17 November 2026 11:15 - 12:00
Panel - AI can build the forecast. You still own it.
AI is doing more of the modeling behind a forecast, but the authority and responsibility for what goes to the board doesn't move with it.
This panel gets into what it actually means to hold that line: how CFOs are making sure models are trained on data they'd actually defend, what a real audit trail looks like so a forecast isn't a black box, and what happens when a number misses and "the model said so" isn't an acceptable answer.
Key takeaways:
- Why authority over the forecast stays with the CFO regardless of how much of the modeling AI handles.
- What "properly trained" actually means in practice, and how CFOs are vetting the data before it ever reaches the model.
- How CFOs are building a genuine audit trail into AI-generated forecasts, so the logic is inspectable, not a black box, when the board asks questions.
17 November 2026 14:15 - 14:45
Modeling AI investment returns under uncertainty
Over half of CFOs admit they can't actually measure the value of new technology, and 71% say standard financial metrics don't work for something that blends technology, data, and people. The usual fix is a single ROI number that everyone quietly knows is a guess dressed up as precision.
This session makes the case for pricing AI investment as a range instead: using Monte Carlo simulation to stress-test a real automation use case across thousands of possible outcomes, showing the actual downside risk, the upside optionality, and the odds of hitting the number the board was promised. Attendees leave with the specific inputs to demand before funding the next AI business case, data readiness, time to value, ongoing governance cost, and where the model's confidence should actually change the capital allocation call.
Key takeaways:
- Why standard ROI and payback models break down for AI investment, and what CFOs should demand instead.
- How Monte Carlo simulation turns a vague assumption into a real distribution of outcomes, downside risk, upside optionality, and the actual odds of hitting the board's target.
- The specific inputs to require from vendors and internal teams before funding the next AI business case: data readiness, time to value, and ongoing governance cost.