What's Happening?
Indirect tax leaders are being advised to expand their metrics for measuring the return on investment (ROI) of artificial intelligence (AI) beyond mere time savings. A new white paper emphasizes that while labor cost reductions are a starting point, they
do not capture the full value AI brings to indirect tax functions. In 2026, 58% of corporate tax and legal professionals faced pressure to accelerate AI adoption, yet only 16% of indirect tax professionals formally measure ROI on their technology investments. The paper suggests tracking four key dimensions of AI value: efficiency, risk reduction, revenue protection, and strategic capacity. Efficiency metrics include processing time and manual interventions. Risk reduction involves error rates and regulatory response time. Revenue protection focuses on overpayment/underpayment corrections and exemption certificate accuracy. Strategic capacity measures the time spent on analysis, planning, and advisory work versus execution. The recommendation is to establish a baseline before AI implementation and track a concise list of metrics against it to build a compelling business case for finance.
Why It's Important?
The current limited approach to measuring AI ROI in indirect tax poses a significant challenge for tax leaders seeking to justify further technology investments. By focusing solely on time savings, organizations overlook critical benefits such as reduced errors, avoided penalties, and enhanced strategic contributions. This narrow view can lead to AI ROI failures being misattributed to technology rather than measurement shortcomings. The proposed broader framework for evaluating AI's impact is crucial because indirect tax functions already struggle with a lack of recognition for their contributions to business performance. A comprehensive ROI measurement can demonstrate how AI not only streamlines operations but also mitigates financial risks, protects revenue, and frees up tax professionals for higher-value advisory work. This shift in measurement strategy is vital for securing executive buy-in and funding for AI initiatives, ultimately enabling tax departments to become more strategic partners within their organizations.
What's Next?
Indirect tax leaders are encouraged to implement a four-step process for building a robust business case for AI: baseline current costs, effort, and performance; pilot metrics to define expected changes and success criteria; project value by quantifying changes where evidence supports it; and establish a regular review cadence to track results against the baseline. This systematic approach will enable tax teams to demonstrate tangible benefits to finance stakeholders. For specific AI applications, such as Thomson Reuters ONESOURCE Sales and Use Tax AI, useful measures include time spent monitoring economic nexus and the thoroughness of line-item validation. For AI-powered Product Classification, metrics like classification speed and accuracy across jurisdictions are key. By adopting these practices, indirect tax leaders can move beyond anecdotal evidence and present a data-driven case for AI, ensuring continued investment and integration of advanced technologies into tax operations. This will also foster a culture of continuous improvement and strategic alignment within tax departments.
Beyond the Headlines
The call for a more comprehensive AI ROI measurement in indirect tax reflects a broader trend in business where the value of technology is increasingly being assessed beyond immediate cost savings. It highlights the evolving role of tax professionals from compliance-focused to strategic advisors. The ability of AI to reduce errors and protect revenue has ethical implications, ensuring fairness and accuracy in tax collection and payment. Furthermore, by freeing up human capital from repetitive tasks, AI can empower tax professionals to engage in more complex problem-solving, policy analysis, and strategic planning, fostering professional development and job satisfaction. This shift also underscores the importance of data literacy and analytical skills for tax teams, as they will need to effectively baseline, track, and interpret AI performance metrics. Ultimately, a more sophisticated understanding of AI's value can drive innovation and efficiency across the entire enterprise, not just within the tax function.
