Playing Civilization V, Part 14

Across industries, a version of the same conversation is playing out in technology leadership meetings. Enterprises have deployed AI broadly, and foundation models keep getting more capable. Yet the outputs still feel generic, shaped by industry patterns rather than by the organization producing them.

McKinsey’s AI Trust Maturity Survey found that while overall AI maturity scores have improved, only about a third of organizations have reached a mature level of strategy and governance. Technical capability is advancing faster than organizational alignment. In my view, the gap is not a model problem. It is a context problem. Enterprises are feeding generic inputs into powerful models because sharing organizational context seamlessly with AI is neither easy nor intuitive today.

Building the analytical and creative capabilities to scale AI, something I explored in a recent piece on the left-brain and right-brain approach to enterprise AI, is necessary but not sufficient. Before either can function effectively, the enterprise needs something more fundamental. AI that actually understands the contextual fabric of the organization it is operating in. A frontier model has processed everything written about your sector, your competitors and your regulatory landscape. It cannot access the reasoning embedded in years of delivery decisions, the patterns encoded in how your teams scope and deliver work over time. That knowledge is organizational memory, and frontier models can’t get that easily. It exists inside every enterprise but has never been structured, connected or made available to any AI system. Without it, even the most capable model answers a generic version of your question.

The next competitive advantage in enterprise AI will not come from a better model. It will come from a better organizational context.

One global technology enterprise set out to solve this across its own operations, building a modular ecosystem of domain-specific agents grounded in its own data across contracting, talent and vendor management workflows. What emerged was not just operational efficiency but a shared intelligence layer connecting decisions across functions for the first time.

Competitive differentiation was never about the tools

Consider what actually separates high-performing enterprises from the rest. In a regulated industry like financial services or healthcare, organizations cannot meaningfully differentiate on product. A bank cannot offer substantially different products or services. A health system uses the same clinical protocols and the same electronic health record (EHR) platforms as its peers. What varies is everything underneath: the rigor of processes, the coherence of cross-functional decisions and the people who carry years of accumulated organizational judgment in how they make those decisions.

An organization with a proper context layer in place can say with precision that for this type of engagement, in this sector, with this risk profile, our institutional history tells us exactly where we stand. That level of specificity is what most enterprises have never made available to AI.

The enterprise AI brain that every organization has but has never assembled

Every enterprise already possesses what I think of as an enterprise AI brain. The problem is that it has never been assembled in one place. The data exists across contracts, project documentation, talent records, delivery metrics and the operational communications of daily execution — the informal reasoning that rarely makes it into formal systems.

None of the standard enterprise platforms were designed to connect this. A customer relationship management (CRM) system captures customer interactions. An enterprise resource planning (ERP) system captures transactions. A project management tool captures tasks and timelines. None of them captures the reasoning behind decisions and none of them surfaces a coherent picture of how the organization actually thinks and operates.

BCG’s study across hundreds of companies found that only 10% of AI value comes from the algorithms and another 20% from the technology that implements them, meaning the remaining 70% depends on people, processes and organizational change. The organizations extracting real value are those that have made their institutional knowledge available to AI in a structured, governed way.

Building a contextual AI fabric

A Contextual AI Fabric is the technical and organizational layer that makes the Enterprise AI Brain usable. It brings together unstructured data ingestion, semantic structuring, retrieval pipelines and governed model access to give AI systems the organizational context they need to produce outputs that are genuinely specific to your enterprise rather than generically accurate about your industry. It rests on three pillars. Core is the secure, governed and interoperable foundation that AI operations run on. Context is reliable, traceable access to the organization’s data, processes, knowledge and history. Coordination connects people, agents, applications and systems into process-driven workflows with clear controls and accountability, so the organization acts as one rather than a set of disconnected functions.

The data layer is where most organizations underestimate the work. Contracts, project reports, talent assessments and operational communications require extraction, chunking, embedding and indexing before a model can retrieve and reason over them meaningfully.

The semantic layer is what makes retrieval meaningful. Even well-ingested data fails if functions use different terminology for the same concepts. What legal calls a contract, delivery calls a scope. Without a shared ontology, AI systems remain precise about the wrong thing. And retrieval alone, however well-structured, only takes an organization so far. Retrieval surfaces the right information at the moment of a query, but it does not give a model genuine memory of the organization. The real source of unique, organization-level relevance comes from training domain-specific small language models on this context directly, models that carry organizational memory forward rather than fetching it fresh every time. That is what ultimately separates a Contextual AI Fabric from a well-organized database.

The governance layer is not an add-on. Access controls, data lineage, approval thresholds and human checkpoints need to be designed in before any agent goes into production. Security is not a layer you add afterward. It is the condition under which organizational AI is worth building. If the institutional intelligence that makes your enterprise distinct gets absorbed into a frontier model’s training data, it becomes everyone’s baseline. That is an architectural decision made, or avoided, at the point of deployment.

Proprietary by design

The institutional knowledge that makes up a contextual AI fabric — delivery history, commercial patterns, talent intelligence and operating culture — is proprietary in ways no external model can replicate. This is as much a security imperative as it is a competitive one. Organizational context, once exposed, cannot be unexposed.

Most enterprises are using AI to automate existing processes rather than questioning whether those processes should be redesigned entirely. The organizations extracting the most value are those willing to ask whether their current operating model, built before GenAI existed, is the one they would build today. That question is harder than any technology decision, and it is also the most consequential one.

From context to coordinated action

Context alone is not enough. When a delivery risk surfaces in project data, the talent function needs to respond. When a commercial signal changes in contract data, operations need to recalibrate. This kind of cross-functional coordination, driven by shared organizational intelligence rather than siloed data, is where the real value of enterprise AI shows up and where the absence of a shared context layer becomes most visible.

A global leader in digital payments and business services found its AI deployments across payroll, HR and risk compliance, each running in isolation, with no shared governance or common data foundation. Once the organization established a unified governance backbone connecting its operational data through a shared retrieval layer, business users could query across domains in plain language and new use cases across fraud analytics, forecasting and policy extraction became extensible without rebuilding infrastructure for each one. The shift was not in the models. It was in the shared foundation underneath them.

The leadership question behind the technology question

The enterprises pulling ahead in AI are not winning on model quality but on organizational memory. The ones that have done the hard work of structuring their institutional knowledge into a governed, secure Contextual AI Fabric are giving their AI something no competitor can replicate: the accumulated intelligence of how the business actually operates.

For CIOs, the question is no longer which model to deploy. It is whether the organization has built the foundation that would make any model worth deploying.

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Last year Anthropic gave away one of the most successful things it has ever built. And, no, I’m not talking about Claude. I’m referring to MCP, the now ubiquitous Model Context Protocol, which Anthropic donated to the Linux Foundation’s new Agentic AI Foundation⁠. At the time, MCP was pulling nearly 100 million monthly SDK downloads across more than 10,000 active servers⁠, prompting the question as to why any company would give up such a popular piece of technology.

Google did much the same months earlier, handing its Agent2Agent (A2A) protocol to the Linux Foundation⁠ with AWS, Cisco, Microsoft, Salesforce, SAP, and ServiceNow signing on as founding members. OpenAI, not to be outdone, supports remote MCP servers in its Responses API⁠, sits on the MCP steering committee, and contributed AGENTS.md to that same foundation alongside its fiercest rival’s protocol.

It’s like Game of Thrones, except the principal AI powers seek regime change through seeming acts of beneficence rather than violence. For those who have been around for a while, it’s also entirely predictable, following a similar script we’ve seen in the cloud, on-premises servers, and more. Platform companies don’t give away technologies they’ve stopped caring about. They give away technologies they no longer need to own because competitive advantage has shifted to new ground.

What does this mean for AI?

Gravity has shifted before

Google has long been an exceptionally active contributor to open source. As I wrote in 2017, Google wasn’t open sourcing TensorFlow and Kubernetes out of generosity but rather turning these open source assets into on-ramps for Google Cloud. Google was playing catch-up to AWS and Microsoft. As then Google product manager Martin Buhr said, the company hoped to “create a gravity well in the market for container-based apps [so] that a significant percentage of them will end up with us.”

In other words, platform companies routinely commoditize one layer of the stack so they can compete somewhere where they hold a stronger hand.

GitHub may be an even better example. Git is open. Anyone can host a Git repository and, once upon a time, different companies did just that. Yet GitHub became the default place software development happens for millions of developers. Nobody pays for Git, but lots of people pay for GitHub. We’re seeing this same phenomenon play out in AI.

Trading contributions for control

Anthropic and OpenAI have both pretended at being all for humanity’s good, but that’s not a good explanation for why they’re racing to give away things like MCP. The deeper reason is that the model itself has turned out to be a poor place to build a lasting moat, and they’re trying to figure out what’s next. As I pointed out recently, the frontier model leaderboards change almost weekly. As such, enterprises shouldn’t build their AI strategy around the assumption that any one vendor will remain permanently ahead on model quality. Instead, as I suggested, AI may be sexy, but the “dull reality” is connecting those models to enterprise data, workflows, etc.

The AI companies understand this better than anyone. Sure, they’ll continue spending billions training ever more capable models because frontier models attract developers, generate headlines, and open enterprise doors. But they’re also quietly acknowledging that benchmark leadership alone doesn’t create a durable platform.

Developers return to the places where their tools, workflows, teammates, and accumulated work already live. Enterprises double down on the systems where their data, permissions, governance, and business processes are already connected. Every new integration makes that destination a little harder to leave, and every new workflow increases its pull. That’s what MCP, A2A, etc., are all about: increasing gravity around the models.

Every major AI company wants to become the place where AI-assisted work naturally happens, and they’re now amassing armies of forward deployed engineers and trying other means to get legacy infrastructure to tie back to their frontier models. The enterprise incumbents want the same thing, but from the opposite direction. They don’t need to own the frontier; instead they need to connect the frontier to the systems that already safely run the business.

That’s why I’m skeptical whenever someone confidently predicts that AI will sweep away enterprise software. I’ve seen this movie before. Developers absolutely live on the frontier, but enterprises don’t. Enterprises create value by connecting new capabilities to decades of accumulated applications, data, policies, and business processes. The newest model matters, and so does the newest agent framework. But neither creates much business value until it’s connected to customer records, financial systems, supply chains, HR data, and everything else enterprises already depend on.

That’s where incumbents still possess enormous gravitational pull. My employer, Oracle, certainly believes so, just as Microsoft, SAP, Salesforce, and ServiceNow do. (Disclosure: I run developer relations at Oracle, which participates in the Agentic AI Foundation.) Ironically, open protocols strengthen that position rather than weaken it. If every model can speak MCP and every agent can interoperate through common standards, enterprises gain the freedom to adopt whichever frontier technology looks best without rebuilding every integration. The protocol becomes interchangeable.

Open standards don’t stop gravity

None of this diminishes the importance of open standards. MCP succeeded because it solves a genuine problem. Developers shouldn’t have to build a custom connector every time an AI application needs access to a database or other business system. Neutral governance also matters because nobody wants foundational infrastructure controlled by a direct competitor. But we shouldn’t confuse open interfaces with open markets.

An enterprise may find it easy to swap one MCP-compatible model for another while still remaining deeply dependent on the place where its prompts, evaluations, security policies, and employee habits have accumulated. Again, we’ve seen this before. Kubernetes made workloads dramatically more portable without making AWS, Microsoft Azure, and Google Cloud interchangeable. SQL has been standardized for decades, yet databases remain fiercely differentiated businesses. Standards reduce friction, but they rarely eliminate competitive advantage. They simply move it.

In like manner, Anthropic, Google, OpenAI, and others are happily standardizing how models, agents, tools, and enterprise systems communicate because they don’t expect the connection itself to determine the winner. Instead they expect to win by becoming the place where AI-assisted work naturally accumulates. Along the way, we’re going to see copious quantities of code given away, increasing developer productivity for all and outsized financial bonanzas for a few. Game on.

Major AI labs are unleashing forward-deployed engineers (FDEs) to try and grab enterprise customers. Large consultancies are dishing out tokens and assembling armies of consultants — both human and agent — to do the same.

But smaller firms are in the mix now, as well. AI is helping 28Stone Consulting, a New York-based, 230-person technology consultancy for capital markets, punch above its weight against larger rivals in the rush to deliver FDEs.

In this Q&A, Thomas Dolan and Frank Erickson, founders of 28Stone, argue that agentic AI isn’t a one-size-fits-all solution in vertical markets; success takes discipline, deep domain expertise, and human involvement to mitigate risk.

Many enterprises continue to struggle with the use of AI agents, which is consultancies are stepping in to get projects off the ground. 28Stone is among those that have published blueprints and methodologies on the development and delivery of agentic AI workflows with humans in the loop.

Computerworld spoke with both founding partners about why companies are still stumbling with agentic AI rollouts, and what a disciplined delivery process actually looks like.

After 15 years of delivering software for capital markets firms, is ‘AI-first’ a real distinction or just positioning?

Dolan: “We’re not shying away from being AI-forward. What needs to shine through is AI done intelligently — not stuff you get by buying some tokens for somebody on the trading desk. We’re an AI-first firm.”

Erickson: “And it’s temporary. At some point, AI is going to be synonymous with software development.

“The whole idea of an AI SDLC (software development lifecycle) versus an SDLC is going to be one and the same, a lot like cloud computing today. To not include AI in your strategy, you’d look like a COBOL vendor.”

What does agentic AI delivery look like?

Dolan: “We’ve got several AI initiatives delivering a pure agentic approach. We’ve doubled down on the human expertise wrapper in the SDLC. That doesn’t mean sacrificing any of the benefits of the AI models — quite the opposite.

“You don’t achieve anywhere near the same level of value from applying AI without keeping that expertise — industry, functional and technical — throughout the process.”

Where do humans stay in the loop once agents are doing the work?

Dolan: “We’re believers in starting with requirements discovery. Someone who knows the analytical nuances of a good business analyst is critically important; shaping a product owner’s business information through a markup file that can be fed into a BA agent, then treating the output as if it came from a very fast junior BA. Only then is the story complete.

“The developer takes that story, transforms it into the most efficient input, then owns the output, because they’re accountable for that code. A developer should own the code on both the input and output side.

“Your product owner, who knows the business, that’s great. But expecting them to interact with an agent and output enterprise code is ridiculous. It’s not a great plan.“

Why not just put one do-everything person in charge of AI and agents?

Erickson: “Every analyst, programmer or software engineer isn’t a great requirements analyst. And a great domain analyst with some technical background won’t know if the agent’s code is garbage, maintainable, performant.

“It’s unrealistic to expect one individual to have that breadth across domain, software engineering, testing, deployment. Clients ask all the time, and we push back: ‘Great, if you can find that guy, they’re few and far between.’ To deliver at the enterprise level, you need the human expertise, at depth.“

Dolan: “There’s system speed and latency, important in parts of finance. Then there’s speed of delivery, because other areas evolve quickly and time-to-market is critical.

“Our human wrapper may at first pass come across as a little slowed down. Maybe it is. But [Erickson] has a good analogy about one of the dangers of AI: you can end up going really fast in the wrong direction. By the time you look up, you’re way off base and have to backtrack.“

What about AI in your sector do you think is overhyped?

Dolan: “The hype around the ease of use of AI and the democratization of enterprise software delivery — that ‘anybody could do it now, it’s all being done by machines’ — is another idea that could prove costly in the long run.

“This do-it-yourself reaction is dangerous for clients, and for trust in the overall AI benefit, which is real. We compare it to the beginning of offshoring 20, 30 years ago: a golden idea that was going to cure everything. A lot of firms did it thoughtlessly, thinking it’s just labor arbitrage, and it almost inevitably failed. That all-or-nothing mentality missed that offshoring is an amazing way of getting better value for your dollar, but it has to be done thoughtfully, so the delivery process — the thing that ties it all together — stays unsevered.

“We’re seeing that now. I’ve heard, ‘We’ll just push a button, the machine’s building the system.’ The machine is not building the system. It might be writing the code, the story, running the tests.

The system is built by a team of engineers you bring in and trust. My fear is that people will say, ‘We don’t need this vendor or this technology team. I’ve got a product team. They might not be able to code at all, but they know the business,’ and it fails dramatically. 

“Then people say, ‘We played with AI, it’s not ready yet,’ and throw it all away. One of the best things we can do is ensure clients know the benefit is real.“

Erickson: “The hype can be summed up in a single phrase: vibe coding. That has done AI a massive disservice, because there’s a huge difference between vibe coding and enterprise software development, and some of the loudest proponents of AI are too latched on to it. In our industry, the only way to succeed would be a stable of unicorns. It just doesn’t scale. I get perturbed when our people internally refer to AI tooling as vibe coding; if they think that’s what they’re doing, they’re misunderstood.“

When you engage clients at different levels of AI maturity, how do you get them to a understand what works?

Dolan: “95% of our take on an agentic approach is in line with everyone else’s, but that 5% matters, especially in requirements discovery, in who’s giving the requirements and how they’re thought of. It can set you up for dramatic errors, given the speed at which you’re moving.

“There’s a dangerous human tendency we’re seeing among clients to try and cut corners at the start of a project and — in lieu of having deep, expert driven discovery sessions — just summarize what they may want using AI.

“We would hope our clients are collaborative, everyone understanding it’s early days. If a client insists on doing something we feel strongly against, like a product owner completely owning everything right up to code generation, that’s an issue we have to either push back strongly on or step out of the accountability for.“

AI body shops — LLM providers and giant consultancies — are emerging to help enterprises deploy AI. Does that model work?

Dolan: “Whether you’re partnering with an LLM or with an AI-first, generic software provider — ‘Hey, we’re not industry guys, but we know AI delivery’ — you end up, if you’re a bank or a broker-dealer, saying: ‘All right, we know our business, these guys know the AI side of it. What could go wrong? Put us together and we’ll have quality engineering.’

“The problem is what you miss: the know-how of putting industry and technical expertise together and actually delivering financial services systems. The people working at the generic delivery firms, whether an AI-only firm or a body shop somewhere, don’t have that capability.“

Does AI change the economics for smaller consultancies like yours competing against the big firms, and does it cut both ways?

Dolan: “Over our 15 years pre-AI, there were two recurring reasons we’d lose a project. One: ‘We’d love to work with you guys, given your subject matter expertise, but the costs just aren’t there compared to my budgets. I’m being forced to go to a body shop or an [offshore] delivery center.’ The other side of that coin: ‘We love your capabilities, but you’re a firm of 230 people and I need 300, 400 people.’

“AI changes the options for clients. You don’t have to sacrifice the niche vendor who knows your space just because you need a larger team or a cost target. AI levels the playing field and should allow smaller firms to compete with the larger, big-box generic firms, the Accentures of the world.“

Erickson: “It redefines what scale means. You can look at velocity as a measure of your cost to deliver, not a rate card. Scale can’t be defined in terms of headcount anymore. It’s got to be defined in terms of output.

“There’s a threat in it, too. If you’re an Accenture with hundreds of thousands of low-cost software engineers, how do you train all those people? I feel for them. But for us, a couple hundred people with a specific domain focus, it’s a huge opportunity.“

How has the profile of the people you and others hire changed with this agentic process?

Erickson: “You’re still looking for people with strong engineering and design backgrounds, and communication skills, because they interact across the software development lifecycle more than in the past.

“Many take too much joy in typing out perfect code. Sorry, I don’t need you writing for-loops and classes anymore. I need you reviewing them, understanding them, operating at a higher level. That’s a different kind of person: an engineer, not a programmer or a coder. On the [business analyst] side it’s similar: people took great pride in detailed user stories covering every path. Now it’s conversations, prompts, reviewing output — less doing, more interacting.

“More than ever, they have to be interested in the domain. They can’t just be, ‘I want to learn everything there is to know about Java.’ That’s too narrow. They don’t have to be an expert; they have to be interested. In our case, capital markets is a specific niche. The biggest challenge is getting familiar with the tools — finding time, while delivering for customers, to ramp up and make the mistakes you need to without jeopardizing projects.“

What about governance? Who’s keeping AI delivery and its costs under control?

Erickson: “This is evolving rapidly. People aren’t sure how to put governance around this. The most obvious is financial governance. People are starting to get hefty bills. One of our clients spent a million dollars on tokens over the last eight weeks alone. Sticker shock. The token-maxing policies are starting to show their flaws. It’s wild west still: learn on the fly, then figure out what needs to be governed.“

Are CIOs actually opening their wallets? And when they do, what’s the smarter way to invest?

Erickson: “There’s still a lot of caution. Forecasts keep going down on how long something should take. So: ‘I could wait three months and maybe still get it delivered by the same date someone’s promising me now, but for half the price. I’m going to wait and see when equilibrium is met.’ We haven’t seen the wallets open up like crazy — it’s slow adoption.“

Dolan: “One of our clients is looking at it from a productivity-boost perspective: instead of doing the same for less, I can do much more for the same. AI lets clients pull the trigger on things they wouldn’t have in the past — projects that might not have been approved pre-AI, where the costs have come down to a point that’s palatable with the business.“

Erickson: “And that’s the story we’re hoping to hear more of. There isn’t a huge cost anymore to exploring a business opportunity. The time and money that would have gone to a return-on-investment study could be spent on a proof-of-concept with AI, and the project done a few weeks later. Maybe [there’s] a hint of things to come, where decisions start being made quicker. 

“There’s a little fear on our side, though: a lot of tiny little projects is tough for a consulting business.“

CIOs remain at the forefront of setting the course for AI adoption in their organizations.

In fact, 82% of CIO respondents to CIO.com’s 2026 State of the CIO survey are responsible for researching and evaluating AI products, with 78% of IT leaders saying their IT departments are driving AI adoption efforts, with business units aligning their strategies accordingly.

As such, CIOs are leading or co-leading AI strategies at the majority of organizations, with many also playing a key role in tackling AI change management. They report encountering numerous factors — from heightened pressure to deliver ROI to challenges with trust in AI outputs — as they formulate and shape those AI strategies.

Here’s a look at seven notable issues impacting AI strategies in 2026.

1. Increasing pressure to show ROI for AI investments

The era of AI experimentation and pilots is over. Boards and CEOs are making it clear they want to see quantifiable returns from their AI investments. Kyndryl’s 2025 Readiness Report, for example, found that 61% of senior business leaders and decision-makers felt more pressure to prove ROI on their AI investments than they had the prior year.

“The era of funding AI is shifting from everything all-in to every project has to have line of sight to some financial value at the end of the day. It’s moving from the experimentation phase to expecting measurable outcomes,” says Jim Piazza, chief AI officer at IT services firm Ensono.

As a result, Piazza says companies, both his own as well as those he advises, are more diligent about building business cases that estimate implementation costs, AI run costs, and expected benefits so they’re primed to pursue AI initiatives that will deliver ROI.

That strategy seems to be paying off. According to the May 2026 AI Momentum Survey from Dun & Bradstreet, 67% of 10,000 businesses surveyed reported seeing early signs or pockets of ROI, 20% reported multiple projects delivering ROI, and 10% reported strong ROI.

That’s a big jump from earlier surveys that found few AI initiatives providing returns. For example, PwC’s 2026 Global CEO Survey, released in January, found that 56% of CEOs saw no significant financial benefit from AI to date, while The GenAI Divide: State of AI in Business 2025 from MIT found that 95% of enterprise generative AI projects failed to show measurable financial returns within six months.

2. The need to harness AI for transformation

The No. 1 concern for CEOs this year, according to PwC’s 2026 Global CEO Survey, is whether they’re transforming fast enough to keep pace with technological change, cited by 42% of respondents as their top concern. And 68% of the 1,120-plus C-suite executives surveyed by KPMG for its May 2026 Adaptability Pulse Survey said they feel pressure to accelerate innovation.

That in turn is influencing AI strategies.

Steve Santana, CIO and head of AI at ETS, the world’s largest private nonprofit educational testing and assessment organization, says his company is “pivoting from working on enterprise efficiencies using AI to figuring out how to deliver assessments,” adding that “AI will enable innovation we couldn’t get to before.”

For ETS, that means reimagining how the company delivers its core products, “finding areas to do something you couldn’t do before because it was too big or too daunting,” such as having more interactive tests and assessments at scale, Santana says.

And while Santana believes organizations can’t move too slowly, he predicts innovation will trump speed. “The winners and losers in the AI race aren’t always going to be the ones that got there the fastest,” he says, observing that those who move too fast “can drive behaviors that are very dangerous.”

He adds, “I’m not advocating for moving slow; I’m advocating moving at pace. It’s better to be measured in your approach.”

3. The black box of AI costs

CIOs are struggling to calculate the full cost to run AI for their use cases, with estimates coming in well under what their actual bills will be. Consider the figures from research firm IDC, which found that global 1,000 companies will underestimate their AI infrastructure costs by 30% through 2027.

That makes identifying which AI use cases will produce quantifiable value much more challenging, which in turn makes determining a winning AI strategy harder to do. CIOs, however, say they can’t let that stop them from advising their C-suite colleagues on which AI use cases are likely to be winners.

“You can’t sit on the sidelines and wait and watch. The general conclusion is you’re going to lose if you do that, so you have to play even though the cost dynamics are not really well understood,” says Mohan Sankararaman, executive vice president and CIO of First Horizon Bank.

Sankararaman says he’s devising his AI strategy with that uncertainty in mind.

“It’s up to me and my team to figure out how to optimize our use for costs, just like we did with cloud,” he says, noting that part of his strategy is to avoid infrastructure choices that could result in AI vendor lock-in and, thus, getting stuck with that vendor’s bills.

“IT has to get the engineering right and not overengineer solutions to make sure the AI strategy we pursue delivers returns,” he adds.

Researchers recommend such approaches. In a blog highlighting the IDC research, Jevin Jensen, research vice president for infrastructure and operations at IDC, wrote that “organizations successfully navigating this challenge are ones that effectively share a common trait: they’ve reimagined FinOps as a strategic team, not an after-the-fact accounting exercise. They treat AI economics as a living ecosystem — measurable, visible, and continuously optimized.”

4. Aligning use cases to business strategy

There are an overwhelming number of potential use cases, so execs must pick and prioritize those that will help them achieve their strategic goals.

That’s easier said than done.

Enterprise Strategy Group’s 2025 report on generative AI’s ROI surveyed 1,900 business and IT leaders across nine countries and found that 71% had more potential use cases that they want to pursue than they can possibly fund; 54% said selecting the right use cases based on objective measures like cost, business impact, and the organization’s ability to execute is hard; and 71% acknowledged that selecting the wrong use cases will hurt their company’s market position. Furthermore, 59% of respondents said advocating for the wrong use cases could cost them their job.

Longtime CIO adviser Larry Wolff says challenges picking and prioritizing use cases stems in part from boards and CEOs commanding their teams “to do AI.” Such directives, he explains, puts the technology first and business goals second — something CIOs have been trying to avoid for years.

“There should not be a technology strategy. There should be a business strategy with a technology component. The same applies to AI,” says Wolff, now CIO of Preferred Travel Group. “We need to talk about business challenges and opportunities first and then talk about how AI can solve for those.”

5. Human readiness to use AI

Even as Sankararaman and his executive colleagues build the bank’s AI strategy, he still sees the need to improve the organization’s understanding of the technology. “Everybody has a basic understanding, but AI fluency isn’t where it should be,” he says, noting that a subpar level of fluency “can hamper creativity.”

“If the strategy is to become top notch in, say, customer experience, we have to determine how to achieve that. And if you start building the road map but you don’t know what the technology can do, then the strategy will be limited,” he adds.

Sankararaman considers running AI boot camps for executives and their direct reports to improve their knowledge of AI and its transformative capabilities. “Not everyone needs to be an AI expert, but we still need to have a level of understanding of, say, what a large language model is and how to apply it and other elementary things like that. The hope is that when we do talk about strategy for business outcomes, everyone will know how to leverage AI,” he explains.

According to Jamaal Justice, principal for people consulting at EY, concern about AI fluency is widespread.

“One of the biggest challenges that impacts the success of an AI strategy is human readiness,” Justice says. He points to EY research showing “that while 88% of employees use AI at work, only 28% of organizations have positioned employees to achieve transformative business impact from AI. This underscores that the challenge is not access, but adoption and readiness.”

Like Sankararaman, Justice acknowledges that it’s OK to have a spectrum of knowledge and use among workers. But success with AI “depends on aligning mindsets, skillsets, and toolsets, by creating the right conditions for both workforce readiness and effective technology use,” he says.

“Organizations that integrate human capability with technology and fundamentally rearchitect work using a human-centered and value-oriented approach will unlock value at scale,” he adds. “Those that don’t risk fragmented adoption and limited returns.”

EY research confirms as much, finding that productivity gains can fall by more than 40% when AI is deployed on weak talent foundations, including poor learning, culture, and incentives.

6. Data readiness for AI use

Data readiness is also lagging at most organizations, further hindering AI ambitions.

According to a 2026 report from Cloudera and Harvard Business Review Analytic Services titled Taming the Complexity of AI Data Readiness, 73% of surveyed business leaders said their organization struggles with AI data preparation. The top obstacles are siloed data and difficulty integrating data sources (56%), lack of a clear data strategy (44%), data quality and bias issues (41%), and regulatory constraints on data use (34%).

To ensure AI success, “a radical reshaping of the data landscape is needed,” says Steve Prewitt, who as chief data and AI officer at IT services firm Genpact advises clients on AI deployments for their own organizations.

That reshaping is more critical today as agentic AI becomes more prevalent, Prewitt observes. Organizations need high-quality well-governed data to enable and trust AI agents to make real-time decisions autonomously. Otherwise, organizations either can’t move forward with deploying agents or, if they do, risk triggering cascading failures.

7. Engendering trust

ETS CIO Santana and his colleagues recognize AI’s potential to deliver faulty outputs, whether from problematic data, drift, or other problems. Everyday users recognize that potential, too.

That’s why the issue of trust has a significant impact on the nonprofit’s AI strategy. Companies such as ETS that provide critical, high-stakes services know they must earn trust by building AI use cases that can consistently and demonstratively deliver accurate outputs, Santana says.

ETS’s strategy is to highlight where AI is making high-stakes decisions and to detail what steps the company must take to ensure that it consistently delivers accurate, trustworthy outputs and that it conforms to established standards and requirements, he says.

“You don’t want someone to feel the results may be wrong if you’re using AI to assess a person and their future depends on it,” he notes. “You want to remove any doubts [in such AI use cases], and the strategy should ensure that. The strategy should include all the work needed to have that trust.”

Last year Anthropic gave away one of the most successful things it has ever built. And, no, I’m not talking about Claude. I’m referring to MCP, the now ubiquitous Model Context Protocol, which Anthropic donated to the Linux Foundation’s new Agentic AI Foundation⁠. At the time, MCP was pulling nearly 100 million monthly SDK downloads across more than 10,000 active servers⁠, prompting the question as to why any company would give up such a popular piece of technology.

Google did much the same months earlier, handing its Agent2Agent (A2A) protocol to the Linux Foundation⁠ with AWS, Cisco, Microsoft, Salesforce, SAP, and ServiceNow signing on as founding members. OpenAI, not to be outdone, supports remote MCP servers in its Responses API⁠, sits on the MCP steering committee, and contributed AGENTS.md to that same foundation alongside its fiercest rival’s protocol.

It’s like Game of Thrones, except the principal AI powers seek regime change through seeming acts of beneficence rather than violence. For those who have been around for a while, it’s also entirely predictable, following a similar script we’ve seen in the cloud, on-premises servers, and more. Platform companies don’t give away technologies they’ve stopped caring about. They give away technologies they no longer need to own because competitive advantage has shifted to new ground.

What does this mean for AI?

Gravity has shifted before

Google has long been an exceptionally active contributor to open source. As I wrote in 2017, Google wasn’t open sourcing TensorFlow and Kubernetes out of generosity but rather turning these open source assets into on-ramps for Google Cloud. Google was playing catch-up to AWS and Microsoft. As then Google product manager Martin Buhr said, the company hoped to “create a gravity well in the market for container-based apps [so] that a significant percentage of them will end up with us.”

In other words, platform companies routinely commoditize one layer of the stack so they can compete somewhere they hold a stronger hand.

GitHub may be an even better example. Git is open. Anyone can host a Git repository and, once upon a time, different companies did just that. Yet GitHub became the default place software development happens for millions of developers. Nobody pays for Git, but lots of people pay for GitHub. We’re seeing this same phenomenon play out in AI.

Trading contributions for control

Anthrophic and OpenAI have both pretended at being all for humanity’s good, but that’s not a good explanation for why they’re racing to give away things like MCP. The deeper reason is that the model itself has turned out to be a poor place to build a lasting moat, and they’re trying to figure out what’s next. As I pointed out recently, the frontier model leaderboards change almost weekly. As such, enterprises shouldn’t build their AI strategy around the assumption that any one vendor will remain permanently ahead on model quality. Instead, as I suggested, AI may be sexy, but the “dull reality” is connecting those models to enterprise data, workflows, etc.

The AI companies understand this better than anyone. Sure, they’ll continue spending billions training ever more capable models because frontier models attract developers, generate headlines, and open enterprise doors. But they’re also quietly acknowledging that benchmark leadership alone doesn’t create a durable platform.

Developers return to the places where their tools, workflows, teammates, and accumulated work already live. Enterprises double down on the systems where their data, permissions, governance, and business processes are already connected. Every new integration makes that destination a little harder to leave, and every new workflow increases its pull. That’s what MCP, A2A, etc., are all about: increasing gravity around the models.

Every major AI company wants to become the place where AI-assisted work naturally happens, and they’re now amassing armies of forward deployed engineers and trying other means to get legacy infrastructure to tie back to their frontier models. The enterprise incumbents want the same thing, but from the opposite direction. They don’t need to own the frontier; instead they need to connect the frontier to the systems that already safely run the business.

That’s why I’m skeptical whenever someone confidently predicts that AI will sweep away enterprise software. I’ve seen this movie before. Developers absolutely live on the frontier, but enterprises don’t. Enterprises create value by connecting new capabilities to decades of accumulated applications, data, policies, and business processes. The newest model matters, and so does the newest agent framework. But neither creates much business value until it’s connected to customer records, financial systems, supply chains, HR data, and everything else enterprises already depend on.

That’s where incumbents still possess enormous gravitational pull. My employer, Oracle, certainly believes so, just as Microsoft, SAP, Salesforce, and ServiceNow do. (Disclosure: I run developer relations at Oracle, which participates in the Agentic AI Foundation.) Ironically, open protocols strengthen that position rather than weaken it. If every model can speak MCP and every agent can interoperate through common standards, enterprises gain the freedom to adopt whichever frontier technology looks best without rebuilding every integration. The protocol becomes interchangeable.

Open standards don’t stop gravity

None of this diminishes the importance of open standards. MCP succeeded because it solves a genuine problem. Developers shouldn’t have to build a custom connector every time an AI application needs access to a database or other business system. Neutral governance also matters because nobody wants foundational infrastructure controlled by a direct competitor. But we shouldn’t confuse open interfaces with open markets.

An enterprise may find it easy to swap one MCP-compatible model for another while still remaining deeply dependent on the place where its prompts, evaluations, security policies, and employee habits have accumulated. Again, we’ve seen this before. Kubernetes made workloads dramatically more portable without making AWS, Azure, and Google Cloud interchangeable. SQL has been standardized for decades, yet databases remain fiercely differentiated businesses. Standards reduce friction, but they rarely eliminate competitive advantage. They simply move it.

In like manner, Anthropic, Google, OpenAI, and others are happily standardizing how models, agents, tools, and enterprise systems communicate because they don’t expect the connection itself to determine the winner. Instead they expect to win by becoming the place where AI-assisted work naturally accumulates. Along the way, we’re going to see copious quantities of code given away, increasing developer productivity for all and outsized financial bonanzas for a few. Game on.

earthphotostock – shutterstock.com

In vielen Unternehmen stoßen IT-Sicherheitsrichtlinien auf Widerstand, da Mitarbeitende sie als hinderlich oder praxisfern empfinden. Dies erschwert die Umsetzung, untergräbt die Wirksamkeit und belastet die Zusammenarbeit zwischen der Sicherheitsabteilung und den Fachbereichen. Statt als Partner wird Cybersecurity oft als Bremser wahrgenommen – ein fatales Sicherheitsrisiko. Für CISOs (Chief Security Information Officer) bedeutet das, dass neben technisch korrekten Richtlinien vor allem die Akzeptanz im Alltag entscheidend ist. Ein neuer Ansatz mit empathischem Policy-Engineering und strategischer Sicherheitskommunikation fördert eine nachhaltige Sicherheitskultur.

IT-Sicherheit: Arbeitsdruck und soziale Einflussfaktoren

In vielen IT-Abteilungen herrscht die Ansicht, dass Anwender wenig motiviert sind, Sicherheitsvorgaben einzuhalten. Unternehmen setzen auf Sanktionen und Schulungen, um regelkonformes Verhalten zu erzwingen. Ein zwei-tägiges Experiment, das untersucht, wie sich Sicherheitsdesigns auf richtlinienkonformes Nutzerverhalten auswirken, zeigte jedoch: Hatten Teilnehmende anfänglich noch eine positive Einstellung gegenüber Sicherheitsrichtlinien, wurden diese unter steigendem Arbeitsdruck zunehmend als hinderlich empfunden, was vermehrt zu Regelverstößen führte. Stress und situative Faktoren hatten einen spürbaren Einfluss auf das sicherheitsrelevante Verhalten der Teilnehmenden.

Sicheres Verhalten entsteht also nicht allein durch Wissensvermittlung, sondern hängt stark von der individuelle Risikoeinschätzung und den konkreten Alltagssituationen ab. Nutzer handeln nicht immer so, wie es die Richtlinien vorsehen. Oft nicht aus Unwillen, sondern weil andere Faktoren überwiegen oder als wichtiger eingeschätzt werden. Ambitionierte Ziele, Zeitdruck und das Bedürfnis nach reibungsloser Zusammenarbeit stehen häufig im Widerspruch zu abstrakten Sicherheitsvorgaben. Diese Interessenkonflikte führen schnell zu Spannungen zwischen Security, IT und den anderen Fachbereichen. Das gefährdet letztlich die Sicherheitskultur.

Sicherheitsverantwortliche können an drei Punkten ansetzen, um dem entgegenzuwirken.

1. Die Anwender verstehen

CISOs sollten sich zunächst die Frage stellen, warum sich Nutzer nicht sicher verhalten. Eine Vielzahl von Faktoren spielen hier eine Rolle: Beispielsweise sind sich Anwender der Bedrohung nicht bewusst, sehen den Nutzen von sicherem Verhalten nicht oder empfinden Sicherheitsmaßnahmen als hinderlich für ihre Arbeit. Eventuell besteht auch ein Interessenkonflikt mit den Zielen der Nutzer oder sie stehen unter Zeitdruck. Oft fehlen schlicht die Mittel – beispielsweise, wenn Vorschriften einen sicheren Datenaustausch mit Zulieferern und Kunden fordern, aber den Mitarbeitenden keine Plattform für einen solchen Datenaustausch zur Verfügung gestellt wird – oder auch Vorbilder im Umfeld.

Vor der Implementierung von Sicherheitsmaßnahmen ist es wichtig, widersprüchliche Ziele und Prioritäten der verschiedenen Interessensgruppen (IT-Abteilung, technische Abteilungen, Management, Verwaltung, Mitarbeitende in der Produktion) zu identifizieren und auszugleichen. Dies ist beispielsweise im Rahmen einer Stakeholder-Analyse möglich – einer Methode aus der Wirtschaftsinformatik, um die Präferenzen aller beteiligten Stakeholder zu erheben. Je mehr Sicherheitsverantwortliche über die Arbeitswirklichkeit und die Ziele der verschiedenen Bereiche wissen, desto besser gelingt es ihnen, Sicherheitsmaßnahmen dazu passend zu gestalten – was zu mehr Akzeptanz und am Ende einer erfolgreichen Umsetzung führt.

2. Sicherheitsrichtlinien mit Blick auf den Anwender gestalten

Unsicheres Verhalten wird häufig den Nutzern angelastet, dabei liegt das Problem oft in der Maßnahme selbst. In der IT-Sicherheitsforschung liegt der Fokus häufig auf dem individuellen Verhalten der Nutzer – beispielsweise auf der Frage, ob sicheres Verhalten von Persönlichkeitsmerkmalen abhängt. Vernachlässigt wird dabei die Frage, wie gut Sicherheitsmaßnahmen überhaupt zur Arbeitsrealität passen – sprich, wie wahrscheinlich es ist, dass sie im Alltag akzeptiert werden.

Für jede Bedrohung gibt es meist mehrere verfügbare Sicherheitsmaßnahmen. Doch Unterschiede in Aufwand, Akzeptanz, Kompatibilität oder Komplexität werden in der Praxis oft nicht berücksichtigt. Stattdessen treffen Sicherheits- oder IT-Abteilungen Entscheidungen häufig ausschließlich auf Grundlage technischer Aspekte.

Um wirksame IT-Sicherheitsrichtlinien zu etablieren, müssen diese nicht nur technisch korrekt sein – sie müssen auch aus Mitarbeitersicht sinnvoll und praktikabel sein. Der Schlüssel dazu liegt im empathischen Policy Engineering: Sicherheitsvorgaben sollten so gestaltet sein, dass sie verständlich sind, akzeptiert werden und mit den alltäglichen Arbeitszielen vereinbar sind. Das gelingt am besten, wenn Mitarbeitende frühzeitig in die Entwicklung eingebunden werden – inklusive ihrer Zielkonflikte und praktischen Herausforderungen.

Ein anschließender Pilotversuch hilft dabei, potenzielle Stolpersteine und Hindernisse frühzeitig zu erkennen und die Maßnahmen entsprechend nach zu justieren. Es hat sich bewährt, dabei mit den “Early Adopters” zu starten – also der Gruppe an Anwendern, die Neuerungen gegenüber aufgeschlossen ist und im Anschluss konstruktives Feedback geben kann. Dieses sollte vor dem großen Roll-out berücksichtig werden. So kann eine Sicherheitskultur entstehen, die wirkt – und im Alltag tatsächlich gelebt wird.

3. Sinnvoll kommunizieren: Der RESPECT-Ansatz

Aktuell werden Sicherheitsmaßnahmen und -richtlinien häufig in einer Art und Weise kommuniziert, die Anwender nicht in ihrer Arbeitsrealität abholen, weil sie gar nicht darauf abzielen, dass Mitarbeitende sich damit beschäftigen und motiviert werden: Etwa über Anweisungen, Standard-Online-Trainings oder zu verspielte Formate wie Comics, die Mitarbeitende nicht ernst nehmen. Besser funktioniert das mit dem RESPECT-Ansatz: Er setzt auf Kommunikation auf Augenhöhe, statt auf Verbote und Strafen.

Der entscheidende Unterschied: Mitarbeitende werden als kompetente, verantwortungsvolle Erwachsene behandelt. Im Zentrum steht ein empathischer Blick auf ihre Bedürfnisse und Arbeitsrealitäten – ohne die Sicherheitsziele aus den Augen zu verlieren.

Es gibt mehrere Techniken, um die Kommunikation von Sicherheitsrichtlinien erfolgreich zu gestalten und Konflikte zu vermeiden:

Taktische Empathie: Diese schafft Anerkennung, stärkt Vertrauen und sorgt so dafür, dass sich Mitarbeitende gehört fühlen und bereit sind, sicherheitsrelevante Informationen anzunehmen.

„Help me to help you“ anstelle von „Nein“: Statt Sicherheitsvorgeben durchzusetzen, können CISOs mit gezielten „Wie“-Fragen Anwender dazu anregen, über die vorgeschlagenen Lösungen nachzudenken. Wenn Anwender Änderungswünsche zu den Sicherheitsvorgaben haben, sollte die Security nicht einfach nur ‘Nein’ sagen. Eine Rückfrage dazu, was die Mitarbeitenden selbst vorschlagen, um sowohl die Sicherheitsvorgaben einzuhalten als auch effizientes Arbeiten zu ermöglichen, ist sinnvoll. So entsteht ein Dialog und es ist leichter, einen für alle Beteiligten tragbaren Kompromiss zu finden.

Praxiserfahrung statt grauer Theorie: Ein Trainingskonzept, das auf direkte Erfahrung setzt, konfrontiert Teilnehmende mit realistischen Szenarien – etwa Cyberangriffe wie Phishing, Ransomware oder USB-Angriffe. Sie erleben hautnah in einer realitätsnahen Umgebung, die typische Arbeitsplätze in kleinen und mittleren Unternehmen abbildet, wie Cyberangriffe ablaufen. So entsteht ein tiefes, nachhaltiges Verständnis für IT-Sicherheit. Statt Belehrungen stehen der Mensch und das Erleben im Mittelpunkt.

Fazit: CISOs als Gestalter wirksamer Sicherheitskultur

Der geringe Erfolg vieler Sicherheitsmaßnahmen liegt nicht allein an den Nutzenden – oft sind es unrealistische Vorgaben, fehlende Einbindung und unzureichende Kommunikation. Für Sicherheitschefs bedeutet das: Statt auf Erziehung und Sanktionen zu setzen, braucht es einen strategischen Paradigmenwechsel. Sie sollten zu einer Art Emphatic Policy Architekt werden, dessen Sicherheitsstrategie nicht nur technisch funktioniert, sondern auch menschlich überzeugt. Er gestaltet Rahmenbedingungen, in denen sich sichere Entscheidungen selbstverständlich in den Arbeitsalltag einfügen. Dafür braucht es ein gutes Gespür für Zielkonflikte, Kommunikation auf Augenhöhe – und die Fähigkeit, Sicherheit als gemeinsamen Wert im Unternehmen zu verankern. (jm)

Lesetipp: So erfüllen Sie Ihre Compliance-Anforderungen

Salesforce’s AI agent platform, Agentforce, is seeing weaker-than-expected customer traction, according to a recent KeyBanc Capital Markets investment research note, which attributed the slowdown in part to the product itself, stating that “Agentforce, as a product, just isn’t there” yet, following customer checks and a CIO survey.

“Our checks and customer conversations have not been strong, nor has the feedback been on Agentforce. What we can piece together in the disclosed numbers does not signal building momentum and, most recently, our CIO survey delivered another blow with Salesforce being a standout for the wrong reasons,” according to a Seeking Alpha that quoted a KeyBanc research note.

“We attend more Salesforce partner and customer events than any other company in our coverage, and feedback from those customers has been consistent in two ways: 1) customers’ data is not in order to do meaningful AI work; and 2) Agentforce, as a product, just isn’t there,” Seeking Alpha reported, quoting the KeyBanc note.

The KeyBanc note quoted by Seeking Alpha also pointed out that conversations with Salesforce “partners” indicate that Agentforce proof-of-concept deployments are only now starting to generate pipeline opportunities, while its CIO survey found more respondents expecting to deprioritize Salesforce within their IT budget than the other way around over the coming 12 months.

The findings in the research note stand in contrast to Salesforce’s sustained push to position Agentforce as its flagship enterprise AI platform. Since introducing the offering nearly two years back, the company has expanded it with new foundation models, integrations, deployment options, and pricing initiatives, most recently introducing its Headless 360 strategy to make Agentforce available beyond conventional CRM workflows through a more flexible consumption model.

Parts of that flexible consumption model and Agentforce pricing, which Salesforce is still evolving, have already come under scrutiny with industry analysts expressing concern that Headless 360’s monetization model could create budgeting headaches for CIOs by making AI spending less predictable and increasing pressure on IT leaders to demonstrate measurable business outcomes and return on investment before expanding deployments.

Concerns over product maturity

Those concerns around Agentforce’s evolving pricing model appear to be intersecting with the latest concerns about product maturity that KeyBanc analysts mention in their report.

“Three pricing model changes in roughly 18 months make procurement committees nervous, and if the commercial model keeps shifting, buyers question whether the product has stabilized either,” said Bhupendra Chopra, chief revenue officer at IT consulting firm Kanerika.

Salesforce’s latest consumption-based pricing model, Chopra pointed out, is a bigger concern: “It is harder to budget for than seat-based licensing. CIOs want a clearer line between spend and outcome. That line isn’t clear enough yet.”

Greyhound Research Chief Analyst Sanchit Vir Gogia, too, said that the pricing model is a fundamental issue for enterprises.

While Salesforce’s pricing model charges enterprises for AI activity, it leaves customers to determine whether those interactions ultimately translate into meaningful business outcomes, Gogia said.

That, according to Gaurav Parab, principal research analyst at NelsonHall, is slowing adoption because enterprise leaders, such as CIOs, are under pressure to evaluate TCO and expected ROI.

“Most enterprises first want confidence that AI deployments will generate measurable business outcomes before committing to broader rollouts,” he said.

Data Cloud and data readiness remain as challenges

Pricing, though, is just one piece of the equation.

The discussion about Agentforce adoption, analysts said, cannot be separated from Salesforce’s broader product strategy, which positions Agentforce alongside Data Cloud as the foundation for enterprise AI deployments.

“Data modernization has become one of the biggest determinants of adoption. Agentforce depends on trusted, unified enterprise data to generate reliable outcomes. For many organizations, preparing that data foundation through Data 360, integration, governance, and data quality initiatives represents a significant part of the implementation effort,” Purab said, backing the KeyBanc research note.

Chopra seconded that assessment, saying Data Cloud has effectively become a prerequisite for production-grade Agentforce: “We worked with a private equity fund administrator where the AI layer only became reliable once we had clean, structured data feeding into Salesforce consistently. Before that, even well-configured automation produced inconsistent outputs.”

In practice, that means many enterprises need to invest separately in Data Cloud, data integration, governance, and cleanup before Agentforce can be deployed reliably at scale, Chopra said.

Implementation challenges are slowing adoption

Product maturity aside, those implementation challenges, Purab pointed out, are also contributing to a slower pace of Agentforce adoption in enterprises than anticipated.

While interest in Agentforce continues to grow, enterprises, according to the analyst, are largely limiting deployments to targeted, high-value use cases while they establish trusted data foundations, integrate with existing systems, and demonstrate measurable business value.

More so because Data Cloud accelerates Agentforce once the foundation is coherent,  it cannot make incoherence disappear, Gogia pointed out.

Chopra, too, said his conversations with enterprise customers closely mirror Purab and KeyBanc’s findings: “The issue isn’t appetite. The issue is that their CRM data is fragmented, partially duplicated, and inconsistently structured. You can’t put an AI agent on top of that and expect reliable outputs.”

“Cleaning that up takes months. That work doesn’t show in a vendor’s deal count, which is why signed agreements and actual production deployments are two very different numbers right now.”

Timing issue or execution gap?

Despite all the challenges, though, Purab said that the slower pace of current adoption is not necessarily indicative of a long-term problem.

“I see it as a timing issue, but it has always been the case. Enterprise AI adoption has consistently followed the maturity of data, governance, and operating models. Salesforce will undoubtedly continue refining the product, pricing, and go-to-market approach, but the larger challenge lies in enterprise readiness,” Purab said.

“As organizations strengthen their data foundations and gain confidence in deploying AI responsibly, Agentforce adoption is likely to broaden significantly,” Purab added.

Chopra, in contrast, offered a more nuanced view: “While data readiness, which is a customer issue, will improve with time, Salesforce needs to fix its go-to-market strategy.”

“Three pricing changes in 18 months, a product that requires significant pre-investment before it delivers value, and implementation complexity that most mid-market buyers aren’t resourced for is definitely a positioning gap Salesforce needs to close,” Chopra said.

Regardless of whether the current slowdown proves temporary or structural, both analysts agreed that the next six to twelve months should provide a clearer picture of Agentforce’s trajectory.

For CIOs evaluating the platform, they said, the focus should be less on headline product announcements and more on tangible indicators of enterprise adoption and operational maturity.

“The key indicators will be an increase in enterprise-scale production deployments rather than pilots, broader adoption beyond customer service into other business functions, stronger customer references demonstrating measurable business outcomes, continued simplification of pricing and deployment models, and greater maturity around governance, security, and operating models for AI agents,” Purab said.

For Chopra, CIOs should go a step further by measuring how AI agents perform in production rather than simply tracking deployment numbers: “Containment rate in production — what percentage of agent interactions are resolved without human escalation — is the real performance signal, not token volume or deal count.”

Salesforce did not immediately respond to a request for comment.

Attacks targeting developer ecosystems are increasing in frequency and sophistication, with Node.js developers firmly in this week’s crosshairs, as multiple npm packages belonging to the open-source AsyncAPI and Jscrambler Code Integrity were poisoned with malware following compromised development credentials.

The incidents highlight the cascading effect of software supply chain attacks in which stolen credentials are then used to perpetrate additional compromises. Security researchers advise organizations to completely rebuild from clean images any developer machines that have installed a poisoned package — and to rotate all npm tokens, source control access, cloud credentials, CI/CD secrets, SSH keys, signing keys, and browser sessions.

Affected packages include: jscrambler@8.14.0, jscrambler@8.16.0, jscrambler@8.17.0, jscrambler@8.18.0, jscrambler@8.20.0, @asyncapi/generator-helpers@1.1.1, @asyncapi/generator-components@0.7.1, @asyncapi/generator@3.3.1, @asyncapi/specs@6.11.2 and @asyncapi/specs@6.11.2-alpha.1.

However, packages that list any of the above poisoned packages as dependencies may also be impacted, including those from the same projects, such as jscrambler-webpack-plugin 8.6.2, gulp-jscrambler 8.6.2, grunt-jscrambler 8.5.2, and jscrambler-metro-plugin 9.0.2.

Vulnerable GitHub Actions workflow used as entry point

The attack against AsyncAPI, an open-source reference specification and toolset for implementing event-driven architectures and asynchronous APIs, occurred on Tuesday and was independently detected by multiple security companies monitoring the npm registry, including Upwind, Socket.dev, Wiz, StepSecurity, and Aikido Security.

According to the researchers’ analysis, attackers took advantage of a known configuration vulnerability in a GitHub Actions CI/CD workflow that had been reported in April. The flaw involves the pull_request_target event, which executes whenever a new pull request is made. When triggered, the workflow automatically checks out and executes the developer’s submitted pull request code in the Actions container, but this is done in the context of the base repository with full access to secrets.

The AsyncAPI project had a proposed fix since May 17, but the fix had not yet gone through the full review and was not merged into the main branch.

“At 05:08 UTC, the attacker opened PR #2155 containing a markdown file with obfuscated JavaScript hidden after approximately 1,000 bytes of whitespace,” researchers from Wiz explained in their report. “The payload was designed to scan the GitHub Actions runner’s environment for secrets and exfiltrate them to a dead-drop URL on the rentry.co pastebin.”

When a GitHub Actions workflow is triggered and is executed in an environment, a temporary GITHUB_TOKEN is generated to allow for authenticated git commands against the repository. Other tokens might also be included.

In this case, the attackers managed to obtain a token associated with asyncapi-bot, a service account that had access across the entire AsyncAPI organization on GitHub. This allowed them to perform malicious code commits in two separate repositories. Those commits then triggered automated build workflows that generated and published the npm packages.

The payload bundled in the packages shares some similarities with a malware framework called Miasma that was used in previous supply chain compromises. However, the malware code appears to be significantly different from previously documented variants.

The first-stage code downloads a secondary trojan payload that has variants for Linux, Windows, and macOS. This is a modular malware framework with credential theft capabilities that targets passwords and cookies saved inside browsers, SSH keys, npm and GitHub tokens, AWS credentials, macOS Keychain, and cryptocurrency wallets.

The trojan communicated with a command-and-control server and can accept remote commands to perform file operations, list directories, and exfiltrate data.

Jscrambler compromised via leaked npm credential

The Jscrambler attack happened over the weekend on July 11 with attackers publishing multiple trojanized versions in two waves. Jscrambler Code Integrity is a client-side security library designed to protect JavaScript-based web and mobile applications against tampering and reverse engineering.

Jscrambler published an advisory in response to the incident in which it clarified that the attackers published malicious versions of the package using a npm publishing credential. However, unlike the AsyncAPI case, how that credential was leaked in the first place is not clear.

Initially the attackers released new package versions with two malicious scripts that get executed at install time using a preinstall hook in the configuration script. The scripts also execute platform-specific binaries for Linux, macOS, and Windows embedded in an obfuscated container.

Because preinstall or postinstall hooks are common ways to deliver malware in npm packages, they are automatically checked by security tools. To avoid detection, the attackers pivoted to a method that involved injecting the malicious code directly in the dist/index.js and dist/bin/jscrambler.js files. This changed the malware execution from package installation time to when the package gets imported into other projects or the Jscrambler CLI is invoked.

The embedded malware executables for different platforms are written in Rust and, according to Socket.dev’s analysis, were “a broad, developer-focused credential and secret harvester” that targeted browser-extension crypto wallets, API keys from AI coding assistants and MCP servers, cloud credentials for AWS, Azure and GCP, authentication tokens for messaging applications (such as Discord, Slack, and Telegram), password stores from browsers, Steam, and KDE.

Enterprises are facing two simultaneous challenges with AI: The risks associated with it are evolving faster than governance frameworks, while the business benefits are often difficult to measure.

This is one of the key findings of The Value of AI, a study commissioned by SAP from Oxford Economics. Now in its second year, the study surveyed 2,600 executives from 13 countries worldwide.

High expectations, limited preparation

On average, the enterprises surveyed plan to spend around $28 million on AI (up from $26.7 million last year), and expect a 21% ROI (from 16% last year). Expectations for AI agents are particularly high, with ROI expected to reach 17% this year, up from 10% last year. Furthermore, 83% of respondents worldwide said agentic AI has the potential to fundamentally transform their organization. On the other hand, only 3% of respondents said their enterprises were fully prepared for the deployment of AI agents.

There are gaps, particularly when it comes to governance:

• Only 12% of respondents said their skills or processes were able to govern AI effectively,
• 38% do not have human-in-the-loop processes in place for oversight of AI agents, and
• only 63% have established permissions and access controls for agents.

Other concerns include weaknesses in the organization of AI deployment, poor data quality, insufficient employee training, and the widespread use of shadow AI.

Governance is the bigger challenge

In an interview, Sean Kask, Chief AI Strategy Officer at SAP, commented on the study’s key findings.

Mr. Kask, in the study’s foreword, you write that companies are currently facing two challenges simultaneously: The risks associated with AI are evolving faster than governance, while the business benefits are often difficult to measure. Which of these poses the greater problem for companies?

Sean Kask: Measuring the business value of IT investments has never been easy. The same applies to AI. That’s why I currently consider the governance issue to be the greater challenge. While traditional governance principles and best practices for secure software development remain important even in the age of large language models and agent-based AI, entirely new risks are emerging at the same time.

For example, as soon as companies roll out AI on a broad scale, they suddenly discover hundreds or even thousands of so-called shadow agents that employees are using without central oversight. Or they find that a significant portion of the workforce is copying content into private ChatGPT accounts. Such risks often only become apparent once AI is already being used productively.

According to your study, German companies invest an average of nearly $40 million in AI, more than companies in all other countries surveyed. Why is that?

Kask: I was less surprised by the amount of investment than by the fact that, overall, the level of investment and the return on investment achieved have developed very similarly across the various countries. There’s no clear answer as to why Germany invests more. In part, it’s likely simply because costs here are higher than in India, for example.

However, we’re also seeing a high level of AI adoption among German companies. SAP has a dashboard that allows us to track how our customers are using AI features. Germany is among the countries with particularly high usage. Added to this are the strong industrial base and the political impetus from Europe, which are driving the use of AI. Accordingly, companies there are making targeted investments in building the necessary expertise.

According to the study, 47% of German companies are satisfied with the return on investment from their AI investments. At the same time, 77% say they are still far from realizing AI’s full potential. Isn’t that a contradiction?

Kask: No, we see this pattern worldwide. Companies initially invest in a few AI use cases and realize: This works; we’re creating added value. Accordingly, they’re satisfied with their investment.

But this is precisely what leads them to identify further use cases. They explore AI agents and want to utilize them as well. However, it is exactly at this point that many encounter new challenges in implementation and scaling.

The study therefore primarily highlights a learning curve: The more experience companies gain with AI, the greater their awareness of its previously untapped potential becomes.

According to the study, only 33% of companies surveyed have KPIs at the executive board level that are directly linked to the implementation of AI. In your view, which metrics should supervisory boards and CEOs definitely be tracking?

Kask: For us, a key indicator is employee enablement. How many employees have already successfully completed training or upskilling programs related to AI? Without the appropriate skills, AI adoption will fall short of its potential.

Transparency is equally important. Companies should know which AI agents are actually in use within their landscape. SAP offers the SAP AI Agent Hub for this purpose, which automatically discovers and inventories agents from SAP and third-party environments. Customers have already been able to identify thousands of agents this way, which highlights the need for centralized governance and transparency.

In addition, companies should have a complete overview of all AI use cases. A robust business case should be in place for each use case. We often see two extremes: Either the executive board is under pressure to implement AI as quickly as possible and allocates a lump-sum budget for this purpose. Or management initially takes a wait-and-see approach. This leads to independent pilot projects springing up throughout the company, with individual departments procuring their own tools and entering into their own contracts.

At SAP, we therefore follow a clearly structured selection process. Each idea first undergoes an assessment of its expected business value. We then examine technical feasibility, data availability, and ethical and governance aspects. From management’s perspective, it is crucial to maintain transparency regarding all ongoing AI projects at all times and to consistently prioritize them based on their business value.

Agents, too, need a ‘hire-to-retire’ lifecycle

Even with the introduction of dozens or even hundreds of AI agents, governance becomes increasingly complex. What capabilities do enterprise platforms need to manage AI agents securely and in a controlled manner at scale?

Kask: We make a conscious effort not to anthropomorphize AI too much. Nevertheless, the analogy is helpful: Agents require a complete hire-to-retire lifecycle. This begins with the detection and registration of an agent. It is then integrated into the enterprise environment, granted the necessary permissions, and given access to the data sources it needs to perform its tasks.

Observability is just as important. Companies must be able to track what an agent is actually doing in the system at all times. In addition, they should track key performance indicators: Is the agent achieving the desired results? How efficiently is it working? How many tokens does it consume? How many processing steps does it require for a task?

Ultimately, this involves several key components: a complete inventory of all agents, appropriate governance, risk, and compliance (GRC) mechanisms, transparency regarding agent behavior, and continuous monitoring. This is the only way to ensure that AI agents consistently operate within defined parameters and deliver the desired business value.

In your estimation, which business processes will companies actually delegate entirely to AI agents over the next two to three years?

Kask: Currently, such agents work particularly well in clearly defined use cases. SAP will release more than 50 (currently 34) specialized AI agents.

One example is periodic financial reporting. In this context, journal entries must be made based on numerous rules stored in documents, emails, or previous transactions. The agent analyzes these various sources of information, derives a recommendation from them, and suggests the appropriate journal entry to the user.

Based on what we’ve heard from customer projects, employees at medium-sized companies currently spend about twelve hours per month on these tasks. With the help of an AI agent, this effort can be reduced to two to three hours.

Another area of application is production planning. If delivery dates change or new orders come in at short notice, the entire production plan must be adjusted. It is precisely these kinds of complex optimization tasks that are ideally suited for AI agents.

In principle, there are virtually no limits to the narrowly defined business processes in which agents can be deployed. However, they will not operate completely autonomously at first.

Trust in AI begins with a stable foundation

Many companies still struggle to trust AI agents. After all, large language models operate probabilistically and can produce false information. This is particularly problematic in financial processes. How do you build trust?

Kask: Trust begins with a stable foundation. ERP systems remain the reliable system of record. They operate deterministically, contain the business logic, and hold the relevant company data. AI agents build upon this foundation. They do not replace it.

Equally important is the human-in-the-loop principle. Employees must be able to understand what the agent is doing, verify its results, and intervene if necessary. That’s why employee training also plays a crucial role. They must understand how generative AI works and where its limitations lie.

Of course, language models can hallucinate. At the same time, we must not forget that humans are not infallible either. The key lies in the collaboration between humans and AI. This allows us to improve both the efficiency and the quality of many business processes.

Another important component is transparency. Our global AI ethics policy, for example, stipulates that users must always be able to recognize when AI is involved. In Joule, it’s possible to trace which data sources the agent used and which steps it went through in reaching its decision. This traceability is an essential prerequisite for trust.

What distinguishes an SAP agent from a general AI agent that merely accesses an ERP system?

Kask: The key difference is that Joule and the SAP agents are directly embedded in the ERP system. There, for example, we’ve built a knowledge graph that describes the semantic relationships between all tables, business objects, and data fields.

To put this into perspective: The SAP S/4HANA Knowledge Graph is based on approximately 452,000 ABAP tables, 7.3 million data fields, and thousands of analytical views. The semantic relationships between these artifacts are modeled in the Knowledge Graph and made available for AI applications.

For example, if a user wants to view all open purchase orders, the agent does not first have to laboriously search for the relevant information. It immediately knows which tables and objects are relevant and also understands the relationships between a purchase order, a purchase requisition, the responsible approvers, and other business objects. As a result, the agent not only works much more precisely but also requires significantly fewer tokens because it can greatly narrow down the search space.

If, instead, one attempts to simply overlay AI onto an existing system or extract data from a relational ERP system, many of these relationships are lost. In a sense, this destroys the semantic context that is crucial for precise answers.

That is why we view the ERP system as an enormous strategic advantage. It has been the system of record for decades and contains roughly 50 years of codified business and process knowledge. This knowledge forms the foundation for what we call the autonomous enterprise. The agents build upon this knowledge and continue to develop it.

In the future, SAP agents will also communicate bidirectionally with agents from other providers via standards such as Agent-to-Agent (A2A).

According to your study, AI currently creates the greatest added value in decision-making, customer interaction, and gaining new insights, rather than in traditional productivity gains. Will this change the way companies justify AI investments in the future?

Kask: In our study, productivity was simply rated slightly lower than, for example, gaining new insights. In the long term, however, productivity remains the ultimate goal. Europe, in particular, has been suffering from comparatively weak productivity growth for years.

At SAP, we therefore first evaluate every new AI feature based on its specific business value. For all agents and AI features that we include in our AI Feature Catalog, we first conduct a value analysis. We ask: What benefit does the feature offer the user? Does it contribute to higher revenue? Does it increase productivity? Only then is it developed further.

At the moment, the greatest added value often still lies in consolidating information from structured and unstructured data sources and making it accessible via natural language. The next step, however, is to translate these insights directly into more efficient business processes. That is precisely where the greatest productivity gains will be realized in the future.

If you could give CIOs just one or two pieces of advice for the transition from generative AI to AI agents, what would they be?

Kask: In my view, the biggest mistake would be to try to transform the entire company all at once or to attempt to perfectly prepare all the data right from the start.

Instead, you should consider what kind of agent can create significant added value, and then implement it. Of course, this agent needs access to consistent and context-rich enterprise data. That’s exactly what we’re working on at SAP with technologies like the knowledge graph, which maps the semantic relationships within enterprise data.

In addition, with data products and the SAP Business Data Cloud, we provide tools that make data from various sources usable for AI agents. Thanks to zero-copy and data fabric approaches, information from legacy systems, Snowflake, or ERP systems can be consolidated without first having to extensively replicate the data. For a procurement agent, this makes it possible to provide exactly the relevant data for the specific use case.

The key point is this: Companies do not have to wait until they have fully migrated to the cloud or consolidated their entire data landscape. With the technologies available today, data can already be made usable for specific AI agents, managed in a controlled manner, and used to quickly generate initial business value. On the other hand, those who wait for the perfect starting point run the risk of falling behind.

This article is adapted from one first published by Computerwoche.

SAP

Unternehmen stehen derzeit vor zwei gleichzeitigen Herausforderungen: Die Risiken rund um KI entwickeln sich schneller als die Governance, während sich der geschäftliche Nutzen oft nur schwer messen lässt.

Das ist eines der Kernergebnisse von „The Value of AI“, einer Studie, die SAP in Zusammenarbeit mit Oxford Economics in diesem Jahr zum zweiten Mal erstellt hat. Für die Studie wurden weltweit 2.600 Führungskräfte aus 13 Ländern befragt, darunter 200 Entscheiderinnen und Entscheider aus Deutschland.

Hohe Erwartungen, geringe Vorbereitung

Im Durchschnitt wollen deutsche Unternehmen in diesem Jahr rund 35 Millionen Euro in KI investieren und rechnen mit einem ROI von 24 Prozent (Vorjahr: 17 Prozent). Besonders hoch sind die Erwartungen an KI-Agenten: Ihr wirtschaftlicher Nutzen soll innerhalb von zwei Jahren auf rund 18 Millionen Euro steigen. Und: 90 Prozent der befragten Unternehmen schreiben Agentic AI das Potenzial zu, ihre Organisation grundlegend zu verändern. Andererseits fühlen sich nur vier Prozent der Unternehmen vollständig auf den Einsatz von KI-Agenten vorbereitet.

Insbesondere beim Thema Governance bestehen Lücken:

• Nur elf Prozent der Befragten halten die Steuerungsfähigkeiten in ihrem Unternehmen für ausreichend,
• mehr als 50 Prozent verfügen nicht über Prozesse zur menschlichen Kontrolle von KI-Agenten, und
• lediglich 30 Prozent haben Berechtigungs- oder Agentenverzeichnisse eingerichtet.

Gleichzeitig befürchten oder akzeptieren viele Unternehmen, KI-Agenten schneller einzuführen, als sie diese kontrollieren können. Weitere Bremsklötze sind Schwächen in der Organisation des KI-Einsatzes, mangelhafte Datenqualität, unzureichende Qualifizierung der Beschäftigten sowie die weit verbreitete Nutzung von Schatten-KI.

Im CW-Gespräch nimmt Sean Kask, Chief AI Strategy Officer bei SAP, zu den wichtigsten Ergebnissen der Studie Stellung.

“Die Governance-Frage ist aktuell die größere Herausforderung”

Herr Kask, im Vorwort der Studie schreiben Sie, dass Unternehmen derzeit vor zwei Herausforderungen gleichzeitig stehen: Die Risiken rund um KI entwickeln sich schneller als die Governance, während sich der geschäftliche Nutzen oft nur schwer messen lässt. Was bereitet Unternehmen die größeren Probleme?

Sean Kask: Den geschäftlichen Nutzen von IT-Investitionen zu messen, war nie einfach. Das gilt auch für KI. Deshalb halte ich die Governance-Frage aktuell für die größere Herausforderung. Klassische Governance-Prinzipien und bewährte Verfahren für sichere Softwareentwicklung bleiben zwar auch im Zeitalter großer Sprachmodelle und agentischer KI wichtig. Gleichzeitig entstehen aber völlig neue Risiken.

Sobald Unternehmen KI breit ausrollen, entdecken sie beispielsweise plötzlich Hunderte oder sogar Tausende sogenannter „Shadow Agents“, die Mitarbeitende ohne zentrale Kontrolle einsetzen. Oder sie stellen fest, dass ein erheblicher Teil der Belegschaft Inhalte in private ChatGPT-Konten kopiert. Solche Risiken werden häufig erst sichtbar, wenn KI bereits produktiv genutzt wird.

Deutsche Unternehmen investieren laut Ihrer Studie mit durchschnittlich fast 40 Millionen Dollar mehr in KI als Unternehmen in allen anderen untersuchten Ländern. Woran liegt das?

Kask: Mich hat weniger die Höhe der Investitionen überrascht als vielmehr die Tatsache, dass sich Investitionsniveau und erzielter Return on Investment über die verschiedenen Länder hinweg insgesamt sehr ähnlich entwickelt haben. Warum Deutschland mehr investiert, lässt sich nicht eindeutig beantworten. Teilweise dürfte es schlicht daran liegen, dass die Kosten hier höher sind als beispielsweise in Indien.

Wir sehen aber auch eine hohe KI-Adoption bei deutschen Unternehmen. SAP verfügt über ein Dashboard, mit dem wir nachvollziehen können, wie unsere Kunden KI-Funktionen einsetzen. Deutschland gehört dabei zu den Ländern mit einer besonders hohen Nutzung. Hinzu kommen die starke industrielle Basis und die politischen Impulse aus Europa, die den Einsatz von KI vorantreiben. Entsprechend investieren Unternehmen hier gezielt in den Aufbau entsprechender Kompetenzen.

Laut Studie sind 47 Prozent der deutschen Unternehmen mit dem Return on Investment ihrer KI-Investitionen zufrieden. Gleichzeitig sagen 77 Prozent, dass sie das Potenzial von KI noch längst nicht ausschöpfen. Ist das nicht ein Widerspruch?

Kask: Nein, dieses Muster sehen wir weltweit. Unternehmen investieren zunächst in einige wenige KI-Anwendungsfälle und stellen fest: Das funktioniert, wir erzielen einen Mehrwert. Entsprechend sind sie mit ihrer Investition zufrieden.

Doch genau dadurch erkennen sie auch weitere Einsatzszenarien. Sie beschäftigen sich mit KI-Agenten und möchten diese ebenfalls nutzen. Genau an diesem Punkt stoßen viele jedoch auf neue Herausforderungen bei der Einführung und Skalierung.

Die Studie zeigt deshalb vor allem eine Lernkurve: Je mehr Erfahrungen Unternehmen mit KI sammeln, desto größer wird ihr Bewusstsein für das bislang ungenutzte Potenzial.

Der Studie zufolge verfügen nur 35 Prozent der deutschen Unternehmen über KPIs auf Vorstandsebene, die direkt an die Einführung von KI gekoppelt sind. Welche Kennzahlen sollten Aufsichtsgremien und CEOs aus Ihrer Sicht unbedingt messen?

Kask: Ein zentraler Indikator ist für uns das Enablement der Mitarbeitenden. Wie viele Beschäftigte haben bereits Schulungs- oder Upskilling-Programme rund um KI erfolgreich absolviert? Ohne entsprechende Kompetenzen bleibt auch die KI-Adoption hinter ihren Möglichkeiten zurück.

Ebenso wichtig ist Transparenz. Unternehmen sollten wissen, welche KI-Agenten in ihrer Landschaft tatsächlich im Einsatz sind. SAP bietet hierfür den SAP AI Agent Hub an, der der Agenten aus SAP- und Drittanbieter-Umgebungen automatisch entdeckt und inventarisiert. Kunden konnten so bereits Tausende Agenten identifiziert, was den Bedarf an zentraler Governance und Transparenz deutlich macht.

Außerdem sollten Unternehmen einen vollständigen Überblick über alle KI-Anwendungsfälle haben. Für jeden Use Case sollte ein belastbarer Business Case vorliegen. Häufig erleben wir zwei Extreme: Entweder steht der Vorstand unter Druck, möglichst schnell KI einzuführen und stellt dafür pauschal Budget bereit. Oder das Management hält sich zunächst zurück. Dann entstehen überall im Unternehmen eigenständig Pilotprojekte, einzelne Fachbereiche beschaffen eigene Werkzeuge und schließen eigene Verträge ab.

Bei SAP verfolgen wir deshalb einen klar strukturierten Auswahlprozess. Jede Idee durchläuft zunächst eine Bewertung des zu erwarteten Geschäftswerts. Danach prüfen wir die technische Machbarkeit, die Datenverfügbarkeit sowie ethische und Governance-Aspekte. Aus Sicht des Managements ist entscheidend, jederzeit Transparenz über alle laufenden KI-Projekte zu haben und diese konsequent nach ihrem Geschäftswert zu priorisieren.

“Auch Agenten brauchen einen ‘Hire-to-Retire’-Lebenszyklus”

Auch mit der Einführung von Dutzenden oder sogar Hunderten KI-Agenten wird die Governance immer komplexer. Welche Funktionen benötigen Enterprise-Plattformen, um KI-Agenten sicher und kontrolliert im großen Maßstab zu verwalten?

Kask: Wir versuchen bewusst, KI nicht zu stark zu vermenschlichen. Dennoch hilft die Analogie: Für Agenten braucht es einen vollständigen „Hire-to-Retire“-Lebenszyklus. Das beginnt mit der Erkennung und Registrierung eines Agenten. Anschließend wird er in die Unternehmensumgebung integriert, erhält die notwendigen Berechtigungen und Zugriff auf die Datenquellen, die er für seine Aufgaben benötigt.

Ebenso wichtig ist die Observability. Unternehmen müssen jederzeit nachvollziehen können, was ein Agent im System tatsächlich macht. Darüber hinaus sollten sie Leistungskennzahlen erfassen: Erreicht der Agent die gewünschten Ergebnisse? Wie effizient arbeitet er? Wie viele Tokens verbraucht er? Wie viele Verarbeitungsschritte benötigt er für eine Aufgabe?

Letztlich geht es um mehrere Bausteine: ein vollständiges Inventar aller Agenten, geeignete Governance-, Risk- und Compliance-Mechanismen (GRC), Transparenz über das Verhalten der Agenten sowie eine kontinuierliche Überwachung. Nur so lässt sich sicherstellen, dass KI-Agenten dauerhaft innerhalb der definierten Leitplanken arbeiten und den gewünschten Geschäftswert liefern.

Welche Geschäftsprozesse werden Unternehmen Ihrer Einschätzung nach in den nächsten zwei bis drei Jahren tatsächlich vollständig an KI-Agenten delegieren?

Kask: Derzeit funktionieren solche Agenten vor allem in klar abgegrenzten Anwendungsfällen besonders gut. SAP wird mehr als 50 (aktuell 34) spezialisierte KI-Agenten veröffentlichen.

Ein Beispiel ist die periodische Rechnungslegung. Dort müssen Buchungen anhand zahlreicher Regeln erfolgen, die in Dokumenten, E-Mails oder früheren Transaktionen hinterlegt sind. Der Agent analysiert diese verschiedenen Informationsquellen, leitet daraus eine Empfehlung ab und schlägt dem Sachbearbeiter die passende Buchung vor.

Nach dem, was wir aus Kundenprojekten hören, verbringen Mitarbeitende mittelgroßer Unternehmen heute oft rund zwölf Stunden pro Monat mit diesen Aufgaben. Mithilfe eines KI-Agenten lässt sich dieser Aufwand auf zwei bis drei Stunden reduzieren.

Ein weiteres Einsatzfeld ist die Produktionsplanung. Ändern sich Liefertermine oder gehen kurzfristig neue Aufträge ein, muss der gesamte Produktionsplan angepasst werden. Genau solche komplexen Optimierungsaufgaben eignen sich hervorragend für KI-Agenten.

Grundsätzlich gibt es kaum Grenzen dafür, in welchen eng umrissenen Geschäftsprozessen Agenten eingesetzt werden können. Vollständig autonom werden sie allerdings zunächst nicht agieren.

“Vertrauen in KI beginnt mit einer stabilen Basis”

Viele Unternehmen tun sich noch schwer, KI-Agenten zu vertrauen. Schließlich arbeiten große Sprachmodelle probabilistisch und können halluzinieren. Gerade bei Finanzprozessen ist das problematisch. Wie schaffen Sie Vertrauen?

Kask: Vertrauen beginnt mit einer stabilen Basis. Die ERP-Systeme bleiben das verlässliche „System of Record“. Sie arbeiten deterministisch, enthalten die Geschäftslogik und die relevanten Unternehmensdaten. KI-Agenten bauen auf dieser Basis auf – sie ersetzen sie nicht.

Ebenso wichtig ist das „Human in the Loop“-Prinzip. Mitarbeitende müssen nachvollziehen können, was der Agent macht, seine Ergebnisse überprüfen und bei Bedarf eingreifen. Deshalb spielt auch die Qualifizierung der Beschäftigten eine entscheidende Rolle. Sie müssen verstehen, wie generative KI funktioniert und wo ihre Grenzen liegen.

Natürlich können Sprachmodelle halluzinieren. Gleichzeitig darf man nicht vergessen, dass auch Menschen nicht fehlerfrei arbeiten. Entscheidend ist das Zusammenspiel von Mensch und KI. So lassen sich sowohl die Effizienz als auch die Qualität vieler Geschäftsprozesse verbessern.

Ein weiterer wichtiger Baustein ist Transparenz. Unsere globale KI-Ethikrichtlinie schreibt beispielsweise vor, dass Nutzer immer erkennen können müssen, wenn KI beteiligt ist. In Joule lässt sich nachvollziehen, welche Datenquellen der Agent genutzt hat und welche Schritte er bei seiner Entscheidung durchlaufen hat. Diese Nachvollziehbarkeit ist eine wesentliche Voraussetzung für Vertrauen.

Was unterscheidet einen SAP-Agenten von einem allgemeinen KI-Agenten, der lediglich auf ein ERP-System zugreift?

Kask: Der entscheidende Unterschied liegt darin, dass Joule und die SAP-Agenten direkt im ERP-System verankert sind. Dort haben wir beispielsweise einen „Knowledge Graph“ aufgebaut, der die semantischen Beziehungen zwischen sämtlichen Tabellen, Geschäftsobjekten und Datenfeldern beschreibt.

Um die Größenordnung zu verdeutlichen: Der SAP S/4HANA Knowledge Graph basiert auf rund 452.000 ABAP-Tabellen, 7,3 Millionen Datenfelder und Tausenden analytischen Sichten. Die semantischen Beziehungen zwischen diesen Artefakten werden im Knowledge Graph modelliert und für KI-Anwendungen nutzbar gemacht.

Wenn ein Anwender beispielsweise sämtliche offenen Bestellungen einsehen möchte, muss der Agent nicht erst mühsam nach den relevanten Informationen suchen. Er weiß sofort, welche Tabellen und Objekte relevant sind und kennt auch die Beziehungen zwischen einer Bestellung, einer Bestellanforderung, den zuständigen Genehmigern und weiteren Geschäftsobjekten. Dadurch arbeitet der Agent nicht nur deutlich präziser, sondern benötigt auch erheblich weniger Tokens, weil er den Suchraum stark eingrenzen kann.

Versucht man stattdessen, KI lediglich auf ein bestehendes System aufzusetzen oder Daten aus einem relationalen ERP-System herauszulösen, gehen viele dieser Beziehungen verloren. Man zerstört gewissermaßen den semantischen Kontext, der für präzise Antworten entscheidend ist.

Deshalb betrachten wir das ERP-System als einen enormen strategischen Vorteil. Es ist seit Jahrzehnten das System of Record und enthält rund 50 Jahre kodiertes Geschäfts- und Prozesswissen. Dieses Wissen bildet die Grundlage für das, was wir als „Autonomous Enterprise“ bezeichnen. Die Agenten bauen auf diesem Wissen auf und entwickeln es weiter.

Künftig werden SAP-Agenten zudem über Standards wie Agent-to-Agent (A2A) bidirektional mit Agenten anderer Anbieter kommunizieren.

Laut Ihrer Studie schafft KI derzeit den größten Mehrwert bei der Entscheidungsfindung, der Kundeninteraktion und der Gewinnung neuer Erkenntnisse – weniger bei klassischen Produktivitätssteigerungen. Wird sich dadurch die Art verändern, wie Unternehmen KI-Investitionen künftig begründen?

Kask: Die Produktivität wurde in unserer Studie lediglich etwas niedriger bewertet als beispielsweise die Gewinnung neuer Erkenntnisse. Langfristig bleibt Produktivität aber das eigentliche Ziel. Gerade Europa leidet seit Jahren unter einem vergleichsweise schwachen Produktivitätswachstum.

Bei SAP bewerten wir deshalb jede neue KI-Funktion zunächst anhand ihres konkreten Geschäftsnutzens. Für alle Agenten und KI-Funktionen, die wir in unserem „AI Feature Catalog“ erfassen, erstellen wir zunächst eine Wertanalyse. Wir fragen: Welchen Nutzen hat die Funktion für den Anwender? Trägt sie zu mehr Umsatz bei? Steigert sie die Produktivität? Erst dann wird sie weiterentwickelt.

Im Moment liegt der größte Mehrwert häufig noch darin, Informationen aus strukturierten und unstrukturierten Datenquellen zusammenzuführen und per natürlicher Sprache zugänglich zu machen. Der nächste Schritt besteht aber darin, diese Erkenntnisse direkt in effizientere Geschäftsprozesse zu überführen. Genau dort werden sich die größten Produktivitätsgewinne künftig realisieren.

“Der größte Fehler wäre, gleich das gesamte Unternehmen zu transformieren”

Wenn Sie CIOs nur einen oder zwei Ratschläge für den Übergang von generativer KI zu KI-Agenten geben könnten – welche wären das?

Kask: Der größte Fehler wäre aus meiner Sicht, gleich das gesamte Unternehmen transformieren oder zunächst sämtliche Daten perfekt aufbereiten zu wollen.

Stattdessen sollte man überlegen, welche Art von Agent einen hohen Mehrwert schaffen kann – und diesen dann umsetzen. Natürlich braucht dieser Agent Zugriff auf konsistente und kontextreiche Unternehmensdaten. Genau daran arbeiten wir bei SAP mit Technologien wie dem „Knowledge Graph“, der die semantischen Zusammenhänge der Unternehmensdaten abbildet.

Darüber hinaus stellen wir mit „Data Products“ und der „SAP Business Data Cloud“ Werkzeuge bereit, die Daten aus unterschiedlichen Quellen für KI-Agenten nutzbar machen. Dank Zero-Copy- und Data-Fabric-Ansätzen können Informationen aus Altsystemen, Snowflake oder ERP-Systemen zusammengeführt werden, ohne dass Daten dafür zunächst umfangreich repliziert werden müssen. Für einen Einkaufsagenten lassen sich so genau die relevanten Daten für den jeweiligen Anwendungsfall bereitstellen.

Der entscheidende Punkt ist: Unternehmen müssen nicht warten, bis sie vollständig in die Cloud migriert oder ihre gesamte Datenlandschaft konsolidiert haben. Mit den heute verfügbaren Technologien lassen sich Daten bereits jetzt für konkrete KI-Agenten nutzbar machen, kontrolliert verwalten und auf dieser Basis schnell erste geschäftliche Mehrwerte erzielen. Wer dagegen erst auf die perfekte Ausgangslage wartet, läuft Gefahr, den Anschluss zu verlieren.

Microsoft’s big lead over AI competitors like Google and others has vanished, and the company is now playing catch up. As a result, Microsoft’s stock has tanked in the last year — down roughly 23% compared to a year ago, due mainly to its massive AI spending and an inability to monetize Copilot. 

The company clearly needs to do something. And last week it did, though not what you might expect. It laid off 4,800 people, a little more than 2% of its worldwide workforce, with its Xbox division hit hardest. And it’s not reducing its massive spending on AI data centers or other AI-related costs.

The New York Times explained the cuts this way: “It is Microsoft’s latest employee culling as it plows tens of billions of dollars into the infrastructure for building artificial intelligence.”

Was cutting back on gaming (while still going all-in on AI) the right move for Microsoft? To answer that, let’s take a look at the details of the company’s July layoffs.

A year of layoffs

The recent cuts come in the wake of larger Microsoft workforce reductions over the last year or so. In May 2025, the company laid off 6,000 employees, about 3% of its workforce. Then a few months later, it laid off 9,000 more, about 4% of its workers. In both rounds of cuts, the company’s gaming division was hit — though it wasn’t the primary target.

This year, in April and May, the company rolled out its first voluntary retirement program for its US employees. Approximately 3,000 people took the money and ran.

Then came last week, when Microsoft primarily targeted gaming. When the cuts take full effect over the next year, 2,850 gaming employees will be let go. In addition, Microsoft is cutting loose several of its gaming studio brands, which will become independent companies or be sold to buyers.

The layoffs hit the two remaining gaming studios, Activision Blizzard, which makes the big-selling games Call of Duty and Candy Crush, and ZeniMax Media, which publishes series including Fallout and The Elder Scrolls. Three years ago, in 2023, Microsoft bought Activision Blizzard for $69 billion. That followed its purchase of ZeniMax Media in 2020 for $7.5 billion. Both seemed like sizable acquisitions at the time. 

Compared to Microsoft’s AI spending now, they’re chump change.

Follow the money

A memo sent to employees about the July layoffs by Amy Coleman, Microsoft executive vice president and chief people officer, made clear the layoffs were more about AI than they were about gaming. 

Of the cuts, she wrote: “The “why” is this: our business is changing because the world around it is changing. The way technology is built, deployed, and used is transforming faster than at any point in my time here. Our customers’ needs are shifting, the business models that serve them are shifting, and that means the work itself — what we do, where we focus, and how we’re organized — has to transform, too.

“Our customers are navigating this same shift, and they’re counting on us to help them through it.”

That last sentence is an oblique reference to the early July launch of the Microsoft Frontier Company, which will embed 6,000 engineers inside customers’ businesses to help them more effectively deploy AI. The cost: $2.5 billion.

That sounds like a substantial amount of money. But it’s only a drop in the bucket of how much money the company plans to spend on AI. In April, Microsoft told investors it would spend $190 billion on data centers and other AI infrastructure this year, a 60% increase over what it spent last year. At the same time, Microsoft said it would shrink its workforce.

Its latest layoffs are clear-cut evidence of that. It’s also evidence that the company recognizes how badly Xbox has performed, and that it needed to do something about it. 

In early June, Microsoft sent a memo to everyone in its Xbox division entitled “Next 100 Days: XBOX Reset.” The memo laid out the problems with its ailing game business and pulled no punches. It noted that beyond the $69 billion the company spent three years ago to buy Activision, “Over the past five years, we have spent over $20 billion on ongoing investments in our content, platform, and hardware subsidy, but our annual revenue has declined nearly half a billion during that time. Going forward, this cannot continue.” 

The layoffs and spinoffs were the first steps. They won’t be the last.

There’s no doubt this is just the beginning of Microsoft’s disinvestment in gaming. The issue isn’t just that the company’s investments haven’t paid off. It’s that Microsoft’s AI ambitions are so large and expensive that it can no longer afford to seriously fund gaming.

Ultimately, it was the right thing to do, at least from a business perspective. The future is AI. It’s not in gaming.

So, for the foreseeable future at Microsoft, when it comes to AI — the sky’s the limit. But when it comes to gaming, things look much less rosy.

Over its more than 15 years in the wild, Google’s Go programming language has evolved from a curiosity for alpha geeks to the battle-tested programming language behind some of the world’s most important cloud-native software projects.

If you’ve ever wondered why Go is the language of choice for projects like Docker and Kubernetes, this article is for you. We’ll discuss Go’s defining characteristics and how it differs from other programming languages. You will also learn what kinds of projects Go is best suited for, including the state of Go development for AI-powered tools. We’ll conclude with an overview of Go’s feature set, some limitations of the language, and where it may be going from here.

Also see: Golang tutorial: Get started with the Go language.

Go is small and simple

Go, or Golang as it’s often called, was created by Google employees—chiefly longtime Unix guru and Google distinguished engineer Rob Pike—but it’s not strictly speaking a “Google project.” Rather, Go is a community-developed open source project, spearheaded by leadership with strong opinions about how Go should be used and the direction the language should take.

Go is meant to be easy to learn and straightforward to use, with syntax that is simple to read and understand. Go does not have a large feature set, especially when compared to languages like C++. Go’s syntax is reminiscent of C, making it relatively easy for longtime C developers to learn. That said, many features of Go, especially its concurrency and functional programming features, harken back to languages like Erlang.

As a C-like language for building and maintaining cross-platform enterprise applications of all sorts, Go has much in common with Java. And as a means for enabling rapid development of code that might run anywhere, you could draw a parallel between Go and Python, though the differences outweigh the similarities.

The Go documentation describes Go as “a fast, statically typed, compiled language that feels like a dynamically typed, interpreted language.” Even a large Go program will compile in a matter of seconds. Plus, Go avoids much of the overhead of C-style include files and libraries.

Advantages of the Go language

Go is a versatile, convenient, fast, portable, interoperable, and widely supported modern language. These characteristics have helped to make it a top choice for large-scale development projects. Let’s look more closely at each of these positive qualities of Go.

Go is versatile and convenient

Go has been compared to interpreted languages like Python in its ability to satisfy many common programming needs. Some of this functionality is built into the language itself, such as goroutines for concurrency and thread-like behavior, while additional capabilities are available in Go standard library packages, like the http package. Like Python, Go provides automatic memory management capabilities including garbage collection.

Unlike interpreted languages, however, Go code compiles to a fast-running native binary. And unlike C or C++, Go compiles extremely fast—fast enough to make working with Go feel more like working with an interpreted language than a compiled one. Further, the Go build system is less complex than those of other compiled languages. It takes few steps and little bookkeeping to build and run a Go project.

Go is faster than many other languages

Go binaries run more slowly than their C counterparts, but the difference in speed is negligible for most applications. Go performance is as good as C for the vast majority of work, and generally much faster than other languages known for speed of development—including JavaScript, Python, and Ruby.

Go is portable and interoperable

Executables created with the Go toolchain can stand alone, with no default external dependencies. The Go toolchain is available for a wide variety of operating systems and hardware platforms, and can be used to compile binaries across platforms. What’s more, Go delivers all of the above without sacrificing access to the underlying system. Go programs can talk to external C libraries or make native system calls. In Docker, for instance, Go interacts with low-level Linux functions, cgroups, and namespaces to work container magic.

Go is widely supported

The Go toolchain is freely available as a Linux, macOS, or Windows binary, or as a Docker container. Go is included by default in many popular Linux distributions, such as Red Hat Enterprise Linux and Fedora, making it somewhat easier to deploy Go source to those platforms. Support for Go is also strong across many third-party development environments, from Microsoft’s Visual Studio Code to ActiveState’s Komodo IDE.

Also see: 8 reasons developers love Go—and 8 reasons they don’t.

Optimal use cases for the Go language

No language is suited to every job, but some languages are suited to more jobs than others. Go shines brightest in cloud-native development projects, distributed network services, and for developing utilities and stand-alone tools. Let’s consider the qualities that make Go especially well-suited to each of these project types.

Cloud-native development

Go’s concurrency and networking features, and its high degree of portability, make it well-suited for building cloud-native apps. In fact, Go was used to build several cornerstones of cloud-native computing including Docker, Kubernetes, and Istio.

Distributed network services

Network applications live and die by concurrency, and Go’s native concurrency features—goroutines and channels, mainly—are well suited for such work. Consequently, many Go projects are for networking, distributed functions, and cloud services. These include APIs, web servers, Kubernetes-ready frameworks for microservices, and much more.

Utilities and standalone tools

Go programs compile to binaries with minimal external dependencies. That makes them ideally suited to creating utilities and other tools, because they launch quickly and can be readily packaged up for redistribution. One example is an access server called Teleport, which can be deployed on servers quickly by compiling it from source or downloading a prebuilt binary.

Limitations of the Go language

Now let’s consider some of the limitations of Go. For one, it omits many language features developers may desire. It also packs everything into its binaries, so Go programs can be large. Furthermore, Go’s garbage collection mechanism delivers automatic memory management at the cost of absolute performance. The language also lacks a standard toolkit for building GUIs, and it is unsuited to systems programming.

Let’s look at each of these issues in detail.

Go omits many desirable language features

Go’s opinionated set of features draws both praise and criticism. Go is designed to err on the side of being small and easy to understand, with certain features deliberately omitted. The result is that some features that are commonplace in other languages simply aren’t available in Go. This is purposeful, but it’s still a drawback for some types of projects.

One thing Go omits that you will find in other languages is macros, commonly defined as the ability to generate program code at compile time. C, C++, and (the rising star) Rust all have macro systems. Go does not have macros, or at least not of the same variety as those languages. What Go does have is a tool command, go generate, which looks for magic comments in Go source and executes them. This can be used to generate Go source code, or even run other commands, but its main use is to programmatically generate code, usually as a precursor to the build process. (Technical blogger Eli Bendersky explains the ‘go generate’ command in detail.)

Another longstanding complaint with Go was, until recently, the lack of generic functions, which allow a function to accept many different types of variables. Go’s development team held out against adding generics to the language for many years because they wanted a syntax and set of behaviors that complemented the rest of Go. But as of Go 1.18, released in early 2022, the language includes a syntax for generics. Because go generate and its code-generation abilities emerged as one possible way to partially address the lack of generics, this functionality is no longer as commonly used in Go.

The fact is that Go adds major language features rarely, and only after much consideration. This works to preserve broad compatibility across versions, but it comes at the cost of slower innovation.

Also see: What you need to know about Go, Rust, and Zig.

Go’s binaries are large

Another potential downside to Go is the size of the generated binaries. Go binaries are statically compiled by default, meaning that everything needed at runtime is included in the binary image. This approach simplifies the build and deployment process, but at the cost of a simple “Hello, world!” weighing in at around 1.5MB on 64-bit Windows. The Go team has been working to reduce the size of those binaries with each successive release. It is also possible to shrink Go binaries with compression or by removing Go’s debug information. This last option may work better for standalone distributed apps than for cloud or network services, where having debug information is useful if a service fails in place.

Go’s garbage collection is resource hungry

Yet another touted feature of Go, automatic memory management, can be seen as a drawback, as garbage collection requires a certain amount of processing overhead. By design, Go doesn’t provide manual memory management, and garbage collection in Go has been criticized for not dealing well with the kinds of memory loads that appear in enterprise applications.

That said, each new version of Go seems to improve the memory management features. For example, Go 1.8 brought significantly shorter lag times for garbage collection, and Go 1.25 introduced a new, experimental garbage collector. While Go developers can use manual memory allocation in a C extension, or by way of a third-party manual memory management library, most prefer native solutions.

Go doesn’t have a standard GUI toolkit

Most Go applications are command-line tools or network services. That said, various projects are working to bring rich GUIs for Go applications. There are bindings for the GTK and GTK3 frameworks. Another project is intended to provide platform-native UIs across platforms, although it focuses on Go 1.24 forward only. But no clear winner or safe long-term bet has emerged in this space. Also, because Go is platform-independent by design, it is unlikely any project in this vein will become a part of the standard package set.

You shouldn’t use Go for systems programming

Finally, although Go can talk to native system functions, it was not designed for developing low-level system components such as kernels, device drivers, or embedded systems. After all, the Go runtime and the garbage collector for Go applications are dependent on the underlying operating system. (Developers interested in a cutting-edge language for that kind of work might look into using Rust.)

The future of the Go language

Go’s development is turning more toward the wants and needs of its developer base, with Go’s minders changing the language to better accommodate this audience rather than leading by stubborn example. A case in point is generics, which were finally added to the language after much deliberation about the best way to do so.

The 2024 Go Developer Survey found developers were overall satisfied with Go. Challenges that surfaced were generally due to the verbosity of error handling, missing or immature frameworks, and using Go’s type system—areas ripe for future development.

Like most languages, Go has gravitated to a core set of use cases over time, finding its niche in network services. In the future, Go is likely to continue expanding its hold there. Other use cases cited in the developer survey include creating APIs or RPC services (74% of respondents), followed by CLI applications (63%), web services (45%), libraries/frameworks (44%), automation (39%), and data processing (37%). While only 4% of respondents mentioned using Go to develop AI technologies, those who did reported that Go was a strong platform for running AI-powered workloads in production. For those wanting to develop ML/AI with Go, lack of tooling (23%) and the fact that Python is the default choice for such work (16%) topped the reasons why.

It remains to be seen how far Go’s speed and development simplicity will take it into other use cases, especially those dominated by other languages and their existing use cases. Rust covers safe and fast systems programming (a space Go is unlikely to enter); Python is still a common default for ML/AI, prototyping, automation, and glue code; and Java remains a stalwart for enterprise applications.

But Go’s future as a major programming language is already assured—certainly in the cloud, where the speed and simplicity of Go ease the development of scalable infrastructure that can be maintained over the long run.

Also see: Go language evolving for future hardware, AI workloads.

A portmanteau of “development” and “operations,” devops emerged as a way of bringing together two previously separate groups responsible for the building and deploying of software.

In the old world, developers (devs) typically wrote code before throwing it over to the system administrators (operations, or ops) to deploy and integrate that code. But as the industry shifted towards agile development and cloud-native computing, many organizations reoriented around modern, cloud-native practices in the pursuit of faster, better releases.

This required a new way to perform these key functions in a more streamlined, efficient, and cohesive way, one where the old frustrations of disconnected dev and ops functions would be eliminated. With two groups working together, developers can rapidly roll out small code enhancements via continuous integration and delivery rather than spending years on “big bang” product releases.

Devops was born at cloud-native companies like Facebook, Netflix, Spotify, and Amazon; but it’s become one of the defining technology industry trends of the past decade, primarily because it bridges so many of the changes that have shaped modern software development.

As agile development and cloud-native computing have become ubiquitous, devops has enabled the entire industry to speed up its software development cycles. Thus, devops has now thoroughly infiltrated the enterprise, especially in organizations that rely on software to run their business, such as banks, airlines, and retailers. And it’s spawned a host of other “ops” practices, some of which we’ll touch on here.[JF1] 

Devops practices

Devops requires a shift in mindset from both sides of the dev and ops divide. Development teams should focus on learning and adopting agile processes, standardizing platforms, and helping drive operational efficiencies. Operations teams must now focus on improving stability and velocity, while also reducing costs by working hand in hand with the developer team.

Broadly speaking, these teams need to all speak a common language and there needs to be a shared goal and understanding of each other’s key skills for devops to thrive.

More specifically, engineers Damon Edwards and John Willis created the CALMS model to bring together what are commonly understood to be the key principles of devops:

• Culture: One that embraces agile methodologies and is open to change, constant improvement, and accountability for the end-to-end quality of software.
• Automation: Automating away toil is a key goal for any devops team.
• Lean: Ensuring the smooth flow of software through key steps as quickly as possible.
• Measurement: You can’t improve what you don’t measure. Devops pushes for a culture of constant measurement and feedback that can be used to improve and pivot as required, on the fly.
• Sharing: Knowledge sharing across an organization is a key tenet of devops.

“Who could go back to the old way of trying to figure out how to get your laptop environment looking the same as the production environment? All these things make it so clear that there’s a better way to work. I think it’s very tough to turn back once you’ve done things like continuous integration, like continuous delivery. Once you’ve experienced it, it’s really tough to go back to the old way of doing things,” Kim told InfoWorld.

What is a devops engineer?

Naturally, the emergence of devops has spawned a whole new set of job titles, most prominent of which is the catch-all devops engineer.

Generally speaking, this role is the natural evolution of the system administrator — but in a world where developers and ops work in close tandem to deliver better software. This person should have a blend of programming and system administrator skills so that he or she can effectively bridge those two sides of the team.

That bridging of the two sides requires strong social skills more than technical. As Kim put it, “one of the most important skills, abilities, traits needed in these pioneering rebellions — using devops to overthrow the ancient powerful order, who are very happy to do things the way they have for 30 to 40 years — are the cross-functional skills to be able to reach across the table to their business counterparts and help solve problems.”

This person, or team of people, will also have to be a born optimizer, tasked with continually improving the speed and quality of software delivery from the team, be that through better practices, removing bottlenecks, or applying automation to smooth out software delivery.

The good news is that these skills are valuable to the enterprise. Salaries for this set of job titles have risen steadily over the years, with 95% of devops practitioners making more than $75,000 a year in salary in 2020 in the United States. In Europe and the UK, where salaries are lower across the board, 71% made more than $50,000 a year in 2020, up from 67% in 2019.

Key devops tools

While devops is at its heart a cultural shift, a set of tools has emerged to help organizations adopt devops practices.

This stack typically includes infrastructure as code, configuration management, collaboration, version control, continuous integration and delivery (CI/CD), deployment automation, testing, and monitoring tools.

Here are some of the tools/categories that are increasingly relevant in 2025, and what is changing:

• CI/CD and delivery automation: Traditional tools like Jenkins remain in many stacks, but newer orchestration tools and CLI-driven or GitOps-centric platforms are growing in importance (e.g. ArgoCD, Flux, Tekton). Also, platforms that integrate more tightly with monitoring, secrets management, drift detection, and policy enforcement are gaining traction.
• Security, compliance, and devsecops tooling: Security tools are increasingly integrated into devops pipelines. Expect to see more use of static analysis (SAST), dynamic testing (DAST), dependency and supply chain scanning (SCA), secret management, and policy as code. The push is toward embedding security earlier and bridging gaps between dev, security, and machine learning teams. (InfoWorld:)
• AI  and automation augmentation: AI-assisted tools are increasingly part of tooling stacks: auto-suggestions in CI/CD, anomaly detection, predictive scaling, intelligent test suite selection, and more. The hope is that these tools will reduce manual interventions and improve reliability. Tools that are “AI ready”—that is, they integrate well with AI or have mature built-in automation or assistance—increasingly stand out from the pack.

Devops challenges

Even as devops becomes more widely adopted, there remain real obstacles that can slow progress or limit impact. One major challenge is the persistent skills gap. The modern devops engineer (or team) is expected to master not just source control, CI/CD, and scripting, but also cloud architecture, infrastructure as code, security best practices, observability, and strong cross-team communication. In many organizations these capabilities are uneven: some teams excel, others lag behind. A 2024 survey showed that while 83% of developers report participating in devops activities, using multiple CI/CD tools was correlated with worse performance — a sign that complexity without deep expertise can backfire.

Toolchain fragmentation and complexity is a related issue. Devops toolchains have sprouted into a sometimes bewildering array of packages and techniques to master: version control, CI build/test, security scanning, artifact management, monitoring, observability, deployment, secret management, and more.

The more tools you have, the more difficult it becomes to integrate them cleanly, manage their versions, ensure compatibility, and avoid duplicated effort. Organizations often get stuck with “tool sprawl” — tools chosen by different teams, legacy systems, or overlapping functionalities — which introduce friction, maintenance burden, and sometimes vulnerabilities.

Finally, although devops has spread far and wide, there is still cultural resistance and alignment. Devops isn’t just about tools and processes; it’s about collaboration, shared responsibility, and continuous feedback. Teams rooted in traditional silos (dev vs ops, or security separate) may resist changes to roles and workflows. Leadership support, communication of shared goals, trust, and allowance for continuous learning are all necessary.

Many CIOs focus too much on tools or implementation first, rather than organizational culture and behaviors; but without addressing culture, even the best tools or processes may not yield the hoped-for velocity, quality, or reliability. Organizations that succeed here tend to have proactive strategies: dedicated training programs, mentorship, internal “guilds,” pairing junior and senior engineers, and making sure leadership supports ongoing learning rather than one-off bootcamps.

Why do devops?

Whoever you ask will tell you that devops is a major culture shift for organizations, so why go through that pain at all?

Devops aims to combine the formerly conflicting aims of developers and system administrators. Under its principles, all software development aims to meet business demands, add functionality, and improve the usability of applications while also ensuring those applications are stable, secure, and reliable. Done right, this improves the velocity and quality of your output, while also improving the lives of those working on these outcomes.

Does devops save money — or add cost?

Devops teams are recognizing that speed and agility are only part of success — unchecked cloud bills and waste undermine long-term sustainability. Waste in devops often comes in the form of “devops debt”— idle cloud capacity, dead code, or false-positive security alerts—which was called a “hidden tax on innovation” in recent Java-environment studies.

 Embedding finops practices can help fight these costs. Teams should shift left on cost: estimating costs when spinning up new environments, resizing instances, and scaling down unused resources before they become runaway expenses.

How to start with devops

There are lots of resources for help getting started with devops, including Kim’s own Devops Handbook, or you can enlist the help of external consultants. But you have to be methodical and focus on your people more than on the tools and technology you will eventually use if you want to ensure lasting buy-in across the business.

A proven route to achieving this is a “land and expand” strategy, where a small group starts by mapping key value streams and identifying a single product team or workload for trialing devops practices. If this team is successful in proving the value of the shift, you will likely start to get interest from other teams and from senior leadership.

If you are at the start of your devops journey, however, make sure you are prepared for the disruption a change like this can have on your organization, and keep your eye on the prize of building better, faster, stronger software.

More on devops:

• Devops debt: The hidden tax on innovation
• 10 big devops mistakes and how to avoid them
• Smarter devops: How to avoid deployment horrors

Nearly five decades after the launch of VisiCalc, AI is reshaping one of the world’s most familiar productivity tools — the humble spreadsheet.

While a lot of knowledge workers interact with spreadsheets on a regular basis, many lack the skills and confidence to access more advanced functions.

“For a very long time, spreadsheets forced you to learn spreadsheet syntax and spreadsheet ways of working,” said Eric Birnbaum, director of product management for Google Sheets. “But think about how many people need to use spreadsheets at work and don’t have the skill set to create the kinds of spreadsheets that can be really helpful for them.”

The addition of artificial intelligence can help handle that issue, he said, making the software more accessible to a wide range of office workers. “AI is unlocking the power of spreadsheets, taking on a lot of the difficult work that’s required to use them. That can be incredibly empowering for users,” said Birnbaum.

Google has steadily expanded generative AI (genAI) capabilities in Sheets since launching Duet AI — now Gemini — for Workspace in 2023.

Gemini in Sheets is available at no extra cost to Google Workspace subscribers, though a paid AI Expanded Access add-on – costing $30 per user each month – is required to remove certain usage limits.

Features that have rolled out in recent months include the ability for a Gemini agent in Sheets to carry out multi-step actions such as formatting, analysis and data entry, and, more recently, the ability to create entire spreadsheets from a single prompt. A Fill with Gemini feature builds on the existing AI function, enabling users to automatically populate selected cells by detecting intent from information within a spreadsheet as well as from the web.

Another feature, Sheets Canvas (currently available in alpha), lets users generate interactive apps that update in real-time based on changes to spreadsheet data. This could be a kanban board for a sales pipeline, for instance, or an analytics dashboard that uses Sheets as its back-end data source. 

Google claims users already see a range of benefits from Gemini in Sheets. According to an August 2025 survey of 200 Sheets users conducted by the company, the majority of knowledge workers (89%) said AI features in Sheets save them at least an hour a week, and 88% believe AI features have made them more confident in their data analysis skills. 

The most popular AI use cases include creating spreadsheets and charts, analyzing data, and fixing broken formulas. How widely these features are actually used is unclear; Google declined to provide weekly usage statistics for Gemini in Sheets.

As the company embeds Gemini deeper into Sheets, questions remain about just how much businesses can trust AI tools to handle important business data, as well as what increased automation means for those who spend much of their day wrangling data. 

Computerworld recently talked with Birnbaum about the potential benefits and challenges of the latest evolution of spreadsheet software. This interview has been condensed and edited for clarity.

As businesses become more focused on seeing value from AI investments, what measurable benefits are customers seeing from Gemini in Sheets – for example, time saved or other forms of return on investment? “Spreadsheets remain one of the universal languages for businesses, and we don’t anticipate that’s going to change anytime soon. But by bringing AI into the product where people work, we think it can significantly reduce the technical tax of data: the time spent understanding it, sourcing it, analyzing it, visualizing it.

“Historically, data professionals spent — let’s estimate it at 80% of their time — on the mechanical groundwork, data cleaning, crafting formulas, formatting, troubleshooting, and maybe only 20% of their time on actual strategic decision-making.

“We think that by offloading the manual, time-consuming, error-prone data work to Gemini, we can flip that ratio a bit, so humans can focus on the things that really matter: the high-value questions to ask and the judgment calls and decisions that get made from them. We’re starting to see evidence of that in our own user base and customer base.

“The second point to make is that AI can democratize data analysis to some extent, and make it available to many more users. For so many years, the spreadsheet was a gatekeeper; if you couldn’t speak the rigid language of spreadsheet formulas, you couldn’t extract the value from data. But by introducing these natural language interfaces, AI is separating the analytical capability from the technical literacy. 

“If you can ask the right questions, or describe what you want in plain language, Gemini and Sheets can help you achieve your goals in a spreadsheet, even if you have minimal spreadsheet skills yourself.

“What we’ve seen so far from users and customers is that it’s incredibly empowering for people who might have been scared off by data analysis or dreaded opening spreadsheets in the past. You don’t need to go and wait for a data analyst to help you; you can go and do this work yourself in a spreadsheet.”

The flip side is, how confident can businesses be if more junior employees can take on higher-level analysis tasks by relying on AI? Given the propensity for AI models to hallucinate, to what degree can businesses trust that these tools won’t introduce errors into important business data? “It’s something we spent a ton of time thinking about. We’ve gone to great lengths to build these AI tools to collaborate with you, to show their work, explain what they did, and make sure that you can take over where they leave off.

“Our Sheets agent, for example, lays out a really explicit, transparent plan for you to review and approve before any data manipulation happens. It’s designed to do that in plain natural language in a way that the average user could understand, and then it gives you back that final summary, so you know exactly what it did and where it did it.

“The other thing is that the model is great at explaining things. If you inherit a spreadsheet that has some complex formula that you don’t understand, for example, the model does an amazing job of explaining how it works and what it’s doing.

“In many ways AI is not only making these features more accessible, but helping users feel more confident in the output. Human error is an inherent risk in manual data management, with or without AI. One misplaced comma or broken cell reference can completely corrupt an entire financial model, and it can be completely undetected. 

“Our approach with AI in Sheets is to create this deliberate verification loop. You now have another spreadsheet expert working along with you, reducing the likelihood of these mistakes.”

Even if humans produce errors too, does it ultimately come down to accountability when AI is involved? “Our point of view here is that AI should be partnering with the knowledge worker who’s doing the work here. And everything that we’ve built is designed to be that partner. You might be able to offload tasks to the model, but we’re citing sources, we’re providing plans and explanations. We’re ultimately relying on the user to do that final verification.

“We spend a humongous amount of time focused on quality. We know that for AI to be useful in spreadsheets, it has to be reliable. When we launched Sheets Gemini Agent, for example, we were really proud that we set a state-of-the-art benchmark on the full SpreadsheetBench data set, which at the time exceeded competitors and near-human expert ability. 

“But we know quality is never ‘good enough’ or done. We’re constantly working to improve quality for our users and customers, and for the use cases where they’re relying on AI most.”

What potential do you see for more agentic functionality in Gemini Sheets — for example, bringing in data from other sources, creating recurring reports, or taking more actions independently? “We’re listening closely to customers and users and building what they’re telling us they need. The agent is already capable of doing very complex multistep workflows, and we see users discover that the agent is extremely capable of doing end-to-end spreadsheet tasks. In terms of connectors, in an alpha we have connections available to HubSpot, Salesforce, and Mailchimp. We hope to expand that over time.

“There’s no path to have AI replacing analysts. I think AI is giving analysts more time back to actually do the more valuable parts of their job. Analysts that I work with are way more productive and impactful than they ever were before, because they can push that uninteresting spreadsheet grunt work off to the model, freeing up time for more interesting and impactful work.

“A great example: the visualizations I’m getting back from analysts nowadays are canvases instead of static charts that I can explore myself. It’s way more informative and useful than what I was accustomed to before.”

Looking ahead, do you expect a larger share of spreadsheet work to be carried out by agents, with humans setting goals and reviewing results? What will be the biggest change in how people use spreadsheets with AI? “The tasks are likely to stay similar and the use cases for spreadsheets are likely to continue to be relevant. The biggest change will be the ability to push the uninteresting spreadsheet grunt work off to a model and free up time for the user to do things that are more impactful, more interesting, more meaningful to the business.

‘Spreadsheets have historically been these like static containers where data goes to rest. I think AI can turn spreadsheets into these dynamic, localized software applications. And I do actually think this sort of changes the game for how people might use spreadsheets moving forward. 

“It’s not just a passive grid full of numbers; spreadsheets are evolving to become these live, long-lived, collaborative applications. Employees can build these on the fly, like a basic CRM or supply chain dashboard in seconds. This is an area of investment for us moving forward, and we’re really excited to see how this evolves.”

AI assistants and agents, such as ChatGPT or Claude, might be able to analyze spreadsheet files and business data without users working inside a spreadsheet application. What do you think will keep Sheets central to the workflow, rather than simply making it one part of a wider AI-driven process?“We’re in constant touch with customers and users; it’s clear work is still happening in spreadsheets. I think spreadsheets remain incredibly popular tools. If we can bring the AI capabilities users need directly into the product where they already are, we’ll transform the way they work.  

“I think we can be the front-end for some of this great AI innovation and the products that users are accustomed to today. Everything we build is guided by user feedback: users are telling us right now they want AI to help them do their everyday or more complex tasks, and they’re starting in Sheets today.”

Most AI agents today are optimised for responsiveness—faster first responses, shorter wait times, higher service rates. And on those metrics, they’re delivering.

It’s no surprise then that 90% of business leaders believe their customers are satisfied with conversational AI experiences. Yet only 59% of consumers agree, according to Twilio’s latest report on conversational AI.

What could explain this 31-point gap? The data is unambiguous. 54% of consumers say AI agents rarely have context about them as a customer. 78% say the ability to escalate to a human is important, yet few get the chance to do so. And only 15% report experiencing a seamless handoff from an AI agent to a human one.

All this to say that the real measure of an AI agent isn’t how fast it answers, but whether it solves the problem. In fact, 72% of consumers would choose an AI over a human if it could resolve their issue more quickly.

Speed without resolution will only result in frustration.

Why most AI agents aren’t great at resolution

It’s not hard to see why most AI agents fail to resolve customer issues: they can’t take action on behalf of the customer, they can’t escalate conversations when they reach their limits, and they lack the real-time context needed to personalise the interaction.

Think about what a typical AI service interaction looks like. A customer calls with a billing question. The AI agent reads their intent accurately enough. But it can’t pull up the customer’s account in real time, process a credit, or route to a human specialist who knows the context of the conversation. So the customer repeats themselves. Or simply gives up.

The root cause is structural. In most stacks, the channel is the system of record, not the conversation. Voice, SMS, chat, and WhatsApp each run as separate sessions, so the moment a customer switches channels or escalates to a human, the interaction resets. Engineering teams paper over this by stuffing full transcripts into AI prompts to fake continuity. This inflates token costs, slows responses, and still truncates older context once the window fills up.

This is the gap between a chatbot and an agent. A chatbot responds. An agent resolves. It’s no wonder that the 59% of organisations planning to fully replace their current conversational AI solution within the year understand this distinction. Their early investments were simply optimised for the wrong outcome.

What resolution actually requires

A smarter agent only gets you so far. Businesses need four capabilities to close the resolution gap:

1. Agency: Agents must be able to take real action, such as scheduling, processing, and updating records, within the conversation itself.
2. Always-on monitoring: Agents should continuously evaluate the quality of interactions and catch failures before they become customer complaints
3. Intelligent routing: Agents should escalate issues with full context so that humans can pick up where they left off.
4. Real-time contextual data: Agents should have the same customer context as a well-prepared human agent. This includes purchase history, past interactions, account status, and preferences.

None of these are speculative. They’re available today, and the companies deploying them are already seeing the difference.

Case in point: OhMD, a healthcare communications platform for physician practices and medical groups. The company built Nia, an AI-powered voice assistant that uses Twilio’s Conversation Relay to handle routine patient calls (scheduling, prescription refills, FAQs). Complex calls are routed to staff with full context, which saves patients from repeating themselves.

The results were immediate. OhMD saw a 60% improvement in self-service first-call resolution, with appointment scheduling flows completing in as little as one minute. By 2026, Nia is projected to handle more than 55 million patient interactions annually.

As Twilio CEO Khozema Shipchandler noted, “What we’re starting to see with OhMD is that they’ve got a 60% lift in self-serve capability to actually resolve calls. They’re able to drive the conclusion of these calls in less than a minute in many instances.”

Patients aren’t impressed because the phone rang once. They’re impressed because the call ended with their problem solved.

Think resolution, not speed

For every customer service leader evaluating their AI agent roadmap, the implication is straightforward. Stop measuring success by response time alone. Start measuring it by resolution rate—specifically, self-service resolution rate.

That means investing not in faster replies, but in smarter infrastructure: agents that act, routing that adapts, data that flows in real time, and monitoring that holds the system accountable.

The future of customer service isn’t about answering faster. It’s about answering fully.         

To learn more about Twilio, visit here.

Intro

• Consumers across APJ are getting more impatient. They expect brands to resolve their issues in just 24 minutes on average.
• If the brand is unsuccessful, 34% say they’ll switch channels to find a faster path to resolution, and 30% say they’ll abandon the effort altogether.
• Most leaders reading this insight would respond by improving agent response times, but speed isn’t the problem. The crux is that consumers want a solution, not to wait less.
• Unfortunately, the AI that most brands have deployed are chatbots who try to handle everything (and then fail).
• What we’re seeing is the opposite. The most productive agents aren’t generalists, but specialists who excel at one thing and know when to hand off when they can’t handle something.
• This intelligent orchestration is a superpower.

The cost of sending customers in circles

• 90% of business leaders believe their customers are satisfied with conversational AI, but only 59% of consumers agree.
• 54% of consumers say AI agents rarely or never have context about them as a customer.
• 78% say it’s important to be able to switch from an AI agent to a human, yet only 15% report experiencing seamless handoffs.
• Most escalations involve lost context, repeated explanations, and restarted conversations. Every misroute doesn’t just waste one interaction—it triggers a cascade. The customer calls back, a second agent asks the same questions, trust erodes, and the next interaction starts from a deficit.
• It’s no wonder that 42% of consumers say AI makes them less patient, not more. 41% are satisfied with how brands currently use AI.
• For customer service teams, the real challenge is routing, not tech.

What self-service really needs

• Brands need four capabilities for self-service AI agents to work.
  • 1. Agency: Specialised agents that can handle defined tasks with precision and don’t attempt everything and resolve nothing.
  • 2. Always-on monitoring: The system must monitor interaction quality in real time and catch failures before they compound into callbacks.
  • 3. Real-time contextual data: Agents must have a unified view of the customer, including history, intent, and recent interactions.
  • 4. Intelligent routing: The system must accurately decide in the moment whether a task stays with AI, moves to a specialist, or escalates to a human with full context.
• Of these four, routing is the most underinvested and the one that determines whether the other three deliver value.

Routing is the intelligence layer

• Think of routing not as plumbing but as decision-making.
• A well-designed system evaluates task complexity, required skills, customer history, sentiment, and more before matching it to the right resource.
• For example, a complex billing dispute will be routed to a senior billing specialist, not the next available generalist. A routing appointment request stays with the AI agent that can solve it in seconds.
• When the AI encounters an edge case it can’t handle, the routing layer transfers the task, along with full context, so the next agent can pick up where the AI left off.
• Twilio’s own research confirms this. 42% of consumers prefer starting with a human agent, even if it takes longer, while 23% are comfortable starting with AI and escalating when needed.
• Both paths demand routing intelligence. The AI-first customer needs seamless escalation, and the human-first customer needs to reach the right human immediately. Neither can afford a misroute.

When resolution compounds

• Every issue resolved on first contact is one fewer repeat call in the queue.
• Fewer repeat calls mean shorter wait times for everyone.
• This compounding effect extends to agent capacity. When routing works, human agents are freed from solving issues that should have been addressed the first time. They spend their time on complex, high-value interactions instead.
• This is what 72% of consumers are signaling when they say they’d choose AI over a human if the issue was guaranteed to be solved faster.
• 46% of consumers say quick service and resolution are their top priorities, and 51% say delays are acceptable if they lead to better customer support.
• Patience is available but only when the brands earn it by sending customers to the right place the first time.
• The brands that treat routing as an afterthought will keep optimising for speed and see their customers quietly leave. Those who optimise for resolution will build trust that compounds with every interaction.

To learn more about Twilio, visit here.

A new era of customer engagement is taking shape. AI agents are quickly becoming integral to the way businesses serve, support, and sell to customers — able to respond, reason, and take action in ways that go far beyond scripted automation.

Many customer journeys, however, are still built on systems that don’t talk to each other. Customer data lives in one place, channel history in another, and AI agents often operate with only part of the picture. Customers feel the pain when they switch between channels like voice and messaging, get transferred, and have to repeat themselves yet again. It doesn’t matter that they’ve been loyal to a brand for years, every interaction feels like a cold start. That is the conversation gap.

It’s clear that AI isn’t the problem, infrastructure is. Closing the gap requires new building blocks that focus on continuity, so context can carry forward across systems, channels, human agents, and AI agents.

To bridge the gap, at SIGNAL 2026, we are introducing a new conversation layer for the Twilio Platform.

Twilio Conversation Orchestrator, Twilio Conversation Memory, and Twilio Conversation Intelligence are now generally available. Together, they help businesses coordinate interactions, preserve context, and connect human and AI agents so every conversation is more continuous and useful.

In addition to the new Conversations layer, we’re also announcing platform updates that make it easier to build, manage, and scale customer engagement on Twilio — from a reimagined Twilio Console to expanded channels and new voice AI capabilities.

New building blocks for connected conversations

The conversation gap does more than create inconsistent customer experiences. It hurts conversion and retention, increases operational costs, adds integration complexity, and makes agents less productive. The new platform capabilities we’re introducing are designed to fix that by coordinating interactions, maintaining context, and surfacing signals as conversations happen.

Conversation Orchestrator

Conversation Orchestrator helps businesses coordinate interactions across Twilio channels without complex custom logic. Teams can configure it in Console or configure their implementation with the API. It connects interactions into a single thread and manages handoffs between human agents and automated systems.

Conversation Memory

Conversation Memory creates a living, identity-resolved profile by connecting customer data with conversation history and customer traits. That means each interaction starts with the right context. It’s built specifically for LLMs to reduce latency and token usage by surfacing the most relevant details when they matter.

A new Enterprise Knowledge API (now generally available) also allows teams to deliver more relevant experiences and ground interactions in trusted business knowledge such as FAQs, policies, and product documentation.

Conversation Intelligence

Conversation Intelligence provides real-time understanding of live interactions. Using prebuilt and custom LLM-based operators, it can detect changes in sentiment, flag potential escalations, and trigger action during a conversation, not only after it ends.

That gives teams the ability to respond sooner, support agents more effectively, and improve customer outcomes while the conversation is still in progress.

Together, these products help businesses create customer experiences that feel more connected across channels.

Open by design

Twilio remains neutral by design. We start with the premise that you know your business. We aren’t here to prescribe a model, framework, or data strategy. We provide the infrastructure that helps you build customer engagement in the way that works best for your business. You pick the model and agent runtime. You own the data.

That doesn’t mean you need to start from scratch, either. We partnered with Microsoft, AWS, and others to create blueprints that support faster development. We are also introducing an open-source developer toolkit, Twilio Agent Connect (now generally available), that lets your teams connect agents built on any LLM or framework directly to Twilio’s infrastructure.

For developers, this means more flexibility. For businesses, it means less lock-in and the ability to get value from existing investments. For partners, it means more ways to build with Twilio.

A new front door

We are also introducing a reimagined Twilio Console, because as customer engagement grows more complex, managing the infrastructure behind it should feel effortless.

The new Console is a single mission control center that brings your communications, identity, and data into one experience: one login, consistent logs across every surface, an intelligent Console Assistant, transparent billing insights, and streamlined compliance workflows that no longer slow you down.

Over the coming months, we’ll roll out this new Console experience to customers automatically. You can also opt in to gain early access.

More channels, more control, smarter conversations

In addition to these launches, we are announcing several updates that expand customer reach, support enterprise requirements, and make it simpler to build on Twilio.

• Apple Messages for Business (Private beta) and Twilio Email (GA) give teams new ways to reach customers on the channels they already use.
• Data Residency for SMS (EU) (Public beta) enables teams to manage personal data locally to support regional data requirements.
• Conversation Relay enhancements add PCI compliance, HIPAA eligibility, Insights, and support for Deepgram Flux for smarter turn detection — helping AI agents better understand when a person has finished speaking.
• Stripe Projects integration enables developers and AI agents to seamlessly provision Twilio within Stripe Projects in a single, programmable CLI workflow.

Built with our customers

Bringing these new products to life required a close partnership with many beta customers and partners. This helped us understand real-world signals and needs to help make the capabilities robust from the start.

Among dozens of others, Centerfield, Constellation Dealerships, Car Finance 247, and Meera.ai leveraged Twilio to solve their own customer engagement challenges. These teams showed what is possible when businesses carry context forward, act on live conversation signals, and connect AI agents with human teams in the moments that matter.

Car Finance 247, a leading UK online car finance broker, is using Twilio to help recover stalled loan applications. When customers miss a field, need to correct information, or still need to confirm terms and conditions, AI-powered outreach across voice, SMS, and RCS, Conversation Memory tracks the application state. Conversation Orchestrator manages the outreach journey, and Flex helps bring in a human agent as needed. As Reg Rix, Co-Founder and CEO, shared:

“Because the platform remembers where each customer left off, we can pick up right where they stopped, helping them cross the finish line in a way that is modern, responsive, and genuinely helpful.”

Centerfield, a technology company powering AI-driven commerce, helps brands connect with consumers across digital and phone-based journeys. With Twilio, the team is connecting real-time conversation data with customer context to guide agents and AI systems in the moment, standardise what works, and improve performance at scale. As Aniketh Parmar, Chief Technology Officer, said:

“Performance comes down to how well every interaction moves a customer forward. We’re capturing each conversation in real time and applying what we already know about the customer to guide our agents and AI systems in the moment. With the Twilio Platform, including Conversation Orchestrator, Conversation Memory, and Conversation Intelligence, we can see what’s driving conversations so we can standardise what works, eliminate what doesn’t, and continuously improve outcomes at scale.”

Constellation Dealerships is using Twilio’s agent infrastructure to accelerate AI-powered engagement across its dealer network, moving from evaluation to measurable outcomes in days. As Richard Pineault, Director of R&D, shared:

“The value of this partnership is evident—our team progressed from evaluating Twilio’s agent infrastructure to realising measurable outcomes within days. This rapid speed-to-value exemplifies the agility and innovation required to propel the dealership industry into the future.”

Meera.ai is building on Twilio to modernise outbound engagement, replacing repeated manual follow-ups with always-on conversations across voice, SMS, and messaging. Vivek Zaveri, Chief Executive Officer, said:

“Meera.ai has partnered with Twilio since our inception to champion a conversation-first future for commerce. As the industry shifts toward real-time LLM-enabled interactions, Twilio’s Platform and the new Conversations products will help us reach customers in the moment.”

Together, these customers and partners show that the Twilio Platform can help businesses recover stalled journeys, improve live interactions, accelerate time to value, and create more connected experiences across AI agents, human teams, and every customer channel.

The next era of customer engagement starts here

As AI agents own more of customer engagement, businesses need infrastructure that keeps conversations connected across channels, systems, and teams. That means preserving context, coordinating handoffs, and acting on what is happening in real time.

That is what we are building with this next generation of the Twilio Platform: a new layer that connects channels, context, intelligence, and human and AI agents, helping businesses make every digital interaction more connected, more useful, and more amazing.

For 17 years, Twilio has helped builders create better ways for businesses to connect with their customers. In this next era, that connection matters more than ever.

Explore the new Conversations layer, try the products, and let’s build what comes next, together.

Voice AI. Conversational AI. You’ve seen both terms everywhere—sometimes in the same sentence, sometimes used as if they mean the same thing.

They don’t. But they’re not opposites either.

One is a category of technology. The other is a specific way to deliver it.

Mix them up and you end up making the wrong platform decisions, building the wrong workflows, and losing 45 minutes in a meeting that didn’t need to happen.

Here’s the difference between voice AI and conversational AI, minus the jargon.

Conversational AI: The intelligence layer

Conversational AI is the broader category. It refers to any AI system designed to understand human language, reason about what was said, and respond in a way that feels natural and contextually relevant. That exchange can happen through text, voice, or any other medium.

What defines conversational AI is the intelligence underneath the interaction:

• Natural language understanding that interprets intent rather than matching keywords
• Dialogue management that tracks what’s been said and what still needs to be resolved
• Response generation that produces output appropriate to the context.

Conversational AI shows up in a lot of forms. A chatbot on a support page is conversational AI. An AI assistant that helps a sales rep draft follow-up emails is conversational AI. A virtual agent that handles inbound customer inquiries is conversational AI.

The intelligence layer makes the interaction feel like a conversation rather than a database lookup.

The channel, the modality, the interface: those are separate from the intelligence. Which brings us to voice AI.

Voice AI: The delivery method

Voice AI is conversational AI delivered through spoken language. It’s the application of conversational AI intelligence to voice-based interactions where the input is speech and the output is speech.

A voice AI system:

• Takes spoken words
• Converts them to text via speech-to-text (STT)
• Runs that text through a conversational AI layer to understand intent and generate a response
• Converts that response back to spoken audio via text-to-speech (TTS)

And it does it all fast enough that the conversation doesn’t feel like it’s buffering.

Voice AI isn’t a fundamentally different kind of intelligence from conversational AI. It’s conversational AI with a voice interface wrapped around it. The reasoning, the context tracking, the dialogue management—those are the same capabilities.

What voice AI adds is the ability to operate through spoken language in real time, with all the additional complexity that introduces: handling interruptions, managing turn-taking, producing natural-sounding speech, and doing all of it with sub-500ms latency.

Ultimately, conversational AI is how the system thinks. Voice AI is how it talks.

How they relate

Voice AI depends on conversational AI to be useful. Without the intelligence layer (intent recognition, context tracking, and coherent response generation), a voice system is just a phone menu with better audio.

The voice interface makes the interaction accessible through speech. The conversational AI makes the interaction worth having.

The relationship goes one way, though.

Every voice AI system uses conversational AI underneath it. But conversational AI doesn’t require voice. A text-based chatbot, messaging bot, or AI assistant embedded in a ticketing system are conversational AI without any voice component.

It’s not really a question of whether you need conversational AI or voice AI. It’s better to ask: does your use case require voice?

• If yes, you need voice AI—which means you also need conversational AI as the foundation.
• If the interaction is text-based, you need conversational AI without the voice layer.

Voice AI vs. conversational AI: Key differences

Side by side, the differences get a lot clearer. Here’s the breakdown across the criteria that matter most for teams building or buying AI for customer service.

When to use conversational AI without voice

Text-based conversational AI makes sense when your customers primarily engage through chat, messaging, or digital channels. And when the nature of the interaction doesn’t require the immediacy of a phone call.

• Support chat on a website
• WhatsApp automation
• AI-assisted email triage
• Messaging bots for transactional notifications

These are all conversational AI use cases where voice doesn’t add much and may introduce unnecessary friction. Not every customer wants to speak out loud, especially in public, at work, or when the question is simple enough to type in thirty seconds.

Text-based conversational AI is also typically faster to deploy, easier to test, and simpler to update. You can iterate on response quality, test new flows, and review transcripts without dealing with audio quality, latency optimisation, or the additional infrastructure that voice requires.

If your primary support and engagement channels are digital and your customers are comfortable typing, starting with text-based conversational AI often makes more sense than jumping straight to voice.

When you need voice AI specifically

Voice AI makes sense when the use case is inherently telephonic, time-sensitive, or requires the kind of nuance that text alone doesn’t capture.

• Inbound phone support: Customers call because they want to talk to someone, or because they’ve always called, or because the issue feels urgent enough that they don’t want to wait for a chat response. An AI that can answer that call, understand the issue, and resolve it in the same interaction replaces one of the most expensive and frustrating moments in customer service.
• Outbound calling: Appointment reminders, fraud alerts, lead follow-up, proactive outreach for at-risk customers. These interactions are harder to execute over text because they require real-time dialogue.
• Context: Tone, urgency, frustration, hesitation—these are signals that a voice AI system can detect and respond to. A customer who speaks with audible frustration is communicating something beyond the literal words, and a well-designed voice AI system can adjust its approach accordingly.

Finally, voice AI matters when your customers are less likely to engage through digital channels. These might be older demographics, industries where phone is still the primary contact method, or use cases where hands-free interaction is a practical requirement.

Do you need both?

For most businesses building serious customer engagement infrastructure: yes.

The customers who prefer chat aren’t going away. Neither are the customers who pick up the phone. A complete AI engagement strategy handles both with a single connected experience rather than two separate systems that don’t know about each other.

And that’s where Twilio Conversations can help.

• Conversation Orchestrator connects voice, SMS, WhatsApp, and chat into one continuous conversation record.
• Conversation Memory gives every agent (AI or human) persistent customer context across channels.
• Conversation Relay handles the voice AI layer: low-latency STT and TTS, bring-your-own-LLM, HIPAA-eligible.
• Agent Connect lets you plug your own AI agents into Twilio channels without rebuilding your communications infrastructure.

Your customers are going to use both voice and text. The question is whether your stack connects them.

Start for free or contact sales to talk through your use case.

Frequently asked questions

What’s the difference between voice AI and conversational AI?

Conversational AI is the intelligence layer that understands human language and generates contextually relevant responses, regardless of channel. Voice AI is conversational AI delivered through spoken language. It adds speech-to-text and text-to-speech components so the interaction happens via voice.

Is voice AI a type of conversational AI?

Yes. Voice AI is a specific application of conversational AI that operates through spoken language. The reasoning, intent recognition, and dialogue management capabilities come from conversational AI. Voice AI adds the speech interface on top to convert spoken input to text, process it through the conversational AI layer, and convert the response back to speech.

Can conversational AI work without voice?

Yes. Text-based chatbots, messaging bots, AI assistants in ticketing systems, and email AI are all forms of conversational AI that don’t use voice.

Does Twilio support both voice AI and conversational AI?

Yes. Twilio Conversation Relay handles voice AI, combining low-latency STT and TTS with bring-your-own-LLM flexibility. The broader Twilio Conversations platform connects voice, SMS, WhatsApp, and chat into a single conversation layer, so the conversational AI intelligence and customer context are shared across every channel.

To learn more about Twilio conversations, visit here.

Technology is evolving at a relentless pace. Headlines proclaim the latest AI breakthroughs and generative models that promise to transform the way we work. Yet, when I sit down with leaders across industries, the conversation quickly shifts. The real questions are not about models, algorithms, or shiny tech investments; they’re about people. How do we equip our teams to thrive amid disruption – not just survive it? What practical steps move us from mere digitisation to lasting transformation?

These are the questions at the heart of a recent episode in our Decoding Business Transformation series. I had the pleasure of hosting Dr Sean Gallagher, founder of Humanova and one of Australia’s foremost voices on the future of work. The insights and recommendations below are drawn directly from that conversation, and I believe every boardroom should confront them head-on: In the era of agentic AI, culture will determine winners, not code.

The fancy tech is table stakes. People are the differentiator.

Let’s debunk a persistent myth: successful AI adoption is not a technology problem – it’s a talent and culture challenge.

Recent BCG research shows that high-performing AI leaders invest 70% of their resources into people and processes, with just 10% going to the algorithms themselves. Real value emerges when we empower individuals at every level – equipping them with the mindset, capabilities, and (crucially) the psychological safety to experiment with and apply new technologies.

As Dr Gallagher put it: “AI is a talent strategy, not just a technology play.” Transformation begins not with a new tool, but with a fundamental reimagining of how we nurture, develop, and inspire our people to explore, experiment, and adapt.

Why most AI projects fail: Ignoring the human element

Here’s a sobering truth, surfaced by Deloitte a decade ago: humans adapt to exponential technologies much faster than organisations do. The mistake? Leaders try to “bolt on” AI to outdated processes – putting a rocket on a jalopy, so to speak.

True transformation happens when we flip the script: empower employees first, technology second.

Frameworks such as the “Work Value Pyramid” can help organisations focus on shifting time away from repetitive administrative work and towards creativity, problem-solving, and strategic innovation. In practice, this means:

• Resisting knee-jerk reductions in headcount. Your people’s tacit knowledge is invaluable capital.
• Rewiring incentives and KPIs to reward learning, experimentation, and sharing.
• Destigmatising “shadow AI” use. Bring your secret AI champions into the open, empower them as peer teachers, and build psychological safety for everyone to explore.

Flatten the org; Redesign the work

The blueprint for winning in the AI age is taking shape: Flatter, Faster, Fitter, Fewer.

• Flatter: Remove unnecessary hierarchy. Push decision-making to the edges of the organisation.
• Faster: AI is about more than simply doing things; it’s about doing them at the speed the market now demands.
• Fitter: Build nimble, AI-literate teams who treat AI as a digital colleague – not a threat.
• Fewer: Growth is not about increasing headcount; it’s about unlocking higher-value work for everyone.

Above all, resist the temptation to simply automate legacy processes. As McKinsey put it, “the fundamental redesign of workflows is the largest factor correlated with real impact.” Start with people and how work creates value – then let AI accelerate, not dictate, those improvements.

Measurement: Macro, not micro

Most companies focus on the wrong metrics: time saved per prompt, or “AI-powered” process widgets. That’s missing the point.

Instead, focus on:

• Business-wide impact: Are you accelerating time-to-market? Opening new revenue streams? Raising the innovation bar?
• Learning culture: Are teams sharing use cases and lessons? Is experimentation a norm?
• Accountability KPIs: Prioritise experimentation, collaboration, and demonstrated learning over mere output.

In closing: The real transformation is human

If there’s one message from my conversation with Dr Gallagher, and from everything I’ve seen working with the world’s most ambitious brands, it’s this:

• Invest deeply in your people – early, intentionally, and continuously.
• Amplify the learning and experiments of your early adopters.
• Model the behaviour you seek, starting with leadership.
• Redefine productivity around effectiveness and innovation – not just efficiency.

Generative AI, and the new breed of AI “agents”, are rapidly becoming our digital colleagues. But only human curiosity, courage, and culture can unlock their full value. In this era, the ultimate competitive advantage is not code. It’s culture.

To learn more about Twilio, visit here.

Customer expectations don’t end when business hours do, which is why delivering a fast, always-on customer experience (CX) has traditionally required large call centres and significant resources. This often placed small businesses at a disadvantage, as many lacked the manpower and budget to provide 24/7 support at scale. Today, AI has completely levelled the playing field. Even small businesses now have access to powerful tools that can answer queries, resolve routine issues, and deliver highly personalised interactions around the clock.

But adopting AI in customer engagement is not just a question of efficiency. For smaller businesses especially, where loyalty is often built on familiarity, trust, and personal service, the real challenge is using AI in ways that strengthen rather than dilute the human connection that customers value most.

Human empathy combined with AI efficiency is a delicate blend. Done right, it ensures that every customer interaction feels personal, thoughtful, and seamless, whether the customer is engaging with a bot at 2 a.m. or a live agent during office hours.

So, how can small businesses embrace always-on virtual agents without losing the human connection that defines their identity? Here’s a practical playbook to guide the transition.

1. Understand what customers want: Speed, simplicity, and empathy

Before diving into AI adoption, it’s critical to understand what customers expect. Twilio’s Digital Patience study suggests that while speed matters, it is not the only thing that customers value. Twilio found that 46% of respondents in the Asia-Pacific and Japan region say quick service and resolution are most important, but 51% say delays are acceptable if they lead to better customer support. The study also notes that customers are open to AI, but still value human touchpoints more highly.

The takeaway: AI should enhance CX, not replace it. Businesses can let natural-sounding AI voice agents handle inbound calls, regardless of peak hours or time zones. These virtual agents act as an intelligent frontline – answering common questions and qualifying leads – before seamlessly routing the conversation to a live human representative. The result? Callers get immediate answers, and the business captures every opportunity without losing the human touch.

2. Map the handover points between AI and humans

One of the most common pitfalls in implementing AI is failing to clearly define when and how customers transition from bots to human agents. To avoid customer frustration, organisations must thoughtfully map out these “handover points” by designing for two key principles: choice and continuity.

Designing for Choice

Give customers the option to reach a human when needed. While AI is perfectly suited for routine inquiries like FAQs or order tracking, customers should never feel trapped in a bot loop. Always provide a clear, accessible option for them to choose to escalate the issue. Additionally, configure your system to proactively step in and offer a human handoff the moment it detects emotion, ambiguity, or complex steps.

Designing for Continuity

Effective handovers rely on technology that recognises when an issue exceeds AI’s scope. By leveraging natural language processing and intelligent routing, organisations can ensure the transition from machine to human is frictionless. Crucially, this means automatically carrying the full history and context of the interaction forward so the customer never needs to repeat themselves.

Achieving this level of continuity requires a new approach to managing interaction data during handovers. Instead of passing along a raw transcript, organisations need a managed memory service that provides agents with persistent context across every conversation, channel, and session. By transforming customer preferences, unresolved issues, and intent into a structured semantic profile—one that continuously evolves and reconciles new interactions as they occur—agents can quickly understand the relationship and continue the interaction without disruption.

To support truly omnichannel experiences, the system must also resolve identity automatically across touchpoints, linking interactions from phone, email, messaging apps, and other channels to a single customer profile. Equally important is the ability to surface only the information that is relevant to the task at hand. By presenting agents with a concise summary of the active issue and customer preferences, grounded in verified business knowledge such as product policies and FAQs, organisations can reduce resolution times while ensuring customers experience a seamless continuation of the conversation.

3. Don’t automate for automation’s sake

AI adoption should never feel like a “set it and forget it” strategy. Instead, it should be approached as a way to solve real business problems. It starts with asking questions like: What are the most time-consuming tasks for the team? What frustrates customers the most?

For instance, a restaurant might automate table reservations and menu queries, while a small online retailer could deploy AI to handle order status updates or product recommendations. These targeted use cases ensure that AI adds tangible value without overwhelming operations.

Take the example of Driva, a fast-growing online finance broker that deployed AI-powered customer service tools to answer routine enquiries and provide immediate assistance while customers wait in the call queue. By automating common interactions, Driva reduced the volume of requests requiring human intervention and achieved a 5% uplift in conversion rates at key points in the customer journey.

4. Invest in AI that connects

While consumers embrace automation, research shows they still draw comfort from the warmth of a human voice. To make your virtual agents feel less robotic and more like an extension of your team, look for tools that:

• Deliver human-like voice AI experiences at scale through natural turn-taking and barge-in capabilities.
• Connect interactions across voice, messaging, and digital channels into a single thread so every exchange builds on the last.
• Leverage Natural Language Processing (NLP) that enables conversational systems to interpret context, mimic human tone, and even recognise sentiment.
• Place orchestration at the heart of the experience. An effective orchestration engine acts as the “conductor,” actively coordinating workflows and routing interactions so the right resource—whether an AI bot or a human—handles the right moment.

When AI bots, automated workflows, and human teams are seamlessly coordinated behind the scenes, the customer simply experiences one unbroken, dynamic dialogue. For small enterprises, this means delivering sophisticated experiences that effortlessly bridge the gap between automation and live support, even at scale.

5. Empower teams with real-time context

AI is not about replacing human workers; it’s here to make jobs easier. However, for teams to fully embrace this new dynamic, organisations must shift their focus from retrospective performance reviews to real-time agent assistance. By feeding agents context as the conversation happens, businesses ensure that every interaction never starts from scratch.

• Leveraging Conversational Intelligence: Use a real-time intelligence layer that turns live conversations into signals and actions. By analysing voice and messaging with generative AI Language Operators, businesses can understand intent, sentiment, and churn risk instantly, allowing human and AI agents to act in the moment with the right response or escalation.
• In-the-Moment Guidance: Give agents instant context and in-the-moment guidance during every interaction. Surfacing relevant customer history, next-best action suggestions, and summaries in real time allows agents to resolve issues faster without switching tools.
• Resolving Complex Customer Needs: AI can handle routine enquiries with low latency, but human agents still excel at nuanced problem-solving. With AI feeding them persistent customer memory and sentiment analysis in real time, human agents can skip the repetitive questions and immediately focus on resolving complex issues, rescuing deals, or preventing churn.

When employees are equipped with real-time customer data and voice-driven insights, SMEs empower their teams to stop reacting to problems and start responding to customers proactively.

Consider global AI platform Genspark, which leverages a Programmable Voice API for its “Call for Me” agent to handle complex outbound tasks like checking supplier pricing or booking international hotels. The AI can conduct real-time, natural conversations across different languages on the user’s behalf, seamlessly navigating the live interactions before delivering a structured summary. Because these natural voice experiences depend entirely on speed and consistency, the underlying infrastructure provides the critical sub-second latency necessary to keep every automated call clear and uninterrupted.

6. Maintain transparency with customers

Finally, a successful AI implementation requires transparency. Customers should always know when they’re communicating with a bot and when they’ve been handed over to a human. AI-powered interactions must offer clarity by providing transparency about when and how AI is used and explaining next steps in plain language.

Transparency builds trust. Small businesses can go a step further by soliciting customer feedback on their AI interactions and using this input to fine-tune their systems.

For small enterprises, the AI-to-human handover isn’t about choosing between humans and machines; it’s about combining the strengths of both to create exceptional customer experiences. AI can provide the speed and efficiency customers expect, while humans deliver the empathy and creativity they value.

By strategically defining handover points, investing in human-like AI, and empowering agents to work alongside technology, organisations can build a CX strategy that’s as scalable as it is personal.

This blended approach ensures that every interaction – whether managed by a bot or a human – is thoughtful, natural, and distinctly on-brand.  

To learn more about Twilio, visit here.

At Amazon Web Services (AWS), artificial intelligence is already compressing security work that once took months into minutes.

In the old world, human red teams would find vulnerabilities, write reports, refine those reports, and eventually hand them to defenders, who would then begin building detections or fixes, Steve Schmidt, chief security officer at AWS, tells CSO. That process could take “two, four, six, eight, 10 months,” Schmidt says.

“Now with proper application of AI, we can have the detections built for the problems the red team finds in 15 minutes-ish,” he says. “I think the outside is about four hours.”

That kind of workflow offers a glimpse of what AI could make possible for the most sophisticated security organizations: AI agents testing systems, other agents generating defenses, and human security engineers validating results and refining the feedback loop.

But it also raises a more uncomfortable question for the rest of the cybersecurity industry: What happens to organizations that cannot build anything close to that?

The concern has become significant enough that the Trump administration recently directed agencies to expand access to AI-enabled cybersecurity capabilities for resource-constrained organizations, including rural hospitals, community banks, and local utilities.

The order reflects a growing fear that AI could deepen a divide that has existed in cybersecurity for years: the divide between organizations with money, expertise, and engineering depth, and those struggling to keep pace with basic security demands.

Yet security leaders and practitioners suggest the impact of AI will be more complicated than a simple widening gap. Some experts say AI is merely adding a new layer to a long-standing security poverty problem. Others argue AI could democratize capabilities once reserved for elite organizations. Still others see today’s divide as real, but potentially temporary, as models become cheaper, more open, and easier to run.

The class divide was already here

For Matt Warner, co-founder and CTO of Blumira, the premise that AI is creating a cybersecurity class divide misses a key point: The divide already exists.

“I would go even a step further and say that there has been a class divide for the last 10 to 15 years,” Warner tells CSO.

What AI changes, he argues, is not necessarily the existence of the divide but how stark it becomes. Larger organizations have money, people, and time to experiment with AI. Smaller organizations often do not.

“The big differences that we’re seeing, especially from where we sit in the world, is the difference is getting starker in having the resources to leverage AI and the time to leverage AI more than anything else,” Warner says.

That distinction matters because many smaller organizations are already overwhelmed. Warner pointed to resource-constrained local governments and small or midmarket organizations that are still far behind large enterprises in basic IT and security maturity.

“I can find you a county in Michigan with two IT people for 2,000 employees,” Warner says. “Those people don’t have time to leverage AI and even learn how to use AI because they’re mostly just trying to put out fires.”

That problem is not unique to AI. Smaller organizations have long struggled to patch systems, prioritize vulnerabilities, monitor environments, and respond to incidents with limited staff. AI may help eventually, but only if those organizations have enough capacity to adopt it.

Wendy Nather’s framework gets an AI layer

Anton Chuvakin, security advisor in the office of the CISO for Google Cloud, sees the AI divide as part of a much older problem.

“I feel like it sends me back to when Wendy Nather invented the security poverty line,” Chuvakin tells CSO, referring to Nather’s 2011 concept describing organizations that lack the money, expertise, capability, or influence to implement effective security.

Chuvakin is skeptical that AI fundamentally changes that model. “I don’t think AI necessarily breaks that model,” he says. “I think it just adds another dimension.”

Cybersecurity has always been shaped by unequal access to top talent, tools, and services, Chuvakin argues. Large organizations could afford better SIEM deployments, advanced DLP programs, threat hunters, application security experts, and incident response retainers. Smaller organizations often could not.

AI may become another scarce resource, but Chuvakin cautions against overstating the role of model cost alone. In his view, the bigger structural issue may be talent rather than tokens.

“Prices for people won’t drop, but prices for LLMs may drop,” he believes.

That means the organizations with the greatest advantage may not simply be those that can afford the most expensive models. They may be the ones that can afford the people who know how to use them — and, as the frontier-access debate below suggests, that talent gap may prove more durable than any gap in model access itself.

AI creates new costs — and new uncertainties

Nather herself, now senior research initiatives director at 1Password, sees AI affecting every dimension of the security poverty line: money, expertise, capability, and influence.

The financial challenges are not limited to whether an organization can pay for an AI tool. In some cases, organizations that cannot afford enterprise licensing may end up making tradeoffs around privacy.

“If an organization can’t afford an enterprise license for the models they’re using, then they can’t keep their data private,” Nather tells CSO. “So, they have to give up privacy because they can’t afford privacy.”

That’s a new twist on an old dimension of the poverty line: It’s not just that under-resourced organizations lack a capability, but that the capability they can afford comes bundled with a risk wealthier organizations don’t have to accept.

Token-based pricing adds another problem: unpredictability. “At this point, nobody knows how much they’re going to burn in tokens at any given time,” she says.

That makes budgeting difficult for organizations that cannot absorb surprise costs. Nather also warns that usage-based pricing is controlled by providers and can change over time, leaving customers with limited leverage.

“The charging practice is in the hands of the providers, and they can change it at any time,” she says.

For organizations already operating below the security poverty line, that uncertainty could make AI adoption harder, even if the technology itself becomes more capable.

Access to frontier models may be a temporary divide

Dave Baggett, SVP/GM of the security suite at Kaseya, agrees there is security class divide dynamic playing out today, particularly around access to frontier models.

“There’s definitely a haves and have-nots issue around Mythos specifically because most people don’t have it,” Baggett tells CSO. But he doesn’t think the divide will have a long-term impact. Open-weight models, quantization, mixture-of-experts architectures, and increasingly powerful commodity hardware, he argues, are closing the gap faster than most people expect.

While not every organization will build a frontier model, he says, more organizations may be able to run capable models locally or use cheaper systems that approximate what today’s elite models can do.

“What it says for finding vulnerabilities is at that point, open-source people can run this stuff,” Baggett says. “Then you’re back to having a symmetrical opportunity where the defenders who are writing the open source can run the same tools the attackers would and have them fix the issues.”

His bottom line is that the divide may be real but short-lived. “Right now, there certainly is a have, have-not schism, but it may not be there for long,” Baggett says — a view Chuvakin shares, though he frames it in terms of the model market rather than open source specifically.

“I don’t think it’s the lowering prices example, but it’s more like you’re a top-tier model maker, I’m a second-tier model maker. My model in a year would do what your model did a year ago,” Chuvakin says.

The real advantage is operational depth

Schmidt’s description of AI use at AWS points to another kind of divide: not access to AI, but the ability to operationalize it.

AWS uses multiple models for different tasks, Schmidt says. One model may discover vulnerabilities, while other models validate results or help build defenses. Humans remain accountable for evaluating what the systems produce.

“Because we believe really strongly in human accountability for the use of AI from end to end, we still have humans take a look at what the systems come up with to determine whether they are reasonable and appropriate,” he says.

That workflow requires more than a model. It requires corporate data, secure infrastructure, feedback loops, security engineers, data scientists, and AI specialists who can work together.

Schmidt also pushes back on the idea that running AI locally on powerful consumer hardware is a substitute for production-grade security infrastructure. “Often the value of the model is also dependent on its proximity to data so that the model can ingest, use, and reason about data,” he says. “As a security person, I do not want that to be on your laptop.”

Experimentation on a laptop is useful, Schmidt says, but it is not the same as a secure production environment.

“I want the data to be somewhere safe that I can control, that I can see, that I can reason about, not sitting on your laptop,” he says. “Experimentation in there, awesome. That’s great. But it is not a production infrastructure component.”

That distinction may define the emerging AI security gap. Many organizations may be able to access AI tools. Far fewer may be able to safely integrate them into real security workflows.

The democratization argument

Phil Venables, a partner at Ballistic Ventures and former CISO of Google Cloud, takes the most optimistic view.

Asked whether AI is widening the gap between well-resourced and under-resourced security organizations, Venables tells CSO, “No, I actually think it’s the exact opposite.”

The reason, he argues, is that AI packages expertise and automation in ways that can be delivered broadly. “One of the fantastic things about AI, and we’re already starting to see this, is [that it’s] a great democratizer of capabilities,” he says. “AI packages up expertise and automation capabilities at a level beyond what prior waves of technology have done, and it makes it available at scale into organizations that have not previously been able to afford these things.”

He points to red teaming as an example. Nearly every organization would like a world-class red team, but few can afford one.

“Pretty much every organization on the planet would love to have a world-class red team to constantly test their security to find and fix things before attackers do,” Venables says. “But very few organizations have ever been able to afford to build a high-end red team.”

AI agents, he argues, could make that kind of capability available more economically. The same pattern could apply to insider threat; third-party risk; software security; governance, risk and compliance; and security operations.

“So even the smallest and resource-constrained organizations can now have access to a higher-end capability,” he maintains.

Venables does see a danger zone, however: under-resourced security teams inside organizations with aggressive AI ambitions. Those teams may struggle to keep up as the rest of the business adopts AI rapidly. But for many small and midsize organizations, he believes AI could improve access to security capabilities they never had before.

A divide over AI — or over readiness?

For elite organizations, AI is already becoming a force multiplier. Security teams with deep engineering talent, mature data infrastructure, and strong governance can use AI to accelerate testing, detection engineering, vulnerability discovery, and risk management.

For smaller organizations, the picture is less clear. AI may eventually package scarce expertise into affordable services. Open models may reduce dependence on expensive frontier systems. But organizations below the security poverty line still face familiar constraints: too few people, too little time, limited expertise, unpredictable costs, and weak leverage over vendors.

The emerging divide may therefore be less about who has access to AI and more about who can turn AI into durable security outcomes.

That makes the question facing cybersecurity more complicated than whether AI will create haves and have-nots. The industry already had them.

The real question is whether AI becomes another technology that rewards the organizations already best positioned to use it — or the first major security advance in years that helps those below the poverty line finally catch up.

The European Commission is ending an antitrust investigation into SAP after the company made numerous concessions worldwide regarding maintenance and support services for on-premises versions of its ERP solution. The Commission began its investigation in September 2025, concerned that SAP forces customers to buy its services for longer, and for more licenses, than they need.

In accepting SAP’s commitments, the Commission makes them binding on the company. SAP could still face fines if it fails to make good on its concessions over the next ten years. SAP’s promises include:

• Greater freedom of choice in support: Customers can divide their SAP landscape into sub-areas and choose different support and maintenance providers for each area.
• Easier license terminations: Maintenance and support contracts can be terminated in certain cases — such as when products are phased out, SAP projects fail, the company files for bankruptcy, there are staff reductions, or parts of the business are sold.
• More flexible licensing models: SAP is expanding access to so-called “single-metric” contracts as an alternative basis for licensing and maintenance fees.
• Relaxation of contractual obligations: In the future, the purchase of new licenses will no longer automatically extend the minimum term of existing support contracts.
• Easier Re-entry: Customers who resume SAP support after a hiatus will no longer have to pay reinstatement fees; back payments will also be reduced.

In addition, SAP is establishing an internal clearinghouse that customers can contact if they suspect violations of the agreed-upon obligations.

Even though SAP said it welcomed the EU Commission’s decision, the Walldorf-based company is unlikely to be truly happy with the concessions it had to make.

User organizations, though, are happy: Conor Riordan, chair of UKISUG, the UK & Ireland SAP User Group, said, “We welcome the proposed changes to SAP’s support and maintenance policies for on-premise customers. Our members have long called for greater flexibility, transparency and predictability, and these changes appear to be a positive move. This should give organizations more room to adapt to changing business conditions and evolve their SAP estates at a pace that suits them.”

More flexibility with SAP support

Michael Bloch, executive director of licensing, contracts and support at DSAG, the German-speaking SAP User Group, went into greater detail in an interview with Computerwoche, here translated from the German:

How do you assess the European Commission’s decision in general?

Michael Bloch: In principle, we think the decision is a good one for now. Many SAP customers will benefit from it, especially those who continue to run their ERP systems on-premises. Our investment survey shows that numerous companies have still not made the move to the SAP Cloud. They now have significantly more freedom to decide how they want to organize support for their existing software.

Who stands to benefit on the provider side? Does the decision open up new opportunities for third-party providers like Rimini Street? Can the potential demand even be met?

Bloch: That will be interesting to watch. At the moment, third-party maintenance is a total niche business, at least in German-speaking countries. Acceptance is significantly higher in the US, but here in Germany, many companies remain rather skeptical of the model. The EU decision could now give this market a new boost. Customers who do not wish to follow SAP’s current strategy will be able to remain on their existing infrastructure in the future and obtain support from another provider. This certainly opens up opportunities for new business models.

Does this decision undermine SAP’s cloud strategy?

Bloch: For customers, the decision now truly becomes a matter of principle: Do I follow SAP’s strategic direction, or do I consciously choose a different path? Those who aren’t convinced that the cloud strategy is the right one for their own company can now more easily decide to stay on their existing ERP landscape and, for example, use a different support provider.

However, one must be clear about the consequences. Those who remain on their current system landscape — even with an alternative maintenance provider — are forgoing the innovations that SAP is currently developing around the Autonomous Enterprise. There is no middle ground here. Companies would have to develop many of these functions themselves or recreate them at considerable expense.

So is this inevitably an either/or decision?

Bloch: No, that’s exactly the key point. Many companies have heavily customized their ERP systems over the years or decades to fit their industry-specific processes — some customers even refer to “refined” systems. These investments don’t simply have to be written off now.

Instead, companies can continue to operate their proven core systems while simultaneously adding targeted cloud components, such as SAP Cloud ERP for financial processes. This allows them to preserve existing investments while also leveraging new innovations. The EU decision thus opens up additional options for action, but it also makes the strategic decision more challenging for CIOs. They must now define even more precisely what their target architecture should look like for the next five to ten years.

2027 – A pivotal year for SAP support

Does this mean that IT decision-makers will now have to forgo a peaceful summer break?

Bloch: I don’t think this will fundamentally increase the pressure. Extended Maintenance doesn’t start until the end of 2027, so there’s still some time left. But companies now have an additional strategic question to answer. The actual decision year is likely to be 2027. By then, many companies will have to determine which system landscape they’ll use in the future and what their long-term SAP strategy will look like.

Even companies that want to stick with their existing ERP landscape will only receive official support until 2030. While that does buy some time, it’s by no means a long planning horizon, especially when alternative ERP strategies or successor solutions need to be evaluated.

That’s why I view it as a positive development that the decision has now been made. It provides clarity and gives companies the opportunity to incorporate these new conditions into their strategic considerations at an early stage.

The new regulations also make it easier for companies to dispose of unused licenses and the associated maintenance fees. How significant could the potential savings be?

Bloch: We don’t have specific figures on that. You have to be very careful here, because it depends heavily on the individual case. The key factor is whether a company actually terminates its SAP support entirely, switches to a third-party provider, or simply no longer needs certain products. After all, a system still has to be operated.

Is there at least a rough estimate?

Bloch: No, we don’t have reliable empirical data. If companies have products in use that they no longer use at all, they’ll be able to cancel support for them more easily in the future and, of course, save money as a result. However, we don’t know how large this so-called “shelfware” portion actually is.

For companies that have already migrated to S/4HANA or SAP Cloud ERP, this issue should largely be resolved anyway. It’s particularly relevant for those who still have the transition ahead of them. They can now review which unused licenses and maintenance contracts they can phase out and whether this is possible within the framework of SAP’s concessions, since regulations must be observed here as well. Those who are still paying substantial support fees today for products that are no longer in use can achieve significant savings by doing so.

Take advantage of the options

Does the EU decision improve companies’ negotiating position with SAP?

Bloch: There’s no one-size-fits-all answer to that. What matters is how a company makes use of its options. In my view, the best results are generally achieved by working with SAP to find a path forward.

Why?

Bloch: For example, if you completely cancel SAP support and later sign a new cloud contract, you should expect to forgo certain incentives from SAP. That’s why every decision should be made with all dependencies in mind.

In addition, SAP isn’t the only one offering incentives to move to the cloud. The hyperscalers also have a strong interest in attracting companies to their platforms and, in some cases, offer very attractive incentive programs. This means that companies’ starting points vary greatly.

Does this make decisions easier for CIOs?

Bloch: Quite the opposite, actually. While the new situation expands the range of options, it makes strategic decision-making significantly more complex. CIOs must now ask themselves: Which existing systems continue to provide business value for our company? Added to this are questions such as: Where is it worthwhile to retain the existing landscape? And in which areas will we actually benefit from the innovations that SAP will provide in the cloud in the future? Finding exactly this balance will be the real challenge.

So does this decision primarily mean that companies gain more time, for example, to weather difficult economic periods or to better prepare for a move to the cloud?

Bloch: Yes, especially for companies that haven’t yet found a compelling business case for moving to the SAP Cloud. They can continue to operate their existing landscape for the time being while simultaneously assessing whether there is still potential for cost savings by eliminating unused licenses and maintenance contracts, in other words, “shelfware.” This can certainly help in individual cases and provides more room to maneuver.

Complexity Is Increasing

Has the EU decision resolved the biggest problem in SAP licensing policy, or are there still issues to address?

Bloch: Extended Maintenance remains an important issue for companies that have not yet migrated to S/4HANA. The EU decision also has an impact here. Companies now have significantly more options for organizing their existing system landscape and support in different ways. They no longer have to treat their entire software portfolio uniformly, but can make differentiated decisions depending on the situation.

However, this also increases complexity. Companies now have a whole toolbox of options and must carefully weigh which combination is right for their strategy.

What’s the next crucial step?

Bloch: The key now is the practical implementation of the promised measures. Together with SAP, we need to clarify how the new regulations will be structured in detail and how companies can actually make use of them. The obligations will also be monitored by an independent trustee. I therefore hope that, together with SAP, we can provide more clarity on the operational implementation in the near future.

Does the EU decision now create additional pressure to act?

Bloch: I don’t think that this will immediately increase the pressure. There’s still some time left until Extended Maintenance begins at the end of 2027. That said, companies now face an additional strategic task: they must assess which new opportunities they want to take advantage of and how these fit into their SAP strategy.

When will things get serious?

Bloch: In my view, 2027 will be the decisive year. By then, many companies will need to determine which system landscape they will use to transition to Extended Maintenance and what their long-term SAP strategy should look like. Even companies that decide against moving to the SAP Cloud will gain some time as a result of the EU decision — but official support for their existing systems will end by 2030 at the latest. Especially when alternative ERP solutions are being evaluated in parallel, that’s not a particularly long planning horizon.

Your conclusion?

Bloch: Overall, it’s positive that the decision has now been made. It would have been much more difficult for companies if uncertainty had persisted until early 2027. Now the framework is clear, and companies can incorporate it into their strategic decisions early on.

Fraunhofer IOSB

Humanoide Roboter verknüpft mit KI stehen derzeit im Fokus einer weiteren Automatisierung. Während Großunternehmen wie Mercedes-Benz mit dem Apollo oder BMW mit Figure und Aeon sowie Siemens mit dem HMND 01 Alpha diese neue Roboter-Spezies in ihren eigenen Werken in Pilotprojekten testen, fehlt KMUs oft diese Möglichkeit.

Hier setzt das Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung (IOSB) mit der Idee des Humanoid Robots Experience Lab an. An den Standorten Karlsruhe, Lemgo und Ilmenau sollen Anwender die Möglichkeit erhalten, an konkreten Anwendungsszenarien zu bewerten, wo und unter welchen Bedingungen der Einsatz der Humanoiden tatsächlich ein Mehrwert bietet.

Mehr als nur zweibeinige Maschinen

Dabei geht es Jürgen Beyerer, Institutsleiter des Fraunhofer IOSB, um weit mehr als nur die äußere Form: „Im Kern steht die Frage, wie Künstliche Intelligenz künftig in unserer realen Welt handeln kann: in Umgebungen, die nicht perfekt vorbereitet sind, und im Zusammenspiel mit Menschen, die die Verantwortung behalten sollen.“ Letztlich dreht es sich also um das Gehirn hinter der Maschine – um verlässliche Autonomie und ein tiefes Verständnis der Umwelt.

Use Cases: Von der Fabrik bis zum Feld

Dazu ist das Lab nicht als isolierte Forschungsstätte konzipert, sondern als standortübergreifende Plattform. So haben Karlsruhe, Lemgo und Ilmenau jeweils andere spezialisierte Schwerpunkte:

• Karlsruhe – Die Retter und Beschützer:

Hier liegt der Fokus auf unstrukturierten und gefährlichen Umgebungen. Ob Verteidigung, zivile Sicherheit oder Katastrophenschutz – in Karlsruhe wird erprobt, wie Roboter-Teams bei der Aufklärung oder Inspektion unterstützen können.

• Lemgo – Die Industrie-Pioniere:

In einer produktionsnahen Umgebung wird untersucht, wie humanoide Roboter ihre Mobilität und Greiffähigkeiten in der industriellen Automation ausspielen können. Das Ziel: Der „Human-in-the-Loop“, bei dem Menschen und Maschinen Hand in Hand arbeiten.

• Ilmenau – Die Alltags-Helfer:

Hier stehen die Landwirtschaft, kommunale Dienste und kritische Infrastrukturen im Mittelpunkt. Die Forscher arbeiten an der Vernetzung der Roboter mit Leitstellen und nutzen Simulationen, um den Sprung in die reale Anwendung zu meistern.

Das Besondere am Humanoid Robots Experience Lab ist sein offener Charakter. Es richtet sich explizit an Anwender aus dem Mittelstand, dem Handwerk, von Blaulichtorganisationen und Kommunen. Unternehmen können hier nicht nur die Technologie erleben, sondern gemeinsam mit Experten prüfen, ob ein Einsatzszenario realisierbar ist und wie hoch der Integrationsaufwand wäre.

Einladung zum Mitgestalten

Wer die humanoiden Helfer der Zukunft live erleben möchte, sollte sich den 23. September 2026 im Kalender markieren – dann macht das Opening Event in Lemgo den Auftakt. Interessierte Organisationen können bereits jetzt Kontakt aufnehmen, um ihre eigenen Fragestellungen und Pilotideen einzubringen. Kontakte und weitere Infos sind hier zu finden: www.iosb.fraunhofer.de/hrel.

Michael Taylor has spent 25 seasons with the Mercedes-AMG Petronas F1 team, working every IT role from trackside support to engineering systems to business transformation. Today, as IT director, he leads an 18-person team responsible for one of the most data-intensive operations in the world.

When a car rolls out of the garage, it carries 300 sensors. When it’s running, it generates more than a million data points per second. Every component, system, and lap produces telemetry that engineers use to find fractions of a second — the difference between winning and losing.

“Formula One has been data-centric for many years,” Taylor says. “The key metric in our sport is the stopwatch, and that’s been true since the World Championship began in the 1950s. But now we instrument everything. If you measure it, you can improve it.”

The challenge isn’t collecting data — Formula One has been streaming live telemetry since the 1980s. It’s making decisions at speed while maintaining the governance that keeps a complex, high-stakes operation running.

For CIOs navigating the pressure to move fast on AI while managing risk, security, and data quality, Taylor’s hard-won lessons from the pit lane offer a useful framework: how to balance speed and control, when to keep humans in the loop, and why “good enough” governance beats perfect governance that never ships.

Innovation vs. stability: ‘A constant battle’

In most enterprises, the tension between innovation and control plays out over quarters or years. In F1, it happens weekly.

“It’s really tough,” Taylor admits. “And something we don’t always get right. This is where we rely on people. Industry experience is really important when making decisions around change.”

The team operates in two distinct modes. Between races, they’re at the factory in Brackley, UK. The site, which is headquarters for the design, manufacturing, and operation of their championship-winning Formula One cars, includes a 60,000-square-meter technology campus. It’s all project and program management, with room for experimentation. But as race weekend approaches, everything shifts to execution.

“We have that kind of normal mode when we’re not racing. We’re back at the factory designing and building and improving,” Taylor explains. “But as we get closer to race weekend, we switch to executing that in the most effective way. We have to not make changes that will impact engineers.”

This duality shapes every technology decision. The same agility that drives innovation during the week must yield to stability when results are on the line. Taylor calls it a “constant battle.”

Modernizing at racing speed

Mercedes-AMG Petronas had run SAP since 1999. The platform underpins the team’s entire design-to-track process — from design release through planning, procurement, manufacturing, testing, and development, all the way to reassembling the car trackside.

“All of those steps are core processes,” Taylor says.

So, when it came time to modernize, the team approached it like a pit stop: planned to the second, executed with precision. They chose RISE with SAP — the vendor’s bundled cloud ERP and migration package — agreeing to the journey in December 2024 and targeting a go-live in August 2025, aligned with the sport’s mandatory two-week shutdown.

“It’s the perfect window to make changes,” Taylor says. “We have to plan everything to perfection so it goes smoothly when we start racing again.”

They finished eight weeks ahead of schedule.

“We are control freaks because of the sport and its time-bound nature,” Taylor says. His team prefers to own and manage systems in-house rather than rely on large systems integrators who “dip their toes in and disappear,” Taylor says. With just 18 people on the IT team, they tap SAP’s expertise for specific problems, then take back the reins. “Once done, we continue to own and manage,” Taylor explains, “and SAP does what they do best.”

The secure path must be the easiest path

Intellectual property in F1 racing has a short shelf life. Once a new component is on the car and photographed in the pit lane, competitors can see it. But that doesn’t diminish the value of what’s behind it.

“The real advantage is not just the part,” Taylor says. “It’s the thinking, the modeling, the simulation, the failure modes, the trade-offs, and the development direction behind it.”

Protecting that requires an offensive security posture. Taylor’s head of information security reports directly to him, and the team actively probes its own defenses.

“Act like, think like, work like a hacker,” Taylor says. “We’re thinking about how we can counter threats without impact on end-users.”

In an engineering-permissive culture where people are empowered to move fast, heavy-handed security backfires. Taylor learned early that perfection is the enemy of progress.

“If security gets in the way of the business, the business will find ways to work around it,” he says. “The job is not to slow the organization down; it’s to make the secure path the easiest path.”

Humans in the loop

With AI evolving weekly, Taylor’s team is running pilots across the organization: machine learning for simulation, agentic workflows in production planning, copilots helping developers write code. But he’s resisting the urge to rush.

“We’re still finding our way,” he says. “There’s no one-size-fits-all. We’re playing with everything available, but in six to ten months we’ll make decisions about what to scale.”

Despite the hype around autonomous AI, Taylor remains committed to human oversight.

“I’m still very much ‘humans should be in the loop,’” he says. “When our workforce is harmonized with AI, that’s where we’ll see real benefit — where it complements our people.”

AI has also raised the bar for data governance. “Good enough now includes stronger visibility, cleaner permissions, and clearer ownership,” Taylor says. “You have to be more deliberate about what data AI is allowed to access.”

Start with consequence

Taylor’s advice to CIOs in other industries wrestling with similar questions is deceptively simple: “Start with consequence, not technology.”

In financial services, it might be customer harm or a regulatory breach. In healthcare, patient safety or loss of public trust. In F1, the consequence of a security failure is loss of competitive advantage.

“Once you understand the consequence, you can decide what needs the strongest control, what needs monitoring, what needs retention, and what simply needs better hygiene,” Taylor says.

It’s a lesson learned over 25 seasons at the edge of what’s technically possible — where decisions happen in milliseconds, and the margin between success and failure is measured in fractions of a second.

DSAG

Dammbruch in Walldorf: Um einer Wettbewerbsklage der EU-Kommission zu entgehen, macht die SAP weltweit zahlreiche Zugeständnisse, was Wartungs- und Support-Leistungen für On-Premises-Versionen ihrer ERP-Lösung angeht. Zusammengefasst verpflichtet sich der Softwareanbieter für die nächsten zehn Jahre zu folgenden Maßnahmen:

• Mehr Wahlfreiheit beim Support: Kunden können ihre SAP-Landschaft in Teilbereiche aufteilen und für jeden Bereich unterschiedliche Support- und Wartungsanbieter wählen.
• Einfachere Lizenzkündigungen: Wartungs- und Support-Verträge können in bestimmten Fällen gekündigt werden – etwa bei auslaufenden Produkten, gescheiterten SAP-Projekten, Insolvenz, Personalabbau oder dem Verkauf von Unternehmensteilen.
• Flexiblere Lizenzmodelle: SAP erweitert den Zugang zu sogenannten Single-Metric-Verträgen als alternative Grundlage für Lizenz- und Wartungsgebühren.
• Lockerung vertraglicher Bindungen: Der Kauf neuer Lizenzen verlängert künftig nicht mehr automatisch die Mindestlaufzeit bestehender Supportverträge.
• Erleichterter Wiedereinstieg: Kunden, die nach einer Unterbrechung wieder SAP-Support beziehen, müssen keine Wiederaufnahmegebühren mehr zahlen; auch Nachzahlungen werden reduziert.

Außerdem richtet SAP eine interne Clearing-Stelle ein, an die sich Kunden wenden können, wenn sie Verstöße gegen die zugesagten Verpflichtungen vermuten.

Selbst wenn die SAP die Entscheidung der EU-Kommission in einer Stellungnahme begrüßt, wirklich glücklich sind die Walldorfer mit ihren Zugeständnissen wohl sicher nicht.

Mehr Freiheiten beim SAP-Support

Was bedeutet das für SAP-Anwender, Drittanbieter und die Cloud-Strategie des Walldorfer Softwarekonzerns? Michael Bloch, Fachvorstand Lizenzen, Vertragswesen & Support bei der DSAG, gibt im CW-Gespräch Auskunft.

Herr Bloch, wie bewerten Sie die Entscheidung der EU-Kommission grundsätzlich?

Michael Bloch: Grundsätzlich finden wir die Entscheidung erstmal gut. Davon profitieren viele SAP-Kunden, insbesondere diejenigen, die ihre ERP-Systeme weiterhin On-Premises betreiben. Unsere Investitionsumfrage zeigt, dass sich nach wie vor zahlreiche Unternehmen noch nicht auf den Weg in die SAP-Cloud gemacht haben. Sie erhalten nun deutlich mehr Entscheidungsfreiheit bei der Frage, wie sie den Support für ihre bestehende Software organisieren möchten.

Wer profitiert auf Anbieterseite? Eröffnet die Entscheidung Drittanbietern wie Rimini Street neue Chancen? Kann der potenzielle Bedarf überhaupt gedeckt werden?

Bloch: Das wird spannend zu beobachten sein. Momentan ist das Thema Drittwartung zumindest im deutschsprachigen Raum ein totales Nischengeschäft. In den USA ist die Akzeptanz deutlich höher, hierzulande stehen viele Unternehmen dem Modell eher skeptisch gegenüber. Die EU-Entscheidung könnte diesem Markt nun neuen Schub verleihen. Kunden, die der aktuellen SAP-Strategie nicht folgen möchten, können künftig auf ihrer bestehenden Landschaft bleiben und den Support von einem anderen Anbieter beziehen. Das eröffnet durchaus Raum für neue Geschäftsmodelle.

Torpediert diese Entscheidung die Cloud-Strategie von SAP?

Bloch: Für die Kunden wird die Entscheidung jetzt tatsächlich zu einer Grundsatzfrage: Folge ich der strategischen Ausrichtung von SAP oder gehe ich bewusst einen anderen Weg? Wer nicht davon überzeugt ist, dass die Cloud-Strategie für das eigene Unternehmen die richtige ist, kann sich nun leichter dafür entscheiden, auf der bestehenden ERP-Landschaft zu bleiben und beispielsweise einen anderen Support-Anbieter zu nutzen.

Man muss sich allerdings über die Konsequenzen im Klaren sein. Wer auf der bisherigen Systemlandschaft bleibt – selbst mit einem alternativen Wartungsanbieter –, verzichtet auf die Innovationen, die SAP derzeit rund um das Autonomous Enterprise entwickelt. Einen Mittelweg gibt es an dieser Stelle nicht. Viele dieser Funktionen müssten Unternehmen selbst entwickeln oder mit erheblichem Aufwand nachbauen.

Ist das dann zwangsläufig eine Entweder-oder-Entscheidung?

Bloch: Nein, genau das ist der entscheidende Punkt. Viele Unternehmen haben ihre ERP-Systeme über Jahre oder Jahrzehnte stark an ihre branchenspezifischen Prozesse angepasst – manche Kunden sprechen sogar von “veredelten” Systemen. Diese Investitionen muss man jetzt nicht einfach abschreiben.

Stattdessen können Unternehmen ihre bewährten Kernsysteme weiterbetreiben und gleichzeitig gezielt Cloud-Komponenten ergänzen, etwa ein SAP Cloud ERP für die Finanzprozesse. So lassen sich bestehende Investitionen erhalten und gleichzeitig neue Innovationen nutzen. Die EU-Entscheidung eröffnet also zusätzliche Handlungsoptionen, macht die strategische Entscheidung für CIOs aber auch anspruchsvoller. Sie müssen jetzt noch genauer festlegen, wie ihre Zielarchitektur für die kommenden fünf bis zehn Jahre aussehen soll.

2027 – Schicksalsjahr für den SAP-Support

Bedeutet das, dass IT-Entscheider nun auf eine ruhige Sommerpause verzichten müssen?

Bloch: Ich glaube nicht, dass der Druck dadurch grundsätzlich größer wird. Die Extended Maintenance läuft erst Ende 2027 an, sodass noch etwas Zeit bleibt. Aber die Unternehmen haben jetzt eine zusätzliche strategische Fragestellung zu beantworten. Das eigentliche Entscheidungsjahr dürfte 2027 werden. Dann müssen viele Unternehmen festlegen, mit welcher Systemlandschaft sie künftig arbeiten und wie ihre langfristige SAP-Strategie aussieht.

Selbst Unternehmen, die auf ihrer bestehenden ERP-Landschaft bleiben möchten, erhalten offiziellen Support nur noch bis 2030. Das verschafft zwar etwas Zeit, ist aber keineswegs ein langer Planungshorizont – insbesondere dann nicht, wenn alternative ERP-Strategien oder Nachfolgelösungen geprüft werden müssen.

Deshalb ist es aus meiner Sicht positiv, dass die Entscheidung jetzt gefallen ist. Sie schafft Klarheit und gibt Unternehmen die Möglichkeit, diese neuen Rahmenbedingungen frühzeitig in ihre strategischen Überlegungen einzubeziehen.

Die neuen Regelungen erlauben es Unternehmen auch, ungenutzte Lizenzen und die dafür anfallenden Wartungsgebühren einfacher loszuwerden. Wie groß könnte das Einsparpotenzial sein?

Bloch: Konkrete Zahlen haben wir dazu nicht. Da muss man sehr vorsichtig sein, denn das hängt stark vom Einzelfall ab. Entscheidend ist, ob ein Unternehmen den SAP-Support tatsächlich vollständig kündigt, zu einem Drittanbieter wechselt oder bestimmte Produkte schlicht nicht mehr benötigt. Schließlich muss ein System auch weiterhin betrieben werden.

Gibt es zumindest eine Größenordnung?

Bloch: Nein, belastbare empirische Daten haben wir nicht. Wenn Unternehmen Produkte im Einsatz haben, die sie gar nicht mehr nutzen, können sie deren Support künftig einfacher kündigen und dadurch natürlich Geld sparen. Wie hoch dieser sogenannte Shelfware-Anteil tatsächlich ist, wissen wir aber nicht.

Für Unternehmen, die bereits auf S/4HANA oder SAP Cloud ERP migriert sind, sollte dieses Thema ohnehin weitgehend erledigt sein. Relevant ist es vor allem für diejenigen, die den Wechsel noch vor sich haben. Sie können nun prüfen, welche ungenutzten Lizenzen und Wartungsverträge sie abbauen und ob das im Rahmen der SAP-Zugeständnisse möglich ist, da auch hier Regularien zu beachten sind. Wer heute noch erhebliche Supportgebühren für Produkte zahlt, die gar nicht mehr im Einsatz sind, kann dadurch durchaus signifikante Einsparungen erzielen.

Die Optionen nutzen

Verbessert die EU-Entscheidung die Verhandlungsposition der Unternehmen gegenüber SAP?

Bloch: Das lässt sich nicht pauschal sagen. Entscheidend ist, wie ein Unternehmen seine Optionen nutzt. Grundsätzlich erzielt man aus meiner Sicht die besten Ergebnisse, wenn man gemeinsam mit SAP einen Weg in die Zukunft findet.

Warum?

Bloch: Wer beispielsweise den SAP-Support vollständig kündigt und später einen neuen Cloud-Vertrag abschließt, muss damit rechnen, auf bestimmte Incentives von SAP verzichten zu müssen. Deshalb sollte jede Entscheidung unter Berücksichtigung aller Abhängigkeiten getroffen werden.

Hinzu kommt, dass nicht nur SAP Anreize für den Wechsel in die Cloud bietet. Auch die Hyperscaler haben ein großes Interesse daran, Unternehmen für ihre Plattformen zu gewinnen und stellen teilweise sehr attraktive Incentive-Programme bereit. Das macht die Ausgangslage von Unternehmen sehr unterschiedlich.

Werden die Entscheidungen für CIOs dadurch einfacher?

Bloch: Eher im Gegenteil. Die neue Situation erweitert zwar die Handlungsmöglichkeiten, macht die strategische Abwägung aber deutlich komplexer. CIOs müssen sich jetzt fragen: Welche bestehenden Systeme haben für unser Unternehmen weiterhin einen betriebswirtschaftlichen Mehrwert? Dazu kommen Fragen wie: Wo lohnt es sich, die vorhandene Landschaft beizubehalten? Und in welchen Bereichen profitieren wir tatsächlich von den Innovationen, die SAP künftig in der Cloud bereitstellt? Genau diese Balance zu finden, wird die eigentliche Herausforderung sein.

Also bedeutet die Entscheidung vor allem, dass Unternehmen mehr Zeit gewinnen – etwa um wirtschaftlich schwierige Phasen zu überbrücken oder einen Cloud-Wechsel besser vorzubereiten?

Bloch: Ja, insbesondere für Unternehmen, die bislang keinen überzeugenden Business Case für den Wechsel in die SAP-Cloud gefunden haben. Sie können ihre bestehende Landschaft zunächst weiterbetreiben und gleichzeitig prüfen, ob sich durch den Abbau ungenutzter Lizenzen und Wartungsverträge – also Shelfware – noch Einsparpotenziale realisieren lassen. Das kann im Einzelfall durchaus helfen und verschafft mehr Handlungsspielraum.

Die Komplexität wächst

Ist mit der EU-Entscheidung das größte Problem in der SAP-Lizenzpolitik gelöst oder bleiben weitere Baustellen?

Bloch: Ein wichtiges Thema bleibt die Extended Maintenance für Unternehmen, die noch nicht auf S/4HANA umgestiegen sind. Auch hierauf wirkt sich die EU-Entscheidung aus. Unternehmen haben jetzt deutlich mehr Möglichkeiten, ihre bestehende Systemlandschaft und den Support unterschiedlich zu organisieren. Sie müssen nicht mehr zwangsläufig den gesamten Softwarebestand einheitlich behandeln, sondern können je nach Situation differenzierte Entscheidungen treffen.

Damit wächst allerdings auch die Komplexität. Unternehmen haben heute einen ganzen Werkzeugkasten an Optionen und müssen sorgfältig abwägen, welche Kombination für ihre Strategie die richtige ist.

Worauf kommt es jetzt als Nächstes an?

Bloch: Entscheidend ist nun die praktische Umsetzung der zugesagten Maßnahmen. Gemeinsam mit SAP müssen wir klären, wie die neuen Regelungen im Detail ausgestaltet werden und wie Unternehmen sie tatsächlich nutzen können. Die Verpflichtungen werden zudem von einem unabhängigen Treuhänder überwacht. Ich hoffe deshalb, dass wir gemeinsam mit SAP zeitnah mehr Klarheit über die operative Umsetzung schaffen.

Sorgt die EU-Entscheidung jetzt für zusätzlichen Handlungsdruck?

Bloch: Ich glaube aber nicht, dass der Druck dadurch unmittelbar größer wird. Bis zum Beginn der Extended Maintenance Ende 2027 bleibt noch etwas Zeit. Allerdings haben Unternehmen jetzt eine zusätzliche strategische Aufgabe: Sie müssen bewerten, welche neuen Möglichkeiten sie nutzen wollen und wie diese in ihre SAP-Strategie passen.

Wann wird es ernst?

Bloch: Das entscheidende Jahr wird aus meiner Sicht 2027. Dann müssen viele Unternehmen festlegen, mit welcher Systemlandschaft sie in die Extended Maintenance gehen und wie ihre langfristige SAP-Strategie aussehen soll. Auch Unternehmen, die sich gegen einen Wechsel in die SAP-Cloud entscheiden, gewinnen durch die EU-Entscheidung zwar etwas Zeit – der offizielle Support für ihre bestehenden Systeme endet aber spätestens 2030. Gerade wenn parallel auch alternative ERP-Lösungen bewertet werden, ist das kein besonders langer Planungshorizont.

Ihr Fazit?

Bloch: Insgesamt ist es positiv, dass die Entscheidung jetzt gefallen ist. Es wäre für die Unternehmen deutlich schwieriger gewesen, wenn bis Anfang 2027 weiter Unklarheit geherrscht hätte. Jetzt liegen die Rahmenbedingungen auf dem Tisch, und Unternehmen können sie frühzeitig in ihre strategischen Entscheidungen einbeziehen.

Check Point Software CTO Jonathan Zanger met with CSO Spain during the software company’s Engage 2026 user conference last week in Paris.

At the event, Check Point executives and representatives discussed how the company is dealing with various types of threats, how it is adopting AI securely, and how Check Point and others can leverage AI for their own benefit.

“That’s why I believe 2026 is a fascinating year to work in this field. Every technological change drastically affects cybersecurity,” Zanger told CSO Spain. “I think we’re currently witnessing the biggest change since the advent of the internet. So, without a doubt, we’re facing significant transformations.”

What follows CSO Spain’s discussion with Zanger, edited for length and clarity.

How are AI agents changing the way we detect and stop cyber threats? What new risks are they creating?

I’ll try to answer on several levels. The first is how we operate differently as a cybersecurity company protecting our clients.

We’ve always had teams of experts monitoring threats, identifying malicious actors, and creating new defenses for our products. Consequently, whenever we detected an APT group, we investigated it and created signatures to protect against it. Or when we saw a suspicious network used by threat actors, we identified its location and blocked it. While it’s true that, in many ways, we were always limited by the number of talented people capable of gathering that intelligence and turning it into actionable defenses, what has AI allowed us to do? Dramatically scale this operation.

Could you give an example?

We’ve always had red teams testing our products to ensure their security. And we’ve always valued those teams highly because they made our products significantly more secure. Now, those teams are incredibly powerful thanks to AI, working 20 times more efficiently. What do we have now? A combination of people and AI agents, with around 300 instances continuously monitoring and testing our systems. This is what allows us to deliver better cybersecurity and scale our capabilities.

But it’s clear that malicious actors are using AI to carry out their operations…

That’s right. Just as it helped us scale our operations, it’s also helped them. We’re now seeing a proliferation of smaller, faster threat groups and malicious actors, capable of conducting phishing campaigns with less expertise than before. Consequently, we’re seeing more people entering the field of offensive cybersecurity. So, perhaps the final piece of the puzzle is that organizations are adopting AI systems, and now we have a new challenge: How are we going to protect them?

In this context, what security challenges arise when AI agents can access and operate enterprise systems? In your opinion, what should organizations prepare for?

Systems used to be deterministic: Given the same input, they produced a predictable output, which made them easier to protect. With AI agents, this changes, as they understand natural language, handle ambiguity, and their behavior isn’t always predictable, requiring a new approach to cybersecurity. Furthermore, something very important must be considered: AI depends on the systems it’s connected to. There’s a tension between security teams, who seek to limit these connections to reduce risks, and those promoting AI, who want to integrate it throughout the organization to gather information from any area.

In other words, the more connections AI has, the greater the attack surface and the security risk.

How is generative AI making it easier for attackers to create cyberattacks? From your perspective, what are the most important defenses today?

AI has transformed software development, making it much faster and more accessible. But what’s happening? Cybercriminals are exploiting this same capability, allowing them to escalate attacks like phishing, ransomware, malware, and vulnerability exploitation, increasing both the speed and volume of threats.

What do you recommend doing in this scenario?

Defenders must also embrace AI. Detection and response are no longer enough when an attack can cause damage in seconds, so prevention plays a key role. While attackers maintain a certain advantage because they only need to hit once and are not subject to regulations, defenders have a differentiating factor: collaboration between teams, organizations, and security companies, which helps to level the playing field.

But you can’t deny that many AI platforms still have vulnerabilities…

That’s right, because innovation often advances faster than security. That’s why I recommend incorporating a layer of security from the very beginning of any AI project and not assuming that a platform is secure simply because it comes from a reputable vendor.

How can AI platforms be used securely in the face of the expanding attack surface? What do you consider to be the most significant risk?

Throughout the evolution of systems, innovation, and organizations, security hasn’t always been a priority from the outset of product development. We see this because we investigate different platforms, especially AI platforms, to assess their security. This has allowed us to uncover serious vulnerabilities in every AI platform we’ve analyzed over the past year, as well as in all major AI development tools.

Now, I’m not criticizing anyone here, because their job is to launch innovative products quickly. But I do believe security gaps exist. That’s why I think that, in many cases, this is the role of organizations like ours: to work with companies to ensure that when they use this innovative technology, they do so in a way that protects their data, safeguards their employees, and doesn’t increase their risk.

Would you share any lessons you’ve learned from adopting AI?

Absolutely. The lesson I take away is that when you adopt AI, whatever the use case, you must do so by incorporating a layer of security. And don’t assume a platform is secure just because it comes from an innovative AI company.

In your opinion, what are the most important innovations in security platforms today? And how do they help customers and organizations stay protected?

I’d like to identify three major areas where AI is transforming cybersecurity. The first is the use of AI to strengthen defense operations. Just as it has revolutionized software development, AI is changing how security teams work, enabling them to detect vulnerabilities, assess security posture, implement changes, and respond to threats more quickly, efficiently, and scalably.

The second area is the protection of the AI ​​applications and agents themselves. As these technologies are integrated into corporate networks, the challenge arises of ensuring they do not become a new attack vector or expose sensitive information. This is a very recent field, driven by the rapid adoption of generative AI, in which there is still ample room for innovation.

Finally, I want to emphasize the need to defend against increasingly rapid and sophisticated AI-driven attacks. To this end, I advocate combining advanced models capable of detecting zero-day vulnerabilities and anomalous behavior with AI systems that simulate the behavior of an ethical attacker. This way, organizations can anticipate cybercriminals, identify their attack surface, and strengthen their defenses before an incident occurs.

The final question concerns the needs of small and medium-sized organizations, which want AI systems to be more transparent and easier to audit when they help detect or respond to threats. What are your thoughts on this?

I believe explainability is a crucial part of what we must offer our clients as cybersecurity advocates. There’s always a tension between blocking something immediately and, at the same time, being able to explain why it was blocked. People like to understand what happened, and this presents a delicate balance. Therefore, my perspective is that we should automatically block as many threats as possible, without requiring human intervention, but we must also enable humans to understand what happened and modify the future behavior of the protection mechanisms.

Víctor Manuel Fernández attending Engage 2026 as a guest of Check Point Software.

Security leaders have been on a spending sprint. The global AI in cybersecurity market is valued at $44 billion in 2026 and is projected to reach $213 billion by 2034, a trajectory that reflects genuine belief that machine learning will close the gap between the volume of threats and the capacity of human analysts. That belief is not wrong. What is wrong is where most organizations focus when the tools stop working.

When AI-driven detection underperforms, the instinct is to tune the algorithm, retrain the model or push the vendor for a better product. The real culprit, in most cases, is sitting upstream in the data pipelines long before any model ever sees an event. Fragmented telemetry, inconsistent schemas and stale behavioral baselines are quietly degrading the performance of AI security systems across the enterprise. Fixing the algorithm without fixing the data is like recalibrating a scale while the input keeps changing.

The tool sprawl problem nobody talks about at the data level

Most large enterprises are not working with clean, unified security data. They are working with decades of accumulated infrastructure decisions. Research shows the average enterprise runs 83 different security products from 29 separate vendors, and SOC teams absorb nearly 3,000 alerts per day, with 63 percent going unaddressed. Each of those tools generates its own telemetry in its own format, with its own field naming conventions, timestamp standards and metadata schemas.

Human analysts develop an intuition for navigating that inconsistency. Machine learning models do not. A behavioral detection model trained to correlate authentication events across your identity platform, your endpoint agent and your cloud access broker will produce unreliable results if those three tools call the same field three different names. The model is not broken. It is being fed structurally incoherent data and asked to find patterns in the noise.

What schema drift actually costs you

This is where the problem becomes invisible and expensive. Schema drift, the gradual mutation of data formats across security pipelines over time, rarely triggers an alert. Log formats change when vendors push updates. New telemetry sources add fields that did not previously exist. Identity platforms rename attributes without notifying the security engineering team. Over months, the statistical patterns that trained your behavioral detection models no longer match the data those models are receiving in production.

The downstream effects are exactly what most CISOs are already experiencing: Elevated false positive rates, analyst fatigue and detection gaps that only become visible after an incident. What most security leaders do not realize is that those symptoms trace back to the data layer, not the algorithm layer. Gartner projects that through 2026, organizations will abandon 60 percent of AI projects due to insufficient data quality, and the pattern is playing out in security operations as visibly as anywhere else.

Stale baselines are an attacker advantage

The data freshness problem is underappreciated as a security risk. Behavioral AI models build baselines from historical activity. In fast-changing enterprise environments, those baselines go stale faster than most security teams recognize.

The shift to hybrid work changed access patterns dramatically. Cloud adoption changed which resources users interact with and when. Mergers and acquisitions introduce new user populations with entirely different behavioral profiles. When AI models evaluate today’s activity against baselines built from a workforce and infrastructure that no longer exist, the results are predictable: Legitimate access triggers anomaly alerts, and sophisticated attackers who study baseline patterns can blend in precisely because the model’s assumptions have not kept up with the environment.

IBM research on data quality costs puts the average annual cost of poor data quality at $12.9 million per organization. In a security context, that figure does not capture the incident response costs, regulatory exposure or reputational damage that follow from a detection failure rooted in bad data architecture.

The organizational gap that keeps this problem in place

The reason this issue persists is structural. Data pipelines are typically managed by data or infrastructure engineering teams. Detection models are owned by SOC analysts or threat intelligence teams. The AI systems that sit between those two functions often belong to neither. When detection quality drops, security teams tune parameters. Engineering teams focus on pipeline cost and availability. Nobody owns the analytical consistency of the data flowing through the system, because no one’s job description covers that specific gap.

This is a leadership problem before it is a technical one. CISOs who want AI security tools to perform as advertised need to close that ownership gap and treat security telemetry with the same rigor applied to other business-critical data assets.

Three priorities for security leaders

Addressing this does not require a platform replacement or a multi-year transformation program. It requires deliberate attention to three areas:

1. Standardize telemetry schemas across your security stack. A unified schema, even an imperfect one, gives machine learning models a consistent foundation. Establish naming conventions for common fields, normalize timestamp formats and document deviations when vendors cannot comply. This is not a one-time project. It is ongoing governance.
2. Build data quality monitoring into every ingestion pipeline. Before any event reaches an ML system, validate it for missing fields, timestamp anomalies and schema deviations. Catching data drift at ingestion is far cheaper than diagnosing detection failures after a real incident or after an attacker has already moved laterally.
3. Apply governance discipline to security data, not just business data. Lineage tracking, validation rules and version-controlled schemas belong in security pipelines as much as they belong in financial reporting pipelines. Security telemetry is a critical business asset and should be managed accordingly.

The AI-powered security tools in your stack are capable of delivering real value against modern threats. But that capability is entirely contingent on the quality, consistency and freshness of the data flowing into them. Before your organization invests another dollar in model tuning or platform upgrades, ask a harder and more productive question: When did anyone last audit the pipelines those models actually depend on?

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