Not long ago, GenAI felt like a distant concept for most tax and finance functions. Interesting in theory, but hard to translate into the structured and highly regulated world of tax. That’s changing faster than many expected.
According to Thomson Reuters’ 2025 Corporate Tax Department Technology Report, 51% of tax leaders say they are now either exploring or actively using Gen-AI. Just a year earlier, that number was under 20%.
The shift isn’t just about automating repetitive work. It reflects a bigger change in how tax teams are beginning to operate. They are more agile, more forward-looking, and more connected to broader business priorities.
This momentum is also raising new questions. Not about whether GenAI is relevant, but about its application with purpose. Where is it already driving value? What risks need to be managed? And what does it take to move from testing tools to transforming how tax delivers impact? And the answers to these questions are making way for a new opportunity—one that goes beyond efficiency and starts to redefine the role of tax itself.
Who’s Leading the Shift Within Tax Teams?
The move toward Generative AI might be strategic, but its impact is starting at a very operational level. While many conversations around Gen-AI begin in the boardroom, adoption within tax functions does not just drive from the top alone. It’s the day-to-day users—the people closest to the work who are actually putting the technology to the test. CFOs judge how GenAI drives their ROI but the CAs or tax professionals are the ones who are actually leveraging Generative AI.
Just look at the tax associates who once spent hours matching GST invoices, now quietly relying on automation to confirm vendor names and extract line-item details in one go. Or the chartered accountants who used to skim dozens of pages of Income-tax circulars, only to have Gen-AI pull out the exact clauses they need.
It’s these mid-level professionals who’ve become the true early adopters. They’re the ones saving extra hours each week. This saved time they can now spend on higher-value analysis, like modeling cash-flow scenarios or testing transfer-pricing strategies. These everyday wins are driving 75% of tax departments to integrate GenAI into at least one core workflow. The numbers are up 20 percentage points from 2024. (2025 Generative AI in Professional Services Report)
So, for now, Gen-AI is growing from the bottom up. By letting the tax professionals’ experiment on real tasks—be it GST reconciliation, TDS verification, or circular interpretation—it’s building trust one function at a time. And those daily wins are exactly why this technology is being adapted so fast, even in a complex tax landscape like India’s.
Where It’s Being Used: Across the Tax Workflow
If mid-level professionals are the ones pushing GenAI forward, the next question is—where exactly are they applying it?
The first and most obvious space is document handling. Tax teams have long dealt with large volumes of invoices, challans, and payment records—most of them unstructured or scanned. Using a combination of Large Language Models and Generative AI, professionals are now extracting key fields automatically, reducing time spent on formatting and entry, and ensuring accuracy right at the source.
Reconciliation has also evolved. Matching values between internal books and government filings like between purchase registers and GSTR-2B once took hours of manual effort. Today, machine learning models train on past reconciliation logic to surface likely mismatches and suggest their resolution, even generating brief explanations for variances when needed.
Then there’s the drafting work. Whether it’s a GST return or a TDS summary, professionals are using LLMs to generate first drafts based on past filings, auto-populate standard disclosures, and even produce dynamic checklists that reflect recent updates. The goal isn’t to automate the submission—it’s to accelerate the groundwork.
Interpretation has changed too. Instead of scanning full-length circulars or income-tax notifications, professionals are querying natural language models to extract only what’s relevant to their case. It’s faster, more focused, and often more accurate.
Even notice response workflows are shifting. When a tax officer sends a query or summons documentation, professionals are using generative text models to structure initial replies, compile relevant annexures, and draw connections to prior cases—all in a more organised, timely way.
Gen-AI is quietly transforming tax and finance functions in procure-to-pay (P2P) processes as well. AI-enabled tools in P2P functions help tax professionals to automate invoice matching, validate tax codes, and perform compliance checks, reducing manual effort and improving accuracy. This streamlines indirect tax determination, input credit validation, and vendor compliance, ensuring better alignment between procurement records and tax filings.
These aren’t isolated improvements. They’re touchpoints across the workflow—quietly redefining how tax gets done and making space for professionals to focus on what actually needs their judgment.
But, all things considered, GenAI’s adoption in various tax functions has seen a significant increase from 2024-2025 as depicted below.
What Risks Are Still Holding Tax Teams Back?
Despite all the early wins, Generative AI adoption in tax isn’t easy. Most teams aren’t resisting the technology but stuck weighing the very real considerations that come with using it responsibly.
1. Reliability Issues
Unlike traditional automation tools that follow a set of pre-defined rules, Gen-AI generates content by understanding and mimicking patterns in data. That’s both its power and its risk. Tax professionals train to be precise, to cite sources, and to defend their positions. On the other hand, a tool that can “sound right” without being verifiably correct can create hesitation and rightly so.
AI automations are now reshaping workflows, yet GenAI remains on the sidelines of key operations. Teams hesitate to rely on it for critical tasks due to concerns over accuracy, auditability, and reliability—without trust in the output, it won’t handle core calculations or final filings.
In fact, a survey by Tolley found that 29% of tax professionals cite lack of trust in AI outputs as the biggest hurdle to adoption, with concerns about content hallucinations (27%) and security (21%) also prominent.
2. Data sensitivity
Tax teams manage sensitive data like financials and legal positions, making Generative AI integration complex. Cloud-based platforms raise concerns over data exposure, while on-premises setups demand strict governance to ensure compliance and confidentiality. This underlines the need for responsible deployment of AI and Analytics—where data usage is ethical, transparent, and governed by clear access and privacy protocols.
3. Talent and training gaps
Gen-AI tools don’t work unless given a task. They require users to prompt effectively, evaluate critically, and apply judgment. That’s a different skill set than traditional tax training. So, the companies still struggle with finding the right talent that is not only capable of using GenAI but also well-versed in tax domain.
The EY Tax and Finance Operations Survey 2024 highlights that 44% of respondents cited lack of skilled talent and limited understanding of Generative AI’s capabilities as major barriers to adoption.
The Real Advantage? People and Gen-AI, Working Together
The hesitation around GenAI in tax isn’t only about capability to adapt by tax experts, it’s rather about standards. Accuracy, confidentiality, auditability, all of these are paramount, when it comes to domains such as tax, insurance, etc. But they also don’t have to stand in the way of progress.
Forward-looking CFOs are adopting a hybrid model—leveraging Generative AI for speed and scale, while HILL (Human-in-the-Loop Learning) safeguards, guide, validate, and contextualize AI. They let AI generate the first draft—let professionals validate it. Use Gen-AI to surface insights—let humans apply context and judgment. Clearly, this balanced model is the most effective way to embed GenAI in tax functions.
The true transformation is about designing systems where the tool supports the professional—not replaces them. That’s the shift more teams are making now: from experimenting in silos to integrating Generative AI in a way that’s practical, responsible, and human-led.
At Binary, our solutions work on this philosophy—infusing AI into tax workflows without compromising trust. We help businesses move beyond experimentation toward sustainable, responsible adoption. This isn’t just automation; it’s intelligent augmentation designed for industries where precision matters most.
The near future of tax isn’t AI-led. It’s human-led—with AI as the catalyst.
