Using AI As a Revenue Engine

​MIT looked at $30 to $40 billion in enterprise AI spending and found 95% of organizations got no measurable return. IBM's own CEO study puts it at a quarter delivering the ROI they promised. Morgan Stanley found only 21% of the S&P 500 could name a measurable AI benefit at all.

​Now hold that next to this: 91% of small businesses using AI say it boosts revenue. Same technology, same year, opposite verdicts. The difference is that one set of numbers was measured and the other was asked. When the Census Bureau applied a stricter instrument, only 17 to 20 percent of U.S. businesses turned out to be running AI in actual production rather than experimentation.

​So the question this session takes seriously isn't whether AI works. It's what the 5% do that the 95% don't, and whether anyone in this county can answer the only question that matters: what did it produce last quarter, in dollars?

​Four panelists who deploy this for a living. One works the exact layer inside a Fortune 50 company where AI projects die. One built and sold an automotive tech company and now runs an AI startup backed by three major accelerators, making him a live test of the finding that buyers beat builders two to one. One spent 25 years across the Fed, Citi, EY, Microsoft, AWS, and PwC and now sells automation to small businesses, holding both scoreboards at once. One has taught over 1,500 people to build AI products and will have to defend building in front of data that says buying wins.

​The back half is a working session. Measurement, relevance, vendor diligence, ownership. You leave with a named action, a named owner, and a 90-day first step, validated or torn apart live by people who do this for a living.

​Bring one number: what AI produced for you last quarter. If you don't have it, that's the most useful thing you'll learn today.