Five Ways to Make AI More Reliable on Your Projects

The recently published PMI Standard for Artificial Intelligence in Portfolio, Program, and Project Management emphasizes that quality and reliability should be designed into AI from the beginning, not added later. Having had the opportunity to review and suggest edits to the standard, I found that many of its principles align closely with my own research and practical experience applying AI to project management. The ideas below combine guidance from the PMI standard with my insights from research and real-world implementation.

Organizations are investing heavily in artificial intelligence, yet many are disappointed with the results. The problem is rarely the AI itself. More often, it is how AI is implemented. If you want more reliable AI recommendations, start with these five practices.

1. Improve your project data.
AI learns from historical information. If schedules are incomplete, costs are inconsistent, or risks are poorly documented, AI will produce unreliable recommendations. Better data almost always leads to better AI.

2. Never accept AI recommendations without validation.
Before acting, ask whether the recommendation reflects the project's current reality. Have priorities changed? Are new risks emerging? AI cannot always recognize recent events that are not represented in its data.

3. Keep humans involved in important decisions.
AI should support project decisions, especially when they are critical to project results. However, strategic trade-offs, stakeholder concerns, and organizational priorities require human judgment.

4. Measure how well AI performs.
Track AI predictions against actual project outcomes. Compare schedule forecasts, cost estimates, and risk predictions with what actually occurs. This allows you to identify where AI adds value and where improvements are needed.

5. Continuously improve the system.
Projects evolve, and AI should evolve with them. Update project data, retrain models when appropriate, and incorporate lessons learned into future recommendations. AI is not a one-time implementation. It is an ongoing capability.

Reliable AI is built through disciplined project management, not just advanced technology. Organizations that combine quality data, human oversight, performance measurement, and continuous improvement are far more likely to achieve meaningful results from AI.