Practical applications of AI for accountants and lawyers

Artificial intelligence (AI) is often discussed in broad or futuristic terms, but many of its most valuable applications are practical and immediate.

Accountants and lawyers can already use AI to support research, document review, financial analysis, drafting and client communications. When these tools are applied to suitable tasks and supported by professional oversight, they can help firms reduce administrative work and improve productivity.

The key is to identify where AI genuinely improves a process rather than adding unnecessary complexity.

Practical applications for accountants

Accountancy work frequently involves reviewing, organising and explaining financial information. AI can support many of these activities, particularly when professionals need to create an initial analysis or communicate technical information clearly.

Financial reporting and commentary

AI can help prepare first drafts of management commentary based on financial data supplied by the user. For example, it may be asked to summarise changes in revenue, costs, margins or cash flow and present them in a clear narrative.

This can reduce the time required to produce an initial draft, but the accountant must verify that the commentary reflects the underlying figures and does not overlook important context.

A change in profitability, for example, may be influenced by one-off costs, seasonality, accounting treatments or wider economic conditions. AI will not always identify these factors unless they are clearly included in the instructions.

Variance analysis

AI can help structure explanations for differences between actual results, forecasts and budgets.

It may identify notable movements, organise them by category and suggest areas for further investigation. This can help professionals move more quickly from reviewing data to discussing the implications with management.

However, AI-generated variance commentary should not be accepted without challenge. The causes of a variance must be confirmed using the underlying records and knowledge of the business.

Forecasting and scenario planning

AI can support the early stages of forecasting by helping professionals identify assumptions, organise scenarios and present potential outcomes.

For example, it could help structure a comparison between a base case, downside case and growth case. It might also suggest factors that should be considered, such as changes in customer demand, staffing costs, interest rates or working capital.

The reliability of any forecast still depends on the quality of the data and assumptions used. AI can support the process, but it cannot remove uncertainty.

Client communications

Many accounting firms use AI to create first drafts of routine emails, technical updates and explanations of financial concepts.

A technical point can be rewritten for a client who does not have a financial background, or the tone of a message can be adjusted to make it clearer and more concise.

This can be particularly useful when communicating complex or sensitive issues. Professional review is still essential to ensure the wording is technically accurate and suitable for the client.

Practical applications for lawyers

Legal work also involves large volumes of information, document review and written communication, making it a natural area for AI-assisted workflows.

Document summarisation

AI can summarise contracts, case documents, correspondence and meeting notes, helping legal professionals identify key themes more quickly.

It may be asked to highlight important dates, obligations, disputed points or areas that require further review. This can provide a useful initial overview, particularly when working with lengthy material.

A summary should never replace examination of the original documents. Important qualifications or exceptions may be lost when complex text is condensed.

Contract review

AI can assist with the initial review of agreements by identifying particular provisions, inconsistencies or missing clauses.

For example, it may help locate termination rights, liability provisions, restrictive covenants or payment obligations. It can also compare the wording of a document against a preferred template.

The lawyer remains responsible for assessing the legal effect of the clauses and advising the client. Contract interpretation often depends on the wider factual and commercial context, which may not be fully captured by the AI tool.

Legal research

AI can help professionals develop research questions, identify relevant themes and organise information.

It may be useful for exploring the initial scope of an issue or generating a list of points to investigate. However, any legal authorities, cases or legislation referenced by AI must be checked against reliable and current sources.

Generative AI tools can invent citations or refer to outdated material. They should not be treated as authoritative legal databases.

Drafting and correspondence

Lawyers may use AI to prepare first drafts of letters, internal notes, document structures and routine client communications.

This can be especially helpful where the substance has already been determined and the main task is to present it clearly. AI may also help adapt the same information for different audiences, such as a client, colleague or other professional adviser.

Improving output through effective prompts

The usefulness of an AI response depends heavily on the instructions provided.

A short or vague prompt may produce a generic answer. A well-structured prompt provides the AI with enough context to generate something more relevant.

Effective prompts usually explain:

• The task to be completed;
• The relevant background;
• The intended audience;
• The desired format;
• The preferred tone;
• Any information that must be included; and
• Any restrictions or areas to avoid.

For example, asking an AI tool to “write an email about the figures” gives it very little direction.

A more effective instruction might explain that the email is for a businessowner, should summarise three key movements in the monthly accounts, use non-technical language and finish with questions for the next management meeting.

The second prompt is more likely to produce a useful first draft because the objective and audience are clear.

Using AI as part of a workflow

AI is generally most effective when it supports a defined process rather than being used in isolation.

A suitable workflow might involve:

1. Identifying the objective;
2. Removing confidential or unnecessary information;
3. Providing the AI with clear instructions;
4. Reviewing the output;
5. Checking facts and sources;
6. Applying professional judgement; and
7. Editing the final content for the intended audience.

This approach helps prevent AI-generated material from being treated as a finished answer.

Starting with the right tasks

Not every task is suitable for AI.

Good starting points are usually repetitive, low-risk activities where the output can be reviewed easily. Examples include:

• Drafting internal communications;
• Creating meeting agendas;
• Summarising non-confidential notes;
• Producing initial document structures;
• Generating questions for further investigation; and
• Rewriting technical explanations in clearer language.

Firms should be more cautious where a task involves confidential information, legal interpretation, significant financial decisions or advice that will be relied upon by a client.

Measuring the value

The success of AI adoption should not be measured only by how frequently the tools are used.

Firms should consider whether AI is:

• Reducing the time required for routine work;
• Improving consistency;
• Allowing faster client responses;
• Creating more capacity for advisory services;
• Helping teams manage information more effectively; or
• Improving the clarity of communications.

If a tool does not produce a meaningful benefit, it may not be the right solution for that particular process.

Key takeaways

AI can already support a wide range of accounting and legal activities, from research and document review to financial commentary and client communications.

The quality of the result depends on clear instructions, reliable information and careful review.

Professionals should begin with practical, low-risk use cases and build AI into structured workflows. The objective is not simply to automate work, but to create more time for analysis, advice and client relationships.

This article is based on the webinar AI for accountants and lawyers – How professional services firms can improve performance, when Dr Stephen Hill, an AI trainer and consultant with more than 25 years’ experience in fraud, forensic accounting and digital investigations, explored how accountants and lawyers can adopt AI responsibly.

The session covered the latest AI tools, practical use cases, effective prompting techniques, emerging technologies and the governance considerations firms should address before implementation.