At the beginning of the year, I started using the phrase “AI PoC Purgatory” to describe what I was seeing across enterprise AI efforts, particularly as organizations were trying to move pilots into production. I wanted to take a moment to explain what I mean by it.
Today, many organizations are investing heavily in AI, but they are running into a very real challenge. AI is moving faster than enterprise systems, governance models, and data infrastructure were ever designed to support. That gap is what led me to define what I call “AI PoC Purgatory.” Companies are running pilots, experimenting with models, and testing ideas, but very few are successfully moving those experiments into controlled, scalable, and repeatable production environments.
Moving from experimentation to production requires more than models, as many are finding out. It requires orchestration, governance, lifecycle management, and the ability to connect AI systems to real enterprise data.
Where this starts to break down, and where most organizations don’t fully appreciate the gap yet, is that we’re still largely framing AI as a compute problem or a model problem, when in reality it’s an execution problem that spans the entire system. It’s not that the models aren’t working. In many cases, they are working exactly as intended in controlled environments. The problem shows up the moment those models have to interact with real enterprise data, real governance requirements, real users, and real operational constraints that were never designed with AI in mind. That’s where the rubber meets the road.
What ends up happening is that organizations prove the concept, they demonstrate value in a pilot, and then they hit a wall when they try to scale it. Data is fragmented across systems, access patterns aren’t consistent, latency becomes unpredictable, and governance requirements introduce friction that wasn’t accounted for early on. At that point, the conversation shifts from “this works” to “how do we operationalize this without breaking everything around it,” and that’s where most efforts stall out.
The reality is, organizations end up in AI PoC Purgatory for a variety of reasons. Some will point to governance, especially in regulated industries where risk models haven’t caught up with probabilistic systems. Others will point to data quality, organizational alignment, or the economics of scaling what started as an experiment. Those are all valid.
What I’ve been focused on is a different layer of the problem, one that tends to show up the moment you try to operationalize AI in a real enterprise environment.
This is why I’ve been pushing on the idea that the constraint is no longer just about storage capacity or raw compute. It’s about how data moves, how it is accessed, and whether it can be made available in a way that aligns with how AI systems actually operate. Memory locality, data movement, and orchestration across the pipeline become more critical than the traditional metrics we’ve used to evaluate infrastructure. You can continue to add more compute, you can continue to optimize individual components, but if the data isn’t where it needs to be at the moment it’s needed, the system breaks down in very practical ways.
What we’re seeing is that most enterprise environments were built for a different era, one where batch processing, well-defined workflows, and predictable aggregate access patterns were the norm. AI introduces a very different dynamic, where access patterns are more fluid, workflows are more iterative, and the system has to respond in near real time to changing inputs. That mismatch is what keeps organizations stuck in this middle ground where they have working pilots but no clear path to production.
So when I talk about AI PoC Purgatory, it’s not just a phrase. It’s a reflection of a structural gap between how AI systems need to operate and how enterprise environments are currently built. It is the result of the body of work I’ve developed around memory, data movement, and access. Closing that gap requires a shift in how we think about the entire lifecycle, from how data is connected and governed, to how and where models are deployed, monitored, and continuously improved in production.
And until that shift happens, we’re going to continue to see a lot of experimentation, a lot of promising results in isolated environments, and a lot of frustration when those results don’t translate into something that can be trusted, scaled, and operationalized in the real world. And organizations will continue to find themselves stuck in AI PoC Purgatory.
If you’ve been following my work, this builds on a broader set of ideas I’ve been writing about around memory, data movement, and execution in AI systems. I’ve pulled that together into a single place for anyone interested in going deeper: The Evolution of AI Infrastructure: Memory, Data Movement, and System Constraints