Highlights
- 65% of tax professionals now use GenAI tools, yet only 26% measure the resulting ROI.
- Three years of data show adoption came first, strategy came next, and measurement remains the biggest obstacle.
- Indirect tax faces higher stakes from shallow AI adoption due to its high transaction volume and tight deadlines.
Ask an indirect tax professional whether they personally use AI, and most already say yes. 65% now use publicly available GenAI tools like ChatGPT for work. That statistic alone used to be the headline. Now it’s barely the starting point.
For years, Thomson Reuters has tracked how AI is reshaping how professionals work. Each year, the question professionals were being asked got harder.
- In 2024, it was simple: Should we adopt this?
- By 2025, it had sharpened: Do we have a strategy?
- This year, in 2026, the question is the one that separates functions that are ready from those that only look ready: Are you measuring what you’re doing, and are you going deep enough?
Indirect tax now has its own answer, and it’s a more complicated one than the adoption number suggests.
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2024: Adoption gained momentum
2025: Strategy became the dividing line
2026: The data caught up, and it’s a mixed picture
Where the data about AI in corporate indirect tax leaves you
2026 Future of Professionals Report
When the AI strategy doesn't reach the desk: a profession under pressure
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2024: Adoption gained momentum
When generative AI first reached professional services, indirect tax moved fast, and for good reason. Rate changes across jurisdictions, e-invoicing mandates, and real-time transaction reporting had already stretched teams thin before AI ever entered the picture. Professionals across tax told us they expected AI to free up nearly 200 hours a year and believed it would have a transformational impact on their work. Skilled labor shortages were straining tax departments more than legal or risk, fraud, and compliance. For a function drowning in rate tables and invoice exceptions, AI looked like relief.
That instinct hasn’t changed. What’s changed is what “adoption” means.
2025: Strategy became the dividing line
By 2025, it was clear that using AI and having a plan for AI were two different things, and only one of them showed up in the numbers. Organizations with a visible AI strategy saw roughly double the revenue growth and three-and-a-half times the benefit of those adopting tools informally. In-house departments with their own strategy, rather than just a borrowed one from the broader organization, captured more of that value than the ones waiting for direction from others.
For indirect tax specifically, this was the year the divide was felt more than it was measured. Some teams were clearly using AI to triage invoice exceptions or pre-screen nexus questions before they hit a human reviewer. Others were still treating AI as a research shortcut and nothing more. The data to prove which approach was providing an advantage wasn’t there yet.
2026: The data caught up, and it’s a mixed picture
This year, for the first time, we have precise numbers on where tax really stands, and it’s a genuine test of depth, not just adoption.
Pressure is strong across the corporate function. Among tax, accounting, and global trade professionals specifically, 64% report facing financial pressure to move faster on AI, and 45% are already seeing, or expect within 12 months, real financial consequences of falling behind. More than half (53%) of corporate professionals now use AI multiple times a day.
Follow-through hasn’t kept pace with that pressure. Among corporate professionals at organizations with a named AI strategy, 44% say their day-to-day practice doesn’t match it: the tools aren’t in place, people aren’t trained on them, or the strategy was never translated into anything specific enough to act on. In indirect tax terms, that’s the difference between a policy that says “use AI for exception review” and a team that can point to how.
The clearest sign of that shortfall is where people turn when the plan doesn’t reach the desk. 36% of corporate professionals are already using AI tools their organization hasn’t sanctioned, in ways it can’t see or govern. For indirect tax, that’s not a footnote. When exception handling or rate research runs through an unvetted tool, the organization loses visibility into what data that tool touched and what oversight applied to the output, in the part of tax where errors compound fastest.
Why this AI discrepancy hits indirect tax harder
Indirect tax runs on volume. There are thousands of transactions, dozens of jurisdictions, and filing deadlines are measured in days, not months. A function operating at that scale without the tools its people actually have access to, or a shared view of where AI use is sanctioned, leaves efficiency on the table and carries risk in the one area of tax where mistakes stack up fastest.
Where the data about AI in corporate indirect tax leaves you
None of these insights mean indirect tax is behind. Strong personal adoption, especially in research and return preparation, is a real advantage. It does mean the next competitive advantage doesn’t lie in asking yourself do we use AI? but rather whether you know what it’s doing for you, across every jurisdiction you file in, and whether you’re willing to let it do more.
Learn more about the current state of professionals in the 2026 Future of Professionals Report.
