Is AI search optimisation worth it for an R&D tax adviser?
One firm in our sweep converted 62% of its AI mentions into a recommendation. A firm cited 36 times converted none. In a market this competitive for claim volume, that gap is the difference between a full pipeline and a firm nobody actually calls.
Steen Stones · Reviewed
The honest answer depends on whether a firm can currently see the number that actually matters, and in this market almost none can. ForrestBrown, in our own sweep, converted 62% of its AI mentions into a specific recommendation. Another firm, named 36 times in the same sweep, more than most firms in the tally, converted zero. Both would look “visible” on a tool that only counts citations. Only one of them is winning claims through it.
Why this market rewards the work more than most
R&D tax claims are high-value and high-trust by nature, a business handing over its claim, and by extension its relationship with HMRC, wants a specific reason to pick one firm over another. Our sweep shows engines already answering that way: every recommendation we found cited a specific compliance process, technical specialism or track-record figure, never a general reputation statement. That's a market that rewards specific, evidenced positioning more than most, which means the return on doing this work properly is higher than in a market where any credible-sounding firm gets picked.
Where the return compounds
Across Avouch's wider research, 6,517 AI answers judged, every verdict stored and independently re-checked, comparison and ranking sites come out ahead of every other channel by a wide margin, however the analysis is read. For R&D tax specifically, our own sweep shows the equivalent is compliance-and-track-record content that reads as independently verifiable, an enquiry rate, a claim-value average, a specific process, not a directory listing in the traditional sense, but functioning the same way: a checkable, third-party-style proof of credibility an engine can point to as the reason.
There's a difference between showing up and being recommended. AI cannot recommend what it doesn't know.
The three questions worth asking before you spend anything
- Do we know our mention-to-recommendation rate, per engine? Our own sweep found one major engine converting zero across two entirely separate markets, an average would hide exactly the progress that matters on the other two.
- Can we state a real, specific compliance figure, enquiry rate, claim-value average, sign-off process? Every recommendation quote in our data was built on a number or a named process, never a general trust claim.
- Is our content dated correctly against the current scheme? A page still describing the pre-merger SME/RDEC split is telling both buyers and engines the firm hasn't kept pace with HMRC's own changes.