In the tax field, an advisor doesn’t just react they anticipate. And in a world where regulatory frameworks change, obligations multiply, and supervisory systems become digitized, the ability to predict risks has become a competitive advantage.

Artificial intelligence (AI) is no longer just a way to “do what we already did, but faster.” It’s now a tool for identifying patterns, predicting contingencies, automating controls, and offering clients proactive tax management.

This article analyzes how AI is transforming tax advisory, which processes can benefit from it, what conditions must be in place (data, quality, supervision), and how you can incorporate this logic into your practice using GenIA-L.

Why predictive compliance is the new standard for tax advisors

Recent reports show that tax administrations are already expanding their use of AI to detect fraud, non-compliance, and anomalous patterns. For example, the OECD notes that many authorities use AI to “analyze and extract data from large volumes in order to identify non-compliance and prioritize resources toward high-risk cases.”

At the same time, tax firms face an environment in which clients expect more added value: not just “we comply,” but “we anticipate what’s coming.”

In short: for the modern tax advisor, predictive AI is a step toward a more strategic, less purely tactical service.

Key processes where AI makes the difference

a) Detection of anomalies and tax risks

AI systems can scan invoicing, accounts, tax returns, accounting entries, and external databases, and identify atypical patterns that might go unnoticed by the human eye. The OECD notes that one of the risks is “inadequate or biased data that may lead to incorrect risk assessments.”

In the advisory setting, this translates into: detecting in advance that a client has an unusual exposure for their sector, that their invoicing is off-norm, or that a deduction is atypical and could draw the tax authority’s attention.

b) Prediction of obligations and scenario simulation

AI doesn’t just analyze “what has happened”; it can help simulate what could happen under different tax circumstances. Predictive analytics tools make it possible to model different current and future tax scenarios. A blog on AI and tax notes that “AI can forecast future tax liabilities based on historical data,” which enables advisors to design anticipatory strategies.

For example: anticipating the tax impact of a restructuring, forecasting regulatory changes, or simulating contingencies in an internal audit.

c) Automation of preventive controls

AI makes it possible to create internal automatic alerts for instance, when a client’s deduction ratios exceed the sector average, or when the provision for tax litigation is inconsistent with the period’s results. This reduces reliance on manual controls, which increases efficiency and reduces errors.

For the tax advisor, this kind of proactive monitoring improves service quality, gives clients peace of mind, and reduces the likelihood of unpleasant surprises.

Conditions the advisor must guarantee for AI to work properly

High-quality data and tax context

As the OECD warns, “using inadequate, incomplete, or poorly structured data can lead to inaccurate results and loss of trust” in AI systems for tax.

In practice: your client data (accounting, invoicing, histories, provisions) must be standardized, clean, and tagged with jurisdiction, sector, and period. Without that, AI will make poor predictions.

Human interpretation and oversight

Even though AI provides predictions, responsibility remains human. AI outputs require extensive human verification in order to be legally defensible.

ResearchGate

The tax advisor must review alerts, put them in context, communicate with the client, and propose a strategy. AI acts as a co-pilot not as an autopilot.

Transparency and explainability

For the advisor and the client to trust AI, they must understand how a conclusion was reached. Otherwise, its value is reduced. This is especially relevant when the result will be used with clients or before a tax authority. Transparency and explainability remain key factors.

For the advisor, this means being able to explain to the client: “the AI flagged this because your deduction ratio in this sector is X, compared with an average of Y.”

Regulatory and ethical coverage

Tax AI is not just a technical matter. You must ensure compliance with professional secrecy and privacy rules and make sure that predictions do not create discrimination or bias. The tax-AI literature warns that frameworks must also address the ethical challenges of AI use.

How to integrate this predictive AI capability into tax advisory practice

Step 1: Identify the highest-impact tax risks

Make an inventory of clients with high volume, sensitive industries, multiple jurisdictions, or recent audits. Prioritize those for whom AI can generate the most value (large data volumes, repetition, high risk).

Step 2: Select pilot use cases

For example:

• Clients with atypical deductions.
• Companies with high contingent tax provisions.
• Subsidiaries in multiple jurisdictions with Base erosion and profit shifting (BEPS) or double-taxation exposure.

Step 3: Provide clean data and prepare integration

Make sure histories are structured and integrated, and that the AI tool (for example, GenIA-L) can access the required data.

Step 4: Set up personalized alerts and scenarios

Configure AI to trigger automatic alerts when it detects out-of-policy parameters, or to simulate different tax options for the client.

Step 5: Human review and strategy proposal

When AI detects a risk or an opportunity, the advisor reviews it, adds context, and proposes an action plan to the client (e.g. adjust provision, change structure, anticipate an audit).

Step 6: Measure results and adjust

Define indicators: number of alerts generated, percentage that led to action, reduction of contingencies, estimated client savings. Adjust the process and the tool based on feedback.

The role of GenIA-L in proactive tax advisory

GenIA-L can be your central platform for predictive compliance and tax risk anticipation.

• Thanks to its specialized legal–tax analysis capabilities, it can help you build risk scenarios.
• Its design based on verified sources, traceability, and professional oversight aligns with the quality, explainability, and accountability requirements of tax advisory work.
• It can integrate with the client’s accounting/historical data, making it easier to predict liabilities, provisions, or tax exposure.
• Its use enables the advisor to move from a “reactive” role (filing returns) to a “predictive” one (anticipating risks, proposing improvements), delivering more value to the client and differentiating the firm.

Conclusion: from compliance services to strategic advisory

AI is transforming tax advisory. It is not just about automating filings or extracting data it is about anticipating, preventing, designing strategies, and offering clients proactive tax management.

The advisor who integrates AI is not merely up to date they are one step ahead.

To achieve this, you must ensure data quality, human oversight, transparency, and a design that allows you to measure results.

With tools like GenIA-L, that transformation becomes feasible and sustainable.

The challenge is not “to use AI,” but to use AI intelligently. And for the modern tax advisor, that means moving from “complying” to “foreseeing.”