Managing AI Projects

AI projects fail at a remarkably high rate — industry estimates range from 60% to 80%. Most failures are not technical. They are management failures: wrong expectations, poor scoping, and misaligned success criteria. Understanding how AI projects differ from traditional software projects is essential.

How AI Projects Are Different

Uncertainty is higher: In traditional software, you define requirements and the team builds to spec. In AI, you often cannot know in advance whether the AI will perform well enough on your specific data. The first phase of any AI project is experimentation, not implementation.

Progress is non-linear: AI performance does not improve steadily. A model might achieve 80% accuracy quickly, then require enormous effort to reach 90%. The last 10% of performance often takes 90% of the effort.

Requirements are harder to specify: "Summarize customer feedback accurately" sounds simple but is deeply ambiguous. What counts as accurate? How long should summaries be? What about sarcasm, mixed sentiment, or technical jargon? AI projects require iterative refinement of what "good enough" means.

A Better Project Structure

Phase 1 — Proof of Concept (2-4 weeks): Test whether AI can solve the problem at all using your real data. Define minimum acceptable performance. Kill the project early if results are poor.

Phase 2 — Pilot (4-8 weeks): Deploy to a small group of real users. Measure performance, collect feedback, identify edge cases. Refine the solution based on real-world usage.

Phase 3 — Production (8-16 weeks): Scale to full deployment with monitoring, error handling, fallback procedures, and user training. Build the operational infrastructure around the AI.

Setting Expectations

Tell stakeholders: "We will know within four weeks whether this approach is viable." This frames the initial investment as a learning exercise, not a commitment to a specific solution. If the proof of concept fails, that is a successful outcome — you learned quickly and cheaply that this was not the right approach.