GUMSEK

Measuring AI visibility for a law firm

Some of it is stuff you undoubtedly already measure, but much of it is probably not. The rest are estimates, educated guesses, blind spots, etc. A lot of AI reporting looks like progress given the dashboards shown, but it's actually noise.

Tell me your need

Before you go off buying AI tracking tools, bear in mind that you haven't sized up the opportunity yet.

15% Of ready-to-hire legal searches showed an AI Overview in Whitespark's 540-search study. The map pack showed on 93%11.

AI visibility is a part of a firm's search presence, but it is not IMHO the primary source of new cases. Keep that in proportion report by report, budget by budget, and you'll be fine.

What Google reports, which is less than you would like

Search Console has a very limited, per-URL AI features report with AI Mode and AI Overview impressions and clicks.

There are two distinct limits in play here1:

  • No sign of which question triggered the appearance
  • AI feature traffic folded into the overall web totals, with no separate line
What follows from that

I want to cheer the report, but how useful is this data when so many wildly different visits are lumped into the web total? However, this "AI traffic" line does not actually exist in Search Console as of yet, and agency figures are based on "the web total" and are hard to verify.

For example, if we got the agency's Search Console report and are sitting with the account manager as we review our AI traffic, it is perfectly valid to ask them how they separated these AI clicks from the web total. You can safely check for direction of travel on a page and that's it.

What Bing reports, which is genuinely more

Bing Webmaster Tools publishes an AI performance report that provides per-URL citation counts and the grounding queries behind them to site owners2.

A grounding query is a behind-the-scenes search with somebody's original question at its root. Few tools can show that, and nobody is telling.

Bing makes up a relatively small portion of search, yet this grounding data really only comes from Bing at this point. It takes a bit of reading, but the picture that emerges of how an answer engine uses a firm's site is quite clear. It costs virtually nothing to check this. Everyone I speak with who sells law firm SEO almost never checks their Bing data, so I made a point to bring the Bing numbers to clients.

The practical use

That means you can assign actual grounding queries to different pages of your site.

Compare that to the target questions and topics of your practice pages and blog articles. That gap is your best content plan.

What the prompt trackers add, and what they do not

Tools like Peec, Otterly, Profound, Gumshoe and Ahrefs Brand Radar offer scheduled runs and multi-engine checks to tell you who gets named for a fixed set of questions2.

So for example (just an example), for each practice area in each market that you serve, you write the question a client would ask, plus one with the firm's name.

The trick is to watch the trend across lots of questions. The problem is that you perceive trackers as a scoreboard. A tracker puts your firm in 11 of 40 questions this month against 9 of 40 last month, which is useful, but it doesn't tell us much about any one answer. Position is fleeting, first place in one answer is just a flash in the pan.

Why a single check proves nothing

AI answers vary based on the run, the user, the date, etc., so your firm might not be at the very top for everybody. If you do not insist that your tracker (or agency) give you repeated samples and run each question across engines, then you have no way to know if your numbers are meaningful3.

The same applies to the platforms' citation data.

60% → 10% ChatGPT's citations of Reddit, falling within weeks, while Reddit held steady on other platforms4.

A monthly figure from one query on one engine is just noise.

This cuts both ways in client conversations

I get sent ChatGPT screenshots of competitors and of clients, and my answer is that while I love seeing a good screenshot, this would be a nonstarter for a report. Try running that question through your own ChatGPT account twenty times and see how often the firm appears.

Three things that cannot be measured at all

1. Answers given without any search.

Only about 18% of ChatGPT conversations end up in a web search5. The other 82% comes from training data, both unseen and unreported. What shapes those answers can change slowly, without warning in three-to-six-month retraining cycles7.

2. Retrieval without citation.

Discarded content is 85% of the material ChatGPT retrieves6. Your best page can simply be read in full and discarded just moments later. The firm has no way of knowing if its page was read.

3. Which fan-out query produced a Google appearance.

The AI features report does not cover fan-out queries or grounding queries behind an impression1. On Bing that data is given, on Google it is not given.

Why I say this out loud to a partner

Better to explain the blind spots to them from the beginning!

The monthly report I would actually send

  1. Share of a fixed question set where the firm is named, across at least two engines and with the question count stated. "Named in 11 of 40 questions, up from 9" certainly beats the usual visibility scores.
  2. Bing's citation counts and grounding queries for the main practice pages. The only free hard citation data, it seems, is the Bing report2.
  3. Search Console AI feature impressions, labelled as directional, with the web total caveat written on the page and not just said once in a meeting1.
  4. Then the numbers that pay the bills. Calls, forms, consultations and signed cases, from the intake and measurement page. For law firms especially, call tracking matters more than just about anything. 56.3% of legal conversions are phone calls, and most reports often can't see them9.
  5. One line on what cannot be measured, repeated in every monthly report so nobody forgets it.

It's a plain, honest report, and long term, it works better than any dashboard with false precision.

The same honesty goes for your average intake desk or front office. I would add that 39% of firms take more than two hours to answer an online enquiry, or never answer it10. No amount of AI visibility, or Bing citations or mentions in the prompt trackers can change that. Keep that number handy and include it whenever you report.

What to do when the numbers move

Treat a citation drop like a ranking drop: let the checks below guide you to a likely cause.

  1. Check the page still ranks. It is often the case that the cited pages you lost in the AI answers are no longer ranking.
  2. Check the page is still eligible. A release with a stray snippet restriction or a blocked AI crawler would easily cause this problem. Take a look at the AI Overviews page and check accordingly.
  3. Check what changed on the page. I favour keeping the answer near the top, since that is what the engines extract.
  4. Check whether the engine changed its sourcing generally. It's fairly common that an engine reshuffles its sources within weeks4. If you look at three unrelated clients dropping in the same week, it is definitely not their pages.

The two biggest factors in the citation literature are accessibility and search rank8, which is often woefully neglected in AI visibility advice. This list is front loaded with those two checks for reasons given above. Most citation problems are pretty much indexing or ranking problems.

References

  1. Google Search Central, AI features and your website (updated 2025-12-10) No special optimisation or files; snippet eligibility; query fan-out; Search Console reporting
  2. Cyrus Shepard, Zyppy, Fan-out framework: 5 steps to optimise for fan-out queries (2026-09-10) Method for collecting fan-out queries and measuring the result
  3. arXiv 2604.07585, Don’t measure once: measuring visibility in AI search Variance between runs and why single measurements mislead
  4. Semrush, The most-cited domains in AI: a three-month study Reddit and Wikipedia citation collapse in ChatGPT; LinkedIn growth
  5. Profound, How ChatGPT sources the web 700,000 conversations: search frequency, turn effects, citation concentration
  6. AirOps, The influence of retrieval, fan-out and Google SERPs on ChatGPT citations 548,534 retrieved pages: 15% cited, fan-out volume, title overlap, Google rank effect
  7. Dan Petrovic, Dejan, Primary bias on selection rate in AI search How a model’s existing view of a brand affects selection
  8. Cyrus Shepard, Zyppy, AI citation ranking factors: evidence from 54 experiments, patents and case studies (2026-09-11) 23 scored factors and the underlying source collection
  9. Ruler Analytics, Legal marketing statistics and conversion benchmarks Conversion rates by channel for legal, and the share of legal conversions that arrive by phone
  10. Hennessey Digital, 2025 lead form response time study How fast law firms reply to online enquiries
  11. Whitespark (Miriam Ellis), AI Overviews for local business searches 540 manual queries across three cities and six industries, personal injury lawyers among them