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Finance AI readiness assessment

How ready is your finance team for AI?

See where you stand, what’s holding you back, and where to start. A practical AI readiness assessment built around the way finance teams work.

  • Free assessment
  • Finance-specific
  • Practical next steps
Let’s find your starting point.

Score your readiness across six dimensions.

Map your stack, data, processes and governance. Get your biggest blockers and a practical roadmap.

Six dimensions · 100 points
  • Use case clarity15
  • Data & stack25
  • Process & models15
  • Governance & control20
  • People & operating model10
  • Execution readiness15
Readiness stages
Foundation not readyExploringExperimenting with constraintsReady to operationalizeReady to scale
01Understand your starting point

What is AI readiness for finance?

AI readiness is your finance team’s ability to use AI effectively in a defined workflow. An assessment helps you examine the data, skills, systems and controls behind that work — so you can decide what to try and what needs attention first.

Having an AI tool is one part of the picture. Your team also needs to check its output and fit it into a repeatable process.

Readiness foundations: Harvard Business School Online

Think in workflows

Ready for one task. Still building toward another.

Drafting commentary from reviewed figures and authorizing a payment need different preparation. Start with the work you want AI to do.

02Check the foundations

An AI readiness checklist for finance teams.

Look for evidence that one workflow can work reliably. These four checks help you identify what to prepare before your next AI pilot.

01

Can you trust the inputs?

Confirm the source, reporting period, units and definitions. Resolve conflicting versions before asking AI to explain the numbers.

Look for: A reconciled actual-versus-budget file with clear labels and an owner.

02

Is the process clear?

Define the task, expected output and what happens when information is missing. Make the pilot repeatable enough to evaluate.

Look for: A documented reporting step with known exceptions.

03

Can the team check the work?

A reviewer needs the finance knowledge to challenge numbers, assumptions and explanations. Practice on work with a known result.

Look for: A named reviewer and a checklist for accepting or correcting a draft.

04

Are the boundaries agreed?

Agree which tools and data are approved, who has access, and what needs review. Keep a way to trace inputs and corrections.

Look for: Clear ownership and approval before outputs are shared or acted on.

The foundations matter. ACCA and CA ANZ’s 2026 research identified data quality (42%), skills gaps (42%) and integrating multiple sources (40%) as barriers to better use of data in finance. Read the research

Global research involving 1,600 finance professionals. These are data-use barriers, not AI project failure rates.

03Start with work you can check

Where can your finance team start with AI?

Choose a narrow task, approved inputs and an output your team can verify. Here are three starting points to consider.

Draft variance commentary

Explain the movement behind the numbers.

Prepare first
Reconciled actuals and budget, consistent periods and approved business context.
What a person checks
Amounts, sign conventions and whether the proposed causes are supported.

Summarize a management report

Turn reviewed information into a clear draft.

Prepare first
A reviewed report, a clear audience and the purpose of the summary.
What a person checks
Missing context, unsupported claims and consistency with the source.

Draft reporting documentation

Make a known process easier to follow.

Prepare first
An agreed process, defined inputs, owners and known exceptions.
What a person checks
Whether the draft reflects the real process and assigns responsibility correctly.

Illustrative pilot ideas, not tested workflows or guaranteed savings. Explore McKinsey’s finance AI examples ↗

Choose the method that fits the task.

Fixed file transformations or recurring calculations may be better served by conventional automation. Posting entries, approving payments or changing a live forecast needs a separate review of permissions, testing and controls.

Find a starting point that fits your team.

Use your assessment to focus the conversation.

04Make the result useful

Your score is the start. The next step is what matters.

Use your AI readiness score to guide a practical conversation about your team and the work you want to improve.

Your readiness snapshotExample result

Your ambitions are ahead of your data path.

A useful next step: strengthen the inputs behind one reporting workflow.

A blocker to work on: Finance data still needs manual preparation.

Your next step: confirm one set of inputs before a scoped pilot.

Illustrative sample, out of 100. This is not a peer benchmark or a score calculated from your answers.

  1. 01

    Read the profile

    Look across the dimensions. Strong team skills and weak data foundations call for different work than strong systems and limited experience.

  2. 02

    Understand the blocker

    Review the gap highlighted in your result. Ask how it affects the workflow you want to try.

  3. 03

    Choose one next step

    Use your action plan to select a task, name an owner and decide what you will check before expanding.

A self-assessment reflects the answers you provide. It does not certify a workflow, approve a deployment or predict the return on an AI investment.

Your next step

Less guessing. A clearer place to start.

Get a view of your team’s readiness, identify the gaps and choose what to work on next.

Your profile
Your blockers
Your next steps
05Questions before you start

AI readiness. Clear answers.

The questions that help finance teams move from interest to a practical next step.

Research updated October 2026.

Sources and methodology ↓

It is a structured self-assessment of how prepared a finance team is to use AI in its work. It helps identify gaps in the foundations behind adoption and prioritize improvements. The result is a starting point for planning; assessing a production workflow also requires evidence and testing. Related assessment guidance.

AI readiness asks whether you have the conditions to adopt or expand AI effectively. AI maturity describes how established AI use already is. A team may use AI regularly while still having gaps in data quality or oversight. Check the definitions in each assessment before comparing results. HBS Online explains the distinction.

A scoped AI-assisted task can use spreadsheet inputs when those inputs are approved and can be checked. For example, a team might draft commentary from reconciled actual-versus-budget figures. Excel use alone does not establish readiness: confirm the file version, formulas, permissions and reviewer for the task.

You need data that is fit for the task you choose. Identify the issues that would make its output unreliable, and address those first. A narrow pilot with approved inputs can help you test an approach while broader improvements continue. That pilot does not establish readiness for every workflow. Read McKinsey’s guidance for CFOs.

Start with understanding a tool’s limits, giving it clear instructions and checking the output against finance evidence. Practice spotting unsupported explanations, missing context and incorrect numbers. A first drafting pilot may not require coding; integrating systems or operating complex workflows can require additional technical skills. ICAEW’s foundational learning topics.

Define ownership, approved data access, output checks and review before action. Test with representative examples and record errors and corrections. Review expectations should reflect the consequences of the task: drafting text differs from executing a financial transaction. NIST’s Generative AI Profile provides a risk-management foundation.

A useful score has a clear methodology and enough detail to guide action. There is no universal cutoff shared by readiness tools. Read the score alongside its dimensions, highlighted gaps and action plan. A high total can still leave an important constraint in the workflow you want to deploy. How to interpret the assessment.

Discuss the result with the people who own the work. Choose one priority, confirm the gap with real evidence and define a next step with an owner. Use your action plan to guide that work. Revisit the assessment after meaningful changes in your data, skills, systems or controls.

Research & methodology

The educational guide draws on published research and professional guidance. The practical checks and pilot examples are FinanceWorkRoom’s synthesis.

These sources inform the content. They do not endorse FinanceWorkRoom or validate its assessment score.

How to interpret the assessment

The assessment is a self-reported diagnostic. Read the total score together with the dimension breakdown, identified blockers and action plan. The result reflects the information you provide, and it does not substitute for testing a particular workflow.

Use the result to frame a discussion with your team. Confirm material gaps with evidence before changing a live process. The example shown on this page is illustrative and is not a peer benchmark.

Find your starting point. Then move it forward.

Make your next AI decision around the work your team actually does.