Most AI strategies are elaborate 50-slide performances designed to impress investors and boards. Steve Blank calls this innovation theater. RAND reports more than 80% of AI projects fail because the boring operational work that creates actual value gets ignored.
Most AI tools will not exist in three years. The economics are brutal: Stanford HAI data shows enterprise AI spending has quadrupled while startups burn through cash twice as fast as a decade ago. Here is how to spot which ones survive and avoid betting your operations on doomed solutions.
Companies waste millions choosing build or buy based on cost spreadsheets. RAND Corporation research shows more than 80 percent of AI projects fail at twice the rate of regular IT projects. The real decision is whether your middle managers understand AI well enough to use whatever you build or buy.
Everyone is jumping between ChatGPT, Claude, Gemini, and Perplexity, but constant AI assistant switching costs up to 40 percent of productive time and is destroying the very productivity gains AI was supposed to deliver
When IDC data shows only 7% of organizations fully scale AI and up to 88% of pilots never reach production, the problem is not the technology. Most AI implementation checklists evaluate features when they should evaluate support, infrastructure readiness, and team preparation.
MIT research shows 95 percent of AI pilots fail to deliver value, yet traditional AI maturity models keep pushing companies through expensive levels. Five contextual factors predict success better than any maturity score.
Only 25% of AI initiatives deliver expected ROI according to IBM research. Executives approve AI when positioned as business value multipliers with clear ROI timelines and risk controls - not technology experiments
Only 7 percent of organizations fully scale AI past the pilot stage, per MIT Sloan research. The approaches that work for 5 people become liabilities at enterprise scale for 50.
Traditional AI readiness assessments measure data quality and infrastructure while missing what actually predicts failure: workflow fragmentation. Microsoft research shows teams toggle between 47 tools, switching contexts 1,200 times daily. That is where most AI projects die, not in the data architecture.