AI in Professional Services: The Trust Issue

For accounting and advisory firms, the message is not "we use AI." It is "we know where to use it, where not to use it, and how important output is reviewed." Thomson Reuters research shows enterprise generative-AI use in tax and accounting firms rose from 8% to 21% in one year, with another 53% planning or considering adoption. Strong use cases include tax research, return preparation, advisory, bookkeeping, and document review. But the trust issue is real. Where an error is cheap and quickly caught—a first draft, an internal summary, a search across your own documents—AI can be tried with little at stake. Where an error is expensive, slow to surface, or reaches a client, the same tool needs review, documentation and a named human owner before it goes anywhere near the workflow. The transferable principle applies to every industry: identify where being wrong actually costs something. That question tends to sort the work quickly and clarifies where AI is an accelerant and where it is a risk.