I have worked alongside enterprise technology for more than 30 years and watched AI evolve from a lab experiment into the modern boardroom’s core focus. However, the last few years of implementing AI alongside our customers have delivered our most profound reality checks.

The initial hype has faded, leaving CIOs to drive real enterprise value. Based on my experience implementing Google, OpenAI and Anthropic technologies, here are the fundamental, technology-agnostic lessons every leader must anchor their strategy around.

AI as a leadership multiplier

The most common tactical error we see is treating AI as an isolated technology project. What I have observed among our customers is that true success does not come from organizations that define a standalone “AI strategy,” but rather from those leaders that integrate AI into their business strategy.

When our customers isolate AI and define an AI strategy, it inevitably treats it like a “technological toy” to experiment with. This approach yields fragmented, orphaned initiatives that fail to scale because they are fundamentally disconnected from their core corporate objectives. What I learned is that AI is not the ultimate destination; it is a powerful catalyst. We have replaced “What can AI do for our customers?” with a more strategic question, “How does AI accelerate their existing business goals?”

Think of AI like electricity. No modern corporation designs a standalone “electricity strategy.” Instead, all companies route it invisibly across the entire organization to illuminate offices, power production lines and drive communication. AI must be woven into the enterprise fabric in the exact same way, acting as an underlying utility that supercharges your existing operational model.

Integrating AI into the broader business strategy also dictates how we measure success. It forces a shift away from short-term tech vanity metrics and anchors the technology into a long-term roadmap.

When AI remains trapped within the IT department of our customers, we notice that it is relegated to a mere “software experiment.” To become a true competitive advantage, we observed that AI requires intense cross-functional orchestration. This perspective does not diminish the merit of the technical team; their expertise is fundamental for establishing the architecture, data governance and tools your enterprise requires. However, while IT builds the foundational infrastructure, it lacks the organizational authority to decide what should be built on top of it. Only the CEO or the owner of the company can step in to ensure AI leaves the “toy project” phase and integrates into the DNA of the organization.

The requirement for top-down, executive ownership stems from three critical realities observed in the field:

• Silo-smashing and data collaboration: True enterprise AI is data-hungry and that data lives across disparate business lines, finance, operations, marketing and customer service. Only the CEO possesses the cross-functional authority to demand that data silos be dismantled.
• Cultural transformation and fear mitigation: AI triggers widespread anxiety over job displacement across all industries and hierarchies. When relegated to an “IT project,” resistance spikes as teams view it as a threat to their livelihoods. When I saw the CEO lead this cultural shift directly is when I noticed the best results.
• C-Suite education and strategic alignment: The mandate for AI capability cannot just be delegated downward; the transformation must begin at the very top. I have conducted more than 70 presentations for the Board of Directors and C-Level teams. These people need to be actively educated not on technical code, but on specific business use cases, return on investment (ROI) frameworks and how AI resolves core organizational bottlenecks.

PwC’s data found that only 12% of enterprises have achieved both cost and revenue benefits from AI. Those elite 12% succeeded precisely because their CEOs embedded AI extensively across strategic decision-making and cross-functional workflows. AI is simply too disruptive and too critical to be left exclusively in the hands of technical experts. If AI is not on the CEO’s weekly agenda, it is fundamentally missing from the company’s true strategy.

AI as a new operational framework

Traditional IT systems have operated on strict algorithmic certainty: if you input a specific set of data, the system executes an immutable line of code and guarantees the same, predictable output every single time.

AI completely breaks this paradigm. Because modern AI is built on probabilistic models, it does not execute static formulas; instead, it predicts the most likely correct response based on mathematical probabilities. This means that AI solutions carry an inherent, small percentage of uncertainty and variability. A prompt entered today might yield a slightly different, though contextually valid, output tomorrow.

Executive leadership and organizational cultures must be actively educated to accept and navigate this fundamental shift. Traditional quality assurance frameworks for software are designed for a 100% success rate. Applying this rigid standard to AI will paralyze your initiatives, keeping 80% of your projects trapped eternally in the pilot phase. This happened to us in a food and beverage company in Latin America a couple of years ago. After this experience, we started to include conditions in our contracts that tolerate statistical margins of error and still define the project as a success.

In terms of cost calculation, we had to teach CIOs and business managers to forget the monthly subscription model for AI and learn to manage the primary unit of exchange in modern AI: the token.

To understand AI costs, executives must understand how large language models process data. AI models do not read full words; instead, they break text, images or code down into “pieces” called tokens. As a baseline, every 100 words process as approximately 130 to 140 tokens. Because the major AI providers use the token as their currency, your business is billed dynamically based on the exact volume of tokens consumed by every query submitted (input) and every response generated (output).

Many leaders believe AI costs are fixed due to flat-rate enterprise tiers ($25–$30/user). This is a temporary illusion. These venture-capital-subsidized rates mask true operational costs and come with dynamic usage limits. Modeling long-term ROI on them guarantees a severe budget shock when true consumption pricing takes over.

The solution is not to halt AI adoption; doing so means losing your competitive edge. Instead, the cost per token must cease to be treated as a technical footnote relegated to the IT department. It must be elevated to a core business variable.

Risks in the AI adoption model

Since the beginning of the AI boom, I have seen all our customers making a critical tactical error that could cost them heavily in the medium term: they are focusing only on operational efficiency (reducing costs with AI).

I have observed that an alarmingly high percentage of companies remain trapped in pilot phases focused exclusively on short-term cost reduction. Bain & Company’s global Automation and AI Pathfinder Survey found that the largest share of companies measuring their AI initiatives (exactly 40%) realized cost reductions of 10% or less, heavily missing their internal targets. Our customers are putting too many resources and effort into marginal financial gains and in doing so, they are jeopardizing their most valuable assets: service quality, resilience and customer trust.

Utilizing AI solely to slash headcount or cut operational corners is a dangerous trap that introduces severe field liabilities. A financial service organization in Latin America announced that they saved $1 million in customer support by replacing humans with AI chatbots. However, the mid-term reality revealed a different story: a damaged brand reputation due to AI errors and an influx of frustrated clients fleeing because the automated system cannot handle special cases.

Putting a company on an extreme AI diet might make it look leaner on next quarter’s financial statement, but over-indexing on cost-cutting will ultimately leave the business too weak to compete when market dynamics shift. We are now inviting our customers to change the question from “How much money will AI save us?” to “How will we leverage AI to exponentially increase the long-term value of our enterprise?”

Deploying enterprise AI is a marathon, not a sprint, and the terrain changes with every mile. The organizations that thrive in this next era will be those that transition from fascination to discipline, treating AI not as a magic bullet for immediate savings, but as a core capability that demands rigorous governance, architectural foresight and cultural maturity. Navigating this shift requires moving past the theoretical hype and anchoring decisions in raw, field-tested reality.

As we continue to deploy these technologies across industries, the blueprint for success is being rewritten in real time. Let’s keep this conversation going as we map out the future of business intelligence together.

This article is published as part of the Foundry Expert Contributor Network.
Want to join?

I have worked alongside enterprise technology for more than 30 years and watched AI evolve from a lab experiment into the modern boardroom’s core focus. However, the last few years of implementing AI alongside our customers have delivered our most profound reality checks.

The initial hype has faded, leaving CIOs to drive real enterprise value. Based on my experience implementing Google, OpenAI and Anthropic technologies, here are the fundamental, technology-agnostic lessons every leader must anchor their strategy around.

AI as a leadership multiplier

The most common tactical error we see is treating AI as an isolated technology project. What I have observed among our customers is that true success does not come from organizations that define a standalone “AI strategy,” but rather from those leaders that integrate AI into their business strategy.

When our customers isolate AI and define an AI strategy, it inevitably treats it like a “technological toy” to experiment with. This approach yields fragmented, orphaned initiatives that fail to scale because they are fundamentally disconnected from their core corporate objectives. What I learned is that AI is not the ultimate destination; it is a powerful catalyst. We have replaced “What can AI do for our customers?” with a more strategic question, “How does AI accelerate their existing business goals?”

Think of AI like electricity. No modern corporation designs a standalone “electricity strategy.” Instead, all companies route it invisibly across the entire organization to illuminate offices, power production lines and drive communication. AI must be woven into the enterprise fabric in the exact same way, acting as an underlying utility that supercharges your existing operational model.

Integrating AI into the broader business strategy also dictates how we measure success. It forces a shift away from short-term tech vanity metrics and anchors the technology into a long-term roadmap.

When AI remains trapped within the IT department of our customers, we notice that it is relegated to a mere “software experiment.” To become a true competitive advantage, we observed that AI requires intense cross-functional orchestration. This perspective does not diminish the merit of the technical team; their expertise is fundamental for establishing the architecture, data governance and tools your enterprise requires. However, while IT builds the foundational infrastructure, it lacks the organizational authority to decide what should be built on top of it. Only the CEO or the owner of the company can step in to ensure AI leaves the “toy project” phase and integrates into the DNA of the organization.

The requirement for top-down, executive ownership stems from three critical realities observed in the field:

• Silo-smashing and data collaboration: True enterprise AI is data-hungry and that data lives across disparate business lines, finance, operations, marketing and customer service. Only the CEO possesses the cross-functional authority to demand that data silos be dismantled.
• Cultural transformation and fear mitigation: AI triggers widespread anxiety over job displacement across all industries and hierarchies. When relegated to an “IT project,” resistance spikes as teams view it as a threat to their livelihoods. When I saw the CEO lead this cultural shift directly is when I noticed the best results.
• C-Suite education and strategic alignment: The mandate for AI capability cannot just be delegated downward; the transformation must begin at the very top. I have conducted more than 70 presentations for the Board of Directors and C-Level teams. These people need to be actively educated not on technical code, but on specific business use cases, return on investment (ROI) frameworks and how AI resolves core organizational bottlenecks.

PwC’s data found that only 12% of enterprises have achieved both cost and revenue benefits from AI. Those elite 12% succeeded precisely because their CEOs embedded AI extensively across strategic decision-making and cross-functional workflows. AI is simply too disruptive and too critical to be left exclusively in the hands of technical experts. If AI is not on the CEO’s weekly agenda, it is fundamentally missing from the company’s true strategy.

AI as a new operational framework

Traditional IT systems have operated on strict algorithmic certainty: if you input a specific set of data, the system executes an immutable line of code and guarantees the same, predictable output every single time.

AI completely breaks this paradigm. Because modern AI is built on probabilistic models, it does not execute static formulas; instead, it predicts the most likely correct response based on mathematical probabilities. This means that AI solutions carry an inherent, small percentage of uncertainty and variability. A prompt entered today might yield a slightly different, though contextually valid, output tomorrow.

Executive leadership and organizational cultures must be actively educated to accept and navigate this fundamental shift. Traditional quality assurance frameworks for software are designed for a 100% success rate. Applying this rigid standard to AI will paralyze your initiatives, keeping 80% of your projects trapped eternally in the pilot phase. This happened to us in a food and beverage company in Latin America a couple of years ago. After this experience, we started to include conditions in our contracts that tolerate statistical margins of error and still define the project as a success.

In terms of cost calculation, we had to teach CIOs and business managers to forget the monthly subscription model for AI and learn to manage the primary unit of exchange in modern AI: the token.

To understand AI costs, executives must understand how large language models process data. AI models do not read full words; instead, they break text, images or code down into “pieces” called tokens. As a baseline, every 100 words process as approximately 130 to 140 tokens. Because the major AI providers use the token as their currency, your business is billed dynamically based on the exact volume of tokens consumed by every query submitted (input) and every response generated (output).

Many leaders believe AI costs are fixed due to flat-rate enterprise tiers ($25–$30/user). This is a temporary illusion. These venture-capital-subsidized rates mask true operational costs and come with dynamic usage limits. Modeling long-term ROI on them guarantees a severe budget shock when true consumption pricing takes over.

The solution is not to halt AI adoption; doing so means losing your competitive edge. Instead, the cost per token must cease to be treated as a technical footnote relegated to the IT department. It must be elevated to a core business variable.

Risks in the AI adoption model

Since the beginning of the AI boom, I have seen all our customers making a critical tactical error that could cost them heavily in the medium term: they are focusing only on operational efficiency (reducing costs with AI).

I have observed that an alarmingly high percentage of companies remain trapped in pilot phases focused exclusively on short-term cost reduction. Bain & Company’s global Automation and AI Pathfinder Survey found that the largest share of companies measuring their AI initiatives (exactly 40%) realized cost reductions of 10% or less, heavily missing their internal targets. Our customers are putting too many resources and effort into marginal financial gains and in doing so, they are jeopardizing their most valuable assets: service quality, resilience and customer trust.

Utilizing AI solely to slash headcount or cut operational corners is a dangerous trap that introduces severe field liabilities. A financial service organization in Latin America announced that they saved $1 million in customer support by replacing humans with AI chatbots. However, the mid-term reality revealed a different story: a damaged brand reputation due to AI errors and an influx of frustrated clients fleeing because the automated system cannot handle special cases.

Putting a company on an extreme AI diet might make it look leaner on next quarter’s financial statement, but over-indexing on cost-cutting will ultimately leave the business too weak to compete when market dynamics shift. We are now inviting our customers to change the question from “How much money will AI save us?” to “How will we leverage AI to exponentially increase the long-term value of our enterprise?”

Deploying enterprise AI is a marathon, not a sprint, and the terrain changes with every mile. The organizations that thrive in this next era will be those that transition from fascination to discipline, treating AI not as a magic bullet for immediate savings, but as a core capability that demands rigorous governance, architectural foresight and cultural maturity. Navigating this shift requires moving past the theoretical hype and anchoring decisions in raw, field-tested reality.

As we continue to deploy these technologies across industries, the blueprint for success is being rewritten in real time. Let’s keep this conversation going as we map out the future of business intelligence together.

This article is published as part of the Foundry Expert Contributor Network.
Want to join?

If you ask us in an official setting, our official position is that software engineering norms still apply. Rigorous CI/CD pipelines, elegant architectural patterns, and an unyielding commitment to maintainable code remain the standard. We will use weighty words like “determinism,” “scalability,” “idempotency,” and “domain-driven design.”

But behind closed doors, late at night, bathed in the glow of a dark-mode IDE, a different and more sordid reality is exposed. Hunched over the console with a manic gleam in the eye, the programmer has become power-drunk on LLMs. Like mad wizards casting spells, we summon the awesome powers of models and agents to satisfy our every programming whim—and commit acts of software engineering that would make Fred Brooks blush.

Let’s just be honest about what is actually happening.

Esoteric knowledge is superfluous

Forget OOP and FP. Forget the CAP theorem, the holy crusade of DRY, and the design patterns. Honestly, you can even forget what frameworks, runtimes, and deployment platforms you are using. The AI will figure out what is best to use and understand what is already in place. We have more mental bandwidth for working on our side project (a novel about AI taking over the world). 

Of course, I exaggerate. A little.

The docs are dead to us

We still say RTFM, but the truth is, we haven’t really read a page of vendor documentation since 2023. Stack Overflow, once our Internet Mecca, is a husk. When a package throws a weird exception, we don’t trace the execution path or read the release notes. We highlight the red text, copy the entire 200-line stack trace, dump it into the chat, and wait for the machine to spoon-feed us the solution.

Better yet, we just have the agentic IDE spot the error, divine a solution, and ask us if it’s OK. We might glance at the problem-solution description, if we have gone around the circle on the problem for a few cycles. Maybe. If we don’t have the agent set up for auto-confirm.

We used to buy heavy tomes like “Rust In Action” that were more like masonry blocks than literature. Now? We just ask an AI to transliterate our JavaScript logic into Rust. We are no longer engineers methodically learning a system. We are glorified copy-paste orchestrators hoping that the stochastic parrot behind the prompt guesses the syntax correctly.

We ignore how the back end is wired

We act like we meticulously designed the data flows, carefully crafted the relational constraints, and mindfully mapped the API relationships. The reality is rather more disturbing: We asked the AI to scaffold a modern deployment, hooked it up to a back-end database, and just sort of… ran it.

It created security rules we don’t fully understand. They do seem to work, however, which is nice. 

It generated a schema that we skimmed for about four seconds. It looks reasonable.

It wrote infrastructure-as-code scripts that provisioned cloud resources we are hoping don’t blow a hole in the budget. Presumably, whoever is in charge of that will manage it by stuffing the metrics into another chatbot.

We nodded, committed the code, and went to lunch. If management asked us to manually deploy the stack from scratch, configure the environment variables, and wire the API routes without our chat window, we would give them a vacant stare.

We understand that management is also using AI to manage the project.

Our tests are uncomfortably incestuous

Test-driven development (TDD) used to be a beautiful dream, ever just beyond reach. It made us feel glorious and despondent at turns. It would burden us with sprawling dependencies if implemented too religiously. (See The Grug Brained Developer in this regard.)

But now we can attain 95% test coverage almost effortlessly. Why not just add them in while we are auto-generating everything else?

We can now wax at length to anyone who will listen about our astounding test coverage and our automated quality assurance. Unit tests, integration tests, smoke tests, you name it. What we conveniently leave out is that the AI wrote the complex application logic, and then we asked the exact same AI to write the test suite to validate the code it just dreamed up.

It is a hermetically sealed loop of algorithmic self-congratulation. The mocks, the edge case, and the assertions are an echo chamber of the model’s original assumptions. The machine is grading its own homework, giving itself an A+.

And we are happy to accept this because, beautifully, when the code has to change, the AI will effortlessly hallucinate new tests to adapt to the churn.

We pass off the AI’s architecture as strategy

AI can produce astonishing design documents. Truly breathtaking. They are cogent, they’re beautifully formatted, and they seamlessly bridge the gap between high-level business goals and granular technical specs. They even include those auto-generated sequence diagrams that wow management.

When we present these spotless architectural proposals in the Tuesday sprint planning meeting, we lean back, take a long sip of coffee, and humbly wave away the team’s praise.

What we don’t mention is that we spent exactly four seconds generating it.

Are these AI-generated documents just as liable as human ones to hide severe, mortal flaws in scope and alignment? Absolutely. They might contain a foundational logic bomb that will eventually doom the entire project. But the markdown is so crisp, and the bullet points are so persuasive, that the eye just glides right over it. We will never truly know the depth of the disaster until it is far too late. But hey, we’ll burn that bridge when production catches fire. Until then, we are strategic visionaries.

We’re addicted to vibe coding (but only in secret)

We loudly mock the term on social media. We roll our eyes in Slack channels when the kids on TikTok talk about vibe coding their new startups. We fiercely cling to our identities as hardened, serious developers who understand memory management, garbage collection, and bitwise operators. We are professionals, damn it.

But late at night, when the managers are asleep and no one is looking? We absolutely love it. We love just throwing a chaotic, half-baked thought at the canvas, pouring a drink, and watching the AI magically build a functioning user interface based entirely on our long-deferred whims. I may finally build that working Ultima V clone. The thrill of typing “Create an app that tracks my cryptocurrency portfolio but makes it look like the interface from Neuromancer” and having it appear 30 seconds later is heady stuff.

The more deeply rooted in the hard, old-school realities of programming, the more profound is the joy the developer finds in the possibility of AI coding. 

We beat the problem into submission with prompts

Like Adam Sandler in “Uncut Gems,” we are convinced the next round will fix everything. This is us with prompts. When things are going really off the rails, instead of putting our boots on and wading into the brambles of complexity, we resort to tonal adjustments. These range from the condescending:

This problem is not fixed. Look at it closely. The error is right here.

To the desperate:

We have been working on this same problem for hours now!

To the pathetic:

Can’t you find a different approach to try?!

The astonishing part? It often works.

But there is no poetry left at the bottom of the rabbit hole; it is verbal warfare. When the context window collapses, when the regressions start cascading, and when the AI stubbornly refuses to follow the most basic rules of temporal logic, the mask of professionalism drops away and something far more atavistic makes its appearance. We stop asking nicely, stop trying to understand the why, delete the pleasantries, and capslock our intent.

What we have here is a failure to communicate!

We feed the same failing stack trace back into the prompt over and over and over again, aggressively hammering the constraints, explicitly forbidding certain libraries, and pasting in release notes just to confirm that the AI lacks the latest APIs. We force the model down a narrower and narrower path until the code finally stops throwing errors. We don’t actually debug anymore, trace variables, or step through functions. We just apply relentless, iterative pressure until the machine surrenders. We beat it into submission. And then, we push to production.

In fact, there is a real skill here—a sheer “will to completion” that remains in the act of building software. We invest just as much time, energy, and heart wrestling the bot as we ever did emitting syntax.

A blacker box

The only profession more given over to using AI like a cursed Level 13 artifact than programming is writing. Writing of course is far more open to public scrutiny than code.

And while my tongue has been firmly in my cheek here, my faith in coders as good guys makes me more curious to see what we create than troubled by the dangers. 

It was once the case that only other programmers could understand what programmers were doing, what they were producing. Now not even that is true. Only the machine knows what the machine is doing. We just keep it tethered to our aims. Hopefully.

Data products help standardize how raw data sets, data warehouse views, and data lake logical views are combined and used to deliver analytics and AI capabilities. By developing data products, teams can streamline much of the upfront data pipelines, governance, and management needed to deliver trusted data assets that people, tools, and AI can then use for different purposes.

The way you cook a meal can serve as a helpful analogy. You can choose to purchase only raw ingredients like tomatoes, wheat flour, eggs, and fresh herbs to make a favorite pasta dish. The approach works well when you have the time and skills to cook from scratch or want to prepare a nice meal for a small family. Otherwise, you may want to buy canned tomatoes, your favorite box of pasta, and a spice mix to cook the same meal, especially if you are time-constrained, are cooking for many people, or want a consistent finished product.  

Like the not-from-scratch pasta meal, data products provide a similar level of time-saving effort, so that analytics and AI capabilities start with consistent, streamlined ingredients. Here are five questions teams should consider as they develop data products and their standards.

When to build a data product?

Most organizations can’t afford to develop data products as intermediaries for every data visualization, machine learning model, or AI agent. There’s cost and time to develop data products, and once they’re deployed or “on the shelves,” their product managers must oversee their ongoing support and life-cycle management. So when should agile data teams develop data products, and how should they prioritize which ones are more important? One starting point is to consider data products built from a single data set and what it means to productize them.

“A data set should really become a data product when multiple teams start relying on it to make decisions or to power applications,” says Danielle Ben-Gera, vice president of engineering at Crunchbase. “Developing proper governance, clear ownership, versioning, and a managed life cycle for changes becomes important, or you’ll just be shipping fragile pipelines that break downstream work.”

A second consideration is treating the use of ungoverned data sets as a form of data debt. Establishing a data product can be a tactical approach to standardize usage and address risks.

“Organizations should build a data product when data sets are being used across teams without strong governance, well-defined processes, or clear ownership,” says Yaad Oren, managing director at SAP Labs US and global head of research and innovation at SAP. “When anchored in a unified data foundation, data products eliminate silos, create shared understanding, and establish secure, standardized access that enables teams to leverage the same assets with confidence.”

A third consideration is to apply manufacturing principles by building data products for defined customers, driving reuse, and creating efficiencies. Drafting the data product’s vision statement and qualifying its business value is particularly important when a data product requires combining multiple data sources. It raises the question of how standardization delivers efficiencies, improves quality, reduces data security risks, and provides other benefits.

Christopher Zangrilli, vice president of technology strategy at Vertex, says, “Leaders should ask whether the data will reduce cycle time, improve decision accuracy, or mitigate compliance risk as a lens on the business impact. When governance, change management for adoption, quality, and value measurement are embedded from the start, data products transform from experimental tools to strategic assets.”

Why define standards for data products?

The products at the grocery store have packaging with a detailed list of ingredients, an expiration date, and a price. Data governance leaders should also standardize how data products are defined, cataloged, and managed. 

“Any modern data product should answer four questions clearly: where the data originates, how it transforms across systems, who or what is consuming it, and what governance obligations apply at every step,” says Abhi Sharma, cofounder and CEO at Relyance AI. “Without that end-to-end context, teams are building features on top of data they don’t fully understand.”

Although food products publish their ingredients and label them for dietary restrictions, few document the sourcing of raw ingredients and the logistics of the path from farm to grocer. But when building data products, capturing data lineage may be required in regulated industries and is particularly important when standardizing data sources for AI applications. 

“Without lineage, teams operate blind, and governance becomes reactive cleanup,” says Carter Page, executive vice president of research and development at Astronomer. “When teams can see where data originated, how it was transformed, and every system that relies on it, updates become predictable, the right pipelines get tested, the target stakeholders are notified, and breaking changes are documented before they cause incidents.”

What is a data product’s life cycle?

Life-cycle management of an API, application, or AI model requires defining a release schedule for delivering improvements, fixes, and other required upgrades. Data product life-cycle management involves several similar disciplines. Ulf Viney, executive vice president of engineering, support, and operations at Precisely, says, “Life-cycle management must include versioning, testing, structured deployment, and stakeholder communication.”

One fundamental difference with data products is that their life-cycle management is closely linked to how their underlying data sets grow or undergo structural changes. Having a data product that works today but isn’t resilient to changes or doesn’t generate alerts when fixes are necessary can break downstream use cases and erode stakeholders’ and users’ trust in the data.     

“Managing data as a product means that data consumers can trust the data from the outset, which requires a sustainable and scalable governance framework that ensures data is easy to find, understand, and use,” says Bethany Sehon, senior director of enterprise data at Capital One. “By embedding observability, quality checks, and interoperability from day one, you can manage the full data life cycle from versioning and testing to measuring adoption and performance.”

Teams managing mission-critical, real-time data products that feed multiple downstream analytics and AI use cases should consider the following devops and data governance practices.

• Establish data governance non-negotiables, especially on setting data quality benchmarks, qualifying any data biases, and adhering to data privacy policies.
• Support advanced continuous integration/continuous delivery (CI/CD) and continuous deployment, with continuous testing and production deployments fully automated.
• Ensure all data integrations have observable dataops with monitoring for data quality issues and alerting when pipelines stop running. IT services should be defined to address requests and incidents. 
• Align with data management technology platform strategies, including data fabrics, data security posture management (DSPM), document processing, and vector databases.

How to encourage adoption?

Unfortunately, building a data product doesn’t guarantee adoption. Think back to the challenges of getting code reuse, API adoption, or standardizing in-house-developed devops tools. These are all examples of intermediary products aimed at reducing developer toil and improving quality, yet many teams adopted “not-invented-here” postures and do-it-yourself practices rather than learning and adopting standards developed by other teams.

Data products face even greater challenges, especially when they aim to consolidate data silos or eliminate spreadsheets. Product managers overseeing data products must develop a change management program to grow adoption and gather feedback.

“A data product earns its place when it drives a real business decision and can be trusted at scale,” says Quais Taraki, CTO at EnterpriseDB. “Treat data products like software, with versioning, testing, and controlled releases, not one-off pipelines. That discipline securely delivers the right data to the right place and turns data into measurable value through adoption, speed, and risk reduction.”

Product managers can accelerate adoption by communicating how a data product aligns with the business’s AI strategy and culture transformation. For example, show how the data product improves AI literacy, democratizes AI through the right business use cases, or prepares the workforce to use AI agents.

How to measure business value?

The value delivered by a customer-facing product is often measured through revenue impact, usage metrics, and customer satisfaction (CSat). Internal, employee-facing products can be measured in terms of workflow efficiency, productivity improvement, and employee satisfaction (ESat). Data products are intermediaries, so quantifying their value can be more challenging.   

“Too many organizations still treat data products as technical outputs instead of strategic assets,” says Daniel Ziv, global vice president of AI and analytics at Verint. “Their true value becomes clear when assessing how uniquely the data is generated, how much measurable impact it can drive across decisions, and how you can safely extract insight while managing risk. When every organization has access to the same AI models, competitive advantage comes from your unique data and how quickly you turn it into action.”

Sunil Kalra, head of the Databricks center of excellence at LatentView Analytics, adds, “Value should be measured through adoption, usage, and outcomes such as faster insights, reduced manual work, and improved revenue or cost performance.”

A best practice is to use digital transformation velocity metrics such as time to data, time to decision, time to innovation, and time to value. As more organizations seek to deliver business value from AI agents, creating data products will be seen as a path to accelerate delivery, reuse data assets, reduce risks, and manage costs.

Ein paar Mal im Jahr tüftelt das Google-Doodle-Team hochwertige Spiele aus, um Jahrestage und besondere Ereignisse zu feiern oder um auf ein aktuelles Thema aufmerksam zu machen. In der Regel dauern diese Games zwischen zwei und zwanzig Minuten – einige können aber auch mehr als eine Stunde in Anspruch nehmen. Wir haben eine Auswahl der besten Google-Doodle-Spiele für Sie zusammengestellt.

Die besten Google-Doodle-Spiele

Pac-Man

Geht es um Arcade-Klassiker aus den 1980er-Jahren, führt kein Weg an Pac-Man vorbei. 2010 veröffentlichte Google das weltberühmte Spiel als Doodle Game, um das 30-jährige Jubiläum des gelben Vielfraßes zu feiern. Erleben Sie pure Nostalgie, während Sie gelbe Punkte, verschiedene Früchte und vielleicht sogar den ein oder anderen Geist verschlingen.

Ziel des Spiels ist es, alle Punkte auf dem Spielfeld abzuräumen und dabei den Geistern auszuweichen. Wird man von einem Geist berührt, heißt es “Game Over”. Pac-Man lässt sich mit den Pfeiltasten auf der Tastatur steuern. Auch eine Partie zu zweit ist möglich: Klicken Sie zweimal auf “Münze einwerfen” und Ms. Pac-Man kommt als spielbarer Charakter dazu. Pacmans Liebste wird mit den Tasten A, S, D und W gesteuert.

Celebrating Pizza

Pizza-Enthusiasten mit Spieltrieb dürften mit dem Google Doodle Game “Celebrating Pizza” aus dem Jahr 2021 Speichelfluss-geschwängerten Spielspaß finden. Das Spielprinzip ist dabei schnell erklärt: Sie müssen Pizzas mit diversen Belägen entsprechend der Bestellungen der Kunden zuschneiden. Dabei werden die Aufgaben zunehmend diffizil – etwa weil Sie auf die Positionierung bestimmter Zutaten achten müssen, um den Task perfekt zu erfüllen.

Celebrating Popcorn

Neben Pizza zählt Popcorn wohl zu den weltweit beliebtesten Snacks, insbesondere in Kombination mit den neuesten (oder klassischen) Blockbustern. Grund genug für Google, auch dem buttrig süßen (oder salzigen) Mais-Produkt ein spielerisches Denkmal zu setzen. Das ist als Multiplayer-Spass konzipiert und versetzt Sie in die Rolle eines Maiskorns, das um jeden Preis verhindern muss, aufzuplatzen. Dazu gilt es zum Beispiel, fiesen Buttergeschossen auszuweichen.

Pani Puri

Das in Südostasien populäre Street Food Panipuri bezeichnet eine frittierte Teighülle, die mit diversen Chutneys, Saucen und Zutaten gefüllt werden. Das verspricht vielfältige, kulinarische Gaumenfreuden – insofern die Teiglinge direkt nach der Herstellung verzehrt werden.

Wie sich das aus Perspektive eines Street-Food-Händlers anfühlt, dürfen Sie mit diesem Google Doodle spielerisch nachempfinden. Dazu müssen Sie “lediglich” die richtige Anzahl von Bestellungen auswählen, bevor die hungrigen Kunden unleidig werden. Der Schwierigkeitsgrad beziehungswiese die Komplexität der Bestellungen steigt dabei kontinuierlich an.

Gerald Lawson

Das Konzept modulbasierter Spielekonsolen wie Atari VCS, Nintendo Entertainment System oder Sega Mega Drive geht auf Gerald “Jerry” Lawson zurück, der bei Fairchild Semiconductor in den 1970er Jahren an der Entwicklung der Spielekonsole Channel F beteiligt war.

Lawsons Beitrag zum künftigen Erfolg der Videospielindustrie fand erst späte Würdigung: 2011 wurde er von der International Game Developers Association geehrt. Er wurde zudem in die World Video Game Hall of Fame aufgenommen. Im Google Doodle Game zu seinen Ehren dürfen Sie seinen Karriereweg spielerisch erkunden.

Great Ghoul Duel

Anlässlich des Halloween-Festes 2022 hat Google ein Online-Multiplayer-Spiel an den Start gebracht, in dem die Benutzer in Viererteams gegeneinander antreten, um “Geisterflammen” zu sammeln und in die eigene “Homebase” zu transportieren – ohne dabei dem gegnerischen Team die Möglichkeit zu geben, diese zu klauen.

Das versprüht nicht nur auf dem Papier “Capture the Flag”-Wettbewerbsflair, der sich per QR-Code auch mit Freunden oder Familie erleben lässt. Oder Sie spielen dank Google-Cloud-Support einfach gegen zufällig ausgewählte Spieler auf der ganzen Welt.

Baseball

Pünktlich zum amerikanischen Unabhängigkeitstag am 4. Juli 2021 veröffentlichte Google Doodle Baseball, um seine Nutzer den Tag mit einer Partie des für Europäer meist undurchsichtigen Ballspiels feiern zu lassen. Um die Sache noch amerikanischer zu machen, spielen Sie als verschiedenartig ausgestaltetes Junk Food, das für Baseball-Happenings typisch ist – etwa Hotdogs oder Nachos mit Käse. Das gegnerische Team besteht übrigens aus Erdnüssen.

Wenn Sie das Spiel beginnen, kommt ein zufälliger Snack auf das Spielfeld. Nun müssen Sie die Leertaste drücken, um Ihren Schläger zu schwingen, sobald der Pitcher den Ball wirft. Ballgeschwindigkeit und Wurfstil des Pitchers verändern sich hierbei, je mehr Punkte bereits erzielt wurden. Das Spiel endet, wenn dem Pitcher ein Strikeout gelingt, Sie also dreimal daneben geschlagen haben. Wie viele Punkte können Sie erzielen?

Basketball

Wir bleiben in der Welt des Sports: Basketball ist ein Einzelspieler-Zeitvertreib, bei dem Sie digitale Körbe werfen können. Das Spiel war Teil einer viertägigen Google-Doodle-Aktion, bei dem Sie auch Ihre Fähigkeiten im Fußball und Slalomkanu testen konnten. Laut Ryan Germick, dem Leiter von Google Doodle, wurden diese vier Spiele innerhalb von vier Tagen über eine Milliarde Mal gespielt.

Die Spielmechanik ist einfach: Halten Sie die Leertaste gedrückt, um Kraft aufzubauen und lassen Sie sie dann los, um den Ball zu werfen. Wird zu viel oder zu wenig Kraft aufgebaut, geht der Wurf daneben. Finden Sie heraus, wie viele Bälle Sie innerhalb von 30 Sekunden im Netz versenken können.

Schuppentier-Spiel im Sonic-Style

Das Schuppentier ist das meist gehandelte Tier der Welt und vom Aussterben bedroht. Um das Bewusstsein für dieses Problem zu schärfen, hat Google am Valentinstag 2017 ein Doodle zu Ehren der gefährdeten Tierart veröffentlicht.

Das Schuppentier-Doodle-Spiel ist ein Side-Scroller, der an Segas Sonic Games erinnert. Statt eines blauen Igels bewegen Sie hier ein Schuppentier mit der linken und rechten Pfeiltaste und lassen es mit der Leertaste über Hindernisse springen. Die Aufgabe: Sammeln Sie so viele Gegenstände wie möglich ein und schaffen Sie es bis zur Ziellinie, bevor die Zeit abläuft.

Coding für Karotten

Anlässlich des 50-jährigen Jubiläums von Kids Coding hat das Google Doodle-Team Coding for Carrots veröffentlicht. Dieses einfache “Drag-and-Drop”-Spiel für Kinder führt in das Konzept des Programmierens ein. Das Prinzip ist einfach: Sie müssen ein Kaninchen zu den Karotten schicken, die auf dem Spielfeld verteilt sind.

Dazu gilt es, Codeblöcke mit der Richtungsangabe für den nächsten Sprung (links, rechts, oben, unten) in eine Leiste zu ziehen. Stimmt die Sequenz der Codeblöcke, landet das Kaninchen bei der Karotte. Das Level ist geschafft, wenn alle Karotten eingesammelt wurden, wobei die erforderliche Sequenz mit jedem Level komplizierter wird.

Insel der Champions

Insel der Champions ist mit zwei Stunden Spielzeit eines der umfangreichsten Google-Doodle-Spiele. Ursprünglich sollte es anlässlich der Olympischen Sommerspiele 2020 veröffentlicht werden – aufgrund der pandemiebedingten Verschiebung nahm sich das Google-Doodle-Team die Zeit, weiter am Spiel zu arbeiten. Das hat sich ausgezahlt.

Mit den Pfeiltasten bewegen Sie Ihren Charakter über die Weltkarte und nehmen Herausforderungen der Inselbewohner an. Die Steuerung ist bei jeder Mission anders, daher sollten Sie die jeweiligen Anweisungen sorgfältig lesen. Nehmen Sie an allen Wettbewerben teil, um zu erfahren, ob Sie es an die Spitze der Rangliste schaffen.

Zauberwürfel

Zauberwürfel (auch Rubik’s Cube), die aus sechs Farben, sechs Seiten und 54 Quadraten bestehen, gibt es bereits seit 1974. Im Jahr 2014 veröffentlichte Google das Rubik’s Cube-Doodle zur Feier von 40 Jahren, in denen die farbigen Würfel Menschen auf der ganzen Welt frustriert haben.

Ziel des Spiels ist es, durch Drehen und Wenden der Blöcke jede Seite des Zauberwürfels auf eine einheitliche Farbe zu bringen. Um die Google-Doodle-Version zu spielen, klicken und ziehen Sie Ihren Cursor, um den Würfel zu drehen. Frustfreies Gelingen!

Ein Hoch auf Gartenzwerge

Haben Sie schon einmal den Drang verspürt, Gartenzwerge mit einem Katapult durch den Garten zu schleudern? So oder so: Jetzt haben Sie die Gelegenheit. Um die geschichtsträchtigen kleinen Statuen – der heute übliche Gartenzwerg entstand im 19. Jahrhundert in Thüringen – zu ehren, präsentierte Google Doodle im Juni 2018 The Garden Gnomes.

Ziel des Spiels ist es, möglichst viele Blumen zu pflanzen, indem Sie die Gartenzwerge so weit wie möglich katapultieren. Zum Spielen brauchen Sie nur die Leertaste: Mit dem ersten Tastendruck setzt sich das Katapult in Bewegung, mit dem zweiten wird der Gartenzwerg abgeworfen. Je besser das Timing beim Loslassen, desto weiter die Flugbahn des Zwergs und desto mehr Saatgut in der Erde.

Loteria

Loteria ist ein traditionelles mexikanisches Kartenspiel, das oft auch als mexikanisches Bingo bezeichnet wird. Der Ansager zeigt eine Karte an und wenn der Spieler die gleiche Karte auf seinem Spielbrett hat, darf er eine Bohne darauflegen. Hat er die Karte nicht auf seinem Spielbrett, muss er auf die nächste warten. Jede Runde erfordert ein bestimmtes Bohnen-Muster, um zu gewinnen.

Neben Pac-Man ist Loteria ein weiteres Google-Doodle-Spiel, bei dem Sie gemeinsam mit anderen Spielern Ihr Glück versuchen dürfen.

Meow-loween

Meow-loween wurde anlässlich von Halloween 2016 entwickelt. In diesem Doodle Game steuern Sie eine schwarze Katze namens Momo, die ihre Zauberschule vor anstürmenden Gespenstern retten muss.

Jedes Gespenst weist dabei ein Symbol auf, das über seinem Kopf erscheint. Um den Geist ins Reich der Dunkelheit zu verbannen, müssen Sie das Symbol mithilfe der Maus auf den Bildschirm zeichnen. Dabei ist Schnelligkeit gefragt, denn im Laufe der Zeit erscheinen immer mehr Geister mit immer längeren Symbolkombinationen.

Scoville

Vermutlich haben auch Sie schon einmal den Fehler begangen, eine vermeintlich milde Chili am Stück in den Mund zu stecken. Wenn Sie dann voller Reue versucht haben, das Gefühl von Höllenfeuer schnellstmöglich loszuwerden, konnten Sie hoffentlich auf das Wissen von Wilbur Scoville zurückgreifen und es mit der lindernden Wirkung von Milch bekämpfen. Scoville führte auch ein Messverfahren für die Schärfe von Chilischoten ein. Zu Ehren des 151. Geburtstags des Pharmakologen bietet das Scoville-Doodle-Spiel die Möglichkeit, verschiedenen Chilisorten zu zeigen, wer der Boss ist.

Der virtuelle Scoville probiert in seinem Labor nacheinander verschiedene Chilisorten und arbeitet sich so die Scoville-Skala nach oben. Nachdem er eine Schote gegessen hat, reicht man ihm zur Schmerzlinderung ein Eis. Hier kommen Sie ins Spiel: Am unteren Spielfeldrand gibt es einen Balken mit einem Kreis, der sich schnell hin und her bewegt. Um die Schote mit der Eiskugel zu treffen, muss man den Kreis so nah wie möglich an der Balkenmitte stoppen – entweder mit der Leertaste oder per Mausklick. Je weiter Sie kommen, desto schärfer werden die Gegner.

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Mit jeder neuen Smartphone- und Tablet-Generation wächst die Zahl der Devices, die ausgemustert werden. In den allermeisten Fällen werden die (oft gar nicht so) alten Gerätschaften dann zu Staubfängern. Was schade ist, denn es gibt diverse Möglichkeiten, um alten Android-Telefonen und –Tablets zu neuem Glanz und Nutzwert zu verhelfen. Zum Beispiel die folgenden.

1. Wireless Trackpad

Mit Hilfe der richtigen Software und einigen Minuten Zeitaufwand für die Konfiguration, können Sie Ihr Android-Gerät im Handumdrehen zu einem Trackpad, beziehungsweise Controller für Windows-, Mac und Linux-Rechner umfunktionieren.

Alles was Sie dazu brauchen, ist die in der Basisversion kostenlose App “Unified Remote” sowie eine Bluetooth- oder WLAN-Verbindung. Mit der Gratis-Version stehen Ihnen grundlegende Maus- und Tastatursteuerung sowie spezielle Remote-Befehle für Medienwiedergabe und Energiemanagement zur Verfügung. Die 4,99 Euro teure Vollversion der App bietet darüber hinaus erweiterte Funktionen – beispielsweise programmspezifische Remote-Funktionen.

Wenn Sie sich für eine Version entschieden haben, brauchen Sie nur noch die zugehörige Software für Ihren Rechner herunterladen.

2. Remote-Terminal

Abgelegte Android Devices eignen sich außerdem auch ganz gut, um sie zu einem Remote-Terminal für Ihren PC umzufunktionieren. Dafür benötigen Sie lediglich die kostenlose Google-Software Chrome Remote Desktop. Die entsprechenden Apps finden Sie im Chrome Web Store sowie dem Google Play Store.

Ein ausführliches Tutorial, was Sie mit dieser Lösung tun können – und vor allem wie – bietet folgender Artikel:

• So geht Fernzugriff per Google-Browser

3. Mobile Storage

Cloud Storage mag heutzutage en vogue sein, aber auch der gute, alte physische Speicher hat seine Vorzüge, sowohl wenn es um Verfügbarkeit als auch den Zugriff geht. Dafür stehen Speichersticks und externe Festplatten in rauen Mengen zur Verfügung – oder Sie nutzen dafür Ihr altes Android-Handy. Das bietet den zusätzlichen Benefit, gleich ein Interface mit an Bord zu haben, um mit dem gespeicherten Content zu interagieren. Optional können Sie diesen dann auch in die Cloud verschieben, per E-Mail versenden oder in kollaborativen Umgebungen nutzen, falls erforderlich oder gewünscht.

Um das alte Android-Gerät zum mobilen Storage-Device umzufunktionieren, empfiehlt es sich, das Gerät zunächst zurückzusetzen, um so viel Speicherplatz wie möglich freizumachen. Anschließend können Sie damit loslegen, Daten zu schaufeln.

4. KI-Chatbot

Generative KI entwickelt sich zu einem immer bedeutenderen Produktivitäts-Tool für Unternehmen. Wenn Sie ebenfalls dem LLM-Fieber erlegen sind oder aus anderen Gründen – beispielsweise zur Akkuschonung des Erstgeräts – ganztags einen dedizierten KI-Chatbot um sich haben möchten, unterstützt Sie dabei ein ausgemustertes Android-Smartphone oder auch –Tablet.

Dazu brauchen Sie nicht viel mehr als eine entsprechende App aus dem Google Play Store. Zum Beispiel:

• ChatGPT,

• Gemini oder

• Copilot.

5. Universal Smart Remote

Sogar wirklich alte Android-Geräte bringen genügend Leistung mit, um sie zur Universal-Fernbedienung für smarte Devices sowie Audio- und Video-Systeme umzufunktionieren. Dazu stehen Ihnen diverse Möglichkeiten in App- und auch Hardware-Form zur Verfügung. Zum Beispiel:

• Smart-Home-Apps wie Nest oder Hue,

• Google TV in Kombination mit einem Chromecast-Dongle oder

• Streaming-Apps wie Roku und Netflix.

Alternativ können Sie auch mit Hilfe von Plex einen vollwertigen Medienserver einrichten und Ihr altes Android Device dazu nutzen, eigene lokale Inhalte auf Ihren TV zu streamen.

6. Forschungsunterstützung

Alte Android-Klötze eignen sich auch, um zur wissenschaftlichen Forschung beizutragen. Auch dazu stehen verschiedene App-Optionen im Google Play Store zur Verfügung. Sie alle funktionieren auf die gleiche Weise: Nach der Installation nutzt die Software (bei aktiver WLAN-Verbindung) die Rechenleistung des Geräts, um den jeweiligen Forschungsbereich voranzutreiben.

Eine erwähnenswerte Optionen in diesem Bereich ist etwa:

• MyShake, eine App der Berkeley-Universität, das die Sensoren des Geräts nutzt, um Erdbeben zu erkennen und zu analysieren.

7. Überwachungskamera

Wenn Sie vorhaben, sich demnächst eine Inhouse-Überwachungskamera anzuschaffen: Sparen Sie sich das Geld und nutzen Sie dafür Ihr altes Android-Handy. Mit der richtigen App behalten Sie Ihre Räumlichkeiten jederzeit im Auge – ohne dabei auf Funktionen wie Videoaufzeichnung oder Bewegungserkennung verzichten zu müssen.

Dazu empfiehlt sich die kostenlose Android-App IP Webcam (die auch in einer 5,49 Euro teuren Pro-Version zur Verfügung steht). Nach der Installation greifen Sie per VLC Player oder einem beliebigen Browser auf den Videofeed zu.

8. Ersatzkamera

Wenn Ihr Android-Smartphone noch relativ jung ist, stehen die Chancen gut, dass es eine relativ hochwertige Kamera an Bord hat. Das können Sie sich in Situationen zunutze machen, in denen Sie Ihr Haupttelefon nicht nutzen wollen – sei es aufgrund von drohenden Umwelteinflüssen oder weil Sie unterwegs keinen Wert auf eingehende Nachrichten legen.

Das Beste daran: Sie brauchen dazu nichts – außer ein Altgerät, das Ihren Foto-Ansprüchen gerecht wird.

9. Videokonferenzstation

Wenn Sie sich schon immer einen fest verankerten Access Point für Videokonferenzen und virtuelle Meetings auf Ihrem Schreibtisch gewünscht haben, befähigt Sie dazu jetzt Ihre Android-Altlast. Dazu benötigen Sie:

• eine ordentliche Docking-Station sowie

• die Collaboration-App Ihrer Wahl (etwa Zoom oder Teams).

Wenn Sie noch einen Schritt weiter gehen möchten, könnten Sie mit ausreichend Android-Altgeräten auch ein Haus- oder Office-weites Videokonferenzsystem auf die Beine stellen.

10. Gourmet-Hub

Falls Sie gerne kochen, können Sie alte Android-Handys oder Tablets auch zu multifunktionalen Kommandozentralen für die Küche umwandeln. Dazu brauchen Sie im Wesentlichen zwei Zutaten: Einen Android Launcher, um den Homescreen des Geräts anzupassen und Sprach- sowie weitere Freihandbefehle starten zu können. Eine Übersicht bietet folgender Artikel:

• Die besten Android- Launcher

Anschließend brauchen Sie nur noch die richtigen Apps für Ihren Zweck zu installieren. Mit Bezug auf kulinarische Genüsse empfehlen sich beispielsweise:

• Rezept– oder

• Notiz-Apps.

11. Webcam-Fenster

Wenn der Blick aus dem Fenster eher für Verdruss sorgt, schaffen Sie mit einem ausgemusterten Android-Gerät Abhilfe. Dazu machen Sie es einfach per App oder Browser zu einem Schaufenster in einen anderen Teil der Welt. Zum Beispiel mit:

• der Webcams-App von EarthCam, die eine beeindruckende Sammlung von Webcam-Feeds auf der ganzen Welt bereithält

• der Website des Zoos von San Diego, der Live-Einblicke in diverse Gehege bereitstellt.

• der Webpräsenz des Monterey Bay Aquariums, die Unterwasser-Einblicke bereithält.

• die Webseite des Zoo Zürich, die Live-Bilder zum Teil auch in 360-Grad-Ansicht liefert.

12. Digitaler Fotorahmen

Mit einem günstigen Standfuß können Sie Ihr Android-Handy von vor ein paar Jahren auch in einen digitalen Bilderrahmen mit Cloud-Verbindung umwandeln.

Dazu brauchen Sie nicht viel mehr als die standardmäßige Google-Fotos-App. Die erzeugt aus Ihren gespeicherten Fotos auf Knopfdruck eine Diashow.

13. E-Reader

Eine unterbrechungsfreie Leseumgebung kann sich auf Geschäftsreisen oder im Pendelstress als sehr wertvoll erweisen. Um Ihr altes Android-Telefon oder –Tablet zu diesem Zweck zu nutzen, brauchen Sie ein weiteres Mal nur die richtige App – zum Beispiel:

• Google Play Bücher,

• Amazon Kindle oder

• Nook.

Nach der Installation deaktivieren Sie App-Benachrichtigungen oder wechseln Sie einfach in den Flugmodus, um ungestört in bestem E-Reader-Stil zu schmökern.

14. Audio-Player

Was für den Lesegenuss gilt, gilt auch für den Hörgenuss: Wer Wert darauf legt, dabei die Zwänge seines Smartphones hinter sich zu lassen, tut gut daran, diesen Task an ein Android-Altgerät auszulagern. Dazu brauchen Sie noch nicht einmal eine App.

15. Digitaler Tischkalender

Auch um ein abgelegtes Android Device zum digitalen Tischkalender zu machen, brauchen Sie nicht viel. Lediglich eine Docking-Station und eine qualitativ hochwertige Kalender-App. Geht es um Letztere, stechen zwei Google-Play-Store-Optionen heraus – nämlich:

• Google Kalender sowie

• DigiCal Kalender.

Dabei zeichnet sich die DigiCal-App insbesondere durch ihr anpassbares Interface aus. Die 5,49 Euro teure, kostenpflichtige Variante der App bietet weitere Konfigurationsoptionen und Themes.

16. Auto-Navigationssystem

Wenn Ihr Auto nicht bereits ab Werk mit einem Navigationssystem ausgestattet ist, können Sie ausgemusterte Android-Gerätschaften auch dazu umfunktionieren. Dazu brauchen Sie lediglich eine passende Docking-Station für Ihr Fahrzeug, einen mobilen Hotspot – und die Google Maps App.

Falls Sie Ihr Primär-Smartphone nicht als Hotspot nutzen können oder wollen, können Sie alternativ auch die benötigten Routen über eine WLAN-Verbindung herunterladen und sie anschließend offline nutzen. Das funktioniert auch per Sprachbefehl, insofern das alte Gerät Support für den Google Assistant mitbringt.

17. Lernwerkzeug

Abgelegte Android-Geräte eignen sich unter Umständen auch gut als Lern- und Spiel-Gagdets für Ihre Kinder. Dazu können Sie relativ einfach ein eingeschränktes Benutzerprofil einrichten – die Google-Support-Seite gibt darüber Auskunft, wie das im Detail vonstatten geht.

Wenn Ihr Android-Altgerät über Android 7.0 (oder höher) verfügt, stehen Ihnen darüber hinaus über Googles Familiy-Link-App zusätzliche Tools zur Verfügung, um den Geräte- und App-Zugriff zu kontrollieren beziehungsweise zu limitieren.

18. Digitaluhr

Die Investition in einen neuen Wecker können Sie sich ebenfalls sparen, wenn Sie einfach ein altes Android-Telefon für diesen Zweck einsetzen. Die kostenlose Google-App “Uhr” ist dazu ideal geeignet.

19. Spende

Sollten die bisherigen Optionen Sie nicht überzeugt haben, können Sie Ihre alten Android Devices auch dazu einsetzen, etwas Gutes zu tun – zum Beispiel, indem Sie sie im Rahmen folgender Projekte spenden:

• Handys für die Umwelt

• Rote Nasen “Recycling4smile”

• Pro Wildlife Althandy-Sammelaktion

(fm)

Dieser Artikel ist im Original bei unserer Schwesterpublikation Computerworld.com erschienen.

Whether you are using an AI code generator, vibe coding, or applying spec-driven development methodologies, your job doesn’t end with AI writing the code. Whether you’re using AI to develop applications, APIs, data pipelines, AI agents, or other automations, writing the code is just one part of the job. Developers must still perform code validation, test applications, automate deployment, and configure infrastructure.

According to one survey, only 16% of a developer’s time is spent writing code. The remaining 84% is spent on other activities including defining requirements, triaging bugs, and addressing vulnerabilities.

Additionally, while AI code generation speeds up development, it can come at the cost of quality and collaboration. In Atlassian’s State of Teams 2026 survey, nearly 50% of respondents say their AI outputs aren’t reliably high quality and admit that using AI is a compromise between speed and quality. Knowledge workers say the pressure to execute is also problematic, with 87% saying they lack time to coordinate and 70% saying their processes aren’t well-optimized for AI.

So, although AI capabilities have changed drastically in the past few years, code-generation tools are not the only ways AI can improve software development. In fact, developers should seek additional AI capabilities to support the full software development life cycle (SDLC). Here are five recommendations for the AI coding stack. 

Scale up testing environments

If coding is faster, development teams should have suitably configured environments that they can use to quickly and easily test changes against real APIs and databases. Testing apps and AI agents against environments that don’t mimic production can slow down development.

“Remote + local” development environments (local execution with remote context) are one option to accelerate testing. Developers can code locally on their own physical or virtual machine, but build and deploy to remote instances. Additionally, when developing AI agents, developers need an execution environment, such as secure sandboxes or ephemeral virtual machines.

“GenAI has been a step-change for developer productivity, absorbing the repetitive work of writing boilerplate, tests, and refactors so engineers can focus on intent and design,” says Aviram Hassan, CEO and cofounder at MetalBear. “But by compressing the time it takes to produce all of this, genAI has also exposed what’s always been the real bottleneck in the SDLC: the feedback loop against the real world. Validating code and configurations against a realistic cloud environment still depends on the same slow build-and-deploy cycles teams have tolerated for years.”

The goal should be to remove the friction and delays from where developers code to a complete, real-world infrastructure they can use to validate changes. Three tools to review are mirrord, Signadot, and Telepresence.

Validate the AI-generated code

At a recent Coffee With Digital Trailblazers LinkedIn Live event that I hosted on AI coding competencies, one speaker shared how he quickly went from a short spec to more than 10,000 lines of AI-generated code. He admitted he didn’t have the time, expertise, or tools to validate the code. He’s not alone. In Sonar’s State of Code Developer Survey, 96% of developers don’t fully trust AI’s output, but only 48% always verify it before committing.

“Agentic software development is generating code faster than any team can manually review it, but speed without confidence only results in technical debt,” says Scott Sanders, corporate vice president of engineering at Sonar. “What’s needed to avoid this is an automated independent verification layer embedded directly into the development workflow—one that unifies code quality and code security into a single, deterministic platform to deliver actionable intelligence before code ever reaches the repository.”

A big concern is that AI-generated code can produce 1.4 times as many critical issues as code created by developers, according to CodeRabbit’s State of AI Versus Human Code Generation Report. Top issues include code readability, cross-site scripting, code formatting errors, and incorrect concurrency control.

Another challenge is that 82.4% of AI tools originate from third-party packages, according to Snyk’s 2026 State of Agentic AI Adoption. The implication is that development teams have much more code to validate than they develop themselves, whether by humans or AI code generators.

“When tools like Cursor are installing dependencies and running actions on a developer’s behalf, they can unintentionally pull in malicious or unvetted packages,” says Randall Degges, vice president of AI engineering and developer relations at Snyk. “That’s why techniques like intercepting tool calls, validating inputs and outputs, enforcing least-privilege access, and isolating credentials are becoming foundational to how AI-driven development systems operate. Without security embedded directly into the agent loop, teams risk shipping faster into more exposure, not less.”

According to Qodo’s report on The AI Coding Paradox, 89% of enterprise engineering teams have experienced an AI-generated code incident and have had a production outage caused by AI-generated code. Development teams building a large portfolio of AI agents or heavily relying on AI code-generation capabilities may want to look at AI code-review tools that provide more contextual analysis than basic static code review tools.

“Current AI coding assistants suffer from a severe amnesia problem, and each session starts without memory of an organization’s unique context, subjective standards, and business logic,” says Itamar Friedman, CEO and cofounder at Qodo. “To safely scale AI, it requires integrating stateful systems equipped with persistent organizational memory that continuously learn from past pull requests and automatically enforce enterprise-specific governance. Ultimately, developers need tools that ensure code is guided by continuously learning organizational experience rather than just raw machine-generated code.”

Tools to review include static application security testing (SAST), software composition analysis (SCA), software bill of materials (SBOM), and AI code review tools.

Security and end-to-end testing

Even when AI-generated code passes all the tests, how can devops teams validate whether it meets business and non-functional technical requirements? Many devops teams have invested in continuous testing, and some support continuous deployment, but the underlying assumptions behind those practices are being challenged now by who is coding and how much code is being generated. 

Some spec-driven development platforms aim to bridge the gap. Tools like Appian Composer and SAP Joule Studio 2.0 generate product requirements documents (PRDs) before coding, enabling the introduction of business acceptance criteria. These tools create knowledge graphs from the business processes implemented on their platforms and provide environments for validating AI agents before deployment.

“For most organizations, the AI code-generation methodology question matters less than the verification question,” says Gal Vered, CEO and cofounder at Checksum.ai.  “Whether your team is prompting from intent or working from specs, AI-generated code still needs to be validated against a production environment before it ships.”

Beyond functional testing, developers must look at new security concerns, especially as AI agents integrate with Model Context Protocol servers. “Most teams are stacking generation tools on top of review tools and on top of testing tools, but without security validation embedded at every stage, you’re just automating the path to your next breach,” says Harshit Agarwal, CEO at Appknox. “Mature teams treat security feedback as a non-negotiable part of the build loop, running automated checks continuously rather than catching issues after the fact.”

Add observability tools

Developers save an average of 3.6 hours per week with AI coding tools, according to one report, and the more experienced engineers achieve the largest productivity gains.

What’s one way to blow these savings? When defects get pushed to production, it’s often the site reliability engineers and senior developers who are left to triage and resolve the issue. Establishing observability practices as a devops non-negotiable is a development investment that pays off significantly to help diagnose issues, resolve errors, and improve performance.

“In data and AI systems, even small changes like model updates, tool decisions, or shifts in data flow can silently cascade into issues no one anticipated, and the AI agent has no way to know that,” says Barr Moses, cofounder and CEO at Monte Carlo. “Leading teams are addressing this by embedding observability across the entire agentic stack, particularly at precommit checkpoints, so agents can surface the true impact of changes before they go live.”

While many devops teams have mature observability practices for APIs, applications, and data integrations, observability practices for AI agents are relatively new. One technique to consider is AI tracing platforms with notation queues for human review and LLM-as-judge evals. A second option is to implement an AI gateway with observability, caching, routing, and cost-tracking capabilities.

Develop reusable agent skills

One last element of the AI stack, especially for organizations heavily investing in AI agent development, is to adopt best practices for developing reusable skills embedded in code-generating tools.

“A key emerging pattern is purpose-built AI skills: reusable, scoped instructions that give agents deep context for specific tasks, rather than relying on general-purpose prompting alongside antagonist agents that challenge other agents’ outputs,” says Phillip Goericke, CTO of NMI. “The defining shift is that developers are no longer writing code with AI assistance—they’re architecting the systems that produce and validate it.”

Development organizations that leverage code-generation tools are recognizing that coding is just one part of delivering business value from AI and resilient AI agents. Developing AI skills and establishing an AI stack are steps toward scaling to a dependable AI software development life cycle.

The humble story of Cosentino starts in marble in southeastern Spain in 1945, and subsequent generations have gradually expanded into more diverse materials and color palettes, so now the company operates in more than 120 countries. And what also began in a small factory is now a vast complex exceeding 27 million square feet where machines, cranes, and robots move freely, loading pallets full of product destined for every corner of the globe.

Together with partner Microsoft, Cosentino is tackling, like many others, how to most effectively adopt and maximize the potential of AI, and it will be the first industrial company in Spain to adopt the Microsoft Discovery platform. This technology, designed to accelerate scientific research, is particularly interesting to a company whose success is based on the discovery and validation of new materials for kitchens, facades, and interiors.

The research platform developed by Microsoft combines agentic AI, high-performance computing, and advanced KM to accelerate scientific and engineering processes by automating tasks such as literature reviews, hypothesis generation, simulations, and analyses, in order to integrate public and private data into a unified environment for researchers and engineers.

For Cosentino, Discovery opens the door to anticipating optimal formulations before production, and reduces the need for physical experimentation. The expected impact is a significant acceleration of innovation, greater research efficiency, and the possibility of exploring new materials.

AI for design

Today, AI is being used at the company to design countertops and building materials, valued precisely for their diversity and color. While Europe generally opts for more subdued kitchen colors, it’s not the same in other parts of the world. So AI can save a significant amount of work when translating designs of particular tastes onto surfaces. Where four or five iterations were previously required for a finish, for instance, now only one is needed. This converts into time savings and increased efficiency, but also into the ability to anticipate trends.

In the sector of interior design, similar to the fashion world, manufacturers release catalogs showcasing new materials and collections in spring and autumn, as the majority of orders are placed before summer or Christmas — periods when people typically display their home improvement projects.

Valentín Tijeras, Cosentino’s corporate VP of product, R&D, and quality, says AI not only reduces costs but also helps develop creativity. “It’s like talking to your thesis advisor, who suggests new ideas,” he says.

CLARity of AI

The latest major contribution of AI to Cosentino’s business isn’t in design or the discovery of new surfaces, however, but in managing its sprawling sales force of 4,000 across the globe. Their biggest concern, the thing that wastes the most time: process management.

The company estimates at least a third of their workday is spent managing schedules and reporting on their activities. In practice, this means more than a thousand people are idle, or not contributing clear value where the company needs it most.  

To streamline these processes, Cosentino developed CLAR, an AI agent for sales management based on Microsoft technology. Its operation is practical and simple, leveraging what a salesman, or anyone for that matter, never loses sight of: their smartphone.

Rafael Domene, Cosentino’s global CIO, has been integral in delivering CLAR, which enables any operator through a mobile device to use voice commands to consult their schedule, prepare meetings, and access key information for each business opportunity without the need to interact directly with multiple applications or processes.

CLAR manages these tasks, automatically capturing, recording, and keeping information up to date. This allows sales teams to focus on higher-value activities and move toward a more agile, personalized, and efficient customer relationship.

Keeping the workforce intact

Ángel Madariaga Álvarez, Cosentino’s VP of engineering and projects, says that even with advancements like CLAR and other AI-enabled points of automation, such innovation doesn’t result in layoffs. “Engineers rotate through different roles, receive training, and learn new functions,” he says, adding that the company continues to train and hire new people every year despite AI’s impact on efficiency.

With these approaches, Cosentino aims to evolve toward an autonomous enterprise, a model in which people, AI, agents, data, and processes work in an integrated, harmonious way to drive smarter and more efficient management based on value creation.

The US government decision to force Anthropic to close down its latest and greatest AI models, Fable 5 and Mythos 5, was only the next step in a burgeoning battle between AI providers and ignorant politicians. 

Anthropic was on top of the world. Its Mythos 5 LLM had everyone excited. (If you believe the hype, it was kind of scary, too.) Even Anthropic CEO Dario Amodei admitted — or boasted? — that Mythos would bring an “enormous increase in the amount of vulnerabilities, in the amount of breaches” to us all. But with that fear came the promise of more AI power than ever. 

Then, the roof caved in.

On June 12, the US Commerce Department used its export-control powers to demand that Anthropic cut off access to its Fable 5 and Mythos 5 models for all foreign nationals, citing national security concerns and fears of jailbreaks. After figuring out it had no way to do that, Anthropic pulled both of its newest frontier AI models offline worldwide.

Just what an AI company needs! All other Claude models, like the Opus and Sonnet series, remain online. But, come on, AI sales are all about the newest and most powerful models.  

Adding insult to injury — and this is true at many high-tech companies — Anthropic has many employees who aren’t US citizens. This means Anthropic’s own programmers can’t work on their latest models.

Of course, this isn’t the first time US President Donald J. Trump and company have tried to put a spoke in Anthropic’s wheels. Back in February, Anthropic refused to give Defense Secretary Pete Hegseth the power to use its models to spy on American citizens and to power autonomous weapons.

This go-around, it wasn’t because Anthropic refused to kowtow to Trump’s officials. It was, they say, out of fear that these new models could be used to attack American interests. 

Mind you, no one in Trump’s regime has the tech chops to know just how dangerous, or not, any AI model is. As I recently noted, Trump’s AI executive order has no teeth. Nor, more to the point, is there anyone in the administration with a clue about AI.

Specifically, at the Department of Commerce, neither Commerce Secretary Howard Lutnick nor William Kimmitt, undersecretary of commerce for international trade, knows a thing about AI. Just what we need, more political hacks deciding tech policy. 

So, how did they discover that Fable and Mythos were theoretically a danger to the US? Good question. According to The Wall Street Journal, it was Amazon CEO Andy Jassy who told the Trump administration that Fable was untrustworthy. Guess what? AWS offers its own full cloud AI stack, starting with Amazon Bedrock for foundation models, Amazon SageMaker for training and deployment, and a growing set of agentic AI tools and services. 

In other words, Amazon is not a neutral party; it’s a competitor. Funny, that.

Sure, Amazon also has partnerships with Anthropic. But, in case you haven’t noticed, all the big AI companies are in bed with each other. That doesn’t stop them from fighting. What’s heating this up is that the AI companies are no longer offering flat-rate subscriptions and are replacing them with far more expensive, token-based pricing schemes. 

Armed with this information, Commerce gave Anthropic 90 minutes to fix its “problem.” Right. AI development is fast, but it’s not that fast. In addition, according to Anthropic, officials haven’t spelled out exactly what’s wrong. They only know that Commerce claims there was a “narrow, non‑universal jailbreak” in Fable.   

That’s it. That’s all. 

Anthropic has also observed, with reason, that similar jailbreaks are possible on other leading models, like OpenAI’s GPT‑5.5. Those others, however, haven’t been hammered with comparable export controls.

The AI and security experts who do have an AI clue believe Commerce is behaving stupidly. (You won’t get any argument there from me.) For example, in an open letter, “On Transparent AI Cyber Protections,” they said Commerce’s directive “has taken the best models away from defenders, created market uncertainty, and risked America’s AI leadership without any real risk to justify it,” warning that pulling capabilities “away from defenders without a good reason when our adversaries are rapidly advancing is dangerous.”

Exactly so. 

Besides, as Alex Zenla, co-founder and CTO of security company Edera, observed, Fable’s capability to identify insecure code sections is the baseline for any model you’d trust to write secure code. The same capability exists in GPT-5.5, Opus, Sonnet, and Kimi 2.7 — it’s not unique to Fable. Pulling Fable from defenders doesn’t remove the capability from the threat landscape. It just removes it from the people trying to build safer systems.”

This is not about AI safety or security. 

What this is really about is opening a new front in Trump’s war against the liberal-leaning Anthropic. Mind you, Anthropic isn’t really liberal. This has more to do with Anthropic not following in other tech firms’ groveling to Trump. 

However, Trump doesn’t seem to realize that by essentially shutting down Anthropic’s biggest move to date, he’s also telling the world that they can’t rely on American AI companies down the road. Sure, in the short run, this hurts Anthropic. In the long run, it’s going to be another reason for Europe and other countries — taking digital sovereignty seriously — to avoid doing business with any American tech company. 

In the meantime, Anthropic and Trump administration officials are in tense talks over whether, and under what safeguards, the models could return to the marketplace. Commerce indicates it might allow a narrower relaunch if jailbreak issues are resolved and additional controls are in place — whatever those might be. 

Since it’s really all about massaging Trump’s ego, I’m not feeling terribly optimistic. Just ask Israeli Prime Minister Benjamin Netanyahu how well letting Trump set the terms of engagement goes. It’s not pretty. 

Attacks on AI systems and disinformation starred as key elements of a ransomware tabletop exercise CSO participated in during this month’s Infosecurity Europe conference.

The “Enter the War Room” exercise — organised and run by cybersecurity vendor Semperis — featured a scenario focused on a cyberattack against a fictional supermarket chain, BlueCart.

CSO took part as one of eight members of a red team of supposed national state–linked attackers (APT 64, AKA Checkout Chaos) that was as much interested in thrashing the reputation of the supermarkets it targeted and causing disruption as in making money.

Last year we took part in a comparable exercise, also organised by Semperis, but on the opposite team as a media advisor to a blue team defending the systems of a fictional water utility from attack.

Rules of engagement

Ransomware tabletop exercises place participants in a realistic but fictional cyberattack scenario where each team takes 10-minute turns to devise their attack and defence plans before reporting their findings to the other side.

Each turn involves an attack-response cycle with Semperis acting as game master. The whole exercise lasted around two hours.

Like many tabletop exercises, this particular simulation was designed to get participants to think outside the box, improve cross-team communications, and develop improved incident response capabilities by exposing blind spots.

Each team was made up of seven participants from public and private sector organisations, including former hackers, security consultants, and incident response execs. Unlike the 2025 edition of the event, the names and identities of those who participated was kept confidential this year.

Data leak on aisle two

As the target of this year’s “Enter the War Room” exercise, grocery retailer BlueCart has an AI-enhanced supply chain command centre, designed to provide visibility across inventory, logistics, and fulfilment. The system has a key role in keeping shelves stocked and deliveries moving.

Logistics, scheduling, warehouse operations, and store fulfilment have been centralised in a new technology and operations centre.

The red team began with reconnaissance to find a supplier or logistics partner that already has trusted connectivity into the AI command centre, then use that foothold to reach shared portals, APIs, or remote-access tooling.

A combination of stolen credentials from developers, weak MFA enforcement, and over-privileged service accounts were used to hack into planning and inventory systems and steal loyalty card data — three of several access vectors typically employed today. The attackers also attempted to break into BlueCart’s Active Directory environment using a combination of phishing and credential theft.

The attackers also sought to exploit the retailer’s poorly segmented building-management network to disrupt heating, cooling, and ventilation operations.

The blue team of defenders decided to rebuff ransomware demands, which the attackers responded to by leaking loyalty scheme data to cause reputational damage against BlueCart.

False alerts and misinformation

The attackers generated thousands of false alerts to confuse the work of security operations analysts and hinder response. To counteract this, the defenders established out-of-band communications channels.

Continuing their attempts to disrupt BlueCoat’s operations, the attackers disrupted payroll operations. Using job losses due to a move to AI-powered operations, attackers took to social media sites such as Reddit and 4chan in attempts to rage bait hacktivists into getting involved with attacks on BlueCoat.

The attackers also created a deepfake of BlueCart’s CEO — made to look as if filmed on his private yacht — saying the job cut will allow BlueCoat to make increased profits and expand its operations.

Fake delivery orders for inappropriate goods, such as sex toys, and perishable items such as ice cream were generated by the attackers.

The blue team said it had established a honeypot so the attackers were only ever in that environment and never had access to its real environment nor customer data.

Testing the relative merits of these claims and counter claims — which seemed at times like a rap battle rather than a game with structured rules such as chess — was beyond the scope of the exercise.

The tabletop exercise offered an immersive experience without featuring any analysis of technical data, such as exercise-specific log files.

Post-mortem

Speaking after the exercise, Guido Grillenmeier, principal technologist at Semperis, explained that Enter the War Room was not a technical tabletop exercise but a way for participants to “broaden their minds and have fun.”

The scenario was designed to hone cyber incident preparedness in a similar way to how war games are used to train military forces during peacetime.

Simon Hodgkinson, strategic advisor at Semperis, said the exercise illustrated how real preparedness and resilience depends more on people and process than on tools.

“The blue team were well structured, thinking about how to minimise the financial and reputational impact on the business and recognising that, should the red team detonate destructive capability, they would need to prioritise and stand up a minimal viable business,” Hodgkinson said.

“The red team were innovative, using techniques like deception to distract the blue team so they could achieve their objective.” Hodgkinson added. “Despite the motivation not being financial they did take the opportunity to make money through media manipulation and shorting stock.”

Vor etlichen Jahren war der Markt für Entwickler-Tools eher überschaubar. Das Angebot umfasste hauptsächlich Compiler, Debugger und IDEs. Als dann die visuelle Softwareentwicklung aufkam, gesellten sich Komponenten-Sets hinzu – dennoch uferte der Markt nicht aus und das Marketing war vor allem unkompliziert. Heute sieht das alles völlig anders aus: Das Web und die Cloud haben inzwischen dazu beigetragen, dass das Angebot an Developer-Werkzeugen explosionsartig gewachsen ist. Und: Die Welt der Softwareentwicklung ist deutlich komplexer geworden. Früher war ein Deployment nicht mehr als kompilieren, auf eine Floppy-Disc packen und ab zum Kunden. Das ist lange passé.

Angesichts der Größe, die der Markt für Entwickler-Lösungen inzwischen erlangt hat, geht es auch um viel Geld. Und um das einzunehmen, müssen die Anbieter der Tools – beziehungsweise deren Marketing-Abteilungen – die Aufmerksamkeit der Entwickler auf ihre Produkte lenken. Dabei gibt es nur ein “kleines” Problem: Developer verweigern sich in aller Regel Marketing- und PR-Offensiven. In diesem Artikel werfen wir einen Blick darauf, warum sich das so verhält – und wie Anbieter und Marketing-Experten die Entwickler erreichen könnten.

Warum Entwickler nicht auf Marketing-Geschwätz stehen

Die Gründe für die Abneigung von Entwicklern gegenüber Werbearien hat diverse Gründe. Diese vier gehören zu den stichhaltigsten.

• Die Benutzer von Dev-Tools sind in der Regel nicht diejenigen, die Budgets managen – sondern ihre Vorgesetzten. Bei einigen Entwicklungsteams werden die Tools von oben vorgegeben. Andere (meist glücklichere) Teams können dem Management diesbezüglich ihre Empfehlungen unterbreiten. Aber am Ende unterschreiben die Entwickler nicht den Scheck.
• Entwickler stehen Hype in jeglicher Form meistens kritisch gegenüber. Wenn Sie es also darauf anlegen wollen, dass Ihr Tool von vornherein durchgängig abgelehnt wird, werfen Sie mit Superlativen um sich. Oder behaupten Sie, dass Ihr Produkt sämtliche Bugs in jeder Software beseitigt. Entwickler lassen sich von Buzzwords nicht blenden – das überlassen sie lieber ihren Managern.
• Entwickler geben mehr auf die Empfehlungen von Kollegen als der von Werbefachleuten. Wenn Devs von einem neuen Tool hören, suchen sie in der Regel eher auf Reddit oder anderen einschlägigen Foren nach Einschätzungen als auf der Webseite eines Herstellers. Das hat zur Folge, dass traditionelle Marketingkampagnen bei Entwicklern eher ins Leere laufen. Wenn Sie jetzt als Anbieter gerade darüber nachdenken, ihr Produkt einfach dort entsprechend zu “platzieren”, dürfen Sie schneller mit einem Lifetime-Bann rechnen als Mark Zuckerberg auf einer beliebigen Datenschutzkonferenz.
• Entwickler wollen sehen, wie die Dinge in der Praxis performen – nicht mit Marketing-Pitches gelangweilt werden. Auch nicht, wenn der die Gestalt eines “Whitepaper” annimmt. So etwas wird sich kein Dev zweimal ansehen. Kommen Sie besser erst gar nicht auf die Idee, ein Verkaufsgespräch mit einem Entwickler-Team anzuberaumen. Das ist reine Zeitverschwendung.

Was für Developer bei Tools wirklich zählt

Was Entwickler wirklich wollen, ist selbst zu recherchieren, selbst auszuprobieren und sich in Eigenregie mit der Dokumentation eines Tools beschäftigen. Eine Webseite für Entwickler-Tools sollte darauf optimiert sein und es möglichst einfach machen, schnell die Informationen zu finden auf die es für Entwickler ankommt. Sie können wochenlang an PR-Botschaften auf Ihrer Produkt-Seite laborieren – die Augen von Entwicklern werden sich dennoch auf die kostenlose Testversion Ihrer Anwendung und die Dokumentation richten. Machen Sie diese Dinge leicht zugänglich und Sie sind der Konkurrenz einen Schritt voraus.

Ein Menüpunkt auf der Seite, der sich dediziert an Developer richtet und Quicklinks zu Sandbox-Umgebungen, funktionierenden Code-Demos, die Ihr Tool in Aktion zeigen, sowie kostenlosen APIs bietet, ist das, was Entwickler wirklich suchen. Empfehlenswert ist außerdem, das Preisgefüge klar und deutlich sichtbar zu machen. Hinweise wie “Kontaktieren Sie unser Experten-Team für ein persönliches Angebo”“ haben in der Regel schon verloren. Die einzige Botschaft, die das an Entwickler sendet, ist “das wird teuer”. Kurzum: Geben Sie Entwicklern, was diese wirklich wollen. Lassen Sie sie in Ruhe Ihr Tool austesten, statt sie mit Anrufen und Webinar-Anfragen zu penetrieren. Setzen Sie auf einfache, simple und leicht zugängliche Botschaften. Der Rest ergibt sich dann von selbst – falls Ihr Tool Developer wirklich weiterbringt. (fm)

Dieser Artikel ist im Original bei unserer Schwesterpublikation Infoworld.com erschienen.

You may have noticed, but people don’t like AI very much. Of course, it depends on the circles you move in, but survey after survey shows that the general perception is becoming increasingly negative. Anecdotal evidence for this can also be found when talking to people outside the IT sphere, or hanging out on the internet. In many comment fields, AI is almost a swear word today.

A rather telling survey came out the other day: One in five Swedes feels ashamed about using AI at work. And it’s easy to imagine how the conversation goes in the workplace to provoke such feelings, when the technology that is supposed to revolutionize how we work is seen as something shameful.

Last week, union think tank Futurion released a report on Swedes’ views on AI, and it’s not exactly fun reading for tech optimists. Swedes believe AI will make almost everything worse, whether it’s about the labor market, democracy, or security. 

The survey also shows that younger people are very, very worried. Six out of 10 students look with horror at their future working life, with fewer jobs and outdated knowledge.

International surveys say the same thing. An American Gallup poll from April showed that Generation Z is becoming increasingly angry and anxious, while their enthusiasm and optimism are rising. We have also seen this physically recently when clips were circulated of students booing prominent graduation speakers who talk positively about AI. 

At the same time, another form of physical resistance to AI development is emerging in the form of a movement that fights against the construction of new data centers. It is mainly occurring in the US, but it is only a matter of time before we see the same thing in Sweden.

There are several reasons for all this. An important key is found in Stanford’s big AI report from last spring, which, among other things, examined the difference in attitudes towards AI between AI experts and ordinary people. And the gap between what tech people think and what ordinary people think is enormous. 

The gap has arisen partly because it is the AI ​​vendors who have driven the development without involving the public. Someone described AI as a technology that is introduced without consent, which is quite accurate. AI is suddenly invading everywhere, at work, in leisure, in culture, and in relationships, and it is all mostly completely unsolicited. People are starting to get quite tired and fed up with AI, rightly so.

But above all, the gap has arisen because AI vendors and AI enthusiasts don’t seem to have really understood where their rhetoric is coming from. After all, it’s the AI ​​people themselves who are constantly telling us how dangerous the technology is, how it will take all jobs, and how hopeless you are if you don’t keep up. At the same time, the technology is being touted as fantastic. It’s no wonder people are confused. And scared.

Because what are the upsides that are highlighted? Absolutely, people like their ChatGPT and fixing their photos with AI, but otherwise most of the gratitude and enthusiasm should come from becoming more efficient and productive at work. And here I think the AI ​​advocates, and the business leaders, don’t really have their finger on the pulse.

I could be wrong, but in my world, most people are not driven by being more efficient at work, at any cost. The primary driver is not the company’s bottom line, but rather being able to use their hard-earned knowledge and professional skills in a way that is both appreciated and that gives personal pride and satisfaction. 

Of course, if the increased productivity is also reflected in the salary envelope, it could be an important driver, but so far it does not look like that is the case. Instead, I suspect that the general feeling is that the use of AI is rather the first step towards the complete elimination of one’s job and professional skills.

Much of this is just fear, as AI’s impact on the job market has so far been marginal from a macro perspective. But as reports of job cuts increase and entry-level jobs become scarcer, concerns are growing, especially among the younger generation.

I also notice a clear difference between generations. A lot of those who are most enthusiastic about technology, especially on LinkedIn, are in my generation (X) and older. While the younger people in the comments sections on TikTok are much more negative.

Of course, this is partly because LinkedIn is “peppy” in nature, but on a deeper level, I think the older generations are actually more enthusiastic because AI is a manifestation of a technological development and future that has been promised for decades. It feels like what we have seen in movies and read in books is now, finally, becoming reality. The future is here!

For the younger generations, the effect is the opposite. Young people today have lived through crisis after crisis throughout their entire upbringing, from financial crisis to pandemic to current global chaos, and are not doing very well. 

When Swedes were asked about their well-being just before AI became widespread, older people were among the happiest in the world, while younger Swedes reported lower life satisfaction, less meaning in life, and worse financial security than older age groups. But the younger people also said they had high expectations and great optimism for the future. 

Then Sam Altman came along and said that the bright future was not theirs anymore.

The debate has begun to call it “AI has a marketing problem,” and that is true to the extent that it is clear that the rhetoric surrounding the technology worries more than it excites. Much of this lies with AI vendors, but also with politicians, business leaders, managers, and AI enthusiasts whose inability to be concrete and responsive in their communication creates more uncertainty and insecurity than faith in the future.

I believe that clarity is the only way to turn this around. Clarity in the advantages and disadvantages of technology, clarity in what it can and cannot do, clarity in what we should use it for and what we should not use it for, clarity in desired and unwanted results, and perhaps above all, clarity in what you know and what you don’t know.

Most people seem to agree that AI will be a big part of our future. The only question is what that future looks like and how we make it as bright as possible.

Too many enterprises remain ignorant of the European Union’s 2024 Cyber Resilience Act, the first elements of which enter force on June 11, according to a new survey.

Two-thirds of respondents to the survey by Open Source Security Foundation said they were unfamiliar with the CRA, which aims to make hardware and software sold in the EU more secure.

As well as the CRA’s demands on vendors, it also has implications for users of open-source software, hence the Foundation’s interest in the topic. Among other measures, the CRA creates the role of open-source steward within the enterprise, with responsibility for ensuring that a security policy is in place for any software being used within the organization.

The first part of the CRA to enter force, on June 11, concerns the designation of conformity assessment bodies by member states. Then, from September 11, manufacturers will be required to begin reporting vulnerabilities in their products to the relevant authorities. The remaining obligations under the Act, which include substantial financial penalties, will apply from December 11, 2027.

The impending sanctions seem not to have concerned businesses: 56 percent of respondents to the OpenSSF survey were unaware that non-compliance fines could reach €15 million or 2.5 percent of global annual turnover.

The lack of knowledge about the implications of the Act surprised OpenSSF CTO Christopher Robinson. “We’ve been speaking on this topic for some time and we’re scratching our heads on why more companies are not aware of the implications of the Act,” he said.

Global concern

He surmised that some companies don’t think EU regulations on hardware and software security apply to them — but such concerns will soon be a global matter. “Other countries, like Japan, are considering similar laws,” he said.

One area of misunderstanding could be that the CRA applies to vendors, and their customers may think that the requirements under the Act didn’t apply to them. He said that this was a misguided approach, particularly when the CRA’s application to open-source software is taken into account.

“There are about 700 million projects in Git Hub. If you work for an organization like a bank, you have little idea which of those projects are being used,” he said.

Under the Act, software companies will have to supply a software bill of materials (SBOM) that has been passed as secure, he said.

Companies that supply US federal government organizations already face this requirement, he said: “If you’re selling to the US government — which is the largest customer on the planet – you should be providing an SBOM.”

Cybersecurity consultant Hans Study said that by addressing the supply chain issue, the CRA is a step in the right direction. “Almost every application has dependencies, whether that is free and open-source software, commercial packages, or some mix of both. The problem has always been responsibility, and the blame game that comes with it. What the CRA does is make it harder for companies to dodge that responsibility when they are building, selling, or placing products with digital elements on the market,” he said.

AI ignorance

According to Michael Callahan, VP of Cyber Strategy at Salt Security, one of the issues that could cause problems in the future is the growing use of AI in the software development process. “The Cyber Resilience Act assumes enterprises know what is in their software. That assumption breaks down when AI coding assistants are generating a significant share of code. An AI assistant has never read your organization’s security policies, your licensing obligations, or your open-source governance standards. The code it produces may contain dependencies, patterns, or vulnerabilities that your security team cannot easily trace back to a specific decision or a specific developer.”

Enterprises are quickly running out time to fix issues and many are pessimistic about their chances. According to the OpenSSF survey, only 41percent of manufacturers expect to be fully compliant by December 2027, while 39 percent do not know when they will be.

It may be that the proposed fines could concentrate minds. Robinson said that it could be like GDPR where a few heavy fines drew companies’ attention to the regulation. The upper limit on fines is per infraction, not per company, he said: “Something like that could wipe out an SME and seriously hit large corporations.” The legislation should be something that all businesses need to be aware of, but there is still a long way to go.

This article first appeared on CIO.

For the past few days, I’ve been immersed in Google’s latest vision of the future — an AI-infused dashboard that taps into info from all of your Google app activity and then uses that data to cook up a series of daily “stories” designed to “connect you with what matters.”

And — believe me, I don’t say this lightly — the experience of interacting with this system has me longing more than ever for the past.

The app is called Dreambeans. Google launched it as an experiment last Wednesday, and I was offered the opportunity to skip the standard waitlist and get immediate access to explore it.

I won’t beat around the bush: Using the app really has been an eye-opening, enlightening experience for me. Just not in the way that Google had presumably wanted.

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Google Dreambeans and the next phase of AI

In many ways, Dreambeans feels like the ultimate example of everything Google’s been gunning for — and AI in general has been building up to — over the past several years.

With your permission, the app accesses your ongoing activity data from Google Workspace (including such services as Gmail, Google Calendar, and Google Drive) along with Google Search, Google Photos, and YouTube to create an evolving profile of your life and interests. That means everything from who you email to what’s on your agenda, what you’re writing or saving files about, and what sorts of subjects you’re searching for, videos you’re watching, and activities you and your friends, family, and other associates are appearing in throughout photos (and even how you all look in those photos) gets constantly analyzed and processed and used as fodder for a personalized feed that updates a few times a day.

On the surface, it sounds a little like Google Now — the excellent and all-too-short-lived proactive intelligence feature Google added into Android for a while back around 2012.

In practice, though, lemme tell ya: It feels dramatically different. Whereas Google Now felt almost magical in its ability to anticipate what you needed before you ever asked for it — with proactive cards on things like flight statuses based on itineraries in your inbox or flight-related searches you’d performed, traffic alerts based on your typical daily routes or appointments in your agenda, and links to maps for businesses you’d been researching — Dreambeans takes those same basic concepts to a whole other level that ends up feeling creepy and invasive, both in the info it’s offering and in the way it’s presenting it.

And, more broadly, it feels indicative of the way AI is heading in general — not just with this one app or with Google but across the industry and in a style that I think most people are increasingly finding off-putting and will only find ever more intrusive in time.

Now, let’s be clear: I’m no technophobe. Far from it: I love clever tech creations and thoughtful new touches that make our lives easier. Heck, I’ve spent much of my life searching for and writing about such feats. And that’s precisely why my reaction to Dreambeans strikes me as so significant: If I’m this put off by this concept, how will average tech users — most of whom are far less tuned into tech trends and intrigued by interesting new options then I am — react?

I’ll tell you more about what I’ve heard so far in a second. First, let me show you exactly what I’ve been seeing, so you can assess this thing for yourself and see how it comes across to your spidey senses.

Here’s a handful of the Dreambeans “story” suggestions that appeared in the app upon its first day working for me, with a few names and personal details blurred for privacy purposes:

I’m honestly not even sure where all of these suggestions came from, but what jumped at me right away were the (occasionally flattering) caricatures of me and my wife and the general sense of invasion from all the slightly too personal stuff and too familiar integration of family members’ names and interests integrated into the material.

For the record: I had been looking into speaker stuff at some point in the not-too-distant past; I’ve never once typed, uttered, or even considered the phrase “hand-loomed textiles” until just now; we had been looking at the arts festival it mentioned; I’ve never specifically searched for or expressed any interest in Scary Movie 6; and I am not into the band Genesis — though, to be fair, I can’t dance.

I showed all this same material to my wife as well as to several other friends and family members I’d categorize more as typical tech users — not tech professionals or card-carrying geeks but just regular people who own and use a variety of devices, as we all do, and rely on ’em for both personal and professional purposes with varying levels of dread, excitement, and/or indifference. Without exception and without any prompting or personal opinions presented to sway them, every single one of ’em responded the same basic way: “Oh. That’s creepy.” And: “I do not like that.” Without fail.

It doesn’t get much better from here, either. Most of the app’s subsequent suggestions have continued to veer just a touch too far onto the “ick” side of the spectrum, as well as occasionally being off-base in some pretty perplexing ways. For instance:

For the record on this set: I do love the show Seinfeld — SERENITY NOW! more than ever — though it’s been some time since I’ve actively watched it; I somewhat famously am allergic to Apple products and avoid ’em whenever possible (notice the name of this column, anyone?); I don’t live in the same city as my brother but do find it creepy to have him pictured in ghoulish caricature form and brought randomly into a discussion about Plex (something he wouldn’t even remotely be interested in hearing from me about); and my various editorial newsletters are all powered by a service called Kit — not Beehiiv — which is mentioned in plenty of places both on my websites and throughout my emails.

Also, while my hairline may not be what it once was, I’m (ahem) not that bald yet — thankyouverymuch, Dreambeans.

Another example that I won’t show here was an item that pictured me in overalls working on installing some “coated stainless steel wire for [my] gallery canvases” — a reference to a community art gallery (with all sorts of details wrong and in some cases flat-out fabricated) connected to my mother’s recent passing. It casually mentioned her by name, too, alongside that eerie illustration of me performing a skill I definitely don’t have in my nonexistent home workshop. I don’t think I have to elaborate on how unsettling, unappreciated, and — again — invasive it felt to have that pop up in this feed.

More than anything, what I’ve been feeling while seeing all of this is a combination of (a) egad, it knows too much — especially when it casually name-drops and caricature-pics my wife, kids, and other family members — and (b) at the same time, the info it’s giving me isn’t especially helpful or insightful. It’s mostly just flat, generic, and — well, more or less exactly what you’d expect from something AI-generated.

More than anything, in other words, it’s a combination of creepy and not particularly useful.

And it’s the “creepy” part that really sticks with me the most.

The fine line Google forgot to avoid crossing

Maybe if the info I’m being served up here were exceptionally useful, this could be a tradeoff I’d be at least a little more likely to accept. Maybe. But in this scenario, it just feels odd and a little too invasive — which I’ve come to realize is a common theme surrounding much of what seems to be the next level of our forced-upon-us AI future.

Take Google’s Gemini Spark, for instance — the “agentic” AI assistant announced at Google I/O that’s meant to be a “proactive” helper tackling tasks on your behalf. David Pierce from The Verge got an early look at the tool in action and called it “the most impressive and terrifying AI experience” he’s had to date, also bringing that “creepy” word into the equation:

I can’t shake the deeply creepy feeling I get from the whole thing. What Spark did feels sort of magical, and very invasive. It’s weird that Spark is so casually telling me the names and ages of my children, reminding me that it knows where I live, and finding information I know for a fact I’ve never volunteered to Google. Intellectually, I know that Google knows an incredible amount about me — add up my emails, my calendar, my photos, and my search history, and you’ve pretty much got me pegged. But seeing Spark treat all that data not as something to be protected, but as something to be mined, just feels bad.

And that, I think, mirrors the exact reaction I’ve been experiencing with Dreambeans. We’ve all always known that Google knows a lot about us, but we’ve also — at least intellectually — understood how all of that data is and isn’t being used. And it’s never been rubbed in our faces just how much the company can figure out about us by putting all the various pieces together and creating an awkward sense of robotic intimacy.

I remember years ago, being in a Google press briefing where someone from the company talked about how much more their systems and services could accomplish but how they deliberately held back on going that far and overdoing the personalization — ’cause even though they had all that info and could make all those connections, they knew (at the time) that people wouldn’t respond well to seeing all their personal activity put together in such shocking ways. They knew (at the time) that most of us weren’t looking for an artificial BFF who knew too much about us. They knew (at the time) that giving us that sensation would cross the line into being creepy.

Well, this just in: That line’s officially been decimated. We’re in AI’s creepy era. And seemingly no one is worrying anymore if it’s actually something any of us want or will appreciate.

It kinda feels now like tech companies are actively rubbing in our faces how much they know about us — and even if there’s nothing truly nefarious going on with what they’re doing, it sure doesn’t feel good. It feels creepy. And at a time when trust in tech titans is shockingly low and most folks outside of the Silicon Valley bubble are feeling more and more frustrated with AI and all of the effects it’s foisting upon us, that isn’t a great look to be giving off.

Put those sensations alongside the sigh-inducing explosion of AI “content creators,” the proliferation of lifeless AI-generated “writing” (been on LinkedIn much lately?), and the troublingly blurry line between photorealistic AI-generated images and actual real-world photographs — not to mention the maddening experience of interacting with an AI bot support agent or encountering the ever-expanding array of AI-powered scams and security threats and AI-generated job cuts, just to name a few other problematic consequences this movement is imposing — and it’s hard not to question if all this purported progress is ultimately more helpful or harmful for us, as living, breathing humans in the real world.

A few months ago, at the three-year mark of Gemini’s launch, I posed the question: Did anyone actually ask for this? And, more pressingly: Is this the future we wanted? As we’re moving now into yet another era of AI innovation and seeing how it’s affecting our lives, it gets tougher every day to imagine many folks outside of the tech industry who’d answer with an emphatic yes.

And, unfortunately, you don’t need any fancy-schmancy AI chatbots to tell you that things are only gonna get more extreme — and, yes, more creepy — from here.

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CIOs racing to experiment with AI models, test AI agents, and use vibe coding to develop applications may find themselves dealing with a new form of technical debt: AI debt. The pressure to accelerate proofs of concept (POCs) into production will likely drive teams to cut corners and leave known improvements as “to-dos” for future releases.

But speed isn’t the only factor that will create AI debt. Even with strong AI governance in place, advances in frontier models and new AI agents from SaaS platforms means that what gets POC’ed and moved to production today will require unanticipated changes in future releases.

Learnings from technical debt

Creating a framework for understanding, avoiding, and resolving AI debt should build on practices for reducing technical debt.

First, not all technical debt carries the same risks or priorities for resolution. Understanding the source of technical debt can help rank the likelihood of the issue impacting business operations and its severity. Seven types of technical debt include performance-limiting data management practices, open-source dependencies, architecture limitations, and cultural inhibitors. Data debt includes data quality issues, manual steps in data pipelines, and data management that lacks observability.

Second, prioritizing the work to address technical debt requires CIOs to communicate in terms that both technologists and business leaders comprehend. When there isn’t a shared understanding, business pressure to upgrade or deliver new capabilities may limit the work needed to address security vulnerabilities, fix defects, or improve operational resiliency.

AI debt can also be categorized by its sources. It also requires CIOs to communicate the risks of deploying AI capabilities before they are fully tested. But as we are early in the AI era, CIOs should put significant focus on ways to avoid the different types of AI debt as part of defining governance and guardrails.

Here are seven AI debt sources to consider, along with ways to avoid them.

AI experiments without targeted outcomes

The pressure to get more employees learning AI and testing AI agents is needed but comes at a cost. In addition to people’s time and rising token costs, prioritizing work without defining objectives can increase AI debt without leaving a clear understanding of whether the AI capability is delivering value. 

“Outcome debt builds when organizations deploy AI without defining the specific, measurable business results they expect it to deliver,” says Rob Scudiere, CTO at Verint. “When teams chase experimentation or hype instead of outcomes, they accumulate systems that are technically impressive but operationally irrelevant. Anchoring every AI initiative to clear, verifiable results prevents this debt and ensures investments translate into stronger, faster, scalable impact.”

Recommendation: Avoid chaotic AI experiments by creating an ideation process before committing resources and by tracking active AI initiatives. Require teams to define their targeted outcomes and establish a risk registry covering any unknowns, concerns, and future fixes related to their AI implementations.

Feeding poor data quality to AI models

Poor data quality, a data risk CIOs should be paranoid about, gets amplified when used with AI models and agents. One key practice is to define a data trust score and prevent teams from allowing AI models and agents to use datasets that fall below targeted thresholds.  

“Data quality debt is one of the most dangerous forms of AI debt because errors in training data and inputs cascade through models, pipelines, and downstream decisions,” says Abhi Sharma, co-founder and CEO at Relyance AI. “The best way to prevent it is to establish continuous data lineage and governance so teams can trace inputs, monitor transformations, and correct issues before they propagate into model behavior.”

A second practice is to extend data catalogs and data fabrics by establishing reusable data products. Data products become shareable multi-purpose assets with their own release cycle, treating AI agents and other users as customers.

“Organizations often don’t recognize data quality issues until an AI agent acts on flawed data, amplifying errors at speed and without human judgment,” says Mayank Mahajan, associate director of engineering at Xebia. “Unlike humans, agents cannot question inconsistencies or fill gaps, so they execute on whatever data is available. Package data as governed, well-documented products and implement observability early so issues like schema drift or stale data are caught before they scale.”

Recommendation: Communicate a set of data governance non-negotiables that establish clear minimal criteria for using data in AI, including compliance requirements around data privacy. Strong data governance practices help reduce the risk of data-related AI debt.

AI model drifts

One key concern when deploying machine learning models is recognizing when real-time inference data drifts from the model’s training data. Model drift is also an issue when using retrieval-augmented generation (RAG) or providing other contextual data to large language models.

“AI model debt builds when models drift or degrade without teams knowing why, leading to compounding performance and reliability issues,” says Amitkumar Rathi, chief product officer at Virtana. “Observability across models, data pipelines, and infrastructure helps identify whether issues stem from data drift, pipeline failures, or resource constraints like GPU contention.”

Recommendation: The key to avoiding AI model debt is through modelops practices, including cataloging models, implementing observability, baselining training data statistics, and monitoring for outcome drift. Rathi recommends, “By continuously linking model outcomes to operating conditions, teams can act early by retraining, fixing data inputs, or rebalancing resources before issues accumulate into long-term model debt.”

Overly entitled AI agents

Should AI agents have the same data access rights as their users? One form of AI debt is when access permissions and the underlying entitlements for AI agents require revisiting and auditing. Worse is when over-permissioned AI agents expose confidential data or make erroneous decisions when accessing sensitive data.

“Enterprises are deploying AI agents that query databases, trigger workflows, and make decisions at machine speed, yet they’re granting these agents broad, static permissions modeled on how humans access data,” says Ganesh Kirti, CEO at TrustLogix. “Every agent running with over-provisioned access or without context-aware controls is quietly accumulating security, compliance, and data integrity risk that compounds over time.”

Part of the challenge is that more organizations are deploying AI agents in mission-critical business processes. Deploying wide-scoped AI agents with broad data access rights can lead to operational risks and AI debt to address them.

“You insert AI into the process with a specific job defined, not any job, and you make sure that the inputs match the job specification,” says Matt Calkins, CEO of Appian. “You must give the AI agent a narrow range of possible outputs.”

Recommendation: To reduce the risk of AI debt from overly empowered AI agents, review the outcomes, decisions, and recommendations AI agents will be responsible for and apply a bottom-up review of the required data entitlements. “To avoid this debt, organizations need to treat AI agents as governed non-human identities with their own entitlement lifecycle, continuous, policy-driven authorization that adapts in real time, and not one-time role grants that nobody revisits,” Kirti recommends.

Teaching AI agents broken business processes

Robotic process automation (RPA) delivered significant ROI when applied to well-defined business processes. With AI agents, some have suggested that role- and task-based agents can perform the required work by providing sufficient context. But this assumes that existing business processes and the data they generate are accurate and reliable.

“Too many organizations are rushing to layer agentic automation on top of their existing processes and infrastructure,” says Don Schuerman, CTO and VP of marketing and technology strategy at Pegasystems. “Anyone can now quickly create an app with gen AI, but they struggle when they try to deploy it to do the real work inside real enterprises with all their complexities and dependencies.”

Recommendation: Avoid the rush to deploy AI agents before conducting upfront audits of existing processes and discussing future needs with business owners. Before generating a single line of code or deploying an AI agent, Schuerman recommends reimagining how work could be optimally done and how best to engage with customers across channels.

AI agent sprawl

AI agents are available across many platforms, and DevOps teams can use spec-driven development practices to build them. One organization’s chief digital officer recently told me they have already deployed over 1,000 AI agents.

The question is whether CIOs will repeat past mistakes when deploying tools that make it easy for business users to create new assets that are challenging to manage.

Consider the debt created by sprawling behaviors:

• Spreadsheets were great for analytics until business users created too many of them, and there were few tools to manage the underlying data practices and change lifecycles.
• Data visualizations were an upgrade, but top CIOs realized that governance practices were needed to manage citizen data science programs.

“Many companies already have more agents than employees, but lack lifecycle management, with no visibility into what agents exist, what data they access, or when they should be retired,” says Saket Srivastava, CIO at Asana. “Each unmanaged agent compounds risk, including security exposure, duplicated logic, and decisions no one can audit. CIOs should manage agents with the same rigor as employees, with clear ownership, role-based access to the right systems and data, and defined onboarding and retirement, before sprawl becomes a costly, hard-to-contain problem.”

Recommendation: CIOs of organizations with SaaS sprawl, especially those with a history of shadow IT, should define processes and controls for selecting, deploying, and managing AI agents.

Security lagging AI-generated code

The speed at which AI agents are developed, integrated with MCP servers, and deployed can compound security vulnerabilities. There are also questions about AI-generated code, with one study reporting that AI pull requests produce 1.4 times as many critical issues as human-generated ones.

Nikhil Mungel, head of AI R&D at Cribl, says, “AI-generated code debt can occur when teams use off-the-shelf tools that generate code that isn’t consistent with the company’s standards.”

Vrajesh Bhavsar, CEO and co-founder at Operant AI, adds, “As organizations scale AI agents and multi-step AI workflows, they inherit a rapidly expanding attack surface where adversaries are actively exploiting new threat patterns, including zero-click attacks that require no user interaction to trigger data exfiltration, poisoning, or leakage mid-workflow.”

Recommendation: Organizations that use AI code generators, vibe coding, or spec-driven development methodologies should consider adding AI code review tools such as CodeRabbit, DeepCode, Qodo, SonarQube, or open source options to their security programs. Mungel recommends embedding engineering best practices into skill frameworks, such as agents.md or skill.md, to ensure outputs comply with internal guidelines. In addition, Bhavsar recommends deploying adaptive, agentic-aware security controls that can detect and stop threats targeting AI agents in real-time.

AI is only reshaping businesses and not transforming them yet. The question for CIOs is whether the pressure to move AI experiments into production and deliver business value will create new forms of AI debt for them to manage in the years to come.