AI for Tax Monitoring: When a Smart Assistant Requires an Even Smarter Owner

Samanta Kondratyeva Project Manager at Cinimex In 2026, the adoption of AI assistants by companies operating under tax monitoring has become a logical step. However, instead of simply reducing the workload, we are seeing a new reality: technology does not eliminate the workload but transforms it, placing higher demands on the professionalism of managers.

Initial expectations — automating routine tasks, eliminating errors, and receiving instant responses from the Federal Tax Service — have given way to a more pragmatic view. The complexity of the transition was underestimated. AI has not replaced the specialist; instead, it has forced professionals to evolve, and the cost of mistakes in this evolution is high.

The main misconception is to view an AI module as a “black box” that can simply be connected and forgotten. In reality, the focus has shifted from configuring accounting policies to training the algorithm. It is not enough to define the rules — they must be “explained” to the machine while accounting for numerous non-standard transactions. The system generates a result, but verifying it requires a deep understanding of the underlying data and the assumptions built into the algorithm. The principle of “garbage in, garbage out” is even more relevant when it comes to AI.

Put simply, a company is investing in a solution whose effectiveness depends to a large extent on the quality of its configuration and ongoing maintenance. This creates a paradox: automation requires a team of highly qualified specialists capable of orchestrating the “digital orchestra” and continuously training and refining the model.

Automation has also created new types of professional risks that simply did not exist before.

The first is the problem of the “button” and the delegation of responsibility. If an AI system recommends a questionable accounting treatment that is later challenged by a tax inspector during the monitoring process, who is responsible? The software developer may point to “parameters trained on your data.” The accountant may argue that they relied on a certified product. Implementing AI therefore requires not only new procedures but also a new understanding of responsibility for machine-generated decisions.

The second risk is a “dialogue between algorithms” and the loss of human context. The Federal Tax Service also uses advanced data analytics systems. This creates a situation in which an AI system on one side generates a request and an AI system on the other side processes it. If a discrepancy arises, explaining it in human terms and taking the underlying business logic into account becomes critically important. This requires more than an accountant — it requires a tax expert who can speak the language of both the business and the regulator.

Ultimately, success is determined not by the technology itself but by the approach to its implementation. A new data culture is essential: data must serve as high-quality “raw material.” The value of a professional is increasingly measured by their ability to formulate tasks for AI, interpret its outputs, and defend the resulting conclusions.

So, is the investment worth it? Would it not be easier to continue tax monitoring the traditional way?

Without such an assistant, specialists spend significant amounts of time collecting, reconciling, structuring, and analyzing data, particularly when preparing a reasoned opinion.

With AI, the balance shifts in the opposite direction. The algorithm takes over routine processing of vast amounts of data, while the specialist focuses on what matters most: meaning, strategy, and communication. This is the key value of the technology: the system becomes a tool for modeling the tax implications of transactions before they are completed and identifying hidden risks. It represents a shift from the tactical goal of “filing reports” to a strategic approach of “forecasting and mitigating risks.”

A smart machine does not think — it processes. But it frees up time for higher-level human thinking. The key asset is the combination of a “smart owner” and a powerful tool. AI does not eliminate the need for an expert; it makes that expert significantly more effective. The future belongs to those who overcome the challenges of implementation and gain in return a level of analytical speed and depth that is impossible to achieve through manual work alone.