Demo vs. Reality

When you point an AI at a business problem and test it on your own example, it often shines. You share it company-wide, and everyone’s impressed. But once real users fire it up, they ask unexpected questions, mix slang, or push edge cases. Suddenly, your demo that worked brilliantly gives wrong answers confidently or simply breaks under load.

• Wrong or weird answers undermine trust when the model hallucinates.

• Slowdowns and crashes frustrate users at scale.

For example, you might demo an AI tool that extracts questions and requirements from an RFP or ITT perfectly. But when the next one uses a different format or adds nuanced clauses, the tool can miss key criteria, turning a seemingly solid feature into a source of errors.

To prevent these failures in the wild, teams can follow a clear process to continuously evaluate and improve AI.

A Simple Evaluation Framework

We follow a four-step loop that keeps AI applications on track:

1. Plan. Pick the key tasks your AI application must handle and decide what “success” looks like.

2. Test. Run those tasks against a small set of real-world examples to reveal early bugs.

3. Monitor. After launch, track performance metrics—error rates, response times—and listen to user feedback.

4. Improve. Update prompts, retrain on new data, or tweak system logic based on what you learn.

By repeating this cycle, you catch new failures and refine your AI application before it surprises customers.

Monitoring in Action

Evaluation doesn’t stop at release. We watch our AI every day:

• Automated health checks. We run representative queries and track key metrics daily.

• User feedback. We gather ratings and support insights to catch subtle problems.

Why Leaders Should Care

Skipping evaluation may speed up your launch but leads to hidden costs—it's like catching a typo before printing thousands of manuals.

• Lost trust. A single error in front of customers can undo months of good PR.

• Rising repair bills. Fixing issues in production can cost significantly more than catching them in staging.

• Unfocused roadmaps. Without clear data on where your AI application fails, it’s hard to prioritise improvements or prove ROI.

As Kevin Weil, CPO of OpenAI, puts it: “The AI models that you're using today are the worst AI models you will ever use for the rest of your life. And when you actually get that in your head, it's kind of wild.”