Why AI Pilots Fail - and How to Fix Them
By Long Nguyen
A recent MIT Sloan study found over 90% of AI pilots never get into production but the reason isn’t the technology - it’s overreach.
The Real Problem
Companies often aim straight for complex use cases like long-range forecasting or budgeting. These demand clean data, cross-functional buy-in, and process maturity most organizations don’t yet have. The result: expectations are high, results are less than optimal = pilots fail.
A Spectrum of Complexity
AI use cases sit on a spectrum:
Low complexity: reporting, KPI dashboards, anomaly alerts, natural-language data queries
Medium complexity: variance analysis, short-term forecasting, benchmarking
High complexity: scenario planning, multi-year forecasting, capital allocation optimization
Too many teams jump to the right side before proving success on the left. Also note that if you’re also fire fighting and fielding ad-hoc requests on the left side, you likely can’t do the right side as well.
The Fix: Inventory and Score
Start by listing all potential use cases. Score each by:
Value– how much time or cost is saved or, what actionable insight it drives
Complexity / readiness – data quality, process maturity, and buy-in required
Add other dimensions if you want to add more richness to the prioritization, but don’t go overboard.
Our recommendation is to start in the upper left and tackle the 'quick-wins' first. This will generate confidence and learnings to help go after the more complex use cases
Build Momentum First
The winners begin with reporting automation, KPI tracking, or anomaly detection. These reduce manual work, show visible impact, and build trust - creating the foundation to move toward advanced forecasting and planning later.
Bottom Line
AI pilots fail not because the tech isn’t ready, but because companies overreach. Treat adoption as a spectrum, score use cases, and focus first on the quick wins. That’s how you avoid the pilot trap and unlock real transformation.
How We Help
We walk our clients through a discovery and prioritization process and help teams align on where to focus. Once use cases are prioritized, we work hand-in-hand with our clients to ensure we are showing ROI (e.g. time saved, cost avoided, insights derived). Reach out here today to learn more.