibbar is executive vice president, chief growth officer, and head of international growth markets at EXL, a global data and AI company.

Why AI in M&A, and why now?

The technology landscape is expanding rapidly, leading to increasingly complex tech stacks that contribute to heightened risks and intricacies in mergers and acquisitions (M&A). Traditional methods such as spreadsheets and manual analyses are proving inadequate, often consuming excessive time and being prone to errors. These approaches also demand specialized skill sets and extensive human input to validate outputs, undermining the efficiency that modern technology aims to achieve.

As M&A strategies evolve, there is a pressing need for swift and precise decision-making. Over the past five decades, approximately 992,000 M&A deals have been executed globally, with a total known value exceeding $57 trillion. This wealth of information underscores the necessity of leveraging AI to navigate the complexities of contemporary M&A activities. (IMA&A).

The M&A pain points AI can solve

1. Data overload during due diligence

Due diligence has become a data problem. IT leaders now face terabytes of structured and unstructured information from ERP and CRM records to contracts, emails and market intelligence. Manual review slows down decisions, introduces errors and increases the risk of missing critical signals. AI shifts this dynamic by automating data extraction, analysis and anomaly detection. Platforms equipped with machine learning can surface risks, highlight synergies and predict integration challenges in minutes (IMA&A).

2. IT integration risks and redundancies

Post-merger integration remains one of the most failure-prone phases of M&A. Overlapping ERP, CRM, cloud and cybersecurity systems create redundancies, inflate costs and cause delays. Traditional integration built on spreadsheets and manual mapping — only adds complexity. AI-driven integration tools can map IT environments, identify duplicate systems and recommend optimal consolidation strategies. Predictive analytics further flags bottlenecks before they derail timelines (Statista).

3. Cultural and workforce misalignment

People-related issues often undermine otherwise strong deals. Cultural friction, communication gaps and talent flight can stall synergy realization. Traditional methods like surveys and workshops aren’t enough at scale. AI tools now analyze communication patterns, sentiment and engagement across collaboration platforms to give leaders real-time insight into morale and alignment. Predictive models identify flight risks and resistance points, enabling targeted interventions that accelerate adoption.

4. Cybersecurity blind spots

Blending two IT ecosystems introduces new vulnerabilities — unpatched systems, unsecured data stores and outdated software. Manual audits rarely catch everything. AI-based cybersecurity tools continuously scan environments, detect anomalies and rank vulnerabilities based on potential impact. These systems help move from reactive security to proactive protection, allowing integration efforts to continue without exposing the business to unnecessary risk. For examples of cybersecurity failures linked to M&A (CISA).

5. Regulatory and compliance hurdles

M&A deals face a growing web of compliance requirements across privacy, antitrust, labor and industry regulations. Manual reviews are slow, expensive and error-prone. AI can automate contract analysis, track regulatory changes and generate real-time compliance insights to accelerate approvals and reduce legal exposure. With global regulations evolving rapidly, automated compliance has become a necessity (Govt. Info).

Real-world impact: How AI has already shaped M&A success

AI has moved from concept to reality in M&A, enabling companies to accelerate deals, uncover hidden risks and unlock greater post-merger value.

Example 1: AI accelerating due diligence (speed + accuracy)

Traditional due diligence can take months, requiring manual review of financial statements, contracts and operational data. AI platforms significantly reduce this time while improving accuracy. Advanced document analysis and financial modeling tools can process thousands of records in hours, identifying inconsistencies, anomalies or hidden liabilities that might otherwise be overlooked. The result is faster deal execution and more informed decision-making.

Example 2: AI driving post-merger IT integration efficiencies

Post-merger IT integration is one of the most resource-intensive phases of M&A. AI tools help map IT systems, detect redundancies and automate consolidation tasks, dramatically improving efficiency. Machine learning algorithms can simulate integration scenarios, predict bottlenecks and recommend optimized workflows, allowing teams to accelerate synergy realization while minimizing operational disruption.

The essential AI toolbox for M&A leaders

AI is transforming M&A by streamlining every stage of the deal lifecycle from automating due diligence and analyzing financials, contracts and market sentiment to mapping IT systems, identifying redundancies and monitoring workforce engagement. By applying machine learning, natural language processing and predictive analytics, AI helps leaders uncover hidden risks, highlight synergies and accelerate integration planning, making complex processes faster, more accurate and insight-driven.

In addition, AI strengthens cybersecurity and compliance by continuously monitoring IT environments, detecting anomalies and ensuring regulatory adherence across multiple jurisdictions. This enables deal teams to make confident, data-driven decisions, improve employee adoption, mitigate operational risks and ultimately capture post-merger value more effectively, turning traditionally labor-intensive and high-risk activities into strategic advantages

Kashif Syed

Your M&A AI roadmap: How to get started

Implementing AI in M&A is not just about adopting technology, it requires a strategic, step-by-step approach to maximize value and minimize risks. IT leaders can follow a structured roadmap to embed AI successfully across the M&A lifecycle

• Assess Current M&A Processes: Identify pain points in due diligence, IT integration, workforce alignment, cybersecurity and compliance where AI can add value.
• Select the Right Tools and Vendors: Choose scalable, user-friendly AI solutions that fit your technology ecosystem and provide actionable insights.
• Pilot in One Phase Before Scaling: Start with a high-impact area to test AI capabilities, measure results and refine processes before full deployment.
• Align with Business and Integration Teams: Ensure collaboration across business, IT and integration teams so AI insights translate into actionable decisions and drive adoption.
• Measure Synergy Realization: Track outcomes like speed, risk reduction, integration efficiency and cost savings to optimize AI’s impact and capture post-merger value.

Kashif Syed

The future of AI in M&A

AI is rapidly becoming a strategic driver in mergers and acquisitions, transforming every phase of the deal lifecycle. Beyond efficiency, AI predicts deal outcomes, simulates integration scenarios and guides execution with minimal manual effort.

Predictive tools can score deal potential, helping executives prioritize targets, allocate resources and make faster, data-driven decisions. Digital twins allow leaders to model IT systems, business processes and workforce dynamics, anticipating challenges and optimizing strategies before committing resources.

Looking ahead, AI is expected to autonomously plan and execute integration tasks, continuously adapting workflows and identifying bottlenecks. Ultimately, AI will become a standard part of the toolkit, embedded across due diligence, IT integration, risk management, cultural analysis, cybersecurity and compliance, turning complexity into clarity and maximizing post-merger value.

Conclusion: From chaos to clarity

M&A deals are complex, but AI provides a clear path to manage that complexity. From accelerating due diligence to optimizing IT integration, workforce alignment and compliance, AI empowers leaders to make faster, smarter decisions.

Leaders who embrace AI strategically can capture post-merger synergies, unlock value and gain a competitive edge. By starting with pilots, scaling across phases and measuring outcomes, organizations can move from operational chaos to strategic clarity.

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Project managers are doing exactly what they were taught to do. They build plans, chase team members for updates, and report status. Despite all the activity, your leadership team is wondering why projects take so long and cost so much.

When projects don’t seem to move fast enough or deliver the ROI you expected, it usually has less to do with effort and more with a set of common mistakes your project managers make because of how they were trained, and what that training left out. Most project teams operate like order takers instead of the business-focused leaders you need to deliver your organization’s strategy.

To accelerate strategy delivery in your organization, something has to change. The way projects are led needs to shift, and traditional project management approaches and mindsets won’t get you there.

Here are the most common project management mistakes we see holding teams back, and what you can do to help your project leaders shift from being order takers to drivers of IMPACT: instilling focus, measuring outcomes, performing, adapting, communicating, and transforming.

Mistake #1: Solving project problems instead of business problems

Project managers are trained to solve project problems. Scope creep. Missed deadlines. Resource bottlenecks. They spend their days managing tasks and chasing status updates, but most of them have no idea whether the work they manage is solving a real business problem.

That’s not their fault. They’ve been taught to stay in their lane in formal training and by many executives. Keep the project moving. Don’t ask questions. Focus on delivery.

But no one is talking to them about the purpose of these projects and what success looks like from a business perspective, so how can they help you achieve it?

You don’t need another project checked off the list. You need the business problem solved.

IMPACT driver mindset: Instill focus

Start by helping your teams understand the business context behind the work. What problem are we trying to solve? Why does this project matter to the organization? What outcome are we aiming for?

Your teams can’t answer those questions unless you bring them into the strategy conversation. When they understand the business goals, not just the project goals, they can start making decisions differently. Their conversations change to ensure everyone knows why their work matters. The entire team begins choosing priorities, tradeoffs, and solutions that are aligned with solving that business problem instead of just checking tasks off the list.

Mistake #2: Tracking progress instead of measuring business value

Your teams are taught to track progress toward delivering outputs. On time, on scope, and on budget are the metrics they hear repeatedly. But those metrics only tell you if deliverables will be created as planned, not if that work will deliver the results the business expects.

Most project managers are taught to measure how busy the team is. Everyone walks around wearing their busy badge of honor as if that proves value. They give updates about what’s done, what’s in progress, and what’s late. But the metrics they use show how busy everyone is at creating outputs, not how they’re tracking toward achieving outcomes.

All of that busyness can look impressive on paper, but it’s not the same as being productive. In fact, busy gets in the way of being productive.

IMPACT driver mindset: Measure outcomes

Now that the team understands what they’re doing and why, the next question to answer is how will we know we’re successful.

Right from the start of the project, you need to define not just the business goal but how you’ll measure it was successful in business terms. Did the project reduce cost, increase revenue, improve the customer experience? That’s what you and your peers care about, but often that’s not the focus you ask the project people to drive toward.

Think about a project that’s intended to drive revenue but ends up costing you twice as much to deliver. If the revenue target stays the same, the project may no longer make sense. Or they might come up with a way to drive even higher revenue because they understood the way you measure success.

Shift how you measure project success from outputs to outcomes and watch how quickly your projects start creating real business value.

Mistake #3: Perfecting process instead of streamlining it

If your teams spend more time tweaking templates, building frameworks, or debating methodology than actually delivering results, processes become inefficient.

Often project managers are hired for their certifications, which leads many of them to believe their value is tied to how much of and how perfectly they create and follow that process. They work hard to make sure every box is checked, every template is filled out, and every report is delivered on time. But if the process becomes the goal, they’re missing the point.

You invested in project management to get business results, not build a deliverable machine, and the faster you achieve those results, the higher your return on your project investments.

IMPACT driver mindset: Perform relentlessly

With a clear plan to drive business value, now we need to show them how to accelerate. That means relentlessly evaluating, streamlining, and optimizing the delivery process so it helps the team achieve the project goals faster.

Give them permission to simplify. When the process slows them down or adds work that doesn’t add value, they should be able to call it out.

This isn’t an excuse to have no process or claim you’re being agile just to skip the necessary steps. It’s about right-sizing the process, simplifying where you can, and being thoughtful about what’s truly needed to deliver the outcome. Do you really need a 30-page document no one will read, or would two pages that people actually use be enough? You don’t need perfection. You need progress.

Mistake #4: Blaming people instead of leading them through change

A lot of leaders start from the belief that people are naturally resistant to change. When projects stall or results fall short, it’s easy to assume someone just didn’t want to change. Project teams blame people, then layer on more governance, more process, and more pressure. Most of the time, it’s not a people problem. It’s how the changes are being done to people instead of with them.

People don’t resist because they’re lazy or difficult. They resist because they don’t understand why it’s happening or what it means for them. And no amount of process will fix that.

IMPACT driver mindset: Adapt to thrive

With an accelerated delivery plan designed to drive business value, your project teams can now turn their attention to bringing people with them through the change process.

Change management is everyone’s job, not something you outsource to HR or a change team. Projects fail without good change management and everyone needs to be involved. Your teams must understand that people aren’t resistant to change. They’re resistant to having change done to them. You have to teach them how to bring others through the change process instead of pushing change at them.

Teach your project teams how to engage stakeholders early and often so they feel part of the change journey. When people are included, feel heard, and involved in shaping the solution, resistance starts to fade and you create a united force that supports your accelerated delivery plan.

Mistake #5: Communicating for compliance instead of engagement

The reason most project communication fails is because it’s treated like a one-way path. Status reports people don’t understand. Steering committee slides read to a room full of executives who aren’t engaged. Unread emails. The information goes out because it’s required, not because it’s helping people make better decisions or take the right action.

But that kind of communication doesn’t create clarity, build engagement, or drive alignment. And it doesn’t inspire anyone to lean in and help solve the real problems.

IMPACT driver mindset: Communicate with purpose

To keep people engaged in the project and help it keep accelerating toward business goals, you need purpose-driven communication designed to drive actions and decisions. Your teams shouldn’t just push information but enable action. That means getting the right people and the right message at the right time, with a clear next step.

If you want your projects to move faster, communication can’t be a formality. When teams, sponsors, and stakeholders know what’s happening and why it matters, they make decisions faster. You don’t need more status reports. You need communication that drives actions and decisions.

Mistake #6: Driving project goals instead of business outcomes

Most organizations still define the project leadership role around task-focused delivery. Get the project done. Hit the date. Stay on budget. Project managers have been trained to believe that finishing the project as planned is the definition of success. But that’s not how you define project success.

If you keep project managers out of the conversations about strategy and business goals, they’ll naturally focus on project outputs instead of business outcomes. This leaves you in the same place you are today. Projects are completed, outputs are delivered, but the business doesn’t always see the impact expected.

IMPACT driver mindset: Transform mindset

When you help your teams instill focus, measure outcomes, perform relentlessly, adapt to thrive, and communicate with purpose, you do more than improve project delivery. You build the foundation for a different kind of leadership.

Shift how you and your organization see the project leadership role. Your project managers are no longer just running projects. You’re developing strategy navigators who partner with you to guide how strategy gets delivered, and help you see around corners, connect initiatives, and decide where to invest next.

When project managers are trusted to think this way and given visibility into the strategy, they learn how the business really works. They stop chasing project success and start driving business success.

More on project management:

• Project management guide: Tips, strategies, best practices
• What is a project manager? The lead role for project success
• 5 early warning signs of project failure
• 10 project management myths to avoid
• 16 tips for a smooth switch to agile project management
• The 22 best project management tools for business
• Scrum vs. Lean vs. Kanban: Comparing agile project management frameworks
• How to pick the right project management methodology for success
• Agile project management: A comprehensive guide
• 8 common project management mistakes — and how to avoid them
• Top 11 project management certifications for 2017
• 10 traits of highly effective project managers
• 8 goals every project manager should aspire to achieve
• Project management: 7 steps to on-time, on-budget, goal-based delivery

AI is everywhere, and CIOs can’t lead AI strategy alone. With 62% of organizations experimenting with AI, its reach is too broad for oversight to live solely with IT. Nearly half (48%) of CIOs still shoulder responsibility for leading AI strategy, even though 88% of generative AI usage happens outside their teams. The result: AI implementation is skyrocketing, but few projects across the business deliver real impact.

The solution is not more IT oversight, but distributed leadership. Department leaders know their teams best. They observe firsthand which processes slow teams down, where AI can automate, and how workflows truly function. This deep expertise makes them uniquely suited to lead AI strategy across their respective departments and realize AI’s full potential. CIOs need to pass the torch and empower them to lead.

The AI Bottleneck: Even the strongest CIOs can’t carry the entire AI agenda alone

CIOs are the champions of innovation – expected to deliver real ROI from AI while keeping the enterprise secure, aligned, and ahead of the curve. But when every AI request, experiment, and implementation lands on their desk, even the best leaders face impossible bottlenecks. On top of this, most generative AI usage now resides outside IT, across finance, marketing, HR, and more.

The consequence is a growing “adoption-value gap.” AI initiatives exist throughout the business, but only 5% deliver measurable ROI. When CIOs try to own every AI project, innovation stalls. To get real value, responsibility must shift to department leaders – those closest to the work who drive meaningful results.

Distributed Leadership: The New Model for AI Success

The most successful and impactful CIOs don’t try to own everything – they orchestrate. Department leaders who understand AI tools and are comfortable using them can step up and take ownership within their teams, relieving the CIO burden. At Freshworks, we’re putting this into practice: AI works alongside our people to remove busy work, accelerate productivity, and unlock higher-value work.

Our teams are seeing measurable efficiency gains across the organization:

• Customer Support: AI agents now handle 34% of chat tickets, allowing human agents to focus on complex, high-value conversations. Productivity per agent has increased 25%, and new agent ramp time has been reduced from six months to three months.
  
• Engineering & Quality: Developers use AI tools to write code, while quality engineers leverage AI for test cases and automation. Cycle times have dropped by up to 50%, and debugging efficiency has improved from hours to minutes in some cases.
  
• Web & Digital Teams: Building new web pages now takes hours instead of weeks, freeing teams to focus on higher-impact initiatives.
  
• IT Teams: AI automates ticketing, categorizes issues, and resolves requests faster, improving employee experience across the business.
  
• HR & Recruiting: AI-powered Slack integrations help review resumes quickly and accurately, streamlining recruiting and onboarding.

Shifting ownership to department leaders unlocks each team’s potential. CIOs move from “owners” to enablers, setting frameworks and guardrails. This approach isn’t about cost-cutting – it frees talent to drive innovation, growth, and problem-solving, benefiting business outcomes and employee engagement.

Building AI-Native Leaders Across the Business

Non-technical leaders may find taking the reins daunting. CIOs can support them by introducing simple, intuitive AI tools, offering literacy programs, and creating “AI champion” groups to share best practices. Teams can explore use cases tied to KPIs—financial forecasting, talent analytics, or operational efficiency—while clear policies encourage responsible experimentation.

From Ownership to Orchestration: The CIO as the Conductor

Think of the CIO as a conductor, not a player. They set the vision, ensure harmony, and provide structure, while department leaders apply their expertise strategically. The result: an AI-fluent organization where experimentation happens faster, and value grows organically.

AI success comes from collaboration across the business. CIOs who empower leaders while providing clear governance unlock AI’s true potential—making it work for people, not against them.

For CIOs seeking concrete examples of driving measurable ITSM value with AI, learn more about Freshservice here.

For marketing teams under pressure to deliver more content at higher quality and in less time,  Firefly Boards introduces a new AI-driven way to brainstorm, explore, and align. Built into Adobe Firefly, Boards provides a centralized space where teams can generate, refine, and collaborate on creative ideas using leading AI models from Adobe and partners including Black Forest Labs, Google, Luma AI, Moonvalley, Pika, Marey, and Runway.

Adobe Firefly AI solutions help businesses meet the increasing demand for content. From accelerating ideation and editing to streamlining bulk production and assembly, Firefly is the leading technology powering creation and production at scale.

A unified workspace for faster ideation

Marketing campaigns rely on quick iteration, strong collaboration, and early alignment. Firefly Boards eliminates the friction of jumping between tools, manually sourcing assets, and navigating long feedback cycles. Instead, your team can work together in one shared environment where:

• Ideas are generated, refined, and evaluated in real time
• Stakeholders collaborate directly on the canvas
• Visual concepts are aligned early in the creative process
• Teams move from inspiration to activation significantly faster

This streamlined workflow supports faster decision-making and more efficient campaign development.

Powerful capabilities for creative exploration

With Firefly Boards, your marketing team can:

• Generate campaign imagery and video concepts using Adobe and partner AI models
• Leverage style and composition references to match brand look and feel
• Remix and combine concepts to test creative directions
• Apply visual elements — such as lighting or color — from one asset to another
• Seamlessly continue production work in Adobe Photoshop or Adobe Express
• Share Boards with cross-functional teams for instant feedback and alignment

These capabilities help marketing teams test more ideas, refine concepts quickly, and produce higher-quality creative outputs.

New features designed for speed and control

Firefly Boards now incorporates new tools that help marketers move from concept to campaign materials more efficiently:

• Presets: One-click generation across key styles — Product, Character, Virtual Try-On, Electric Party, and more — powered by Adobe Firefly and partner models.
• Generative Text Edit (beta): Update or replace text inside visuals directly within Boards, enabling quick iteration without disrupting workflows.
• Describe Image: Automatically generate a ready-to-edit prompt based on any existing image, making it easy to adapt and evolve campaign concepts.

These enhancements provide greater control, reduce guesswork, and accelerate the creative review process.

Ease management of your company’s marketing software

Firefly Boards is one AI tool within the broad scope of Adobe Firefly and Adobe Creative Cloud.  If your marketing team is already using Creative Cloud for teams, they already have access to many Firefly tools.  Firefly complements the design work your team is doing to create campaigns and brand tools that make your company stand out.  From a software deployment and management perspective, you gain ease of deployment and management when your marketing teams work with integrated apps managed at a unified console level.  Get 24/7 technical support and explore step-by-step product tutorials.

Learn more about Firefly Board here.

“We leverage big data and artificial intelligence to gain a deeper understanding of our guests’ needs. Smart systems help us optimize queue management, visitor flow, and energy efficiency. By integrating online and offline experiences, we enable visitors to conveniently plan and interact through their mobile devices. Our ultimate pursuit is to make Resorts World Sentosa more than just a resort—it is an evolving destination that blends innovation with seamless hospitality.”  – Lee Shi Ruh, CEO of RWS

The digital revolution is bringing cutting-edge technologies like AI, IoT, and big data to every corner of modern life, fundamentally transforming how we live and interact with the world. Nowhere is this shift more evident than in the tourism sector, where innovation is redefining the very essence of exploration. Far from being mere hype, smart tourism solutions have emerged as game-changers; they have refreshed guest experiences, streamlined operations, and paved the way for sustainable growth in one of the world’s most dynamic industries.

Nestled on Singapore’s vibrant Sentosa Island, Resorts World Sentosa (RWS) stands as Asia’s premier integrated resort. They are pioneers in creating immersive travel experiences with modern technology.

Transforming from a tropical resort to an entire smart ecosystem

Since its launch in 2010, RWS has captivated over 200 million guests with its unique attractions. Spanning 49 hectares, this award-winning destination is home to world-class attractions like Universal Studios Singapore, the mesmerizing Adventure Cove Waterpark, and the thrilling Singapore Oceanarium. Guests have luxurious boutique hotels and state-of-the-art facilities at their disposal throughout the resort, like the International Conference Centre. For ten years, RWS has proudly held the prestigious title of Best Comprehensive Resort, solidifying its status as a global leader in entertainment and hospitality.

Behind the honour is RWS’ relentless pursuit of sustainable development and technological innovation. By harmonizing tourism with environmental stewardship and fostering mutual growth between businesses and communities, RWS sets a transformative standard for progress. To this end, RWS unveiled its ambitious RWS 2.0 expansion plan, investing SGD 6.8 billion into transformative development. Beyond iconic additions like Minion Land, The Laurus, and WEAVE, the initiative prioritizes the adoption of extensive digital and intelligent innovations, aiming to create new surprises and lasting memories for every visitor.

When I joined a major retail digital transformation project as a senior specialist with a consulting firm, the first meeting went exactly as I expected: “We need more tools.” Every discussion about efficiency seemed to end with another vendor demo or platform suggestion — a new SaaS for workflow, a chatbot for customer service, an AI dashboard for analytics.

It’s a reflex many organizations have developed. Digital transformation has become synonymous with tool adoption and executives often measure progress by the number of technologies purchased. But as I listened to the project sponsors enthusiastically list the “missing systems,” one question came to mind: Were we transforming the business or simply expanding the toolbox?

I had seen this pattern before — a well-intentioned accumulation of platforms that ultimately increased complexity rather than capability. So I proposed something that sounded contrarian at the time: Instead of adding more, let’s first see what we can remove.

The suggestion earned a few surprised looks, but that conversation became the turning point of one of the most effective transformation programs I’ve been part of.

The hidden cost of tool sprawl

In the modern enterprise, tool sprawl is a quiet but costly epidemic. Across industries, organizations accumulate overlapping software faster than they can integrate it, creating confusion, duplicating data and raising subscription costs.

The retail client I supported was no exception. Over time, every department had picked its own favorites — project management boards here, customer feedback tools there and a half-dozen collaboration apps in between.

During my first month, I conducted an audit of the digital ecosystem. The result was startling: 142 separate applications in active use across the organization, with more than a dozen dedicated to collaboration alone. Some departments used three tools to manage the same process, creating duplicate records and confusion about ownership.

In one workshop, a team leader joked that “finding the right file is harder than finding a new employee.” It was meant humorously, but it captured the frustration perfectly. Instead of driving productivity, technology was slowing people down.

Leaders often try to fix this kind of chaos by layering on new software. But as Harvard Business Review notes, more tools don’t automatically equal more productivity — they often amplify the noise.

Choosing subtraction over addition

I started with two simple questions:

1. What tools do people actually use every day?
2. What measurable value does each tool provide?

Those questions changed the conversation. Instead of “what’s missing?” we began asking “what’s working?”

We hosted a series of discovery sessions where employees anonymously rated the tools they used by usefulness and ease of adoption. The results were eye-opening. Nearly 40% of tools had fewer than 15% active users. Some were bought for pilot programs that never scaled; others replicated capabilities already available in enterprise systems.

For example, three departments paid for separate survey tools even though their CRM could already handle feedback collection. Another analytics platform generated reports nearly identical to what finance could produce internally.

When I presented the findings, one executive asked, “So you’re saying we could cut a third of our software and no one would notice?”

I replied, “You might notice — in a good way.”

That comment led to our first pilot consolidation: collaboration and analytics, the two most fragmented areas. The pushback came not from IT, but from end-users attached to their preferred systems. We countered that by focusing on the user experience. We showed how simplification would mean fewer logins, consistent data and faster response times. Slowly, the idea gained traction.

Streamlining the stack

Our first step was to standardize the collaboration environment. We reduced 13 communication platforms down to four, implemented single sign-on and integrated the approved tools with the company’s intranet. Teams no longer had to jump between chat windows, task trackers and document portals to find information.

Next, we rationalized analytics and reporting. Instead of maintaining multiple dashboards across BI tools, we centralized key metrics into one unified view. We created a data-governance checklist and automated report scheduling to eliminate manual reconciliation.

The process took about five months, but the results were immediate and measurable:

• 25% reduction in software licensing costs within the first year.
• 38% increase in active usage across remaining tools.
• Simplified security posture, reducing the number of vendor integrations by half.
• Improved employee satisfaction scores, especially among frontline retail managers who no longer needed to toggle between systems.

One of my favorite moments came a few months later when a department head told me, “I can finally find what I need without calling three people.” That’s when I knew simplification had worked.

Research supports this experience. According to Reworked.co, employees forced to juggle multiple collaboration tools lose up to an hour of productive time per day. Our data lined up almost exactly with that estimate.

Lessons from a less-is-more approach

Looking back, what surprised me most wasn’t how much we saved, but how much clarity we gained. Simplification forced alignment. Once redundant tools disappeared, teams began to talk more — not through software, but through collaboration. Decisions became faster because everyone used the same data and systems.

One executive commented that it felt as if “the fog had lifted.” They weren’t just working with fewer tools — they were working with purpose.

The hardest part of the project wasn’t technical execution. It was changing the perception that transformation equals accumulation. For years, digital maturity was measured by how many tools an organization deployed. But as Harvard Business Review points out, tools don’t create productivity — people and processes do. Our experience mirrored that insight exactly.

Here’s what I learned:

• Simplification enables innovation. You can’t innovate when your foundation is unstable. Consolidation frees up both mental and operational capacity.
• Tool governance matters. Every new platform should justify its existence — what value does it add beyond what’s already in place?
• Culture drives adoption. Removing tools can feel threatening unless employees understand the why. Transparent communication makes all the difference.

Digital transformation isn’t a race to deploy more technology. It’s a discipline of ensuring the right technology delivers consistent value.

Building for the long term

After the consolidation phase, the client’s IT roadmap looked dramatically different. Instead of dozens of disconnected platforms, they invested in optimizing integrations, training and analytics literacy. By strengthening the foundation first, they could later introduce new technologies — AI forecasting, advanced personalization and process automation — with confidence that the systems underneath would support them.

That’s a crucial shift many organizations overlook. As tempting as it is to chase the next big tool, real transformation comes from improving how people and processes work together.

A year later, when I reconnected with the client’s CIO, she told me, “Our best decision wasn’t the AI tool we added. It was everything we removed before it.”

That’s the paradox of transformation: progress sometimes starts by taking things away.

[Note: The views expressed in this article are my own and do not represent the views of Deloitte or its clients.]

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Enterprises have flocked to adopt platforms that automate and streamline the software development lifecycle, with a huge majority of organizations completing migrations to DevOps or CI/CD platforms during the past two years, according to a recent survey.

In addition to streamlining coding practices, these platforms are also seen as a way to cut back on the number of developer tools in use — and to make the most of AI-powered coding copilots.

Gartner projects the use of DevOps platforms, including GitHub, GitLab, and Harness, will rise to 80% of organizations by 2027, up from 25% just two years ago. In a recently commissioned survey, software delivery solutions provider CloudBees found that 85% of the IT leader respondents had migrated to DevOps platforms in the past two years.

Several factors are driving enterprises the trend, including a desire to modernize app development, cut costs, and reduce the number of apps developers use, says Anuj Kapur, CloudBees’ president and CEO.

“Tool sprawl creates a procurement challenge, having to deal with multiple vendors,” he says. “It creates an integration challenge, having to get tools to talk to each other.”

Many software development leaders also want to eliminate the often-fierce debates within their organizations about the best coding apps and editors, Kapur adds.

“You create these religious wars inside organizations about whose tool is better,” he says. “There’s an effort to address that once and for all with this panacea that becomes, ‘If only we could convert everyone’s religion into one, we’d all be so much better.’”

Cost overruns

But the CloudBees survey comes with a warning: Rip-and-replace migrations, popular for DevOps platforms, come with complications, including unanticipated cost overruns.

Nearly six in 10 of the IT leaders surveyed say their organizations spent over $1 million on DevOps migrations in the past year, with those using rip-and-replace spending an average of $1.75 million. Moreover, organizations that moved to a DevOps platform in a single coordinated event overspent their migration budgets by 18%, more than $300,000 per enterprise. Worse, more than a third of the IT leaders said 25% of that budget turned into sunk costs with no lasting business impact.

As an alternative, more than nine in 10 IT leaders surveyed observe greater efficiencies by integrating coding tools rather than replacing them.

Despite CloudBees being listed as a challenger in the 2024 Gartner Magic Quadrant for DevOps platforms, Kapur recommends that enterprises take the integration approach.

These platforms often offer AI coding assistants, but while there’s been heavy competition among copilots in the past year, much of the generated code has been “so straightforward and arguably so undifferentiated,” he says.

Developers at most organizations prefer a variety of coding tools, Kapur adds. “We have believed from the start that telling developers what tools to use is like telling a teenager what to wear,” he says. “It’s only partially effective.”

Moving to one DevOps platform limits developer choice and locks organizations into one vendor, he adds.

“We don’t believe consolidation is the answer, because in some ways, consolidation goes against the spirit of innovation,” Kapur says. “It basically assumes that a single vendor will be able to address everything.”

Governance vs. flexibility

Warnings about the cost of rip-and-replace migrations resonated with some developers. There’s a real trend toward integrated DevOps solutions, as organizations look for faster delivery, increased security, and easier governance, says Milankumar Rana, architect at software development firm Headstorm.

Unified DevOps platforms streamline governance, auditing, and security, while separate technologies, such as Jenkins, Jira, and SonarQube, provide more flexibility but complicate upkeep, he adds.

For enterprises considering a migration, the cost of retraining developers, creating new continuous integration and delivery pipelines, and improving YAML templates can create budget overruns of 15% to 20%, Rana says.

During Headstorm’s migration between providers, the most significant hidden costs were related to the time spent disassembling systems for compliance evaluations, identity management, permission revisions, and regulatory changes, he adds.

He recommends that organizations take a step-by-step approach to DevOps platform migration. “Begin with non-production pipelines and proof-of-concepts before moving on to critical workloads,” Rana says.

In defense of platforms

While CloudBees’ report raises concerns about migrations, representatives of other DevOps platforms say the problems may be overstated.

The Harness DevOps platform offers tools for continuous delivery and integration, as well as code and security testing, but developers can keep their preferred integrated development environment (IDE), says Jignesh Patel, field CTO at Harness.

“Developers care most about what tools they want to use when it comes to development, but not the tools to do their testing, not the tools to do their security testing, not the tools to do their releases, and not the tools to do their cost optimization,” he says. “They care about their IDEs.”

Patel, who previously served as director of cloud and DevOps at financial services firm Morningstar, directed a rip-and-replace migration to Harness when he was there, and the process went smoothly, he says.

The biggest challenge during a migration is the up-front buy-in, Patel says. “It’s more an organizational than a tooling challenge,” he adds. “Technology is the easiest part of the puzzle. It’s usually the people who are the tougher challenge, and it’s making people believe that this is truly going to add value to their everyday lives.”

Patel also discounts concerns that rip-and-replace migrations can lead to cost overruns. “You’re still letting go of your other tools that you have in your repertoire,” he says. “If you have 15 isolated tools today, once you do rip and replace it with a solution like Harness, you’re not keeping those 15.”

GitHub COO Kyle Daigle also says developers using the platform can keep their preferred IDEs, CI/CD systems, and AI models.

“GitHub has always been built around developer choice,” he says. “We don’t believe in locking developers into a single set of tools nor a single way of working.”

GitHub’s data shows that 90% of businesses rely on open-source code that lives on the platform, Daigle notes. “We believe the real benefit comes from bringing the entire developer lifecycle and the community to the code,” he says. “When you build on GitHub, you aren’t being locked into a platform; you’re plugging directly into the supply chain your software depends on. That proximity is what accelerates innovation.”

Broadcom has announced standards-based network updates to help customers build and extend modern private clouds, as well as a Cisco partnership designed to simplify operations and improve interoperability in those environments.

Broadcom is expanding its open hardware certification program to increase participation and streamlining its certification process to help customers adopt next-generation CPU, GPU, and accelerator technologies. The company is also announcing support for edge-optimized nodes for rugged, compact servers in industrial, defense, retail, and other remote site applications, according to a statement.

The integration with Cisco and its Nexus platform will deliver a unified fabric and improved networking capabilities for Broadcom’s VMware Cloud Foundation (VCF) for customers using Cisco in their networking environments.

“Broadcom is taking a lead here in terms of unifying all of these various networking fabrics so they can truly incorporate using standards-based technology, which is BGP and EVPN, which is the market,” said Prashanth Shenoy, CMO and vice president of marketing for the VMware Cloud Foundation (VCF) Division at Broadcom, during a press briefing. “We truly believe that it will drastically simplify networking operations in the modern, private cloud world, and this unified standards-based fabric approach will also enhance interoperability between the application environment and the underlying network infrastructure, providing that cloud-like simplicity that our customers really, really require.”

Broadcom explained its strategy to use Ethernet VPN (EVPN) and Border Gateway Protocol (BGP) networking to enhance interoperability between application environments and the network. The strategy is based on the standards EVPN and BGP: EVPN extends Layer 2 Ethernet connectivity across a Layer 3 network, such as MPLS or IP, which integrates different control planes and simplifies network management; and BGP which enables capabilities such as network redundancy and traffic management.

Broadcom said its standards approach will help enterprises overcome the challenges associated with adopting AI at scale, which can intensify the requirements for performance, security, and resiliency. “Doing that, we truly believe that it will drastically simplify networking operations in the modern, private cloud world, and this unified standards-based fabric approach will also enhance interoperability between the application environment and the underlying network infrastructure, providing that cloud-like simplicity that our customers really, really require,” Shenoy said.

The partnership aligns VCF with Cisco’s Nexus One fabric, which implements a standard-based VXLAN EVPN control and data planes. The integration will create “more ingrained visibility for the network admin so that they can rapidly deploy and scale the networking environment for the modern, private cloud platform,” according to Shenoy. Broadcom says customers will benefit from virtual private cloud (VPC) protection, consistent network operations, routing, and visibility across VCF Networking domains and third-party networking solutions.

Cisco recently began shipping an integrated Nexus Dashboard that will let customers see and manage its Application Centric Infrastructure and Nexus NX-OS VXLAN EVPN fabrics with unified data, control, and policy enforcement. The dashboard will allow customers to consolidate and control AI, LAN, SAN, and Cisco IP Fabric for Media (IPFM) systems from a single pane of glass. 

“Cisco’s foundational work in VXLAN EVPN, backed by the industry’s most comprehensive IETF standards authorship, reflects our long-standing commitment to open networking,” said Murali Gandluru, vice president of data center networking at Cisco, in a statement. “This collaboration with Broadcom demonstrates our shared commitment to open standards—giving our customers architectural flexibility and choice as they connect, protect, and scale both AI and traditional, business-critical workloads.”

Today’s CIOs face a digital paradox: The more technologies their organizations adopt, the more complex their environments become. Legacy infrastructure and decades of regulation have combined to create technical debt and disconnected silos. Even as workloads shift to the cloud, CIOs struggle with integration; expanding toolsets often create fragmented estates rather than streamlined operations.

Amit Mehrotra, Vice President and Head of United Kingdom and Ireland at Tata Communications, says: “CIOs today are under huge pressure to drive transformation – navigating regulatory complexity, cyber risk, and tight cost constraints – often all at once.”

Nick Reeks, Director of IT at Tata Steel, agrees: “There’s always pressure to get to the end result quickly, but in IT you must get to the detail first. If you don’t, costs spiral – with systems and data burning money without delivering value. That’s why CIOs must be involved early in transformation discussions, otherwise something critical gets missed.”

Without a streamlined digital foundation, agility stalls, innovation slows, and security vulnerabilities multiply. CIOs must transform their organizations’ digital fabrics, turning potential liabilities into an integrated and modular foundation for long-term growth.

A complex web  

Today’s hyperconnected ecosystems bring added pressure. By 2034, 40.6 billion Internet of Things (IoT) devices are forecast to be connected globally. This explosion of endpoints expands the attack surface and strains already fragmented infrastructures, and puts agility and resilience at risk.

Across Europe, challenges vary by sector. In Germany, manufacturers are accelerating digitalization but remain tied to legacy OT systems, while fluctuating energy costs and supply chain pressures complicate transformation efforts. Within the U.K. financial services industry, some banks operate up to seven years behind on tech updates, which are driven by regulations, but also hindered by limited budgets.

The costs of inaction

AI also introduces complexity. In 2024, only 14% of European Union (E.U.) businesses with 10 or more employees were using AI technologies, highlighting how many E.U. organizations remain in the early stages of their AI adoption journey – and risk being left behind.

The gap between ambition and execution is costly. For example, 43% of European CEOs say they won’t fully achieve their transformation goals, despite heavy investment in digital initiatives. The results: slower growth, weaker customer engagement, and declining competitiveness.

“Post-COVID, many U.K. enterprises are still defining what ‘digital-first’ means across hybrid working and customer channels,” says Mehrotra. “Getting the basics right is still a work in progress.”

Reeks adds: “Today, organizations need more data – held for longer and traceable across the whole supply chain. Yet many are still struggling to make sense of it. AI could be seen as a quick fix, but without the right data structure, quality controls, and guardrails, it risks adding complexity rather than solving it.”

Integrate, uncomplicate, and innovate

Tata Communications addresses this challenge through its digital fabric, which integrates and uncomplicates infrastructure, enabling CIOs to innovate with speed and confidence.

The process starts with integrating disparate technology stacks across cloud, network infrastructure, IoT, and applications.

In manufacturing, for example, this means bridging IT and OT – modernizing legacy systems while ensuring continuity and real-time visibility.

Amit Kapoor, Vice President and Head of Europe, Tata Communications, says: “Across Europe, we’re seeing manufacturers adopt AI and machine learning as innovation tools and enablers of autonomous operations. AI-driven edge computing is becoming critical for real-time decision making and localized data processing.”

A notable example is Tata Motors, which uses an edge-native solution to optimize energy consumption in its paint shops – integrating OT data, AI algorithms, and 5G connectivity for real-time efficiency.

Next comes uncomplicate: streamlining the IT estate by consolidating platforms, eliminating redundant systems, and optimizing workflows. A typical European enterprise network might involve hundreds of vendors, complicated by mergers and legacy contracts that have created sprawling infrastructures.

Networks are critical, but technology refresh cycles now outpace contracts – forcing upgrades sooner than planned. Untangling the IT estate boosts agility, resilience, and performance, while reducing risk.

Once simplified, organizations can focus on innovation. Visibility unlocks smarter insights and quicker decisions, which can be the difference between winning and losing in today’s market.

Mehrotra adds: “And of course, security is at the core of every digital discussion we’re having in the U.K. Boards want reassurance that their transformation journeys are secure by design and resilient by default.”

A fabric for the future

CIOs increasingly recognize that a foundational digital fabric is the key factor to achieve long-term value. In financial services, manufacturing, logistics, and retail, this approach simplifies complexity, reduces costs and brings order to fragmented ecosystems.

Alexandru Duca, Director, Head of Automation and Operation Technology at Maersk, a shipping and logistics company, says: “Businesses need systems to talk to each other – from the terminal to vessel to inland logistics. Without integration, companies lose visibility and can’t plan properly. For us, digital transformation isn’t optional; connecting digital and physical worlds is the foundation for growth.”

For many mid-sized enterprises, the challenge isn’t ambition; it’s execution. This is where Tata Communications can help. It supports businesses with a co-creation model, aligning infrastructure design with business priorities and growth objectives.

By unlocking real-time visibility across the value chain, Tata Communications helps organizations make faster decisions, embed resilience, and scale with confidence.

Ankur Jindal, Vice President and Global Head at Tata Communications, says: “Enterprises today face major challenges from disparate systems, networks, and processes powering their applications. To unlock value, businesses must reweave their digital fabric with integrated, ‘right-fit’ technologies embedded seamlessly into work and customer experiences.”

With the right foundation of a resilient network, integrated infrastructure, and trusted partnerships, CIOs can move beyond reactive transformation to build a digital fabric designed not just to keep up, but to lead. Doing so enables innovation, manages risk, and drives long-term competitive advantage.

Click here to learn more about how a digital fabric helps your business ensure long-term growth.

“Retail is detail” has long been an industry catchphrase. Its insight is that consumers notice small elements of their shopping experience, and these moments often decide whether a purchase happens.

Today, most industries must deliver outstanding customer experiences (CX) as they operate and sell online. Whether it’s a financial services firm providing insurance, a manufacturing business selling direct, or an automotive maker showcasing vehicles, CX is a top priority.

What businesses may overlook is that small CX moments increasingly rely on their digital networks. Quality, speed, and security remain essential, but integration and the ability to turn data into insights determine how personalized experiences feel. When these align across the end-to-end customer journey, conversion, and loyalty rise.

How infrastructure drives experience

Hidden beneath every customer touch, there is a technology stack supporting it – from the CX platform to the digital infrastructure. When networks are optimized, secure data is accessible, tools are integrated, and companies deliver fast, personalized interactions. But all of these tasks can be difficult to achieve.

Amit Mehrotra, Vice President and Head of the United Kingdom and Ireland region at Tata Communications, says: “For many organizations, the biggest blocker to good CX is the complexity and fragmentation of their tech stack. Customer data sits in siloed systems that don’t talk to each other. We find that organizations need help streamlining their CX IT environments to set them up for success.”

Breaking down internal fragmentation is vital. A unified experience across channels requires an integrated and unified CX technology platform.

Integration is a particular problem for companies operating in the U.K. and Europe, where multiple languages, different regulatory concerns, and overlapping tools can quickly create IT complexity. But done right, joined-up infrastructure can help organizations execute hyper-personalized campaigns to drive exceptional results.

Nick Reeks, director of IT at Tata Steel says: “Organizations need more data for longer periods of time – and it must be traceable across the whole supply chain. Artificial intelligence can help connect the dots, but only if the data is structured properly, with quality controls and guardrails.”

Orchestrating with AI

With solid tech foundations in place, companies are then better placed to innovate and enhance their CX. Deploying AI-based tools helps improve personalization, speed, scale, and reliability. Two effective methods are to utilize AI in marketing orchestration and to deploy AI agents to automate key steps in the marketing process.

“Agentic AI can deliver ‘next best actions’ with considerably greater accuracy – and at a far greater speed – than was possible just a few years ago,” says Mehrotra. “With our Interaction Fabric, we connect back-office employees, systems, and data with front-office customer support – whether that’s agents, bots, or self-service channels – so that CX is seamless across every customer touchpoint and channel.”

Yet, agentic AI can go even further. At a tactical level, it can segment audiences based on customer data, generate tailored content, decide the most effective delivery channel and timing, and then analyze results using conversational analytics.

These are not fixed, deterministic journeys; the AI solution or technology can continuously adapt in real time to orchestrate dynamic, goal-driven interactions. For example, a retailer might set a target to sell 1,000 units of a product. The AI agent can identify the right customers, craft personalized offers, and deliver them at the optimal moment across email, SMS or social platforms – maximizing sales while minimizing opt-outs.

AI orchestration in CX scales this intelligence across the enterprise. In a marketing use case, instead of optimizing a single campaign, orchestration uses the power of AI to crunch huge quantities of data to build rich customer personas, generate multiple iterations of personalized offers, and coordinate journeys across many channels and regions.

Orchestration ensures consistency of experience at scale, even in complex markets with diverse languages, regulations, and customer behaviors. Crucially, a human is in control throughout, steering the process by setting goals, approving AI outputs and ensuring campaigns stay creative, compliant, and on brand.

Amit Kapoor, Vice President and Head of Europe at Tata Communications, says: “Thanks to AI, content and messages can be targeted to be more personalized at greater speed and accuracy – all delivered with the assurance of global reach and rigorous compliance with local data regulations. This is transformative for companies and their marketing teams, ensuring they can innovate at scale without compromising on trust, compliance, or security. And the impact on CX is huge.”

The Hyper-personalization imperative

The development of agentic AI is already transforming how companies serve their customers.

Bajaj Auto Credit Limited (BACL) worked with Tata Communications to launch a secure, compliant digital lending platform in just 90 days. Delivering 30% month-on-month growth in platform usage and 12-15% cost savings, while ensuring faster, more seamless customer experiences and meeting strict regulatory requirements.

“The biggest IT barriers to customer experience are legacy systems, siloed data and inconsistent platforms. By adopting cloud-smart strategies and a unified service catalogue, we have been able to deliver more predictable, high-quality services,” says Ravi Rao, Cloud Operations Manager, EMEA Infrastructure, Ricoh Europe.

By unifying CX platforms, network connectivity, and cloud environments, Tata Communications creates an end-to-end system without third-party dependencies. The result is AI-powered customer experiences that are not only seamless, but also efficient, compliant and consistent worldwide.

Reimagine your organization’s customer experience. Learn more here.

“Retail is detail” has long been an industry catchphrase. Its insight is that consumers notice small elements of their shopping experience, and these moments often decide whether a purchase happens.

Today, most industries must deliver outstanding customer experiences (CX) as they operate and sell online. Whether it’s a financial services firm providing insurance, a manufacturing business selling direct, or an automotive maker showcasing vehicles, CX is a top priority.

What businesses may overlook is that small CX moments increasingly rely on their digital networks. Quality, speed, and security remain essential, but integration and the ability to turn data into insights determine how personalized experiences feel. When these align across the end-to-end customer journey, conversion, and loyalty rise.

How infrastructure drives experience

Hidden beneath every customer touch, there is a technology stack supporting it – from the CX platform to the digital infrastructure. When networks are optimized, secure data is accessible, tools are integrated, and companies deliver fast, personalized interactions. But all of these tasks can be difficult to achieve.

Amit Mehrotra, Vice President and Head of the United Kingdom and Ireland region at Tata Communications, says: “For many organizations, the biggest blocker to good CX is the complexity and fragmentation of their tech stack. Customer data sits in siloed systems that don’t talk to each other. We find that organizations need help streamlining their CX IT environments to set them up for success.”

Breaking down internal fragmentation is vital. A unified experience across channels requires an integrated and unified CX technology platform.

Integration is a particular problem for companies operating in the U.K. and Europe, where multiple languages, different regulatory concerns, and overlapping tools can quickly create IT complexity. But done right, joined-up infrastructure can help organizations execute hyper-personalized campaigns to drive exceptional results.

Nick Reeks, director of IT at Tata Steel says: “Organizations need more data for longer periods of time – and it must be traceable across the whole supply chain. Artificial intelligence can help connect the dots, but only if the data is structured properly, with quality controls and guardrails.”

Orchestrating with AI

With solid tech foundations in place, companies are then better placed to innovate and enhance their CX. Deploying AI-based tools helps improve personalization, speed, scale, and reliability. Two effective methods are to utilize AI in marketing orchestration and to deploy AI agents to automate key steps in the marketing process.

“Agentic AI can deliver ‘next best actions’ with considerably greater accuracy – and at a far greater speed – than was possible just a few years ago,” says Mehrotra. “With our Interaction Fabric, we connect back-office employees, systems, and data with front-office customer support – whether that’s agents, bots, or self-service channels – so that CX is seamless across every customer touchpoint and channel.”

Yet, agentic AI can go even further. At a tactical level, it can segment audiences based on customer data, generate tailored content, decide the most effective delivery channel and timing, and then analyze results using conversational analytics.

These are not fixed, deterministic journeys; the AI solution or technology can continuously adapt in real time to orchestrate dynamic, goal-driven interactions. For example, a retailer might set a target to sell 1,000 units of a product. The AI agent can identify the right customers, craft personalized offers, and deliver them at the optimal moment across email, SMS or social platforms – maximizing sales while minimizing opt-outs.

AI orchestration in CX scales this intelligence across the enterprise. In a marketing use case, instead of optimizing a single campaign, orchestration uses the power of AI to crunch huge quantities of data to build rich customer personas, generate multiple iterations of personalized offers, and coordinate journeys across many channels and regions.

Orchestration ensures consistency of experience at scale, even in complex markets with diverse languages, regulations, and customer behaviors. Crucially, a human is in control throughout, steering the process by setting goals, approving AI outputs and ensuring campaigns stay creative, compliant, and on brand.

Amit Kapoor, Vice President and Head of Europe at Tata Communications, says: “Thanks to AI, content and messages can be targeted to be more personalized at greater speed and accuracy – all delivered with the assurance of global reach and rigorous compliance with local data regulations. This is transformative for companies and their marketing teams, ensuring they can innovate at scale without compromising on trust, compliance, or security. And the impact on CX is huge.”

The Hyper-personalization imperative

The development of agentic AI is already transforming how companies serve their customers.

Bajaj Auto Credit Limited (BACL) worked with Tata Communications to launch a secure, compliant digital lending platform in just 90 days. Delivering 30% month-on-month growth in platform usage and 12-15% cost savings, while ensuring faster, more seamless customer experiences and meeting strict regulatory requirements.

“The biggest IT barriers to customer experience are legacy systems, siloed data and inconsistent platforms. By adopting cloud-smart strategies and a unified service catalogue, we have been able to deliver more predictable, high-quality services,” says Ravi Rao, Cloud Operations Manager, EMEA Infrastructure, Ricoh Europe.

By unifying CX platforms, network connectivity, and cloud environments, Tata Communications creates an end-to-end system without third-party dependencies. The result is AI-powered customer experiences that are not only seamless, but also efficient, compliant and consistent worldwide.

Reimagine your organization’s customer experience. Learn more here.

IBM will cut a “low single-digit percentage” of its 270,000-strong workforce in the fourth quarter, the company said Tuesday. The reduction could affect between 2,700 and 5,400 employees if there is an employee reduction of just 1% or 2%.

“We routinely review our workforce through this lens and at times rebalance accordingly. In the fourth quarter we are executing an action that will impact a low single-digit percentage of our global workforce,” an IBM spokesperson said.

IBM said that US employment will remain “flat year over year,” suggesting the company will backfill domestic roles in other geographies. The company currently lists 2,466 job openings in India compared to 370 in the US, according to its Careers portal.

This marks IBM’s third major workforce reduction since September 2024, when the company eliminated an estimated 8,000 to 10,000 positions. Another 5,000 to 7,000 positions were cut in March 2025.

Slowing growth in a key software business

The timing of the cuts is notable. Three weeks before announcing the layoffs, IBM reported slowing growth in Red Hat, its highest-margin hybrid cloud business, and the centerpiece of CEO Arvind Krishna’s transformation strategy.

IBM’s third-quarter earnings report on October 22 showed software revenue growth of 10% to $7.2 billion. Within that segment, the Red Hat hybrid cloud unit grew 14%, down from 16% in the prior quarter. Analysts had expected 16% growth.

Sanchit Vir Gogia, chief analyst and CEO at Greyhound Research, said the deceleration reflects internal execution challenges. “This shift exposes delivery strain inside a business unit expected to move faster than it currently does,” Gogia said. “While enterprise clients continue to view IBM as a dependable partner in complex environments, the way hybrid cloud is bought and measured has changed. Organisations are no longer buying platforms in isolation. They are buying the ability to act quickly.”

Red Hat is central to CEO Arvind Krishna’s strategy to shift IBM toward higher-margin software and cloud services. During the earnings call, Krishna said he expects Red Hat to return to “mid-teen percentage growth, or close to that level, entering 2026.”

Streamlining for efficiency

Gogia said the workforce reduction is designed to streamline operations rather than signal financial distress. “Over the past year, IBM has redesigned more than seventy internal workflows through automation and AI, creating the space to reduce headcount without cutting capacity where it matters most,” he said. “From what we have observed, engineering and delivery teams remain protected.”

The cuts aim to reduce internal complexity, Gogia said. “These decisions are designed to reduce internal complexity and channel effort into the parts of the business with the clearest return. This is not about shrinking the company. It is about shaping it to scale more effectively.”

However, execution issues remain a concern. “In many client settings, the technology is not the issue. It is how IBM’s internal teams pass work between groups that slows things down,” Gogia said. “This becomes especially visible in deployments that cross product lines, such as OpenShift tied to AI models or hybrid data orchestration.”

Increased client oversight recommended

The combination of workforce cuts and internal execution challenges is prompting analysts to advise closer monitoring of IBM engagements.

Greyhound Research is advising clients to increase oversight of IBM delivery operations during the reorganization.

“We are advising CIOs to step in early and request formal documentation around coverage, support depth, and continuity of leadership across their accounts,” Gogia said. “In recent months, we have seen examples where IBM’s internal transitions slowed deployment, not because of missing capability, but due to unclear ownership between business units.”

The issue typically affects projects that cross organizational boundaries, such as deployments that combine Red Hat with consulting services or blend software delivery with infrastructure planning.

IBM’s internal structure is still adjusting, Gogia said. “During this time, it is essential for clients to clarify who is accountable for each phase of delivery and to lock those roles in place across the full term of execution. This is not a sign of instability. It is a normal stage in any large-scale realignment. But it does require CIOs to shift from passive trust to active engagement.”

CIOs should validate support agreements and establish clear accountability for each phase of delivery. Organizations should also clarify how the workforce changes affect product roadmap timelines, particularly for implementations of IBM’s watsonx AI platform, Gogia added. “The coming months will need to show that the reshaped teams can respond faster, maintain roadmap delivery, and carry client work forward without hesitation,” Gogia said. “If that happens, the decision will be seen not as a cost cut, but as an investment in operational clarity.”

What is AIOps? AIOps, defined

AIOps — short for AI for IT operations — is an emerging operational practice that uses machine learning and automation to help organizations monitor, manage, and troubleshoot complex digital systems. Companies that implement AIOps use AI-driven tools to combine data from logs, metrics, and events across infrastructure and applications to detect problems early, identify root causes, and trigger responses before users even notice an issue.

AIOps has been around since before the current wave of generative AI, taking its name from the sense of AI/ML more common in the last decade. Monika Malik, lead data/AI engineer at AT&T, describes that era’s model as straightforward: “ingest → correlations → detect anecdotal events → predict root likely cause → orchestrate some remediation.”

That workflow still forms the backbone of AIOps today, she says, but large language models are adding a new layer of intelligence. “GenAI is additive, not a replacement,” she says. “LLMs sit atop reasoning/summarization, ops copilots, and knowledge retrieval—but data plus rules plus ML still needs to exist.”

In short, AIOps began as a way to automate IT operations through analytics and machine learning. Today, generative AI is enhancing that foundation with conversational interfaces and contextual reasoning, helping teams work faster and boosting cloud and IT operations.

AIOps vs. devops: What’s the difference?

Devops and AIOps share some philosophical DNA — both are about bringing automation, feedback loops, and responsiveness to technology systems — but they operate at different levels of the stack.

As Kostas Pardalis, data infrastructure engineer and co-founder of Typedef, puts it, “Devops is about automating and streamlining the software development lifecycle. AIOps extends that philosophy into operations by applying machine learning and inference as first-class operations.” In other words: devops helps you ship and deploy reliably and quickly; AIOps helps you monitor, detect, and remediate in production more intelligently.

Greg Ingino, CTO of Litera, frames the two as complementary: devops governs how we build and deliver systems, while “AIOps governs how we operate and optimize those systems in production. Devops drives speed, while AIOps ensures stability.”

In practice, you can think of devops as the foundation of continuous delivery and infrastructure automation, and AIOps as adding a layer of smart monitoring and autonomous operations. Over time, as systems grow increasingly complex, that added intelligence becomes essential to keeping environments resilient, especially at scale.

What are the components of an AIOps platform?

Typedef’s Pardalis explains that an AIOps platform needs three layers. Start with “data collection and normalization across logs, metrics, traces, and unstructured events,” he says. Next comes “inference-first pipelines that can classify, enrich, and correlate signals probabilistically, not just deterministically. Finally, you need “observability and governance, so teams can trust the AI outputs —  lineage, evaluations, cost controls. Without those, you either drown in data or end up with a black box no one trusts.”

Milankumar Rana, software engineer advisor and senior cloud engineer at FedEx, lays out a more detailed architecture that blends traditional observability with generative intelligence. He notes that many deployments rely on open-source stacks such as ELK, Prometheus, and OpenTelemetry, while commercial tools like Splunk, Elastic Observability, LogicMonitor, and IBM’s AIOps suite add “generative AI for natural language query, incident summarization, and autonomous remediation.” Cloud providers have joined in too, with AWS and Azure adding AIOps-powered incident insights and anomaly detection.

According to Rana, “any AIOps platform has interconnected parts”: data ingestion and normalization; scalable analytics stores; ML models that predict and correlate incidents; and newer generative layers that summarize events and suggest next actions. Noise reduction, feedback loops, visualization dashboards, and strong governance complete the picture. Few organizations implement every component, but these elements together define what a trustworthy AIOps system looks like.

AIOps implementation strategies

A carefully considered AIOps rollout rarely starts with a grand sweep—instead, success comes from incremental steps, measurable wins, and building trust. AT&T’s Malik recommends the following steps:

• Start thin: Pick two or three chronically noisy services and define success criteria—for example, 30% less noise, 20% faster MTTR.
• Hybrid detection: Combine hard rules for SLO breaches with ML-based anomaly detection. Avoid going “pure ML” too early.
• Make explainability visible: Every dashboard or prompt should show why something is being brought to the attention of the user—similar past incidents, knowledge-base references, etc.
• Phase in automation: Begin with read-only insights, then allow the system to begin suggesting actions with human approval, and then move on to limited auto-execute (with rollback protection).
• Measure and publish weekly: Track metrics like MTTA/MTTR, false positives, L1 deflection, and on-call hours saved.

FedEx’s Rana emphasizes that many successful adopters first conduct a “data readiness examination” to expose issues like excessive false positives that intelligent automation can help mitigate. He calls for a domain-specific proof of concept that boosts confidence, surfaces data quality gaps, and enables incremental evolution of services, telemetry, and automation. He also warns that “autonomous systems without audit trails or rollback need safety and governance,” and stresses that educating AI users and ops teams is as essential as deploying new tools.

Litera’s Ingino echoes the “start small, prove value” ethos: his team began with a single product line to reduce alert noise and improve MTTR, won early buy-in, and then expanded AIOps across environments. “Our engineers saw early wins, and that built confidence,” he says. He notes that the key is trust — making AIOps a reliable partner rather than an experiment.

Benefits and challenges with AIOps rollouts

When AIOps works, its advantages are immediate and measurable. Ingino says that at Litera, the payoff has been “faster incident detection, fewer false alarms, and greater system reliability.” Beyond improving uptime, he notes that “AIOps has significantly reduced the cognitive load on our operations teams, allowing them to focus on higher-value engineering work.”

Nagmani Lnu, director of quality engineering at SWBC, agrees that the biggest benefits come from earlier, more accurate detection and resolution. When AIOps is implemented successfully, he says, “the company will really see benefits in detecting issues proactively and addressing them in real time, and will improve their MTTR and hence improve the IT experience for the business.” Typedef’s Pardalis adds that AIOps provides “the ability to handle scale that humans simply can’t,” turning mountains of telemetry into actionable insight.

The challenges, however, can be as significant as the rewards. Ingino says the hardest problems are “data quality and cultural change.” AIOps “is only as smart as the data it sees,” he explains, so ensuring consistent, contextual ingestion is critical. Trust is another recurring theme. “Teams need to trust the AI,” Pardalis warns. “That means transparency, lineage, and the ability to debug.” He also points to practical barriers—“models are probabilistic, so you need guardrails,” and “costs can spike if you don’t optimize inference.” Lnu adds that poor use-case selection can derail an entire rollout: “Poor selection can deter management confidence and hence risk any future innovation.”

What is the role of an AIOps engineer?

An AIOps engineer takes on an interdisciplinary role, combining the skills of a site reliability engineer, a data scientist, and an automation specialist. Typedef’s Pardalis describes the job as “an evolution of the site reliability engineer. An AIOps engineer isn’t just automating playbooks,” he says. “They’re designing pipelines where inference is in the loop.” That includes “curating data for observability, training or fine-tuning models for anomaly detection, and deploying inference-first workflows that make sense of logs, traces, and metrics in real time.”

Chirag Agrawal, lead engineer and tech expert, emphasizes that while some think of an AIOps engineer as a mere tool configuration tech, their real impact lies in understanding, managing, and curating the data that AIOps tools will make use of. “When poor-quality data is ingested, poor outcomes are produced,” he says, adding that the best AIOps engineers are those with “deep understanding of logs, metrics, and dependencies specific to their environments,” not necessarily formal AI backgrounds.

SWBC’s Lnu frames the role more systematically. An AIOps engineer’s responsibilities, he says, include:

• Defining objectives and scope, identifying pain points like alert fatigue or performance bottlenecks and setting success metrics such as reduced MTTR.
• Assessing the current IT environment, from servers and containers to monitoring tools like CloudWatch, Prometheus, and Grafana.
• Creating a data strategy, ensuring standardized, enriched, and centralized telemetry.
• Selecting the right AIOps platform, evaluating integration capabilities and AI/ML features.
• Developing automation playbooks, from restarting instances to triggering service tickets or scaling workloads via orchestration tools.

The AIOps engineer is a bridge between human operators and intelligent systems—someone who not only builds automation but also instills trust, establishes governance, and offers insight into how AI makes operational decisions.

Real-world AIOps examples

AIOps is increasingly proving its value in production environments across industries—from cloud-native infrastructure to publishing and cybersecurity. SWBC’s Lnu says real-world deployments vary by environment. In cloud-native contexts, organizations use AIOps to “monitor container health, detecting abnormal CPU, memory, or network usage across containers,” and to “predict high traffic periods to pre-warm Lambda functions to avoid cold start latency.” Other use cases include “auto-scaling ECS tasks based on historical load, controlling cost by limiting over-provisioned containers, and predicting EC2 instance failure before they crash.” The same systems can automatically “reboot, replace, or resize” affected instances, helping reduce downtime while optimizing spend.

Chirag Agrawal offers a people-focused success story. His team developed “an AI agent that recognized tickets commonly reassigned between teams. The tickets were automatically routed correctly without requiring any human intervention.” The result: hundreds of hours saved per quarter and a clear ROI. Agrawal attributes that success to groundwork—“years of carefully studying, cleaning, and labeling historical data”—and emphasizes that “the model wasn’t simply left to run on raw data; it was trained under human supervision.”

Typedef’s Pardalis has seen similar gains in other sectors. “Media companies use AI pipelines to classify and enrich thousands of documents daily,” he says, while cybersecurity teams “use inference to extract structure from unstructured logs, enabling faster threat detection without drowning analysts in alerts.”

Litera’s Ingino recounts a scenario where AIOps tools detected “a subtle performance drift in a service that standard monitoring would have otherwise missed.” The platform “correlated anomalies across several microservices, pinpointed the source, and triggered a response before users experienced any degradation.” That single event, he says, “validated the entire investment.” Since then, Litera has seen “incident resolution times drop by more than 70%,” with PagerDuty automation ensuring the right engineers engage immediately.

Are humans still needed in an AIOps world?

Even as AIOps grows more capable—correlating events, summarizing incidents, and recommending fixes—human expertise remains essential. Chirag Agrawal puts it plainly: “AI can automate pattern recognition, but context and intent must be provided by people who understand how those systems behave in real-world environments.”

AIOps excels at sifting through telemetry, detecting anomalies, and accelerating root-cause analysis, but it still depends on human judgment to interpret meaning, verify impact, and decide how automation should evolve. “AIOps works best when human insight and machine intelligence are developed side by side, not when one replaces the other,” Agrawal says.

That partnership also fuels long-term progress. Every resolved incident strengthens the system’s knowledge base, improving future responses and reducing toil. “The true promise of AIOps,” Agrawal concludes, “is seen not only in automation but in the collective memory that is built.”

In that sense, AIOps doesn’t make humans obsolete—it amplifies them. The more context engineers share with these systems, the better they become at turning raw data into operational intelligence.

For decades, Molex has been at the forefront of connectivity innovation, driven by the belief that every connection has the power to transform. With more than 80 years of experience in the interconnect industry, Molex has grown into a global leader providing advanced interconnect solutions used across a multitude of industries, including transportation, telecommunications, consumer device, data center, industrial, and healthcare. Today, the Illinois-based company has more than 100,000 products supporting customer innovation and connectivity on a global scale. Despite this deep expertise and extensive product portfolio, one critical area of the business required next-level support: supply chain management.

A case  for supply chain improvements

For years, Molex relied on a custom-built supply chain solution combining manual processes with legacy software. While this system supported operations in the past, it struggled to keep pace with the demands of a modern, digitally connected supply ecosystem. The solution hindered real-time communication and collaboration with suppliers worldwide, limiting visibility and making it difficult to manage part on-time delivery and reliably.

An opportunity to enhance communication

Molex annually sources 70,000 parts from suppliers worldwide. However, adoption of the legacy Molex supply chain solution by suppliers has been limited, with only 30% of purchase orders (POs) being confirmed through the system. Without a way of confirming the other 70%, Molex employees estimated when and where those associated parts would be delivered, with the possibility of creating a chain reaction that had the potential to impact customer deliveries and introduce unnecessary risk. 

The uncertainty of dates and locations for parts delivery could affect the company’s ability to effectively manage its “clear-to-build” process of determining the number of products Molex can produce in a given time frame. In that process, Molex analyzes inventory levels, POs, and supplier lead times to help avoid production delays and smooth workflows across manufacturing plants. 

Any guesswork about parts delivery could not only impact the clear-to-build process but, in turn, the scheduling of products for shipment and the on-time delivery to customers. This situation jeopardized delivery reliability and heightened the risk of customer dissatisfaction. 

To keep production moving, and ensure both supplier and customers experience, Molex employees spent each week manually inputting and confirming POs on behalf of its suppliers. Nearly 90% of confirmations were created manually—a time-consuming, repetitive task that diverted valuable employee focus from other critical tasks.

In addition, the legacy solution was unable to connect with the suppliers’ ERP systems, hampering Molex employees’ ability to collaborate with the suppliers. And the solution lacked the capability to provide Molex with visibility or insights into global challenges that could help with managing supply and demand.

Making the connection with SAP

Since Molex creates products that keep industries running, it’s essential that it count on a direct part supply that is available when and where needed. There is no room for guesswork, which is why Molex turned to SAP for a new solution. The company adopted SAP Business Network as part of a broad digital transformation initiative to update its domestic and overseas operations. A business-to-business (B2B) cloud platform, SAP Business Network connects companies, processes, and systems across the supply chain, digitizing transactions, including PO ordering, streamlining procurement, enhancing visibility into networks and supply chains, and boosting collaboration among teams and systems. The network was first implemented in Asia, where a significant portion of Molex’s supply chain is based. 

A win-win for customers, employees, and suppliers

By adopting SAP Business Network, Molex has transformed its supply chain process from requisition to invoice. The network has connected Molex’s SAP ERP with suppliers’ ERP systems to improve system accuracy and supply chain alignment. It has boosted the company’s order confirmation rate, which improves visibility, parts delivery, and the clear-to-build process, benefiting everyone involved—including customers, Molex employees, and suppliers.

Suppliers are now entering 90% of order confirmations versus the 30% of orders when the Molex previous solution was in use. The SAP solution has also led to a deep collaboration between Molex and suppliers that wasn’t possible before. 

“We wanted to go beyond the pure tactical side of collaboration and move into building an intelligent collaboration tool—not just passing information back and forth but bringing intelligence to it,” says Tony Gainsford, Director of Procurement Business Systems, Data, and Processes at Molex.

Thanks to the SAP solution with automation, Molex employees have almost completely eliminated manual confirmation handling. This has reduced the time spent on repetitive administrative tasks from several hours each week down to virtually none, enabling them to focus on higher-value activities and significantly boost their productivity.. 

A smooth, profitable transition

Suppliers have been onboarded to SAP Business Network with minimal disruption. In addition, US $1 billion in transactions have been processed during the first 18 months after the solution’s deployment, reaching a run rate of $200 million per month.  

The numbers speak 

The company that grew to become a connectivity leader has moved beyond its initial supply chain challenges, gaining credibility and industry recognition from its digital transformation.  

Connecting with a Performance Award

For its success in transforming its supply chain process, Molex was named the winner of the Business Network Innovator category at the SAP Innovation Awards 2025. The award is presented to companies that adopt SAP cloud solutions to improve supply chains, processes, or business networks. 

To learn more about the Molex transformation, access the company’s entry pitch deck.

Nvidia showcased a variety of advances at its GTC event (GPU Technology Conference) this week – everything from quantum computing inroads to a 6G wireless stack  and improved partnerships with core vendors, including Cisco. 

Not to mention, the Nvidia became the first company to hit a market value of $5 trillion or what the Wall Street Journal called: “the latest milestone in an unprecedented surge that reflects the growing influence of artificial intelligence on markets and the economy.”

With all of that as a backdrop Here we look at some of the news that may have flown a little below the radar at the event.

HPE Launches Second Generation AI Factories

Hewlett Packard Enterprise (HPE) announced an expansion of its Nvidia AI Computing  by HPE portfolio, including a variety of hardware and service updates. The expanded offering includes turnkey AI factory solutions, unified data strategies, and updated server platforms featuring the latest Nvidia AI infrastructure and software.

Topping the list of new offerings is the second generation of HPE’s turnkey Private Cloud AI service co-developed with Nvidia. HPE claims its new Private Cloud AI for the HPE Gen12 ProLiant DL 280a offers three times better performance than the first-generation offering.

It is also upgradeable, allowing customers to start small with their deployment and expand over time. The new Private Cloud AI can also be integrated into an air-gapped secure private cloud, which are often required by governments, sovereign entities, and regulated industries.

HPE Services, meanwhile, is providing a new offering that leverages Nvidia NeMo frameworks to create what HPE calls “digital avatar assistants” designed to deliver AI-based customer transactions for applications for smart cities, retail, manufacturing, health care, banking, finance and education.

There are several new hardware products available from HPE:

•        HPE ProLiant Compute XD685 now supports eight Nvidia Blackwell Ultra (B300 GPUs) in a 5U, direct-liquid cooled HGX chassis. The XD685 is optimized for AI service providers and neoclouds, as well as ambitious enterprises and model builders that need to stand up large, secure, and validated AI clusters.

•        Nvidia GB300 NVL72 by HPE, optimized for AI models of more than 1 trillion parameters, is now available to order and expected to ship December 2025.

•        HPE ProLiant Compute DL380 Gen12 Server Premier Solution for Azure Local, designed to offer Azure services on a customer’s network as well as AI services.

•        Future models of HPE servers will offer support for Nvidia ConnectX-9 SuperNIC and the Nvidia BlueField-4 DPU, announced yesterday.

HPE cited a 2025 Architecting an AI Advantage report that found nearly 60% of organizations have fragmented AI goals and strategies, and a similar proportion lacking comprehensive data management for AI.

The two firms are collaborating to address these challenges by providing a suite of solutions for AI factories, supporting a range of users from developers to sovereign AI cloud builders. The goal is to enable organizations to develop a holistic AI infrastructure strategy.

Flex and Nvidia Collaborate on Giga-Scale AI Factories

Nvidia struck a deal with manufacturing developer Flex to develop modular data center systems designed to meet the surging demand for AI infrastructure across the United States. The collaboration is built on Flex’s advanced manufacturing capabilities and global footprint to deliver high-performance, energy-efficient AI factories at scale.

Flex is using Nvidia’s cuOpt for capacity planning, route simulations, and process optimization. cuOpt is a GPU-accelerated optimization engine developed by Nvidia for solving large decision-optimization problems such as route planning and scheduling for vehicle fleets.

In the case of Flex, it will use digital twins to unify inventory, labor, and freight operations, streamlining logistics across Flex’s worldwide network. Flex’s new 400,000 sq. ft. facility in Dallas is purpose-built for data center infrastructure, aiming to significantly shorten lead times for U.S. customers.

The Flex/Nvidia partnership aims to address the country’s labor shortages and drive innovation in manufacturing, pharmaceuticals, and technology. The companies believe the partnership sets the stage for a new era of giga-scale AI factories.

Nvidia and Oracle to Build DOE’s Largest AI Supercomputer

Oracle continues its aggressive push into supercomputing with a deal to build the largest AI supercomputer for scientific discovery — Using Nvidia GPUs, obviously — at a Department of Energy facility.

The system, dubbed Solstice, will feature an incredible 100,000 Nvidia Blackwell GPUs. A second system, dubbed Equinox, will include 10,000 Blackwell GPUs and is expected to be available in the first half of 2026. Both systems will be interconnected by Nvidia networking and deliver a combined 2,200 exaflops of AI performance.

The Solstice and Equinox supercomputers will be located at Argonne National Laboratory, the home to the Aurora supercomputer, built using all Intel parts. They will enable scientists and researchers to develop and train new frontier models and AI reasoning models for open science using the Nvidia Megatron-Core library and scale them using the Nvidia TensorRT inference software stack.