Why AI Proofs of Concept Fail When They Reach Production

BIZTECH: Many organizations build an impressive AI proof of concept but struggle to deploy it in production. Where does that transition usually break down?

WEISS:  The biggest issue isn’t unique to AI. It’s true of almost every software development or digital transformation project. Proofs of concept are built around the happy path. They demonstrate how the solution works under ideal conditions. But production environments aren’t ideal. They’re full of exceptions, unusual business scenarios and edge cases.

People are often surprised by how quickly they can build something that handles 90% of the use cases. Then they discover that the remaining 10% can take as much work as the first 90% combined.

That’s simply the nature of software engineering. AI has compressed the time it takes to get from zero to a working prototype, so organizations naturally feel like they’ve solved the problem. But getting from an impressive demo to a production system that works reliably 99.9% of the time is a much longer journey.

Some organizations become frustrated and conclude the technology doesn’t work. More often, they simply underestimate the investment required to finish the job. AI is still software, and it has to be engineered, maintained and managed like any other software system.

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BIZTECH: How much of that “last mile” challenge is really a data problem?

WEISS:  Data is certainly part of it. Many proof-of-concept projects rely on carefully selected or manually prepared data. Sometimes it’s hard-coded. Sometimes it’s a small, clean sample that isn’t connected to live enterprise systems. That’s perfectly reasonable for proving an idea, but production is different.

Once you begin integrating with real business systems, you’re dealing with live data that isn’t perfectly organized or cleaned up. New scenarios emerge that the prototype never encountered.

The good news is that AI is actually quite good at handling messy data. The bigger issue is often that the necessary information may not exist at all.

Organizations frequently rely on institutional knowledge that’s never been documented. There may not be a written standard operating procedure because employees simply train one another over time. That knowledge lives inside people’s heads, which makes it difficult for AI systems to consume.

One of the most valuable things organizations can do is capture that expertise. Ironically, AI can help with that process. Recording conversations with subject matter experts, transcribing them and organizing that information makes it much easier to build AI systems around knowledge that previously wasn’t documented. Creating usable data has never been easier, but organizations still have to do the work of capturing it.

BIZTECH: AI projects often move very quickly at first and then slow dramatically. Why does progress become so much harder after the initial success?

WEISS:  AI is exceptionally good at helping you move quickly in the beginning, but it can’t identify every edge case for you. That work comes from understanding business processes at a very deep level.

That’s why I believe subject matter experts are among the most important people involved in building AI applications. Engineers can infer many scenarios because they’ve designed complex systems before. But they haven’t necessarily lived the business process every day. The people who perform that work understand the unusual situations that happen once every few months; they’ve already figured out how to respond when those situations occur. Those decision paths have to be incorporated into the AI system.

Without that operational knowledge, it’s very difficult to build software that succeeds in production.

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BIZTECH: Many organizations expect AI to deliver immediate cost savings or revenue growth. Are they approaching AI ROI the right way?

WEISS:  I think many organizations are looking at AI in a much more academic way than they should. There’s an assumption that AI will operate independently and you’ll simply measure everything it does. You can certainly count prompts, conversations or tokens, but AI rarely works that way in practice. Most AI operates alongside people. It enhances human work instead of replacing it. That creates an attribution problem.

Take our own seller assistant at CDW. It doesn’t replace the salesperson. It extends that person’s capabilities. I think of it like Iron Man’s suit: The suit isn’t useful without Tony Stark, and Tony Stark is better because he has the suit. They’re more effective together.

That’s how AI works in most enterprises today.

Eventually, I think we’ll stop asking for AI ROI in quite the same way because AI will become another business tool. Nobody asks for the ROI of PowerPoint or electricity. They’re simply part of how work gets done.

That doesn’t mean businesses shouldn’t evaluate costs. AI often represents a new expense, and executives naturally want to understand the return. But over time, as costs decline and AI becomes embedded in everyday workflows, I think those conversations will evolve.

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BIZTECH: In that case, how should organizations evaluate whether an AI initiative is succeeding?

WEISS:  One of the most overlooked metrics is simply whether people are using the tools. If employees aren’t using them, you’ve already learned something important. People don’t open Microsoft Copilot because it’s entertaining. If you see repeated use increasing over time, that’s a strong behavioral signal that employees are changing how they work and finding value in the technology. Those utilization metrics are relatively easy to measure, and they’re leading indicators of business outcomes.

Ultimately, AI increases the velocity of work. If you shorten every step involved in preparing proposals, analyzing information or completing routine tasks, the entire business moves faster.

That has implications beyond a single organization. More work gets completed in less time, deals close faster, and businesses become more productive. What’s interesting is that economists have struggled to identify meaningful productivity gains from many recent technology waves because much of that innovation centered on consumer experiences or entertainment. AI is different because it directly accelerates knowledge work. I think that’s one of the most exciting aspects of this technology.

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BIZTECH: What’s the biggest misconception organizations still have about operationalizing AI?

WEISS:  The biggest misconception is believing AI can fix a broken process. If the underlying process doesn’t work well today, adding AI won’t magically solve it.

Organizations need to step back and understand what they’re actually trying to accomplish. They should rethink the workflow from first principles before deciding where AI belongs. Once you’ve redesigned the process, AI can become an incredible accelerator for specific steps. It can help employees think through problems, generate ideas and complete work faster.

But AI isn’t something you simply throw at a problem and expect it to repair everything.

The organizations seeing the greatest success understand their own processes deeply. They know where the friction exists, and they apply AI deliberately where it creates the greatest value. That’s very different from assuming AI will somehow compensate for operational weaknesses that already exist.