The 80% AI failure rate isn't mysterious or inevitable. Projects fail for predictable reasons that organizations can prevent. This guide provides the specific actions that separate the 20% that succeed from the 80% that fail.
Frequently Asked Questions
The single biggest differentiator is starting with a well-defined business problem rather than a technology solution. Organisations that begin with "we have this specific problem" succeed far more often than those starting with "we should use AI."
SMBs can avoid failure by starting smaller (single use case, single department), choosing proven rather than bleeding-edge AI, ensuring strong data foundations before deployment, and building internal AI literacy so teams can maintain and evolve solutions.
A structured prevention framework includes: validating the business case first, assessing data readiness, securing cross-functional stakeholder buy-in, defining measurable success criteria before starting, implementing proper change management, and building governance from day one.