01
Confirm the business problem
Describe the problem without mentioning AI. Make sure the use case creates measurable cost, delay, rework, risk, or lost capacity and supports a defined finance priority.
Resources · The Finance AI ROI Checklist
15 practical tests before you scale AI.
A controller-led framework for moving from experimentation to measurable value. Use it to expose gaps before they become expensive surprises, and to prove ROI before the board asks for it.
102 KB · PDF · 9 pages
AI cannot leapfrog the maturity of the finance operating model. It inherits it.
The checklist principle
15 tests · read in order
From problem definition to continuous value monitoring.
15 questions → one score
02
Establish the current baseline
Measure current preparation and review hours, manual touches, rework rates, exception volumes, and resolution times. No baseline means no credible ROI.
03
Assess process maturity
Know whether the process is manual, digitised, automated, governed, or agentic. Do not expect AI to compensate for a missing foundational stage.
04
Redesign before automating
Map the end-to-end workflow, remove duplicate entry and unnecessary handoffs, and identify what work will stop when AI begins.
05
Make the accounting logic explicit
Document the relevant accounting policy, recognition and measurement rules, judgments, thresholds, and evidence requirements. Review AI outputs against policy, not just historical practice.
06
Prepare the data
Identify required data sources and owners, resolve inconsistent definitions and mappings, and include data preparation time in the ROI model.
07
Select a high-value use case
Choose a use case with sufficient volume, repetition, and measurable effort. Do not choose it because it demos well — choose it because solving it creates value.
08
Define the roles of AI and humans
Classify each activity as assist, recommend, prepare, route, execute, monitor, or escalate. Keep final accountability clear and document overrides.
09
Embed governance and controls
Assign a named business owner, configure authority limits and segregation of duties, trace inputs and outputs, and create a process to challenge or suspend the automation.
10
Plan for adoption
Employees must understand why the process is changing, what work should stop, and how to challenge outputs. Monitor usage, workarounds, and shadow processes.
11
Integrate into the operating environment
Connect the solution to the relevant ERP, subledger, data warehouse, or workflow. Remove excessive exporting, uploading, and copying.
12
Define ROI before launch
Assign named owners and timeframes to expected benefits, include all implementation and ongoing costs, and set a clear break-even point.
13
Convert time savings into realised value
Verify saved time, know whose capacity was released, and give that capacity a deliberate use. Shift senior review time toward higher-risk work.
14
Move beyond the pilot
Set clear success criteria, test exceptions, controls, security, and operating conditions with users outside the project team, and identify a production owner and budget.
15
Monitor value continuously
Compare actual results with the baseline, review benefits, usage, accuracy, exceptions, and manual interventions, and reassess the control environment as automation expands.
Scorecard
Give the initiative one point for every “Yes”.
The scorecard is deliberately blunt. It turns a board conversation about “AI potential” into a conversation about readiness, risk, and the work still to do.
13–15 yes answers
Ready to scale
9–12 yes answers
Promising, but exposed
5–8 yes answers
Pilot with caution
0–4 yes answers
Redesign before investing
Download the full checklist
Take the 15 tests into your next AI review.
The PDF includes the complete framework, the CFO final readiness test, and a printable scorecard.
Ready when you are
Want to apply the checklist to your use case?
We can walk through the 15 tests against your actual process, baseline, and governance — no scripted demo.