Enterprises started buying AI faster than they could see what it returned.
In two years, enterprises have bought licenses, integrated models and deployed agents across nearly every function. Most still can't say what AI is returning, or where.
With our customers, we kept seeing the same waste: spend scattered across vendors and teams and noticed only after the fact, unused licenses, heavy models on light tasks, prompts paying for the same context on every call, agents running unchecked. Real costs, and the easiest ones to find.
Adoption is harder to measure. Logins say little about whether work has changed, and within one company some teams build AI into their workflow while others try it once and leave. The pressure to show adoption has created what we call tokenmaxing: treating consumption as the goal. More usage isn't more value.
Governance lags too. Much of AI arrived outside procurement, bringing sensitive data into prompts, unapproved tools into daily use, and unclear ownership. When security and compliance ask for assurance, the visibility often isn't there.
Cutting costs solves half the problem. Leading organizations don't want smaller AI budgets. They want to know which investments deliver, so they can move money from what underperforms to what works.
ZuzAI was built on that premise. It connects usage to adoption and adoption to impact, and applies governance before spend is committed. AI leadership, IT, engineering and finance get one evidence-based view of what is used, by whom, at what cost and to what effect. We believe the advantage in AI will go to those who see most clearly and allocate most deliberately, not those who spend the most.
The problem was never getting AI into the business.
It was seeing what it was doing once it got there.
That’s when we knew we had to build ZuzAI.