In an accounting firm, artificial intelligence is not intended to replace the accountant, nor to make decisions on their behalf. Its benefit is more concrete: helping teams save time on repetitive tasks, structure information more quickly, and produce clearer deliverables for clients.

This approach is particularly relevant in a context where firms must simultaneously absorb a large volume of production, respond more quickly to client requests and develop higher value-added consulting missions.

Used as a co-pilot , AI can support several daily tasks: preparing for a prospect meeting, onboarding a new client, improving document collection, analyzing accounting data, writing balance sheet comments, or even monitoring regulatory developments. Imoran's training materials emphasize this approach: AI supports the process, but the accountant remains in control.

However, AI must be integrated methodically. A firm handles sensitive data: financial information, social data, personal information, and client documents. Its use must therefore be governed by clear rules: anonymization, selection of professional tools, systematic human oversight, and a prohibition on entering confidential information into uncontrolled public tools.

In this article, we will review 5 concrete uses of AI for an accounting firm , with operational examples, expected benefits and points of vigilance to keep in mind.

1. Prepare for prospect meetings more methodically

The first use of AI in accounting firms is to better prepare for meetings with prospective clients. Before an initial meeting, the firm often has some information: sector of activity, size of the company, current management style, presence or absence of employees, tools used, upcoming deadlines.

Based on these elements, AI can help structure the main themes to be addressed during the interview: administrative organization, manager's expectations, difficulties encountered, quality of documents transmitted, support needs or points of vigilance specific to the activity.

The benefit is simple: arriving at the meeting with a clearer understanding of the topics to be explored. The firm avoids overly generic discussions and quickly demonstrates that it grasps the company's specific challenges.

However, AI should not replace professional experience. It serves as a preparation tool, not a definitive analytical framework. It is the chartered accountant who selects the right questions, interprets the answers, and identifies the relevant engagements.

This use case is particularly interesting for firms that wish to strengthen their advisory stance from the first exchange, without burdening their sales preparation.

2. Better integrate new customers

Onboarding a new customer is an often underestimated step.

However, it is at this moment that good habits are built: what documents to transmit, through which channel, how often, with which interlocutors and according to what deadlines.

AI can help the firm to create clearer onboarding materials. For example, it can produce a first draft of a welcome booklet, a welcome message, or a document outlining the onboarding process.

The goal is not to create a standard document applicable to all clients, but rather to have a structured basis, which the firm then adapts according to its organization, its tools, its level of service and the client's profile.

When used properly, AI can make the onboarding process smoother.

The customer better understands what is expected of them.

Employees have a clearer framework.

Oversights and misunderstandings are decreasing.

The added value then lies in the firm's ability to build a coherent, simple and reproducible customer journey.

3. Improve the quality of parts submitted by customers

The collection of accounting documents remains a sensitive issue for many firms.

Documents sent late, illegible photos, incomplete supporting documents, files scattered across multiple channels: these irritants waste teams' time and generate numerous follow-ups.

AI can help produce simple educational content to support clients in their best practices.

For example, the firm can use it to explain how to transmit usable supporting documents, how to avoid common mistakes, or how to better organize documents before an important deadline.

This content can take the form of short fact sheets, educational emails, or messages integrated into the client portal. It must remain simple, accessible, and suitable for managers who are not accounting specialists.

The aim is to gradually reduce friction in the customer relationship.

Fewer unusable parts, fewer follow-ups, fewer back-and-forth trips and better processing quality.

The firm retains control over tone, instructions, and internal rules.

AI is mainly used to accelerate the formalization of messages that teams often repeat.

4. Facilitate accounting analysis and preparation for balance sheet meetings

AI can also help firms to better leverage accounting data.

Business software already produces reports, balances, ledgers, ratios, and dashboards.

AI can complement this work by helping to formulate a clearer interpretation of the available information.

In particular, it can be used to prepare an initial summary of key points before a review meeting: significant developments, elements to explain, topics to explore further with the client, or messages to make more educational.

This practice is particularly useful when the firm wishes to transform the presentation of accounts into a genuine management exchange.

The leader is not just looking to know the year's results.

He wants to understand what the numbers say about his business, his profitability, his cash flow, or his priorities.

Caution remains essential.

AI can help to formulate, compare or synthesize, but it does not guarantee the accuracy of the analysis.

She can be wrong, interpret too quickly, or produce overly assertive comments.

The role of the chartered accountant therefore remains central: to control the data, to verify the conclusions, to qualify the messages and to link the figures to the reality of the company.

It is the combination of AI's analytical capabilities with the accountant's interpretive skills that makes it possible to offer this in-depth analysis in record time.

5. Save time on monitoring and internal firm tasks

The last use relates to the internal workings of the firm.

A significant portion of the time is spent reading, sorting, summarizing, reformulating, writing or following up on information: emails, meetings, internal notes, tax news, social developments, customer messages, reports.

AI can be useful in reducing this administrative burden. It can help summarize a long conversation, prepare a first draft of a report, rephrase a technical note in more accessible language, or transform a news item into a clearer customer message.

These uses are often the easiest to test, as they don't necessarily require major changes to business processes. They allow teams to save time on frequent tasks, while improving the clarity of communication.

Monitoring is a good example. AI can help synthesize dense information, extract its potential impacts for clients, or prepare an initial communication outline. However, it doesn't eliminate the need to verify sources, dates, the regulatory context, and the actual applicability to each situation.

For a firm, the challenge is therefore to find the right level of use: to automate what can be automated, without losing control over quality, confidentiality and professional responsibility.

How to regulate the use of AI in an accounting firm?

These five uses demonstrate that AI can bring value at several levels: customer relations, internal organization, production, analysis, and communication. But for an accounting firm, the question isn't simply what AI can do. The real question is under what conditions it can be used reliably, effectively, and securely .

A firm handles sensitive information: financial data, social data, tax documents, personal information, confidential communications with executives. The use of AI must therefore be regulated from the outset.

The first rule is to distinguish between low-risk and sensitive uses. Summarizing an internal meeting, rewriting a generic email, or preparing a meeting outline does not present the same level of risk as analyzing a client file or writing a financial statement commentary. Therefore, not all uses require the same level of control.

The second rule concerns confidentiality. Employees must know which data can be used, which data must be anonymized, and which information should never be entered into an uncontrolled tool. This rule must be simple, understood by everyone, and regularly reiterated.

The third rule is human validation. AI can suggest, structure, reformulate, or accelerate. It must not make decisions on its own. In an accounting firm, professional responsibility remains with the chartered accountant and their team. Every deliverable produced with the help of AI must therefore be reviewed, corrected, and validated before being sent to a client.

Finally, AI should be integrated gradually. It's better to start with a few simple, measurable, and useful everyday use cases, rather than trying to transform all processes at once.

This approach allows for testing, adjustment, team training, and the establishment of a culture of responsible use.

Where to begin in an accounting firm?

To begin, the most effective approach is often to start with the existing irritants.

• Where are the teams wasting time?

• What tasks are repeated each week?

• Which documents are regularly written using the same information?

• Which customer interactions generate the most follow-ups?

These questions help to identify the first relevant use cases.

A firm can, for example, start by preparing appointments, rewriting client messages, writing meeting minutes or creating simple training materials.

These uses are accessible, minimally intrusive, and quickly understood by employees.

It is also very important to involve the teams.

AI adoption does not work when it is imposed from the top down.

Employees need to understand the purpose of the tool, its limitations, the rules to be followed, and the concrete benefits for their daily work.

A demonstration using a real-world business case is often more effective than a general presentation on artificial intelligence.

The right starting point is therefore not the tool.

That's the process.

Mistakes to avoid

The first mistake would be to consider AI as a magic solution.

An AI tool cannot compensate for a poorly defined process, poor quality data, or a confusing organization.

Before automating or assisting a task, you must first understand how it works today.

The second mistake is to multiply tests without a framework.

Many firms start by trying several tools, several prompts, several uses, without a clear method. This creates dispersion and can generate mistrust among the teams.

It is better to choose a few priority uses, test them thoroughly, and then decide if they are worth deploying.

The third mistake is neglecting data security.

AI may seem simple to use, but it raises important questions:

• Where does the data go?

• Are they preserved?

• Are they used to train models?

• Who can access it?

These issues must be clarified before any use on customer information.

The fourth mistake is underestimating training.

An employee who misuses AI may obtain inaccurate, incomplete, or overly generic results.

Conversely, with a clear method, he can save time and improve the quality of his output. The skill lies not simply in "asking a question" to the AI, but in formulating a precise, contextualized, and verifiable request.

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AI opens up concrete opportunities for accounting firms. It can help better prepare for appointments, streamline the onboarding of new clients, improve the quality of documents submitted, facilitate accounting analysis, and reduce certain internal tasks.

But its value does not come solely from the tool.

It depends primarily on how the firm chooses its uses, frames practices, protects data and supports its teams.

The goal is not to automate everything.

The goal is to identify the right use cases: those that actually save time, improve service quality, or strengthen the firm's advisory capacity.

To move forward, a firm can start with a simple approach:

• map your repetitive tasks,

• identify the most frequent irritants,

• select a few priority uses,

• then test them in a safe environment.

This gradual approach helps to avoid fads and to build truly useful practices.

For firms wishing to move from experimentation to a structured approach, Imoran can support the identification of priority use cases, the training of teams and the implementation of a usage framework adapted to the realities of the firm.

Request an AI diagnostic to identify the first relevant use cases in your practice.