CMO Agenda in the AI Era: A Board Framework for AI Visibility

Semrush’s Cannes discussion referenced a proprietary database of more than 300 million AI prompts, a concrete sign that customer discovery can now be observed beyond a conventional search-results page. The CMO agenda in the AI era is to turn that fragmented visibility into board-ready evidence: a clear view of where the brand appears, whether its website can support customer trust, and what commercial outcomes can reasonably be associated with the work.

The panel, hosted by Wall Street Journal Leadership Institute chair Maryam Banikarim and featuring Adobe marketing leaders Rachel Thornton and Andrew Warden, focused on a central executive tension. Discovery is spreading across search, AI platforms, social channels, communities, reviews, and brand-owned content. A recommendation from ChatGPT, Google Gemini, Perplexity, or Claude may shape consideration before a prospective buyer reaches a brand site.

That does not establish that every answer-engine mention produces revenue, or that a brand can control how any model responds. It does mean that visibility, factual representation, and destination-page quality deserve executive attention. The framework below is editorial guidance informed by that discussion and by Audra’s site-audit perspective; it is not presented as a reporting model endorsed by Semrush, Adobe, or the panelists.

Why the CMO agenda in the AI era begins with discovery risk

The event’s strongest practical point was that brands are no longer discovered through one channel alone. A prospective customer comparing haircare products, for example, may encounter product pages, retailer reviews, creator videos, Reddit discussions, and an AI-generated response in the same research journey. The precise sources used by a particular answer engine can vary by product, prompt, location, model version, and time.

For a CMO, the resulting risk is not simply a lower ranking. It is a gap between what the company intends customers to understand and what customers may actually encounter. That gap can include absence from relevant recommendations, outdated product information, contradictory claims across owned pages, or a poor experience after the click.

A useful working definition for leadership teams is:

> Brand visibility is the ability to be discovered, accurately represented, and evaluated with confidence across human-led and AI-led customer journeys.

This is an editorial definition, not a Semrush measurement standard. Its value is that it prevents a narrow focus on appearance rate alone. A brand that appears in an answer but sends visitors to a broken pricing page has not created a dependable discovery path.

Responsibility one: define the growth questions worth measuring

The panel’s reference to a database exceeding 300 million prompts illustrates the scale of possible monitoring. It does not mean a company should track millions of questions. A board needs a disciplined sample tied to priority audiences, categories, markets, and revenue motions.

Build a prompt portfolio around customer decisions

A B2B cybersecurity company might start with 30 to 50 prompts rather than an unmanageable universe. Its list could include:

1. “Best endpoint security platform for a 500-person healthcare company.”
2. “Endpoint security tools with compliance reporting.”
3. “Alternatives to [competitor] for midsize IT teams.”
4. “How should a hospital evaluate endpoint security software?”
5. “Endpoint security pricing for a distributed workforce.”

The first two are recommendation-oriented questions, the third tests competitive consideration, and the fourth is an educational query. The company can record responses from ChatGPT, Gemini, Perplexity, and Claude where those platforms are relevant to its buyers. A response should be dated because answer outputs can change.

The initial record should capture five observable fields:

• Whether the brand appears at all.
• Whether it is described accurately for the intended use case.
• Which competitors or alternatives appear alongside it.
• Whether the response names, cites, or links to a relevant destination.
• Whether the destination page is live, current, usable, and aligned with the claim.

This produces a visibility baseline, not causal proof of growth. It gives the marketing team a repeatable way to find information gaps and assess whether the brand’s most important pages support the customer questions it wants to earn.

Separate AI visibility signals from revenue evidence

A recurring error in AI-search reporting is treating a mention as equivalent to revenue. No supplied panel evidence establishes a direct, universal connection between answer-engine visibility and sales. The relationship will differ by category, purchase cycle, geography, brand familiarity, and the ability to measure downstream behavior.

CMOs can still make a credible commercial case by separating leading indicators from outcome indicators. Answer visibility is a leading signal. Qualified visits, demo requests, opportunities, retained revenue, customer lifetime value (CLV), and return on marketing investment (ROMI) are commercial outcomes that need first-party data and stated attribution assumptions.

Use an evidence chain rather than a single score

For a specific initiative, a report can show the chain below:

A ROMI calculation should be explicit about its scope. A common expression is:

Incremental gross profit attributed under the chosen method ÷ total initiative cost = ROMI

The difficult phrase is “attributed under the chosen method.” Teams may use controlled page releases, holdout groups, matched markets, assisted-conversion analysis, sales-call feedback, or before-and-after comparisons. Each has limitations. The board should see those limitations rather than a falsely precise claim that an AI answer caused every result.

For prioritization mechanics, Audra’s internal article Boost Your SEO: An Audit Plan That Prioritizes Fixes is a practical companion, not independent proof that audit fixes improve AI visibility.

Treat site quality as recommendation readiness

The Cannes conversation discussed changing discovery and content ingestion. It did not establish that SEO is a “trust-signal system” or that any particular technical factor directly controls AI answers. Those are better understood as editorial interpretations: a site that is difficult to access, confusing to navigate, or factually inconsistent creates avoidable friction for people and systems that need information from it.

Recommendation readiness is therefore a practical standard for the pages most likely to support consideration: product pages, services pages, pricing pages, comparisons, documentation, case studies, and local pages. The question is not whether a technical fix guarantees an answer-engine mention. The question is whether the company’s owned evidence holds up once a prospect arrives.

Audit the destination, not just the mention

A page-level review can examine concrete issues such as:

• A product page returning a 404 or redirecting through multiple URLs.
• A pricing page that still displays a discontinued plan.
• A comparison page that makes a claim unsupported by linked evidence.
• A form whose labels cannot be interpreted clearly by assistive technology.
• A key landing page with a slow largest visible content element or shifting layout.
• An orphaned documentation page with no useful internal path from product content.

Performance, accessibility, links, technical SEO, and content accuracy are distinct disciplines. They should not be collapsed into an unsupported claim that they are direct answer-engine ranking factors. Together, however, they help determine whether a customer can verify a claim and complete the next step.

Audra is a local-first desktop audit app for macOS and Windows that brings AI answer-engine visibility checks together with technical SEO, performance, accessibility, best-practice, and link audits. Its role in this framework is diagnostic: it can help teams create page-level evidence and client-ready reports, not promise a particular AI-platform outcome.

Use content governance to reduce factual drift

The panel described a world in which AI systems and people may encounter information across websites, social platforms, and communities. That makes source consistency a CMO concern. It does not require publishing a separate version of the brand for AI agents.

The stronger operating approach is one maintained source of truth for material claims. Product marketing, legal, web, and subject-matter experts should know which pages contain definitive statements about capabilities, limitations, eligibility, pricing approach, implementation, certifications, or regulated topics.

Start with the claims customers most need to verify

For a software company, that may mean reviewing the top 10 product and comparison pages quarterly. For a healthcare or financial-services brand, the review cadence may need to reflect regulatory and product-change requirements. The correct interval is not universal and should be set according to the cost of stale or inaccurate information.

Each review can ask:

1. Is the statement current as of the review date?
2. Can a customer locate supporting details without excessive navigation?
3. Do product, sales, support, and marketing materials describe the same limitation or capability?
4. Is an owner accountable for correcting the page when the product changes?
5. Is the page available to users on mobile devices and through normal site navigation?

This is not an argument for content volume. It is an argument for accountable content. Audra’s internal analysis, AI Crawler Content Visibility: What 300 Sites Actually Serve, is relevant as an exploratory audit perspective on what sites serve to crawlers; it should not be read as a universal explanation of how every AI system retrieves content.

Responsibility two: make trust observable after the click

Trust is often discussed as a brand-perception outcome, but customers also experience it through basic interactions. A recommendation can be undermined by an expired offer, a broken checkout path, a non-functioning contact form, an inaccessible control, or a page that never reaches a usable state.

This is where CMOs need a concise operating view rather than a long list of technical tickets. A board does not need to inspect every Lighthouse result. It does need to understand whether high-value customer journeys have unresolved experience risks and who owns remediation.

Report exposure, owners, and resolution status

A practical monthly review can use five categories:

Named tools can make the review more concrete. Lighthouse can surface performance, accessibility, and best-practice findings; a crawl can identify status-code errors, redirects, and internal-link gaps; a manual review remains necessary for claims and customer context. No audit tool can independently determine whether a commercial statement is legally approved or genuinely useful.

The leadership value is prioritization. A broken demo form on a page associated with 12 high-intent prompts is likely more consequential than a low-severity issue on an archived article. This keeps the CMO agenda tied to business exposure rather than generic “AI readiness.”

Responsibility three: lead a growth system, not an AI tool rollout

BCG’s 2025 article describes an AI-first CMO as a growth architect who works across technology, talent, customer signals, and creative work. That is BCG’s model, not a conclusion drawn from the Semrush panel. It is useful here because fragmented discovery cannot be managed by a campaign team acting alone.

The CMO does not need to own every technical decision. The role is to set commercial priorities, establish shared measurement, remove conflicts between functions, and ensure that customer promises remain coherent across the journey.

A narrowly defined decision model prevents the work from becoming generic transformation language:

• Marketing sets: priority audiences, categories, positioning questions, and investment cases.
• SEO and content set: discovery requirements, information architecture, content briefs, and accuracy checks.
• Web and engineering set: deployment standards, performance remediation, rendering, and reliability priorities.
• Analytics sets: definitions, first-party data controls, attribution methods, and reporting limitations.
• Product, legal, and communications set: approved claims, change notices, privacy boundaries, and escalation paths.

First-party data has a specific role. CRM records, consented web analytics, product usage, support themes, and sales outcomes can reveal whether a priority audience is valuable and whether a changed journey is associated with better outcomes. It should not be used as a vague slogan or fed into AI agents without agreed privacy, access, retention, and approval rules.

A six-measure board dashboard for brand visibility

The following dashboard is Audra’s editorial framework, built to make the growth, trust, and leadership themes operational. It is not claimed to be derived from the Cannes panel, and organizations should adapt it to their business model.

1. Priority-prompt visibility: the percentage of a dated, defined prompt portfolio where the brand appears.
2. Representation quality: the percentage of reviewed appearances that describe the brand, offer, and intended use accurately.
3. Competitive context: the competitors, publishers, communities, or alternatives repeatedly appearing in the same prompt set.
4. Destination readiness: the share of priority landing pages without agreed critical availability, link, performance, or accessibility issues.
5. Trust exceptions: material inaccuracies, stale claims, broken customer journeys, or compliance escalations, with severity and owner.
6. Commercial evidence: qualified sessions, conversions, pipeline, retained revenue, CLV, and ROMI reported with documented methodology.

Consider a company monitoring 40 comparison prompts in Q4 2026. If its appearance rate moves from 22% to 37%, that is a useful observation. A board report should also disclose whether the 15 additional appearances were accurate, whether the associated pages were functional, and whether qualified demand changed relative to a defined comparison period.

The dashboard gains credibility when it includes negative evidence. If a model repeatedly displays old pricing while visibility rises, that exception belongs in the report. Visibility without accurate representation is not an unqualified win.

A focused 90-day operating plan

This 90-day plan is an editorial implementation sequence, not a prescription from Semrush, Adobe, or BCG. Its purpose is to create a baseline before a company makes broad claims about AI-led discovery performance.

Days 1-30: establish the baseline

Choose 25 to 100 prompts based on category complexity and available team capacity. Record the platform, exact wording, date, brand appearance, competitors, descriptive accuracy, and any named destination. Audit the pages connected to the highest-value themes for crawlability, links, performance, accessibility, and current material claims.

Days 31-60: resolve high-exposure gaps

Use a prioritization rule: commercial importance × observed gap × confidence in the proposed fix. For example, a broken pricing page associated with a high-intent comparison query should usually take priority over a minor heading change on a low-value article. Log the hypothesis for every change rather than assuming a fix will change an AI response.

Days 61-90: review evidence and set ownership

Re-run the same dated prompt set, review changes in destination quality, and compare first-party business data using the agreed methodology. The executive readout should separate gains worth scaling, material trust risks, and unknowns that need more testing. It should assign a durable owner to each unresolved issue.

Teams using agent workflows for audit work can also consult AI Agents Fix Lighthouse Errors with Chrome DevTools. That internal resource describes a workflow for remediation assistance; it is not evidence that AI agents can safely make unattended production changes.

FAQ

What are the top priorities on the CMO agenda in the AI era?

The priorities are profitable growth, accurate representation across fragmented discovery, dependable destination experiences, and accountable measurement. A practical CMO agenda defines the customer questions that matter, monitors a manageable prompt sample, maintains current evidence on priority pages, and connects those leading signals to qualified demand, CLV, and ROMI with clear attribution limits.

How is AI changing the role of the CMO?

AI expands the CMO’s coordination role because customer discovery can involve answer engines, social channels, communities, search, and owned content. BCG characterizes the AI-first CMO as a growth architect. In practice, that means setting commercial priorities and aligning marketing, web, analytics, product, legal, and technology teams around measurable customer journeys rather than managing an isolated AI-tool rollout.

How should CMOs measure brand visibility in AI search and answer engines?

CMOs should define a dated portfolio of high-value prompts and test relevant platforms such as ChatGPT, Google Gemini, Perplexity, and Claude. Record appearance, accuracy, competitive context, cited or named destinations, and page readiness. These are leading indicators, so they should be reported separately from qualified traffic, conversions, pipeline, revenue, retention, CLV, and ROMI.

How can marketing leaders prove AI-driven growth and ROI to the board?

They should avoid claiming that a mention caused revenue without evidence. Instead, report an evidence chain: prompt visibility, representation quality, destination-page condition, qualified demand, and commercial outcomes. Controlled releases, comparison periods, holdouts where feasible, and documented attribution assumptions are more credible than a single “AI impact” score.

How can brands use AI without losing customer trust?

Brands can use AI for research, monitoring, issue detection, and audit prioritization while retaining human accountability for material claims, sensitive content, customer data, and publication decisions. Trust depends on current information, accessible customer journeys, clear consent practices, and an escalation process when an AI-generated output or external narrative conflicts with verified facts.