SEO isn’t dead. It’s becoming findability in the age of AI buying.

Where enterprise tech marketers stand today

In client work, strategy sessions, and early pilots, we’re hearing the same set of questions surface:

• “How do we show up in AI-generated shortlists, not just Google results?”
• “How do we measure whether our category presence is improving?”
• “What content actually influences what AI systems cite and repeat?”

These discussions are quickly evolving into strategic priorities for marketing teams as AI’s influence over the buyer journey intensifies.

The buying committee is bigger, and now includes AI agents

Committee buying has been the reality in enterprise tech for years. What’s changed is the scale and who participates.

Forrester’s buyer research puts the average buying group at 13 people, with 89% of purchases involving two or more departments. Gartner cites up to 16 people across as many as four functions. That’s not a messaging problem you can solve with a single audience-agnostic thought leadership piece. It’s a consensus problem across security, IT, finance, procurement, and functional owners, each with different risk tolerances and success criteria.

Now add a new entrant: AI agents.

Forrester reports that almost 95% of buyers anticipate using generative AI to support their decision and purchase process. McKinsey is already describing “procurement agents” as systems that ingest context, plan work, suggest options, and act autonomously. This accelerates the shift toward a hybrid workforce where procurement professionals collaborate with digital coworkers.

The implication for B2B tech marketers is that the buying committee is becoming part human, part machine.

Dual-track content

Most teams already tailor content by persona. The new layer is tailoring content by consumer type:

• Humans reward narrative, clarity, visual proof, and interactive experience.
• Machines (LLMs/agents) reward structure, consistency, explicitness, and retrievability.

This is the pivot. The same truth needs to ship in two optimized forms.

Track A: human-optimized content

This is where you win attention, belief, and internal alignment.

If you’re marketing an enterprise SaaS platform (say, a FinOps + governance solution), human-track assets increasingly need to do more than “explain.” They need to help stakeholders decide:

• Interactive ROI/TCO tools and budget planners
• Visually dynamic demos (journey maps, workflows, architecture explainers)
• Outcome-led case studies that show before and after states with constraints and tradeoffs
• Buying committee enablement modules (security briefs, implementation plans, procurement-friendly packaging)

Track B: machine-optimized content

This is where you win shortlist inclusion and reduce mis-positioning when buyers rely on AI summaries.

Machine-track deliverables look different:

• Markdown “factsheets” for each solution and industry (what it is / who it’s for / key differentiators / proof)
• FAQ content that answers shortlist questions directly
• Schema-based markup (e.g., Organization, Product/SoftwareApplication, FAQPage, HowTo)
• A canonical “claims library” so your positioning stays consistent across web surfaces

The game plan is to make it easy for AI systems to retrieve, cite, and repeat the right story.

Findability is now a deliverable

When buyers use genAI to shortlist vendors, your discoverability becomes upstream of your funnel. And because committees are large, different stakeholders (and their agents) are each asking different questions:

• Security asks: “Which vendors meet SOC2/ISO requirements and support private deployment?”
• Finance asks: “Which solutions reduce spend fastest, and what’s the payback period?”
• IT asks: “Which platforms integrate with our stack and have proven implementation playbooks?”
• Procurement’s agent asks: “Compare vendors across contract terms, support SLAs, and compliance posture.”

Dual-track content lets you meet those questions in two ways:

• a compelling human experience that builds preference, and
• a structured machine-readable layer that ensures you show up and show up correctly.

From rankings to Share of Answers

If findability is real, it has to be measurable.

We’re seeing leading teams treat AI discovery like a new channel, with its own equivalent of share-of-voice: Share of Answers.

A simple operating model works:

1. Build a stable “prompt pack” across buyer intent: category discovery, shortlist intent, comparisons, proof-seeking
2. Score responses consistently: mentioned (Y/N), tier (Top 3/5/10), positioning accuracy, evidence quality, link/citation quality
3. Ship controlled interventions: update one cluster (owned, earned, or explanatory) at a time
4. Re-score monthly and track movement

This demonstrates how findability is anchored in data-driven insights rather than being merely an intuitive process.

The three surfaces of retrieval

Across AI-sourced recommendation sets, we consistently see retrieval fall into three content classes:

1. Owned (“things that look like answers”): your solution pages, use cases, integrations, customer stories, security/IT documentation.
2. Earned (“things that rank answers”): directories, review platforms, partner ecosystems, credible roundups.
3. Explanatory (“things that explain the topic”): category guides, decision frameworks, glossaries, and thought leadership.

Winning findability means designing all three as a system.