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Generating code with AI is fast, but getting that code to run reliably inside a large enterprise, integrated with live systems, governed for compliance, and maintainable over years requires foundational work that most organizations underestimate.

While 81% of all organizations have a detailed strategy, only 12–16% reach AI‑driven execution , says SAP's Michael Ameling, CPO of SAP Business Technology Platform, and the reasons rarely come down to the quality of the generated code.

"Across industries, enterprises that have invested heavily in AI tooling are hitting a wall when generated code meets the reality of their existing environments, because generating code and operationalizing it are not the same problem," Ameling says.

There are specific requirements for deploying AI-generated logic at enterprise scale: what data and integration readiness actually look like, how governance works when AI agents move from producing recommendations to executing workflows, and how development teams are changing their role as AI takes over more of the coding work.

Why AI code generation fails in enterprise production environments

The productivity gains from AI code generation are real and well-documented, but the ease of prototyping has given many organizations a misleading sense of how far along they actually are.

"Generating code is one thing," Ameling says. "Enterprise customers, including multinationals and large organizations, need to ensure there are no compromises in compliance or security. Code that runs reliably for ten or twenty years, as it does at many of SAP's largest customers, also has to be maintained, patched, and understood by whoever inherits it. Life cycle management, in other words, does not generate itself."

The issue is rarely the generation quality. Teams build something compelling, then discover they lack access to the data it depends on, or the integrations it assumes, or the permissions required to run it in a real environment. The problem is essentially that AI amplifies an organization's existing data and process maturity, but it can't substitute for it.

This dynamic intensifies as AI moves from producing code to executing actions. Latency, cost, and system load all increase when logic runs continuously against live data rather than rendering a one-time output. The performance requirements of an autonomous agent operating across a multinational's transaction systems are categorically different from those of a developer copilot.

How to connect AI-generated logic to fragmented enterprise systems

The architecture challenge that most enterprise AI projects underestimate is integration. Real enterprise environments are not clean slates: they combine cloud systems, legacy on-premise infrastructure, fragmented data stores, and dozens of business applications that were never designed to talk to each other. Getting AI-generated logic to operate reliably across all of them requires a layer that unifies data access, process context, and governance, and it has to be in place before any agent starts executing. And organizations that see AI as a reason to defer infrastructure modernization are making a mistake.

"The question is not whether to modernize or not. Of course you need to modernize," Ameling says. "But the value you get on top of this is much higher with AI. Federated data access and harmonized process layers are not alternatives to upgrading a fragmented landscape, they're what make the upgrade worthwhile."

At the platform level, this translates into a set of practical requirements: structured data integration, end-to-end process visibility, and the ability to discover and connect to APIs across both modern and legacy systems. SAP's approach with the Business AI Platform draws on tools including its Joule Studio, Integration Suite, Business Data Cloud, and SAP AI Agent Hub enterprise architecture layer to provide that context. The goal is to give AI-generated logic accurate, current knowledge of what a business is doing and how, rather than just access to raw data.

AI agents handle large challenges by dividing them into smaller, autonomous tasks, with each agent responsible for a specific domain, and all coordinated toward a shared outcome. A financial close, for example, involves dozens of discrete sub-processes. Agents handling each task in parallel, within defined constraints, can compress cycle times dramatically, but only if the underlying systems they interact with are coherent and accessible.

The governance and oversight that AI agents require in production

When AI moves from assistant to operational actor, the governance questions loom large, because agents that trigger workflows, update records, and interact with live business systems need the same accountability framework that applies to human employees, i.e., identities, defined privileges, and auditable behavior.

There are two distinct models:

Principal propagation, where an agent acts on a user’s behalf, inheriting that user’s permissions and scope.

System-triggered agents, where the agent operates under its own identity and role-defined privileges, functioning more like an automated HR role than a personal assistant.

Both models require the same underlying infrastructure: an agent hub where operators can see which agents exist, what APIs they can access, and what they are authorized to do. Observability also needs to be operationalized correctly for AI, combined with both technical and business evals.

"In production, openness is very important," Ameling says. "We use OpenTelemetry as a framework, so we can integrate with other solutions, for end-to-end observability of the tool, third-party agents and the like."

On top of that, standard technical evals, which test whether an agent produces consistent outputs, are necessary but not enough. Business evals assess whether an agent is actually moving the performance indicators it was deployed to improve, but it has to work end-to-end.

Where the testing happens is equally important. The traditional software development cycle across dev, test, and production environments breaks down when a model produces different outputs depending on whether it is running against test data or live data. Getting to trustworthy AI in production means accepting that validation looks fundamentally different from what engineering teams have practiced for decades, with live environment testing, even A/B/C testing to ensure outcomes are reliable.

How AI-driven code generation is changing software engineering roles

The role of the developer is not disappearing in this environment, but its center of gravity is shifting. The productivity multiplier is significant when developers can run multiple coding agents in parallel across open terminals, each working on a separate problem and each taking several minutes to complete. But it introduces a new kind of cognitive demand, because humans have to stay in the loop. That means tracking context across concurrent workstreams, evaluating outputs that range across large codebases, and making architectural judgments that no agent can be trusted to make alone.

"The more specific and complete the prompt, the less intervention is required, and developers are learning that bringing more context upfront pays dividends in reduced back-and-forth," Ameling says. "But the output still needs to be understood, not just accepted."

The competitive edge will remain intellectual property, not tooling. The companies that pull ahead will be those that most effectively encode their domain knowledge into the systems they build.

"A manufacturer's process expertise, a financial institution's risk logic, a logistics firm's routing intelligence, these are the assets that AI can accelerate, but only if the organizations that hold them do the work to make them accessible and usable," Ameling says. "Protect that, and apply AI to accelerate your differentiation."

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Is there life on Mars … in clay? Scientists think that the minerals in clay could be the key to finding signs of ancient life on the Red Planet.

The European Space Agency is still working toward launching its ExoMars Rosalind Franklin rover to Mars to search for signs of life. And, according to a statement from the space agency, the rover is now aiming to land at Oxia Planum, a depression on the Martian surface where it's thought that water was once plentiful. There, scientists think that they might find major clues in the hunt for life in the basin's clay, according to a new paper.

"We will use the instruments on board to ground truth the discoveries made from orbit, learn about the ancient environment in which they formed, and if they preserve any evidence of Martian life. Warmth and nutrients on an early martian seabed could have provided habitats for early life," ExoMars deputy project scientist Elliot Sefton-Nash added in the statement.

Scientists have spent years searching for signs that life once existed on Mars. It's thought that water on Mars evaporated around three billion years ago, but before then the planet likely had a more substantial atmosphere and water flowing in rivers and into lakes all across its surface. Because of the planet's history, many scientists think that it's most likely that at some point in the ancient past, the planet must have supported life. While this has yet to be confirmed, last year scientists found what is currently thought of as the strongest possible biosignature, or physical evidence of life, on Mars.

This image, captured by the HiRISE camera on NASA's Mars Reconnaissance Orbiter shows the location of clay on Mars. (Image credit: NASA/JPL-Caltech) In a new study, researchers have found extensive clay deposits at Rosalind Franklin's proposed landing site. They found this clay to reach roughly 186 miles (300 kilometers) outward from Oxia Planum, stretching as far as a Martian valley called Mawrth Vallis. To spot the clay, they first studied the planet from orbit.

Researchers used the OMEGA instrument on ESA's Mars Express orbiter and NASA's Mars Reconnaissance Orbiter to explore the minerals and rock layers on Mars between Oxia Planum and Mawrth Vallid, finding mineral layers at both sites as well as markers showing changes in water chemistry over time. These observations add to other studies pointing to water on ancient Mars .

With ESA's upcoming rover, some scientists think that the clues to life on Mars could be hiding in this clay in the Oxia Planum region.

"By landing at Oxia Planum, we’ll uncover a large-scale process that shaped ancient clays across Mars," lead author Inés Torres Auré of the University of Lyon in France said in the statement.

Scientists think that it's possible that the area of Oxia Planum could have once been home to a body of water as big as an ocean or the region could possibly have experienced incredible flooding some four billion years ago, according to the statement.

"Because the area is so large, we are not talking about a localised occurrence, but rather a regional or global process that would have required immense amounts of water. We are targeting the oldest deposits in the sequence, which makes the potential implications for the geology and early climate of Mars very relevant for the Rosalind Franklin mission in its search for life," ExoMars project scientist Jorge Vago explained in the statement.

While we have never confirmed life off-Earth and it could be different from the life we know, as far as life on Earth is concerned, water is a necessary ingredient.

ESA's Rosalind Franklin rover is projected to launch to the Red Planet in 2028. The rover will be part of ESA's ExoMars program alongside the agency's Trace Gas Orbiter which is already traveling around Mars. Rosalind Franklin will have a drill, allowing it to explore below the planet's surface as the pair work together from orbit and on the Martian surface to hunt for signs of ancient life.

This work was described in a study published in the journal Science Direct.

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Do you remember the metaverse ? If you don’t, don’t worry. In 2026, four years into the AI revolution that’s changing the world forever, you could easily be forgiven for thinking it was a strange fever dream you had back in 2021. You might even have odd memories of seeing a blocky version of Mark Zuckerberg floating about in a Minecraft-inspired hellscape, conducting meetings with people who could walk with no legs , while the real Mark Zuckerberg was looking at the whole thing through VR goggles. At least, that’s my memory of it.

I could be suffering from the Mandela effect , but I distinctly remember something off about the legs. They fixed that in a later version, but that’s my overriding memory — no legs. Oh, and Mark Zuckerberg assuring us that this was the future. He’d spent tens of billions of dollars on it, even changing the company name from Facebook to Meta, just to let us know he was really serious about the metaverse. Even if the legs didn’t work.

The problem was, it looked laughable. While everything in the technology world to do with games and special effects was moving in the direction of hyper-realism, the metaverse was moving in the opposite direction, towards the sort of blocky graphics that small children enjoy. But even that didn’t really answer the most basic question about the metaverse. Why? What possible advantage was there for us all to meet in a VR space where clunky avatars of ourselves could interact… badly?

Then AI happened and Meta abruptly forgot about the metaverse and pivoted towards the mission of putting personal superintelligence in all our hands instead, which sounds as terrifying and dangerous as it actually is, but we are where we are. At least when Zuckerberg was obsessed with the metaverse, we could ignore him. It existed somewhere “over there”, in Meta-land, where we could let him get on with it. Now he’s right up in our business again.

Careless People , by Sarah Wynn-Williams. (Image credit: Future) The rise of a bad idea

I’ve always been fascinated by how Zuckerberg got into the metaverse and why he became so obsessed with it. The origins of the metaverse go way back. In March 2014, Facebook bought Oculus, the VR company, for about $2 billion. This was where his passion for VR started. Think of it as the seed, not the full obsession.

By July 2021, Zuckerberg gave a long interview to Casey Newton at The Verge about Facebook becoming a “metaverse company” and described it as an “embodied internet”. Then, on October 28, 2021, his obsession became the company identity. Zuckerberg announced that Facebook the company was becoming Meta at Connect 2021 , saying the new company brand would focus on bringing the metaverse to life.

I can see the logic. As a business strategy, that made sense. As a product ordinary people were expected to use, it was much harder to understand. Zuckerberg did not want Meta/Facebook to be trapped inside someone else’s platform again. Facebook had won on social, but on mobile it remained dependent on Apple and Google for distribution, privacy rules, app-store policies and hardware. The metaverse looked like a chance to own the next operating system of social life: hardware, avatars, identity, payments, meetings, gaming, work, commerce — the whole stack. In his 2021 founder’s letter, he framed the metaverse as the “next chapter of the internet” and said Meta would become “metaverse-first, not Facebook-first.”

Facebook had obviously had its problems — it was scandal-ridden. It had let advertisers target vulnerable teenagers, helped fake news spread, and enabled the spread of hate speech linked to atrocities in Myanmar. Perhaps Zuckerberg was looking for a way out of Facebook, and the metaverse offered that.

What I still didn’t understand was why he didn’t see what the rest of us saw — that it looked terrible and offered no real benefit to users. Then I read Sarah Wynn-Williams’ tell-all book about Facebook, Careless People , and it all started to make sense.

To say the book made my jaw hit the floor on several occasions would be an understatement. It’s an absolute page-turner, and your reactions grow from mild amusement to shock, then disbelief, then absolute outrage the further through the book you get. I’m aware of the criticisms of Wynn-Williams: that it is a book written by a disgruntled employee, and that she dodges a lot of personal responsibility for her part in the various misdeeds of the company. However, in another perfect example of the Streisand effect , the fact that Meta obtained a legal order in the United States to prevent her from saying anything negative about the company — at all — made me want to pick it up, and I’m glad I did.

Because now I get it — Zuckerberg seems to have spent years in an environment where too few people were willing to tell him when his ideas weren’t good. According to Wynn-Williams, he was surrounded by sycophants. When he had bad ideas, like the ill-fated Internet.org, he wouldn’t let them go and persisted with them, even when they were obviously going to fail. The people around him enabled him because he was simply too powerful. They even let him win at the board games he liked to play with them at his house or on his jet, and — crucially — he didn’t notice that they were letting him win. I can imagine that in that environment, nobody inside Meta would want to tell Zuckerberg that his metaverse was the equivalent of the emperor’s new clothes, especially if they wouldn’t even risk beating him at Settlers of Catan .

Wynn-Williams only mentions the metaverse in her epilogue. It happened after she was brutally fired from Facebook. Perhaps selfishly, I wish she’d been there for the metaverse period, because I would love to read firsthand accounts of how and why Zuckerberg persisted with such an obviously bad idea.

An avatar of Mark Zuckerberg, chief executive officer of Facebook Inc., rides a hydrofoil during the virtual Facebook Connect event. (Image credit: Getty Images / Bloomberg) The fall of the metaverse

Maybe I’m being too harsh on Zuckerberg. The metaverse graphics did get better over time and Apple ventured slightly into the same territory with its Apple Vision Pro , even after the metaverse had turned into a smoldering wasteland. The fact is, people don’t enjoy wearing VR goggles for extended periods of time, and for normal people, VR lacks that one killer app. There doesn’t seem to be anything you can do in a VR space that you can’t do elsewhere much more easily.

The metaverse didn’t really die with a bang, but with a whimper. It faded through layoffs, spending cuts and the AI pivot. If I had to put a date on it, I’d say early 2023 was when Meta’s narrative moved on. In February and March 2023, Zuckerberg started talking about Meta’s “year of efficiency” and announced huge layoffs and cost-cutting. OpenAI had launched ChatGPT in November 2022, and by early 2023, generative AI had swallowed the oxygen that ideas like the metaverse need to survive. Every tech company was talking about AI now, not virtual offices and avatar legs.

The metaverse was over. We all forgot about it and moved on.

I’m glad I read Wynn-Williams’ book, because now I can understand how Facebook let the metaverse happen. And if there’s one thing I learned from reading it, it’s that money and power can bring you a lot of things, but common sense requires neither.

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After hiding in the shadows for over a decade, the renowned Thief series of video games re-emerged last year with a new installment made for virtual reality, titled Thief VR: Legacy of Shadows . It marked the first adventure within the dark fantasy stealth franchise not to star master thief Garrett, or a reimagining of him, but instead introduced an orphan thief named Magpie who happened upon a locket possessed by Garrett's spirit and began her own quest within the oppressive city. For those looking to see through the original antihero's mechanical eye once more, however, there's good news. Bit Bot Media is prepping a BackerKit campaign for an officially licensed graphic novel titled Thief: Pulse of Promise , offering a new, self-contained story set between the events of the groundbreaking title that started it all and its sequel, Thief II: The Metal Age , and Collider can exclusively share a look at the cover art for the Deluxe Hardcover Edition.