Why AI POCs fail (and how to succeed)

Most AI proof of concepts (POCs) fail in production because teams are testing for the wrong thing. POCs tested on a documented best case scenario tend to look like they’ll succeed, but fail in production because they aren’t tested on whether they can keep succeeding as conditions change. 

Of the 60% of organizations that evaluated enterprise AI tools last year, only 20% made it to pilot and only 5% reached production. That systematic failure points to a fundamental misunderstanding about how to evaluate and implement AI. In order for AI to succeed in production, you must not only be confident that it will succeed today, but that it can keep doing the work as your business changes.

Your POC worked, so why did production fail?

When you ask executives why their AI projects fail to deliver, the answers are remarkably consistent. A 2025 Gartner survey of 701 executives found that CEOs most frequently cite "lack of clear business case," followed by "poor data quality" and "uncertain governance." But the same survey revealed something more telling; CEOs say the greatest obstacle to realizing AI's potential is aligning it to processes, incentives, and culture.

That alignment gap exists because most POCs test too narrowly. Testing whether AI is accurate on documented workflows is a good start, but accuracy on today's processes won’t predict production success. You need to know if your AI can adapt when conditions change, access institutional knowledge that was never written down, and recognize when it doesn't know something instead of guessing.

1. You only tested accuracy, not adaptability

According to a study published in Nature, 91% of machine learning models degrade within one to two years of deployment. In order to be truly successful in production, you must plan for this degradation by having a process to keep AI accurate as conditions change. Without that process, AI will make mistakes.

Zillow's home-buying algorithm, for example, worked well during a hot housing market. But when the market cooled, the model kept making purchase decisions based on outdated patterns and the company lost over $500 million on bad real estate investments.

IBM Watson for Oncology is another example where a model worked well enough in a U.S. hospital where it was trained. When the model was deployed internationally, it wasn’t built to adapt to different treatment standards, drug availability, and protocols in other regions. It failed not because it was incapable, but because it was trained to be static.

These mistakes happened because the models weren't trained continuously to handle new markets, new regions, or shifting conditions.

2. You only tested against documented processes, not reality

The second reason POCs tend to fail is that teams assume documentation reflects how work actually gets done. If you’re running a POC on documented workflows like SOPs, process maps, and best-case scenarios, the model will appear to perform well because the test conditions match the training conditions.

Production, however, is a different reality. According to Panopto, 42% of institutional knowledge is never documented. That might be the knowledge a veteran employee uses when they recognize that an old receipt format from a specific vendor is still legitimate, or when they remember a particular SKU is always mispriced by $2 in the system. Those seemingly small pieces of knowledge are critical to accuracy.

If AI has no mechanism to learn from people who’ve handled these situations because they’ve seen them before, it’ll either make a guess or fail to move the workflow forward. If instead you test whether the model can handle undocumented edge cases, and continuously learn from how your people actually work, your model will be much more likely to succeed in production.

3. You didn’t test for self-awareness 

Most AI systems don't recognize their own uncertainty. They give you an answer whether they're confident or guessing, which means you're stuck manually checking every output to catch mistakes. If your AI is 90% accurate but can't tell you which 10% is wrong, you haven't automated anything. You've just added a verification step.

For AI to actually work in production, it needs to flag uncertainty and escalate when it doesn't know something. Air Canada's failed chatbot is one example. When a customer asked about bereavement fare discounts, the bot confidently told them to book now and apply for a refund later. The actual policy required requesting the discount before booking. The customer sued, and Air Canada lost.

The bot should have been tested during POC for whether it could recognize it was uncertain about a high-stakes policy question, and escalate to a human to avoid this failure.

How Hellmann built production-ready AI

Hellmann Worldwide Logistics, a $4.5 billion freight forwarder handling 20 million shipments a year, needed to fix their quote process. It was taking days, and customers were choosing faster competitors. When they ran a POC with Reindeer, they tested for adaptability, incorporated edge cases and continuous learning, and set guardrails so that they’d be confident their agent could flag its own mistakes.

1. They built for adaptability

Instead of running the POC on a frozen dataset, Hellmann built continuous learning into the evaluation. They started with 20 sample requests to establish a baseline, then created a dedicated training inbox where their pricing team could correct quotes in real time during the pilot. Each correction became new training data, and a dashboard tracked whether the system was actually improving.

2. They tested against reality

Hellmann’s POC used live quote requests from a shared inbox. Those samples included everything from bundled shipments to messaged in multiple languages, and handwritten notes. When the system hit something it hadn't seen before, it asked the pricing team for guidance and learned from the answer.

For example, if a shipping address was missing, the system would ask: "Use last shipment's address?" The rep would confirm, and the AI captured that decision. Over time, it learned institutional knowledge like which customers always bundle requests from three cities or which document formats were legitimate even if they looked wrong.

3. They tested for self-awareness

Lastly, Hellmann evaluated whether the system knew when it didn't know something. The agent was designed to flag missing information and escalate cases it couldn't resolve. Every action had an audit trail, and status was color-coded so the team could see at a glance when the AI was confident versus when it needed help.

Quote turnaround time ultimately went from days to hours, win rates improved, and the pricing team shifted from data entry to consultative work. The system moved to production with reliable execution because the POC tested for production, not just the best case scenario.

Production readiness is about continuous learning

In order for AI to be successful in production, continuous learning has to be a part of the POC process. It should be tested with an intention to include human-in-the-loop feedback both at the POC stage and in production. Without that process, you're building static automation that decays over time.

The companies that win with AI aren't the ones with the biggest budgets or the most advanced models. They're the ones who stopped treating AI like software that gets deployed once and started treating it like a system that learns.

Contact us to see how the experts at Reindeer could guide you through a successful POC process.