What the PAVA Framework engagement involved, and what category-leading AI visibility actually looks like in B2B.
A commercial interior décor client engaged us last quarter operating in a category most CMOs would describe as well-managed. Established trade publication coverage, healthy specifier relationships, an active project portfolio on owned channels. Inside the AI platforms architects and project managers actually use for early-stage research, the brand was barely being mentioned. Ninety days later, the brand held 19.78% of category share of voice against five tracked competitors, with the nearest competitor at 4.7%.
Below is what the work involved, and what I would take from it.
THE STARTING POSITION
In commercial interior décor, the first comparison a specifier sees is no longer a brochure or a trade-publication article. It is an AI summary. Architects, project managers, and procurement leads are running comparison-stage queries on AI platforms before the brand ever knows the project exists.
That is not theoretical. It is observable in the data the moment you start measuring it. And most B2B brands in this category do not measure it, because traditional SEO dashboards and trade media coverage reports do not surface AI mention data.
The client had been investing properly in the channels they could see. The channel they could not see was carrying the early comparison conversation, and competitors with less product depth were collecting the mentions.
We baselined them across ChatGPT, Gemini, Perplexity, Google AI Overviews, and Google AI Mode. We mapped the high-intent commercial interior and specification queries that specifiers actually run. We benchmarked against five competitors in the category.
The picture was uneven. Some queries the brand was already winning. Others it was completely absent from. The category was fragmented in a way that rewarded the first brand to systematise AI visibility coverage.
WHAT THE WORK INVOLVED
The engagement ran across all four pillars of the PAVA Framework: Presence, Authority, Visibility, Amplification. Each pillar addresses a question AI platforms appear to weigh before deciding whether to surface a brand in an answer.
1. PRESENCE. Entity integrity.
The brand’s category positioning was clear on its own site but inconsistent across the third-party sources AI ingests. We rebuilt the canonical brand description, aligned it across LinkedIn, Crunchbase, and the relevant industry and specifier directories. Wikipedia work went through the standard contributor process with paid-contributor disclosure where the policy requires it. Schema deployment covered product, organisation, and FAQ types across the site.
2. AUTHORITY. Earned editorial standing.
We mapped the third-party publications AI platforms actually cite in this category. Trade press, specifier resources, architect-focused publications. We briefed the client’s existing PR partner against that target list with angle briefs the firm could pursue on the merits of the work. Citation outcomes were measured against each placement to verify which sources translated into AI mentions and which did not.
The brand’s PR partner now operates against a citation-outcome target, not a coverage-volume one. This is what AI Citation Enablement looks like on the earned side. AICE is the discipline we run across all four PAVA pillars: making the brand’s expertise easier for AI to find, verify, and accurately attribute, on the brand’s own merits.
3. VISIBILITY. Content architecture.
We mapped the prompt universe, our term for the high-intent specification and comparison queries specifiers actually run. We then categorised them into three working groups: queries the brand was already winning (Brand Leaders), queries where multiple brands competed (Battlegrounds), and queries the brand was absent from (Blind Spots).
Owned content was restructured into the formats AI retrieval systems extract most reliably: direct answers at the top of a section, specification comparison tables, named methodology frameworks, and statistics sourced with publisher, sample, and year. Blind Spots became the priority content commissioning queue.
4. AMPLIFICATION. Sustained presence.
The fourth line of work runs continuously. Share of voice across the five named AI platforms was tracked weekly. Competitor density per prompt cluster was monitored, with reinforcement work scheduled wherever a competitor was gaining ground. Sentiment patterns across cited sources were watched continuously.
The compounding effect on this engagement was unusual. As the brand moved from absent to consistently cited across multiple platforms, AI confidence in the brand’s category position increased, which produced higher placement positions on subsequent queries.
THE NUMBERS
By the end of 90 days, against five tracked competitors:
• Brand mention coverage: 66.67%
• Citation rate: 47.13%
• Share of voice: 19.78% (nearest competitor: 4.7%)
• Average AI ranking position: #2.31
• Brand mentions: 116 (vs 17–42 for competitors)
• Citations: 82 (highest in the tracked set)
• Sentiment score: 78
• AI-referred sessions (Jan 17 – Apr 16): 297
• Engaged AI sessions: 156
• Engagement rate from AI traffic: 52.53%
We measure outcomes against what we call the M-C-R stack: Mention, Citation, Recommendation. This engagement moved the first two materially and held an average AI ranking position of #2.31, which is meaningful proximity to the Recommendation tier. Recommendation rate, where the brand is the suggested choice in a commercial-intent answer, is the third tier and the next horizon for the measurement programme.
The other number worth highlighting is the 52.53% engagement rate from AI traffic. AI-referred volume is naturally lower in B2B specification categories than in consumer ones. Engagement quality compensates. A 52.53% engaged-session rate means the specifiers landing on the site from AI have already completed the early comparison work inside the chat window. They arrive past evaluation. In a category with long buying cycles and high project values, that quality of traffic compounds in ways AI session volume alone does not capture.
THREE OBSERVATIONS
B2B SPECIFICATION CATEGORIES MAY BE THE EASIEST TO DOMINATE IN AI SEARCH.
The volume is lower than consumer queries. The buyers are more concentrated. The competitive set is small and uneven in its AI investment. A category leader who systematises AI visibility coverage early can hold disproportionate share of voice for years. This client moved from category participant to category default in 90 days.
CATEGORY LEADERSHIP IN AI SHOWS UP AS AVERAGE RANKING POSITION, NOT JUST MENTION RATE.
A #2.31 average AI ranking matters more than a 66.67% mention rate, because it measures where the brand appears in the answer, not just whether. Top-three positioning is the precondition for Recommendation outcomes. Most AI visibility programmes only report mention rate. The ranking position is the leading indicator of category default status.
THE BLIND SPOTS MAP IS WHERE THE NEXT QUARTER OF GROWTH SITS.
Brand Leaders maintain themselves. Battlegrounds need defensive work. The compounding lift in this engagement came from systematically covering queries the brand was previously absent from. Most AI visibility programmes optimise where they already have a presence. The expansion frontier is everywhere they do not.
WHAT WAS NOT IN SCOPE
The work explicitly excluded several practices. This matters in 2026 because Google’s May 15 spam-policy update on generative search formally classified most of them as spam.
• No paid placements with passing links. Any sponsored editorial carried rel=”sponsored” or rel=”nofollow”.
• No syndicated press release distribution with optimised anchor text.
• No anonymous Wikipedia editing on the brand’s behalf.
• No AI-generated content published at scale.
• No incentivised or coordinated reviews on platforms AI reads.
These are not stylistic preferences. They are the line between sustainable AI visibility work and tactics that are about to be audited out of the market.
In May we renamed our signature practice from “AI Citation Engineering” to “AI Citation Enablement”. The acronym (AICE) stayed, the framing changed. “Engineering” implies working on AI’s evaluation. “Enablement” describes working on the brand. The second framing is the only one I am comfortable defending in client engagements over the next two years.
WHERE TO START
If your brand operates in a B2B specification category and has not been benchmarked against the major AI platforms, that is the place to start. We run a complimentary AI Visibility Audit. It takes a week and produces a board-ready benchmark against your top three competitors across all five major AI platforms. The audit is the diagnostic phase of the AI Visibility Operating System we build for enterprise CMOs.
DM me if that is useful.