Can AI do the actual financial calculations, or only the writing around them?

Only the writing, in practice. Language models produce confident-sounding errors around arithmetic, specific figures and cited references, so the workflow that holds up is: calculate and verify the numbers yourself, then hand the verified figures to the model to turn into prose. AI compresses the gap between having the numbers and having the document — it does not close the gap between having data and having an answer.

What is the highest-value first use case for a finance team?

Variance analysis narration. Give the model actual versus budget by category, a short note on what drove each variance, and the intended audience, and you get a management narrative draft in seconds. The analyst still supplies the interpretation; what disappears is the time cost of turning that interpretation into polished prose.

Is it safe to use AI for contract review in a finance team?

As a first pass, yes; as a substitute for legal review, no. A structured summary that flags financial obligations, renewal terms, payment conditions and risk areas helps a finance team understand what it is holding before escalating. It does not tell you whether a clause is enforceable, and it should not be the last set of eyes on anything consequential.

Why does AI work less well in finance than in marketing or communications?

Because precision matters more than speed here, which narrows the usable set of tasks. A plausible-sounding paragraph is an acceptable first draft in marketing and an unacceptable one in a board pack. The finance use cases that work are the structural, repetitive, language-heavy ones — narration, summarisation, documentation — where a human check against source data is quick and unambiguous.

What skills does a finance professional actually need to use AI well?

Two: prompt fluency and verification discipline. Prompt fluency is giving the model enough context — format, audience, constraints, an example — that the output needs editing rather than rebuilding. Verification discipline is treating every output as a first draft to be checked against source data. In finance the second is not optional.

AI in Finance and Accounting: What You…

Finance and AI: where the hype ends and utility begins

Finance is a field where precision matters more than speed. That means the use cases for AI in finance are more constrained than in, say, marketing or communications — but the ones that work, work well.

The professionals seeing real gains are not replacing their judgment with AI. They are using AI to handle the structural, repetitive, language-heavy parts of their workflow so they can apply their judgment to higher-value problems.

Variance analysis narration

One of the most immediate use cases in FP&A: generating the narrative explanation of variances from a structured data input. A finance professional can provide actual versus budget figures by category, context about what drove the variances, and a target audience — and receive a first-draft management narrative in seconds.

This does not replace the analyst's interpretation. It removes the time cost of translating that interpretation into polished prose.

Forecasting first drafts

AI cannot replace a well-constructed financial model. It can, however, generate a structured starting point for a forecast narrative, a set of scenario assumptions, or a sensitivity analysis summary — particularly when the underlying data and logic has already been defined by the analyst.

The finance professional still owns the model, the assumptions, and the sign-off. AI compresses the time between having the numbers and having the document.

Warning

AI outputs in finance must be verified against source data before use. LLMs can produce confident-sounding errors around calculations, specific figures, and cited references. Treating AI output as a final draft — rather than a starting point — is one of the most common and costly mistakes finance professionals make.

Contract review and summarization

Accounting and finance teams that deal with contracts — vendor agreements, customer terms, partnership arrangements — are using AI to generate structured summaries that highlight key financial obligations, renewal terms, payment conditions, and risk flags.

This is not a substitute for legal review. It is a first-pass tool that helps finance teams understand what they are looking at before escalating to legal or starting their own analysis.

Board report and management pack summaries

Senior finance professionals are using AI to generate executive summary sections for board packs and management reports. Given the full data and context, tools like Claude handle longer documents well and produce clear, structured summaries with appropriate tone for a board audience.

Review and editing is still required. But the time to first draft is dramatically shorter.

Audit preparation

Audit prep involves significant document organization, process documentation, and narrative explanation. AI can help write process descriptions, summarize control environments, and draft responses to auditor queries — reducing the administrative burden on finance teams during high-pressure audit periods.

Tip

For variance analysis narration, give the model actual versus budget figures by category, a brief note on what drove each variance, and the intended audience. The more structured your input, the closer the first draft is to what you actually want to send.

The skills finance professionals need

The finance professionals who are using AI effectively have two things: prompt fluency and critical verification habits.

Prompt fluency means knowing how to give AI enough context — format, audience, constraints, examples — to produce an output that needs editing rather than rebuilding. A poorly structured prompt produces a plausible-sounding but unusable output; a well-structured prompt produces a first draft that is 80% of the way there.

Critical verification is equally important. AI outputs in finance must be verified against source data before use. LLMs can produce confident-sounding errors, particularly around calculations, citations, and specific figures. Treating AI output as a first draft, not a final answer, is not optional in finance — it is the baseline.

The AI for Finance and Accounting Professionals course covers both the capabilities and the failure modes of AI systems in a finance context. The AI Strategy and Leadership course is relevant for finance leaders who need to evaluate AI adoption at an organizational level — including risk, governance, and build-versus-buy decisions.

AI in Finance and Accounting: What You Actually Need to Know