How to Know if Your AI Investment Is Actually Working

The measurement gap most organizations don’t know they have

McKinsey reports that 78% of organizations claim they’ve “adopted” AI. But when BCG applied rigorous criteria, only 4% are seeing real business value. That’s not a typo — ninety-six percent of companies investing in AI can’t demonstrate meaningful results.

The problem isn’t the technology. It’s that technical teams and leadership teams are measuring different things. Engineers report model accuracy, latency, and uptime. Leaders need to know: did it change a decision? Save money? Reduce errors that matter?

Most organizations build measurement systems around whatever data is easy to collect, rather than the data that would actually inform decisions. They end up measuring what the technology produces instead of what the business needs.

A framework built for leaders, not engineers

This guide gives you four questions that work for any AI implementation — chatbots, predictive models, automation tools, recommendation engines. You don’t need technical expertise to ask them or evaluate the answers.

Is it doing what we said it would do? Go back to the original business case. If the team can’t connect what the AI does to the original justification, that’s your first signal.

Is it doing it well enough? A model that’s 95% accurate sounds impressive — but if the 5% errors are the high-value cases that cost real money, 95% might not cut it. “Well enough” is a business judgment, not a technical one.

Is it getting better or worse? AI systems degrade over time. Data gets stale. Providers push updates. I’ve watched teams go 30 days without noticing that their AI output quality degraded — not because anyone was negligent, but because nobody had a measurement system to detect the shift.

Is it worth what we’re paying? The visible costs — licenses, consulting fees, infrastructure — typically represent about 30% of the total investment. The rest is hidden. If you don’t know total cost, that’s worth solving before anything else.

Who this guide is for

You’re a CEO, COO, VP, or director who approved an AI investment somewhere between six months and two years ago. The technical team says everything is running fine. But you can’t independently evaluate whether the thing is delivering what it was supposed to deliver.

This guide gives you a practical framework for that evaluation — plus six red flags you can spot without a data science degree, and a set of questions you can bring to your next leadership meeting.

Download is free. No spam — just the guide and occasional insights on AI decisions.