HappyFunCorp

AI agents are changing how we think about system design, but they also introduce new complexity around reliability, transparency and control. Nonfunctional requirements, things like safety, explainability, and auditability matter more than ever in this next wave of AI-driven systems. I had the chance to contribute some commentary to this excellent piece on how to define those standards thoughtfully and practically here: https://lnkd.in/eyatArZW

Two groups of companies. Same budgets. Same leadership buy-in. Same access to talent. One group scales AI at a 77% success rate. The other at 37%. The difference comes down to a single operational capability that most organizations skip entirely. It explains the gap between scaling AI and stalling after delivery more than any other factor in the study.

98% of the most mature AI organizations plan to engage an external AI partner in the next six months. That's a striking number on its own. It gets more interesting when you look at why. They've proven AI works internally. But scaling past early wins creates a different kind of problem, one that's pushing even the most advanced teams toward outside partners. The full report includes the data on what's behind that number, broken down by maturity tier and industry.

44% of mid-market companies have shipped AI initiatives that are now going nowhere. Not failed. Not abandoned. Just...stuck. The pilot worked. Leadership is bought in. Budget exists. And somehow it won't scale. The reasons are consistent, specific, and fixable, but they're operational patterns most teams don't see from the inside.

92% of mid-market technology leaders say AI is a strategic priority at their organization. Only 17% have results to show for it. We surveyed 501 VPs, Directors, and C-suite leaders across four industries to find out why. Three patterns kept showing up. One of them explains most of the gap on its own.

Can your team prove your AI investments are actually working? We asked 501 technology leaders that question. 76% said no. That measurement gap matters. Teams that can prove impact are far more likely to scale AI successfully. Download the report to see what separates AI initiatives that scale from the ones that stall.