Question 1

Why are mid-market consulting firms losing visibility in ChatGPT and Perplexity?

Accepted Answer

Mid-market firms are losing AI search visibility because the citation economy that AI assistants run on rewards two specific assets that mid-market firms have historically underinvested in: published thought leadership tied to named individuals, and case-study content that is openly readable on the public web. The Big Four and the MBB consultancies have spent two decades publishing partner-authored frameworks, McKinsey Quarterly articles, BCG perspectives, and Bain Insights essays — all of it freely accessible, all of it linked to identifiable experts. Mid-market firms have instead invested in sales enablement and lead-gen content gated behind email forms, which AI crawlers cannot access. When ChatGPT is asked who to hire for a supply chain transformation, it has tens of thousands of indexed McKinsey passages to draw from and roughly zero from a regional services firm in Manchester. The visibility gap is not about firm quality — it is about content surface area and structured authorship signals.

Question 2

Should B2B services firms ungate their case studies for AEO?

Accepted Answer

Yes, for the majority of case studies. The instinct to gate case studies behind a form was rational in a paid-acquisition SEO world where email capture justified the friction. In an AEO world the calculus is different. A gated case study is invisible to ChatGPT, Claude, Perplexity, and Gemini — these crawlers do not fill in forms, and they cannot cite content they cannot read. The strategic move is to publish the full case study openly with structured data (Article schema, named client, measurable outcome, named author), then offer a richer downloadable artifact — board-ready deck, full methodology appendix, financial model — behind the gate. The first version powers AI citation and brand discovery. The second version captures intent. Most mid-market services firms will see meaningful citation lift within 60-90 days of publishing five to ten ungated case studies with proper schema, named clients, and named delivery partners.

Question 3

How important is the named author signal for B2B services AEO?

Accepted Answer

Author-level entity signals are now among the strongest factors in AI search citation, and they are dramatically underused by mid-market services firms. AI assistants build internal models of who is an authority on a given topic, and those models are reinforced when content is consistently bylined by an identifiable person with Person schema, a stable author page, a LinkedIn presence with consistent NAP (name, address, position) metadata, and external citations from other authoritative entities. McKinsey's edge in AI search is not only the volume of McKinsey content — it is that McKinsey content is consistently signed by a named partner with a verified profile, linked across LinkedIn, university faculty pages, conference speaker bios, and trade press. Mid-market firms publishing under a generic firm byline are leaving the highest-leverage AEO signal on the table. The fix is operationally cheap: assign each substantive piece of content to a named partner, mark up Person schema, and link the partner's content from their LinkedIn and bio pages.

Question 4

What schema markup should a consulting firm or agency use?

Accepted Answer

A serviceable AEO schema stack for a B2B services firm includes five core types and a small number of supporting properties. First, Organization schema for the firm — including legal name, founding date, sameAs links to LinkedIn, Crunchbase, and registry pages, and an areaServed array listing the markets the firm operates in. Second, Person schema for every partner and senior practitioner with worksFor, jobTitle, alumniOf, knowsAbout, and sameAs links to LinkedIn and external bios. Third, Service schema for each distinct service line with serviceType, provider, areaServed, and audience properties. Fourth, Article schema for thought-leadership content with author, datePublished, dateModified, and citation properties. Fifth, FAQPage schema on service pages so that questions a buyer might ask AI assistants are answered in machine-readable form on your own site. The combination produces an entity-context graph that AI crawlers can resolve cleanly. Schema alone will not save mediocre content, but mediocre content with no schema is structurally invisible.

Question 5

Why does McKinsey rank everywhere in AI search?

Accepted Answer

McKinsey ranks everywhere because, for roughly two decades, it has been operating an unintentional AEO program through McKinsey Quarterly, McKinsey Insights, and the steady stream of partner-bylined research published openly on mckinsey.com. The site has approximately 30,000 indexed pages of research content, virtually all of it bylined by named partners with stable profile pages, virtually all of it cited by trade press, business school curricula, and Wikipedia. By the time AI training pipelines started ingesting business content at scale, McKinsey content was already overrepresented in the training corpus relative to the firm's market share. That training-data advantage compounds: AI assistants citing McKinsey reinforce McKinsey's perceived authority, which drives more press citations, which feeds back into the training signal. The mid-market lesson is not to copy McKinsey's content volume — that is unwinnable — but to copy its content architecture: named authors, open access, consistent topic clustering, and structural authority signals.

Question 6

Can a $10M services firm realistically compete with the Big Four on AEO?

Accepted Answer

Not on breadth, but yes on depth. A $10M services firm cannot match Deloitte's 80,000 indexed pages, and trying to do so by ramping content production will produce thin, generic content that AI assistants ignore. The realistic strategy is narrow-and-deep entity authority on two or three specific topics where the firm has demonstrable expertise — a specific industry vertical, a specific methodology, or a specific transformation type. A boutique firm publishing 40 substantive, named-author articles on, say, post-merger integration in mid-market industrial businesses can plausibly out-cite a Big Four firm on that specific query set, because the Big Four content is general and the boutique content is precise. The compounding bet is to become the canonical entity for a narrow topic before the AI training cycle next refreshes, then expand outward. This is the strategy that allowed a16z to out-cite older venture firms on portfolio-construction topics despite being a fraction of their AUM.