How CFOs should evaluate AI and automation business cases
AI cases tend to be optimistic about benefit and vague about realization. The job of finance is not to slow the work — it is to make the value real.
The pattern finance keeps seeing
An AI or automation proposal arrives with a credible technology design, an aspirational benefit number, and a thin path from output to operating P&L. The CFO is asked to fund it on faith.
The discipline is not to argue with the technology. It is to insist on a benefit chain that can be tracked.
Five questions to ask of every AI case
- Which decision changes when this works? If no decision changes, no value moves.
- Where does the benefit show up — labor reallocation, working capital, revenue, risk avoidance? Each has a different proof standard.
- Who owns the operating change required to capture it?
- What is the value at stake on day 30, day 90, day 365? If only day 365 is credible, it is too far away.
- How will value be tracked in the operating P&L, not in a side log?
"Benefit you cannot point to in the operating P&L is not benefit. It is hope."
Use the diagnostic, not the deck
A short, focused diagnostic on real client data is a faster route to a credible business case than another round of internal estimation. It also exposes the unglamorous work — master data, exception handling, controls — that determines whether the case actually lands.
The Value Lab exists precisely for this: a working environment where the case is pressure-tested before the investment commits.
"Benefit you cannot point to in the operating P&L is not benefit. It is hope."
- Pin every benefit to a decision that changes.
- Insist on day-30 / day-90 / day-365 value clarity.
- Track value in the operating P&L, not in a side log.
- Pressure-test the case on real data before committing.