AI projects rarely fail suddenly. They show warning signs months before collapse. Organizations that recognize red flags early can intervene and save projects. Those that ignore warnings join the 80% that fail.
Frequently Asked Questions
Key red flags include no clearly defined success metrics, lack of executive sponsorship, data quality issues surfacing late, scope creep beyond the original use case, vendor-driven rather than problem-driven selection, and no change management plan for end users.
Most AI project failures can be predicted within the first 4-6 weeks by evaluating data readiness, stakeholder alignment, clarity of success metrics, and the gap between vendor promises and technical reality. The checklist framework helps identify these signals early.
Immediately escalate to the project sponsor, conduct a root cause assessment, determine if the red flags are fixable or fundamental, and be willing to pivot, rescope, or pause the project rather than continuing to invest in a failing approach.