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Transfer pricing (TP) specialists globally have long grappled with one of their most labor-intensive tasks: manual benchmarking. Traditionally, the benchmarking process, along with its extensive documentation, demands significant time from tax professionals, often consuming hours, days, and even weeks to complete.
This intensive time commitment often diverts professionals from other crucial tasks. However, in the past year, generative AI has significantly altered this landscape, alleviating much of the manual effort and dramatically accelerating the benchmarking process.
While this technological advancement appears promising, it raises critical questions about the future role of humans in benchmarking and its potential impact on employment within the tax industry.
The Headache of TP
Transfer pricing refers to the pricing of goods or services transferred between different units of an organization located in various countries. This practice serves two primary purposes: ensuring that intra-group transactions occur at fair market rates and preventing profit shifting to low-tax jurisdictions to minimize global tax liability.
For large corporations, TP benchmarking becomes increasingly time-consuming. TP specialists must compare the internal prices of goods and services with those of comparable third parties to ensure alignment. The entire process, meticulously documented for tax purposes, often spans hundreds of pages, making manual completion exceptionally laborious.
AI is Redefining the Benchmarking Process
The advent of generative AI in late 2022 revolutionized various sectors, including the tax industry, particularly TP. Generative AI excels at analyzing databases, extracting relevant information based on specific criteria, and gathering supporting evidence—precisely the tasks required for TP benchmarking.
AI can perform these tasks in a fraction of the time it would take even the most efficient human, potentially accelerating the TP process by up to 80%. For understaffed tax teams, this development is a significant boon, allowing them to leverage AI for the heavy lifting and focus on more critical tasks. However, this advancement also raises concerns about whether humans will soon become redundant in the process.
Humans Will Always Have a Critical Role
Despite AI's capabilities, human involvement remains indispensable. AI applications require extensive training on high-quality data sets to function effectively. Without such training, the risk of inaccurate outputs increases, potentially necessitating more time to correct errors than to perform the task manually.
Moreover, even well-trained AI results should be considered suggestions rather than definitive answers. They require close scrutiny and verification by tax professionals before being used officially. Hence, humans will continue to play a vital role in the benchmarking process, with AI acting as a supportive tool.
AI should be viewed as the "keen intern" capable of handling much of the heavy lifting, such as taking screenshots of websites. However, like interns, AI is prone to mistakes. Thus, human experts remain crucial to oversee and validate AI's work.
Conclusion
Benchmarking is a critical yet time-consuming component of TP. Fortunately, AI provides an effective solution to its most labor-intensive aspects, allowing tax professionals to reallocate their time to more valuable tasks. While AI is a powerful tool, it is not a standalone solution. It requires continuous monitoring and assessment to ensure accuracy, reaffirming the indispensable role of humans in the benchmarking process.
Hank Moonen's perspective underscores that AI, while transformative, does not replace the need for human expertise in TP. Instead, it enhances efficiency, enabling professionals to work smarter and more effectively.
By fLEXI tEAM