Pilots were easy to approve when AI felt like R&D. Budgets now ask a colder question: did this investment pay off once you count tokens too? Companies that treat AI as an experiment forever never learn. Companies that only chase vanity metrics learn the wrong lesson.
What ROI should mean here
Time saved on a defined process & error rates down. Revenue influenced by better response times. Cost avoided - including token spend, not only salaries. Soft wins (morale, speed of learning) count only if you write them down before the pilot starts.
How serious teams check the numbers
Baseline the old way of working for two weeks. Run AI on a slice of traffic, not the whole company. Compare with a control group when you can. Include failure cost: rework, escalations, brand risk.
Kill or redesign pilots that cannot show movement after a fixed window.
Common self-deception
Counting demos as adoption. Ignoring shadow spend. Crediting AI for gains that came from a process cleanup. Declaring victory because the model “feels smart.”
Vendor sprawl is part of the bill - see whether SaaS faces a crisis as teams build their own apps.
A weak premise still fails no matter the model - back to what makes a business idea actually brilliant.
AI leaves the experiment phase when someone owns a metric, a budget, and a date to decide keep, cut, or change — not when the slide deck says “transformational.”
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