The AI Prototype-to-Production Checklist

This week, MIT confirmed what we all suspected: 95% of generative AI pilots are failing.

I don't see failure as a problem per se. Prototypes are for testing whether new technology can solve existing problems in novel ways. It's clear that most of them won't work (otherwise the problem would have probably been solved already).

What is problematic, though, is the cost of failure.

When teams spend months on 'prototypes' or push AI projects into production that were never production-ready in the first place, that is a problem. Because it burns budget, erodes trust and comes with heavy opportunity cost. The cost could be as high as your entire AI journey.

I'm far away from telling which prototype will be successful in production. But after 5+ years in applied AI consulting and over 50+ hands-on projects, I have observed some patterns that are "red flags" for moving a project forward.

Today, I'm sharing this 7-point checklist that separates genuine production candidates from well-intentioned experiments. It takes 10 minutes to complete and could save you months of wasted effort.

So let's dive in!

Why Most AI Projects Die Between Prototype and Production

I've written before about the difference between discovery and delivery phases in AI projects. Discovery is about testing assumptions fast. Delivery is about building something that works reliably at scale.

You can see the complexity explosion that happens between prototype and production. What starts as a simple idea grows into a system with users, funding requirements, support needs, and DevOps complexity.

The problem? Most teams never make the conscious decision to switch phases. They just keep "discovering" for months, burning through budget while calling it a "pilot."

Here's what the consequences look like in practice:

• Week 1-2: Exciting prototype demo

• Month 2-6: "Pilot phase" (really just extended discovery)

• Month 7: Pressure to show production results

• Month 8: Quiet project burial

The "root" issue (quite literally if you look at the picture) is that teams lack a clear framework for when a prototype has graduated from "let's see if this works" to "let's make this work reliably".

That's where this checklist comes in. It helps you spot the difference between genuine production readiness and wishful thinking. The goal is to iterate quickly in discovery, figure out if you’ve found something real and then scale as you go.

The 7-Point Production Checklist

Here's the framework I run in my head whenever I see an AI prototype that gets considered to be "moved further".

✅ 1. Value Clearly Validated

What I'm looking for: The prototype has demonstrated clear and measurable business value or impact (e.g., reduced costs, time savings, higher user satisfaction).

You'd be surprised how many "successful" demos fall apart when you ask basic questions like "How much time does this actually save?" or "What happens to our costs if we scale this?"

"Cool demo" isn't the same as "business impact." I've seen chatbots that impressed executives but didn't move any metrics that actually mattered to the business.

Red flag: Your value story relies on future assumptions rather than current measurements.

✅ 2. Real User Feedback

What I'm looking for: Initial users have consistently provided positive feedback or have shown strong engagement for the prototype.

Real feedback sounds like "Can I get more access to this?" Fake feedback sounds like "That's really interesting, we should definitely explore this further."

Too many teams use "production" as an excuse to avoid putting their idea in front of real people. They want to make everything perfect first. But you need actual user feedback before you decide to serve it to more people. That's how digital products work.

Red flag: Users praise the concept but find excuses not to use it regularly, or there's no feedback at all.

✅ 3. Technical Feasibility Proven

What I'm looking for: Core technical questions or risks have been answered or mitigated. In other words: there's high confidence the AI solution is technically viable.

"It works in the demo" and "it works when John from IT is babysitting it" are not the same thing.

The key question isn't "Does it work?" but "Do we understand what it takes to make it work reliably?" Some problems are easy to fix by throwing engineering hours at them - integrations, user interfaces, performance issues. These often have a direct relationship between effort and results.

AI problems don't work that way. See my post on the 80% Fallacy. With AI, more time doesn't automatically mean more progress.

Red flag: The current AI performance is not sufficient to deliver value right now. There's an expectation that the AI will "get better" in production or "learn over time".

✅ 4. Demand Is Growing

What I'm looking for: Increasing user demand indicates broader organizational interest or clear potential for wider adoption.

This is about pull vs. push. You're not having to sell people on trying it - they're hearing about the project and getting interested on their own.

The pattern I look for: "I heard you're working on this. Maybe this could help with..." or departments asking if they can be part of the test group. When word spreads organically and people start seeing applications in their own work, that's a strong signal.

Red flag: You're still having to convince people to try it, or usage stays flat within your original test group.

✅ 5. Leadership Support Secured

What I'm looking for: Senior stakeholders or decision-makers have explicitly signaled willingness to fund the AI solution moving forward.

This isn't about getting a polite nod or having leaders say "interesting work." Some leaders back AI projects because they need to report AI initiatives to their board or senior management. They'll lose interest the moment that PowerPoint slide gets delivered.

Real leadership support looks different. It's leaders asking detailed questions about scaling, wanting to understand the business model, or bringing up the project in unrelated meetings. They're thinking about it as part of their strategy, not just their AI checkbox.

Red flag: Leadership says "keep up the good work" but won't commit specific resources.

✅ 6. Ethics & Compliance Checked

What I'm looking for: While you perhaps haven't considered all ethical and compliance-relevant intricacies up to the last detail, it's clear that in general your AI solution will meet both ethical and compliance standards of your organization.

I've seen great AI projects get killed overnight because someone in legal finally took a look, or the works council found out. Better to have those conversations early than emergency meetings later.

Red flag: Your team actively avoids compliance discussions or has no plan for risk management.

✅ 7. Integration Path Understood

What I'm looking for: There's clarity around how the AI prototype will integrate within existing workflows, systems, and processes.

The most elegant AI solution is worthless if it requires people to completely change how they work. When your innovative solution requires users to switch between three different systems to complete one task, you'll never reach true mass adoption.

Red flag: Your integration plan starts with "Users will just need to adapt their workflow a bit."

How to Use This Checklist

There's no "scoring" system here.

The only hard rule: If everything gets checked, move to delivery planning.

Beyond that, I weight these differently based on the situation. #1 (Value), #2 (User Feedback), and #3 (Technical Feasibility) tend to be deal-breakers for me - if you can't demonstrate clear value or if the stuff just doesn't work, the other factors won't save you.

Some combinations are more problematic than others. A project with great user feedback (#2) but weak leadership support (#5) might survive. A project with strong leadership support but terrible user feedback usually won't.

Take this checklist to surface the real issues, not to generate a neat score. The goal is to have honest conversations about what's actually working and what needs fixing before you invest more time and money.

Conclusion

Don’t try to avoid failure. Try to fail early, when it’s still cheap. The goal is to fail fast on the wrong projects and scale aggressively on the right ones.

If your current AI project checks most of these boxes, you're ready to start planning for production. If it doesn't, use this checklist to identify what needs attention before you invest any more time or resources.

Because the most expensive AI failure isn't the one that happens in week one – it's the one that happens in month six.

Which boxes can your current AI project actually check?

See you next Friday!
Tobias