CPMAI™ Phase 5: Model Evaluation

Or impressing the board with a fancy dashboard.

Real success is delivering business value — sustainably.

In Phase 5 of the CPMAI Framework, Model Evaluation, we determine if the AI solution's performance from both a technical and business perspective is effective before deciding if is ready for large scale deployment.

Is it accurate, aligned to business goals, and robust enough to handle changing data conditions over time?

Too many AI projects measure success by technical milestones: model accuracy, deployment speed, or data processed. But if the business problem still exists — or worse, new ones emerge — was it really a success?

Here’s what success should look like in an AI project:

🎯 The business problem is measurably improved — revenue, efficiency, customer satisfaction, etc.

🧑‍🤝‍🧑 Stakeholders trust and adopt the solution in their daily workflows.

🛠️ The solution can be maintained and improved over time — not a one-off experiment.

Here is a real-world example of an Epic AI Project Implementation Fail.

In 2012, Knight Capital Group, a major trading firm, deployed a new AI-driven algorithm to manage high-frequency stock trades. But during rollout, they accidentally activated outdated code that hadn’t been properly tested or integrated. Within 45 minutes, the system flooded the market with erroneous trades—buying high, selling low—and racked up a staggering $440 million loss. The glitch caused wild price swings and nearly bankrupted the firm. The AI didn’t fail on its own—the disaster came from poor deployment practices and a lack of safeguards around legacy code.

AI isn’t a trophy to display. It’s a tool to create real, lasting impact. This is why you need to hire an awesome project manager like me who understands business, tech and AI.