On-Demand Webinar
An honest conversation on what AI readiness in finance actually requires
Boards are asking about AI. Vendors are pitching it. Peers are claiming wins. But most finance teams are still spending the first week of every month assembling numbers before they can analyze anything — and AI layered on top of messy data isn't a shortcut, it's a liability.
Nidhi Shah (Global VP Finance, Level Access) has seen this play out for two decades across PE-backed mid-market companies — including KKR, H.I.G., and PSG portfolio companies — and the pattern holds: the finance teams that move fastest on AI are the ones that treated their data foundation as strategy, not cleanup.
At Level Access, a PE-backed company where board reporting, diligence readiness, and trust in the numbers aren't optional, she's putting that playbook to work. In this candid interview with Paul Barnhurst (The FP&A Guy), she'll share what she fixed first, what she's still deciding, where AI genuinely earns a place in her team's workflow, and how the finance org itself has to be built differently to make it stick.
What you'll take away
- What a clean data foundation means in practice: one source of truth, maintained continuously — not assembled at month-end
- Patterns from two decades of PE-backed finance: why some data strategies stick and others stall across KKR, H.I.G., and PSG portcos
- Where to start when you can't fix everything: which data to prioritize and what can wait
- Why AI on messy data goes wrong: the reliability and compliance risks of garbage in, garbage out
- Where AI fits first: forecasting, variance analysis, reporting — where Nidhi trusts it today, and where she doesn't
- How the finance org needs to evolve: team design, hiring, and skill mix as work shifts from assembly to analysis
- A 90-day starting point: one thing to stop doing, one thing to start
Who should attend
CFOs, VPs of Finance, and FP&A leaders who feel the pressure to adopt AI but want to get the foundation right before they automate.
