AI Search Is Not a Channel-It’s an Operating System

What’s Inside: Navigate the OS

1. The Operating System That Runs Without You
2. What AI Search Is (A Definition That Actually Matters)
3. Why Siloed Teams Send Conflicting Entity Signals
4. The 5 Functions That Must Work Together
5. Myth vs. Reality: How Brands Are Getting This Wrong
6. Industry Updates: What’s Happening Right Now
7. What a Unified AI Search Model Looks Like
8. How Pepper Executes This as One System
9. FAQ

The Operating System That Runs Without You

Your buyers have already switched. Over 50% of B2B buyers now start their research in an LLM – not Google. At Pepper’s Index event, a former G2 CMO shared the data: in early 2025, about 24% of buyers started searches in AI. By mid-2026, that figure had crossed 50%.

For some Silicon Valley companies, it’s already 80-90%.

Meanwhile, across the Fortune 500, organic traffic from Google dropped 30-40% on average in 2024-2025. For some categories, it fell 70-80%. As Dave Gruen, Partner at Lightspeed Venture Partners and investor in Pepper, put it at the Index event: the floor gave out – and it happened fast.

The problem isn’t AI search. The problem is that marketing teams are still treating it like a channel – something to ‘optimize,’ hand to one team, measure with one metric. That’s the wrong mental model entirely.

AI search is an operating system. And right now, most organizations are running it like a side project.

What AI Search Is (A Definition That Actually Matters)

A brand cited three times in a ChatGPT response beats a brand ranked #1 on Google for the same query – because the buyer never leaves the chat interface. This isn’t a subtle shift. It’s structural.

Kishan Panpalia, in his talk at Pepper’s Index event, made this point precisely: ranking #1 on Google does not mean ranking #1 on LLMs. Most of the time, it isn’t even close.

Why Siloed Teams Send Conflicting Entity Signals

Here’s how the breakdown happens in practice.

Your SEO team optimizes pages for keywords. Your PR team secures editorial mentions but doesn’t share the language used. Your brand team updates your Wikipedia summary – but the SEO team doesn’t know it happened. Your product marketing team builds your G2 profile with messaging that differs from what your content team uses on-site.

Now an LLM is trying to build a coherent entity model of your brand. It pulls from G2 (one description), Wikipedia (a different one), your blog (a third angle), and an article in TechCrunch (written by a journalist who may have used outdated messaging). The model synthesizes these into a response – and what comes out is vague, inaccurate, or dominated by a competitor.

Conflicting entity signals don’t just reduce your visibility. They actively confuse the model about who you are.

According to Pepper’s Atlas platform data, directory and review platform presence can carry 20-70% citation weight in LLM responses. Authoritative list inclusions carry 40-65%. Yet most brands still allocate the majority of their GEO budget to blog content alone – which carries far less weight as a standalone signal.

Christine, Head of Marketing at a work management company, described this exact realization at Pepper’s Index event:

The 5 Functions That Must Work Together

There are 5 functions that collectively determine your brand’s performance in AI search. Here’s what each one contributes – and what breaks when they operate independently.

1. SEO & Technical

Responsible for crawlability, llms.txt, schema markup, and content architecture. If LLM crawlers can’t retrieve and parse your pages, none of the other work matters. Technical errors at this layer create a ceiling on everything else. Kishan Panpalia’s Index talk emphasized one key principle: one fact per section. AI extracts structured facts – not prose elegance.

2. Content

Responsible for creating content that directly answers the prompts your buyers run – not keywords, but prompts. Content that isn’t structured for LLM extraction (short paragraphs, FAQ schemas, definition blocks, H2/H3 hierarchy) won’t get cited regardless of how well it ranks on Google.

3. PR & Earned Media

The most underrated function in AI search. According to Pepper’s GEO research, 61% of LLM responses are influenced by mentions in trusted editorial sources. PR is now a growth marketing function – not just a brand function. Editorial mentions in Search Engine Journal, Forbes, or Content Marketing Institute directly feed LLM training data and RAG retrieval.

4. Brand & Entity Management

Your Wikipedia entry, Wikidata record, Crunchbase profile, and Knowledge Graph presence determine how LLMs resolve your brand entity. If these are incomplete or inconsistent, the model can’t confidently anchor signals to your brand. This is the ‘who are you’ layer – and it’s often neglected entirely. Linda Kaplinger, who leads AIO and GEO at NVIDIA, flagged this clearly at Index: brand and trustworthiness are going to be huge for agentic search.

5. Product Marketing & Reviews

G2, Capterra, and similar review platforms carry disproportionate citation weight – 20-35% of LLM citations. Your product marketing team’s messaging on these platforms becomes part of your AI search profile, whether or not they know that. Visit atlas.pepper.inc to see how your brand stacks up.

Each of these functions feeds into the same LLM output. None of them can win independently.

Myth vs. Reality: How Brands Are Getting This Wrong

MYTH: “We hired an SEO agency to handle GEO. We’re covered.”

REALITY: A traditional SEO agency can optimize your technical structure and your content. It cannot build your PR presence, manage your entity records, or shape your G2 reviews. Those require different teams, different relationships, and different execution tracks – all aligned around the same brand signals.

MYTH: “We’re already doing content. GEO is just an extension.”

REALITY: Content written for Google rankings and content optimized for LLM retrieval are structurally different. LLMs weight definition blocks, FAQ schemas, and authoritative claim structures. Most legacy SEO content isn’t chunked, expert-attributed, or schema-marked in a way that AI crawlers can extract and cite.

MYTH: “Our brand is well-known. We’ll naturally appear in AI search.”

REALITY: At Pepper’s Index event, a Dropbox marketing leader described exactly this failure: ‘The brand representation of the AI overview summaries was inaccurate to our strategy.’ Brand recognition in human memory doesn’t automatically translate to LLM citation. Visibility in AI search is built deliberately – or it isn’t built at all. They now track LLM impact as a line item on their weekly scorecard.

Industry Updates: What’s Happening Right Now

The signals are converging fast. Here’s what the data says as of mid-2026.

EY, May 2026

Traditional search volume will fall 25% by 2026 as AI chatbots and virtual agents capture market share. AI is shifting marketing ‘from campaign cycles to an always-on operating model that adapts continuously to customer signals.’ Structure, context, and machine readability become part of the craft of marketing – not technical afterthoughts.

Gartner CMO Spend Survey, 2025

Marketing budgets flatlined at 7.7% of company revenue. Yet CMOs are now allocating 15.3% of those constrained budgets specifically to AI initiatives. AI is no longer just a marketing conversation. It’s a capital allocation conversation.

McKinsey

42% of organizations are now using AI in sales or marketing functions. Teams with AI capabilities make faster decisions, allocate budget more efficiently, and scale analytical capabilities without scaling headcount. The gap between early adopters and laggards is widening.

G2 Research (Reported at Pepper’s Index 2026)

Over 50% of buyers now begin their search in LLMs. For B2B categories especially, the majority of the buyer’s discovery journey is now happening in an interface your traditional marketing stack cannot track. Last year, that figure was 24%. The shift is not gradual – it’s a step change.

Board-Level Accountability

At Pepper’s Index event, a Dropbox marketing leader described their wake-up call: an audit showed brand representation in AI overview summaries was ‘inaccurate to our strategy.’ That moment catalyzed board-level attention. They now show LLM funnel impact on their weekly scorecard alongside pipeline – and track prompt clusters the way they once tracked keyword clusters.

What a Unified AI Search Model Looks Like

The brands winning AI search right now share one thing: they’ve stopped treating GEO as a project and started treating it as infrastructure.

There are 4 characteristics of a unified AI search operating model:

1. One source of truth for how your brand is described across Wikipedia, Wikidata, G2, PR mentions, and on-site content. All teams use it. All content aligns to it. A shared entity record.
2. Instead of keywords, the team runs on prompt clusters – the actual questions your buyers type into ChatGPT. Every content brief, PR pitch, and product review request maps to a specific prompt cluster. Cross-functional prompt ownership.
3. Metrics like share-of-answer, citation frequency, and prompt coverage are tracked at the organization level – not siloed inside one team’s reporting. Platforms like Atlas (Pepper’s intelligence layer) make this possible at scale. Visit atlas.pepper.inc to see it in action. Unified visibility tracking.
4. PR is no longer just about awareness. Editorial placements are actively tracked for their LLM citation contribution. Story angles are developed specifically to match the prompts LLMs retrieve at the moment of buyer research. PR as a citation engine.

Heidi, an enterprise CMO speaking at Pepper’s Index event, described the org shift required:

That’s the right instinct. AI search requires systems thinkers, not channel specialists.

How Pepper Executes This as One System

Most brands have the pieces. What they lack is the connective tissue.

Pepper functions as the unified execution partner that coordinates SEO, content, PR, entity management, and performance tracking under one operating model – anchored by Atlas, Pepper’s AI-native intelligence layer (atlas.pepper.inc).

• Strategy: Pepper’s team maps the brand’s full AI search landscape using Atlas, identifying which prompts your buyers run, which competitors are being cited, and which signal gaps are causing your brand to be absent.
• Content: Expert-led content produced specifically for LLM retrieval – structured, schema-marked, and authored by named subject matter experts who build E-E-A-T credibility.
• LLM Indexability: Technical execution including llms.txt deployment, schema markup, and semantic architecture so AI crawlers can parse and cite your content reliably.
• Sustained Monitoring: Atlas tracks citation frequency, share-of-answer, and competitive movement across ChatGPT, Gemini, and Perplexity – feeding insights back into strategy continuously.

The results speak for themselves. Freshworks: $330K annual contract renewed two consecutive years while maintaining citation presence over Zendesk and ServiceNow. Mutual of Omaha: 189% month-over-month click growth and 199% impression growth in six months. Atlassian: Pepper-written articles generated 2.8x more average clicks than non-Pepper content.

AI search optimization doesn’t work as a piecemeal effort. It works as a system.

The Bottom Line

AI search is the new discovery layer for your buyers. It synthesizes signals from every function in your organization simultaneously. When those functions are misaligned, you lose visibility – not because your content is bad, but because your signals are incoherent.

The brands that win aren’t the ones with the best SEO. They’re the ones that have built a cross-functional operating model where every team is working toward the same entity – and every signal reinforces the same story.

That’s not a marketing campaign. That’s an operating system.

FAQ

What is an AI search operating model?

An AI search operating model is a cross-functional organizational approach that aligns SEO, content, PR, brand, and product marketing around a shared goal: ensuring the brand is consistently visible, cited, and accurately represented across LLM-powered search surfaces like ChatGPT, Perplexity, and Google’s AI Overviews.

Why can’t one team own AI search optimization?

Because LLMs synthesize signals from multiple sources simultaneously – editorial coverage, review platforms, structured content, entity records, and social discussions. Each of those sources is typically owned by a different team. No single team controls all the inputs that determine your AI search presence.

What are entity signals in AI search?

Entity signals are the data points LLMs use to understand who your brand is – including your Wikipedia entry, Wikidata record, G2 profile, PR-earned editorial mentions, and the language patterns used across your owned content. Inconsistencies across these signals cause LLMs to form an inaccurate or vague picture of your brand.

How do you measure AI search performance?

The key metrics are share-of-answer (how often your brand appears in AI responses to tracked prompts), citation frequency, brand mention rate, and domain coverage (the percentage of relevant prompts where your site is cited). Platforms like Atlas by Pepper (atlas.pepper.inc) track these across ChatGPT, Gemini, and Perplexity.

How is GEO different from traditional SEO?

Traditional SEO optimizes for keyword rankings and click-through rates. GEO (Generative Engine Optimization) optimizes for LLM citation and answer inclusion. Backlinks carry only 5% weight in AI search versus 45% in traditional SEO, while authoritative list inclusions and review platform presence carry 40-65% weight in LLM responses.