AI is perfect for accounting, handling high volumes of documents and repetitive tasks with great efficiency, thereby unburdening the accountant. It unburdens, "takes away their work," or perhaps simply changes the role of the accountant. Artificial intelligence takes over entering invoices into the system and generating reports. What remains on the human side is judgment, exceptions, control, and advice, and that is where the accountant's role shifts. This text is not about whether AI will replace people in finance (I write about that here), but about what AI already automates in accounting today and how it changes the daily work of an accountant.
- What does AI actually do in accounting, and what is just automation?
- What won't AI do, and can its results be trusted?
- Will AI replace an accountant?
- How is the role of an accountant changing, and what competencies matter?
- Should you implement AI in accounting, and where to start?
- What risks does AI pose in accounting?
- Summary
What does AI actually do in accounting, and what is just automation?
The automation of accounting processes began long before discussions about AI. Mass invoicing, bank statement reconciliation, and OCR systems are already commonplace. And this is not AI at all. This is automation, very often mistakenly called AI. Transferring data between systems or posting according to defined rules is Robotic Process Automation (RPA). Solutions based on artificial intelligence involve the system learning from data and coping with variability: recognizing patterns, suggesting solutions, and detecting deviations.
What won't AI do, and can its results be trusted?
I like the saying that AI is like an intern who has read all the books but completely lacks the experience of how and where to apply that knowledge. Artificial intelligence tools are effective in elements of processes such as calculating, analyzing, creating comments, and can even make decisions. To post invoices, prepare declarations, and send them to the tax office. Is that everything? Definitely not. Because someone has to take responsibility for the data; someone, meaning a human, has to decide whether the quality of documents (reliability and correctness) complies with the law, procedures, and regulatory requirements. This element cannot be automated.
Will AI replace an accountant?
So, we have an answer to the question of whether AI will replace an accountant. No. AI, or rather the digitalization of accounting, will eliminate some tasks that are currently part of an accountant's duties. 30 years ago, accounting focused primarily on entering numbers into large ledgers called "amerikanki" (American journals). A good accountant was meticulous, accurate, and could write neatly. Today, meticulousness and accuracy are still important competencies, but they have a completely different character. It's not about comparing entries in paper documentation, but, for example, about assessing data completeness. The automation of accounting processes, which we have observed for years, has progressively changed the skills required in the accounting profession. AI is merely a continuation of these changes, not a revolution.
How is the role of an accountant changing, and what competencies matter?
The role of an accountant, the skills required in this profession, and the competencies needed to perform the work are evolving just as in other professions. Instead of the ability to quickly and accurately enter data into the system, verifying correctness, interpreting legal regulations, or assessing exceptions is now more important.
In addition, there's consulting and communication: talking to clients or management, explaining what the numbers mean, supporting decisions, and contacting authorities. Finally, working with tools and data matters: the ability to use AI and ensuring the quality of the data on which the model operates, because poor data leads to poor suggestions.
Should you implement AI in accounting, and where to start?
In companies where accounting department automation is already implemented, AI will be the next, natural step. Not a revolution, but rather an evolution of what has already been developed.
What does a good step-by-step implementation look like?
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The first step to implementing accounting automation is to separate automation from AI. Where there are patterns, repetitive actions, rules, etc., we use RPA.
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Models come into play only when "judgment" based on historical data and patterns (not schemes) is needed. To train an AI model, data is necessary. Without a sufficient number of organized, correct examples (how similar cases were accounted for), the model has nothing to learn from. This is where the assessment of readiness and data comes in.
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At the end of the process, a human with the knowledge needed to make a decision steps in.
How will the size and role of the accounting team change?
The role of the accounting team changes as technology changes. What raises questions and controversies is "how many people will AI take jobs from." The answer is simple: as many as those who do not want to develop new competencies. AI is a model, it is statistics, it is not even a machine. It cannot take anyone's job. It is the human being who deprives themselves of work by not adapting their skills to market demands. The profession of elevator operator no longer exists. Will we say that "something" took their job?
What risks does AI pose in accounting?
The answer is simple: liability for errors. Automation is the first stage. And errors can already occur there. For example, an incorrectly read date, amount, or supplier from a document. If AI comes into play in the next process, there is a risk that it will not correct the earlier error, but the tool will replicate it in its process. To this, we add the risk of AI hallucination, meaning that it is a model based on statistics, not 100% certainty.
Regardless of how many stages of the entire process are performed by RPA or an AI-based tool, at the end there is responsibility that only a human can bear. Excessive "trust" in automation and a lack of control processes significantly increase the level of risk. At some stage, the judgment and assessment of an experienced accountant are necessary.
Summary
AI in accounting is the next stage of automation, which has been ongoing for years, and it works best where the work is mass and repetitive. It takes over data entry, reconciliations, and initial reports, giving the accountant what is most valuable: time for judgment, interpretation, and advice.
As a result, the accounting profession is maturing. Added value is not in quick data entry, but in understanding, assessing exceptions, and knowing regulations. The position is strengthened by those who develop these competencies, just as skills in this profession have changed over time, from meticulous handwriting in American journals to working with systems.
One thing remains constant. Regardless of how many stages automation and AI perform, at the end there is a decision and responsibility that only a human can take. That's why AI in accounting is only as good as the control that oversees it.
Do you want to prepare your accounting department for this change and develop your team's competencies before the market does it for you? Check out our training courses.
