Guides
Practical, vendor-neutral guidance for finance teams putting AI to work. The benchmark measures what AI costs per successful outcome; these guides cover everything around that number — how to run an evaluation, where AI fits in the finance function, what controls to put in place, and how the pricing actually works. No paid placement applies here either (independence policy).
- How to evaluate AI for a finance workflow — a pilot playbook
Before you buy or build, run a two-week evaluation that measures what actually matters — cost per acceptable result, not demo quality.
- Where AI fits in the finance function — seven workflow patterns
A map of the finance jobs AI can do today, organized by the shape of the work rather than the hype around it — and where each pattern is proven versus experimental.
- Controls and risk management for AI in finance workflows
How to deploy AI in a controlled finance environment — review design, audit trails, data handling, and change management that will survive your auditors.
- Understanding AI pricing — from tokens to cost per outcome
A finance professional's guide to how AI is actually billed — tokens, caching, batch tiers — and the arithmetic that converts a price sheet into a budgetable cost per unit of work.
- The vendor question checklist — ten questions that cut through an AI sales call
Bring these to the demo. The questions that separate measurable products from confident decks, with the answers you should expect to hear.