​The AI Consumption Trap: Why Your Enterprise Is Measuring The Wrong Thing

Gregg Aldana, Global SVP at Appian, advises CIOs, CTOs and business leaders globally on enterprise AI and transformation.

I spend a lot of time with CIOs, CTOs and business leaders talking about AI, and one question keeps surfacing: “Why did our AI bill jump again?” IT can usually explain the mechanics. More users, more agents, more API calls, more tokens. But then somebody asks the harder question: “What did we actually get for it?” That is where the conversation gets more interesting.

I have started calling this the “AI consumption trap.” Companies are getting good at measuring AI consumption, while many still struggle to explain what it is worth. I have seen this problem through generations of enterprise technology. Earlier in my career, while leading large modernization work at the FDIC during the global financial crisis, nobody cared how many servers we were consuming or how many lines of code we had written. The questions were more practical. Could we process the work faster? Could we reduce risk? Could people make better decisions? Could the system handle what the mission demanded of it? I think AI deserves the same standard.

AI Activity Is Not Business Value

Enterprise leaders naturally gravitate toward what is easy to measure. That is not unique to AI. A sales organization can count calls and emails long before it knows whether those activities are producing better pipeline. A factory can add shifts without improving profit. AI has given us a new activity metric in consumption.

That metric is useful for budgeting, but it is not value. A rising token bill may mean waste. It may also mean the organization is successfully using AI at scale. Without knowing what process changed, the number tells you little. In my experience, this is why the question “What is the ROI of AI?” is often too broad to be useful.