Highlights
- Hours saved only shows part of AI's value, so indirect tax teams should track efficiency, risk, revenue, and capacity.
- Only 16% of indirect tax professionals formally measure ROI, making AI spend hard to defend to Finance.
- Set a baseline before rollout, then track a short metric list against it to build a Finance-ready case.
Tax leaders keep hearing the same question: what are we getting for our AI spend? In 2026, 58% of corporate tax and legal professionals said stakeholders were pressing them to speed up adoption. However, only 16% of indirect tax professionals formally measure ROI on their technology investments.
That makes spend on AI hard to defend. Here is a practical way to measure AI profitability in indirect tax with four kinds of value to track, how to establish a baseline first, and a short process for building a case Finance will accept.
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The four dimensions of AI value for indirect tax
Build the business case for AI in four steps
Where can indirect tax leaders start with justifying AI?
Why hours saved isn’t enough
Most technology business cases start with labor. Hours saved x labor cost = value created. For indirect tax, that’s a fair starting point. The Contribution Gap report found that 87% of organizations run across multiple systems, and 85% call manual reconciliation a major challenge. Giving those hours back matters, it just doesn’t present the whole story.
Hours saved says nothing about errors that never happened, tax dollars that stayed in the business, or what your team does with the extra time. That’s a problem for a function that already struggles with a lack of recognition. Of indirect tax professionals, 88% say their work contributes significantly or moderately to business performance, but only 32% say leadership sees that contribution as significant.
The four dimensions of AI value for indirect tax
Some outcomes turn into dollars and belong in a financial model while others are leading indicators. Sorting your metrics into four groups keeps the two apart.
1. Efficiency
Start here, since it’s the familiar one. Track processing time, manual interventions, reconciliation hours, and filing cost per jurisdiction. The Contribution Gap report recommends these for a quarterly technology ROI dashboard.
2. Risk reduction
Fast and wrong is worse than slow. Pair your productivity numbers with error rates, regulatory response time, and audit exceptions. Tax already has influence here: 63% of indirect tax functions say they directly helped avoid a regulatory penalty or business disruption in the past year.
3. Revenue protection
Look at overpayment and underpayment corrections and exemption certificate accuracy. Dan LeCompte, a Thomson Reuters industry specialist, describes one large organization whose pre-payment invoice review found about $4 million in annual overpayments and other tax issues. The review took heavy manual effort, which is the kind of work AI can speed up while people still make the call on exceptions.
4. Strategic capacity
Strategic capacity involves what your team does with the time it gets back. Track the share of hours spent on review, analysis, planning, and advisory work compared with execution. Then watch your influence. Indirect tax teams already weigh in on supply chain decisions (59%), pricing (54%), and contract terms (50%), so more involvement there is a good sign.
How to measure true AI value
Only 18% of organizations measure AI ROI, according to Thomson Reuters research, and many of those count usage or user satisfaction. Usage tells you people logged in. It doesn’t tell you what changed.
So set the baseline before rollout. Document the workflow you plan to improve, the data that feeds it, how long it takes, and the outcomes you hope AI will move. Prior-period logs, error and audit records, and time-allocation data all work. Then track a short list against it: cycle time, reconciliation hours, exception rates, payment corrections, manual reviews, and regulatory response time. As Dan LeCompte puts it, “To understand whether AI is truly adding value, you need to start with the goal and understand the process you’re automating.”
Benchmark against peers like you
In corporate tax, 81% of departments are still exploring or considering AI, and among those tracking technology success metrics, 41% measure ROI or cost savings. Compare yourself with teams of similar size and maturity, not one industry-wide number.
Build the business case for AI in four steps
- Baseline: Document today’s cost, effort, and performance.
- Pilot metrics: Decide what change you expect, such as fewer manual reviews or faster product classification, and what result counts as a win.
- Projected value: Turn expected changes into dollars only where the evidence supports it. Avoided overpayments may justify a direct estimate. More time for advisory work often reads better as a strategic indicator than as an invented dollar figure. Write down your assumptions.
- Review cadence: Check results against the baseline on a regular schedule, and adjust.
The same steps apply across use cases. For Thomson Reuters ONESOURCE Sales and Use Tax AI powered by CoCounsel, useful measures include time spent monitoring economic nexus and whether you validate every line item instead of spot checking. For AI-powered Product Classification in ONESOURCE Determination, look at classification speed and accuracy across jurisdictions. For a related view on the financial side, see How to build a business case for indirect tax process automation.
Where can indirect tax leaders start with justifying AI?
Many AI ROI failures are measurement failures rather than technology failures. Pick one or two metrics you can baseline this quarter and track the change. Build toward consistent, platform-level visibility from there. Teams that start now will be able to show where AI pays off and make the case for what comes next.
Get the full framework
Download the white paper, Measuring AI ROI in indirect tax, for the metrics, benchmarks, and business case process in detail.
