How CMOs should think about discovery in an AI-first world

Last month, I asked a VP of marketing how prospects were finding her company. “Organic search, some paid, a little social,” she said. I opened ChatGPT and entered a query that her ideal customer would use: “Best customer data platforms for healthcare SaaS teams with small RevOps staff.” Her brand never surfaced.

That gap matters more than any missed ranking. She was missing from the summaries buyers now trust. Buyer journeys now start with a conversation that distills the market and shapes the buyer’s final consideration set.

Here’s how that shift changes discovery, what visibility means inside LLMs, which KPIs reveal early risk and how to prepare your team for the next 18 months.

Discovery is now a conversation, not a search box

Many prospects now frame their pain to an AI system and expect synthesized guidance. They include constraints, budgets, compliance needs, team structure, and urgency in a single query. The system returns condensed recommendations and evaluation guidance. Your brand either earns inclusion in that guidance or fades from the buying moment.

Buyers offload research to one interface and expect a narrative that frames the problem and highlights credible options. Prompts now sound like:

What platforms help mid-market SaaS teams manage compliance training with limited staff?
Which CDPs work well for healthcare brands with small engineering teams?

Those questions carry nuance that keyword tools rarely capture. SERP-first thinking loses relevance when the buyer never sees the results page. Content written solely to rank, without defining problems or trade-offs, seldom gets cited.

This change in how prospects research creates a new problem for CMOs. Ranking no longer defines visibility. LLMs decide which brands appear in AI-generated answers, so marketing leaders need to understand how that selection process works.

Practical takeaway

Treat AI platforms as discovery channels. Open ChatGPT, Perplexity and Gemini. Enter the questions prospects use when research begins with problem statements. Capture each response. Note which brands surface, how they get described and which attributes stand out.

Create a tracking sheet with three columns: prompt, brands mentioned and positioning language. Review the same prompts monthly to spot narrative drift.

Dig deeper: AI is rewriting visibility in the zero-click search era

Visibility in the LLM era is about being citable, not clickable

LLMs synthesize information and present concise guidance rather than lists of links. That shift rewards brands that function as reference points inside those summaries. They learn from repeated patterns across the web and associate brands with specific workflows, outcomes and use cases over time.

Brands that earn consistent inclusion tend to share three traits:

Precise positioning that defines who they serve and where they fit.
Repeatable language across blogs, product pages, case studies and PR.
Domain authority built through original insight.

Top-of-funnel now lives inside the first paragraph a buyer reads. That paragraph frames the category, sets evaluation criteria and shapes the final consideration set.

Practical takeaway

Shift content goals from traffic growth to answer inclusion. Review core pages and ask one question. Would an AI system quote this content to explain the category?

Rewrite anything that feels vague. In real buyer workflows, prompt logic now carries the same weight as keyword research.

Dig deeper: Why AI visibility is now a C-suite mandate

The KPI reset: Measuring what no analytics platform shows you

Dashboards track traffic, conversions and pipeline. They reveal little about whether your brand earns citation in AI summaries. After teams capture a baseline, the next task is to identify which pages and assets block inclusion.

New metrics CMOs should track include:

Synthetic visibility: Track how often your brand gets cited inside AI-generated summaries for priority prompts.
Prompt recall: Test whether your product surfaces when the category appears without your name.
Answer share of voice: Calculate your share of mentions inside responses.
Narrative control: Review how the system describes your differentiators.

Practical takeaway

Build a monthly AI visibility report. List 20 to 30 buyer research queries. Run each across ChatGPT, Perplexity and Gemini. Log brand mentions, phrasing and omissions. Share trends with leadership.

Operationalizing AI discovery visibility

Below is an example outline for an AI visibility report. This report highlights which parts of your content portfolio fail to support how buyers now evaluate solutions.

Executive summary: Synthetic visibility changes and competitive movement.
Prompt performance: Top prompts with brand inclusion status and narrative shifts.
Share of voice: Mentions by category.
Narrative control: Accuracy of differentiators.
Next actions: Content gaps and PR or partnership opportunities.

Sustaining AI discovery visibility requires tools that support monitoring, interpretation and action across buyer prompts:

Prompt monitoring: Manual testing inside ChatGPT, Perplexity and Gemini and AI monitoring platforms that log answer inclusion.
Narrative tracking: Spreadsheet dashboards or lightweight BI tools.
Content refactor workflows: Editorial templates for problem definitions and playbooks.
PR and backlink intelligence: Media monitoring and link analysis tools.

AI recall compounds slowly. For most enterprise teams, early progress appears as positioning consistency rather than dominance.

During the first month, teams establish a baseline for synthetic visibility and identify where category framing breaks down.
In the second and third months, refactored content for priority problem areas begins to influence prompt recall. One earned placement that reinforces category positioning often marks the first measurable shift.
By the end of the third month, the answer share of voice typically shows early movement and the first quarterly AI visibility report reaches leadership.

Review trends monthly. Treat quarter-over-quarter change as the first performance benchmark.

Dig deeper: AI is forcing a shift from data silos to shared customer context

What content gets cited in AI-driven discovery

LLMs reference content that feels usable for honest buying conversations. In practice, that means material that clearly defines problems, lays out how teams evaluate options and anchors opinions in evidence.

High-level commentary fades because it rarely explains anything with precision. Articles built around trends or inspiration leave AI systems with nothing concrete to reuse. Content that performs in AI-generated answers reads more like a buyer playbook than a brand manifesto.

It tends to include:

Plain-language definitions that clarify where a product fits and who it serves.
Decision frameworks that outline how teams move from problem to evaluation.
Data-backed points of view grounded in benchmarks or operational insight.

Practical takeaway

Refactor flagship assets into resources buyers would reference during active evaluation. Focus on problem definitions, decision criteria, comparison tables and step-by-step guides that map how teams actually choose solutions.

Dig deeper: How digital visibility drives — or destroys — brand trust

Improving content clarity helps, but recall is shaped by signals that extend far beyond your own site. PR coverage, analyst conversations, community participation and partnership content all contribute to the language LLMs learn to associate with your brand. High-authority backlinks continue to play a role in reinforcing how your category and use cases are described.

Those signals change how teams plan discovery work. As off-site mentions shape recall, responsibility no longer rests solely with content. It spans PR, partnerships and brand, with each function contributing to how your positioning language travels across the market.

How CMOs should reorganize now

AI discovery requires clear ownership and tighter integration across teams. Pair content engineering with SEO and assign accountability for how the brand appears in AI-generated answers. Designate one leader responsible for AI discovery and set a Q2 goal to baseline synthetic visibility across priority segments.

Most teams struggle to decide where to start. Prioritization should be based on revenue exposure, not content volume. Begin with product lines tied directly to the pipeline. Define buyer questions for each segment, audit current inclusion in AI-generated answers and focus the first 90 days on gaps that carry clear revenue risk.

Revenue dashboards hide how demand now forms. Pipeline reports reflect past behavior and offer no visibility into the AI conversations that shape decisions long before a site visit. When citation presence declines inside AI-generated answers, the impact often surfaces months later. The brands winning in 2026 are already building for AI answers today.

Do this in the next 30 days: