Every C-suite is heads-down on AI right now. Leaders are thinking about how to build the right AI strategy, automate internal processes, and add AI capabilities to their product roadmap. With AI updates and releases accelerating faster than anyone can keep up, being a first-mover is important, albeit urgent.

That said, very few are stepping back to ask a simpler question: When buyers use AI systems to research vendors, are we visible and positioned to win?

The problem is that this space is currently inundated by marketing or technical “SEO language;” Answer engine optimization (AEO), generative engine optimization (GEO), prompts, schema, fan-out queries, LLMs.txt and more. (And yes, we’ve succumbed to this fate as well!)

Underneath all the acronyms, the real issue is simple: buyers are changing how they research. According to Forrester’s 2025 Buyers’ Journey Survey, 94% of buyers now use AI in their research process, and twice as many say generative AI or conversational search is a more meaningful source of information than vendor websites, product experts, or sales.

From an executive perspective, this isn’t about chasing trends or keeping pace with the latest technology releases. It’s about revenue, competitive position, and ensuring your marketing foundation is built to surface at the earliest stages of buyer decision-making.

So, if you’re an executive or board member of an enterprise, start-up, or emerging company, the question isn’t whether AI will influence your buyers; it’s whether your organization will show up when AI is shaping buyer decisions.

Here are the 6 questions executives should be asking marketing teams to ensure their business is visible and accurately represented in AI-driven search and discovery environments.

Question 1: How Fast are Buyers Shifting to AI Search?

AI platforms such as ChatGPT, Gemini, and Perplexity keep user-level data private to protect consumer privacy. As a business leader, you cannot access detailed audience demographics or see exactly who of your ICP is using these systems to research vendors unless the platform providers themselves choose to publish aggregated insights.

What you can rely on are broader market studies from firms such as Forrester and McKinsey, which provide directional guidance on adoption trends, buyer behavior shifts, and how AI is influencing research and decision-making. While they don’t offer individual-level data, these reports outline a trajectory that helps leadership understand where the market is moving.

The gist is that for a CEO, the exact percentage of people moving to AI matters less than the trajectory. Adoption is clearly rising, especially among decision-makers and evaluators. Waiting for perfect measurement risks reacting only after competitors are already embedded in AI-driven shortlists.

We have seen this pattern before. When the internet first emerged, many companies dismissed it. Those that moved early built durable advantages, while those that waited either spent years catching up, or disappeared altogether. AI-driven discovery is a similar inflection point. This is a first-mover moment defined by urgency, not perfect quantification.

Question 2: What Search Queries or Buyer Questions (e.g. prompts) are Your Customers Using the Most?

First and foremost, this is a trick question; external vendors (you, your clients, most SaaS tools) cannot see exactly what your customers are typing into AI tools like ChatGPT, Gemini, or Perplexity because of privacy and legal restrictions. There is no honest way to pull a master list of “top prompts” from inside those platforms, so any promise to do that should be treated with serious skepticism.

What matters for the C suite is not the exact wording of every prompt, but whether your company shows up as the obvious answer when buyers ask AI for help at key moments: when they first realize they have a problem, when they compare options, when they choose a partner, and when they look for proof they made the right call. This is NOT just 2-3 word search terms; these are specific, deep sentence inquiries customers are asking. In inquiring with ChatGPT ourselves, it says the average is 20-40+ words based on observed conversational behaviors, B2B buyer prompt modeling, and overall experimentation.

TL;DR: , Exact prompts are unknowable and ultimately less important. What matters is intent coverage. Buyers use AI at predictable moments: defining a category, comparing vendors, validating credibility, and shortlisting options. Organizations need to leverage the customer insight they already own; conversations with sales, support, Google keyword research, public discussion research, and existing clients, to shape  messaging to align with these critical decision points. The executive question is simple: “when these moments occur, are we seen as a credible answer?” We test 150 questions across all buyer stages, each targeting  a different dimension of visibility in AI answers.

Question 3: Does Your Business Appear Among “Best” or “Top” Provider Recommendations?

When users ask for recommendations, AI systems typically return three to five companies. That list functions as an algorithmic shortlist. These are the kinds of prompts you can run across AI search engines (do so multiple times) to see whether you appear as a recommended solutions:

• “Who are the leading [category] companies for [audience or use case]?”
• “What are the best [category] firms for [industry]?”
• “Which providers are recommended for [problem or outcome]?”
• “What is [your brand] known for in [category]?”.

If you aren’t appearing in these recommendations, you may be missing influence at critical decision points, where buyer shortlists and perceptions are being shaped before your team ever has a chance to getinvolved. The revenue impact is subtle but real. Sales teams may hear, “We already shortlisted someone else.” Pipeline quality weakens before anyone sees a clear cause.

Absence at this stage is a competitive disadvantage.

Question 4: How is AI Positioning Your Company (and its solutions) versus Competitors?

AI systems position brands based on signals they can confidently extract. Vague, generic, or inconsistent positioning create three risks:

Missing high-fit opportunities because AI cannot clearly match your organization to specific use cases.

AI needs to understand who you best serve, use cases, why, and real data.

Competing in broad comparisons where incumbents dominate.

It is difficult to be included in AI-generated answers if your business is not clearly focused. We call this product-to-AI-search fit (a little change on product-market fit): the specific moment where your expertise is more relevant than the category leader’s authority.

For example: If you resell Oracle, Cisco, Microsoft, or any major platform, the vendor will dominate broad prompts. BUT buyers may ask AI-specific things that the vendor rarely covers:

• “Best Oracle hospitality partner for OPERA Cloud migration”
• “Companies that fix Symphony integration errors”
• “Top OPERA Cloud support firms on the East Coast”

The same dynamic applies to consumer markets. If you sell jeans, you won’t beat Levi’s for “best jeans.” But you can compete in more precise prompts like: “best petite stretch jeans under $100,” “jeans for curvy athletes,” “mid-rise jeans no-gap waistband reviews.”

LLMs reward specificity and context to the user’s question, not brand fame.

Not showing up at all.

If digital signals, website, 3rd party mentions, UGC, social channels, directories, content, and more, aren’t complete or aren’t formatted for AI extraction, AI will fill gaps with vendors that have more clarity.

This 64-point checklist helped us evaluate whether a brand’s digital presence is positioned for AI to recognize and recommend it. This is not just about clarity; it is about consistent signals across channels. Your website, media coverage, citations, and positioning should reinforce the same story everywhere you appear.

For a CEO, this is about competitive clarity. If AI cannot confidently articulate your differentiation based on the signals available, it will default to competitors it understands better.

Question 5: Are Customers Leaning on AI for Support?

AI search is not limited to pre-sale research. 77% of global consumers are comfortable with AI resolving questions or issues, according to a 2026 industry survey. Customers are increasingly using AI tools to troubleshoot issues, understand features, and validate decisions, rather than navigating documentation or contacting support.

If AI surfaces inaccurate or outdated guidance, trust erodes. If it provides clear, accurate information, it becomes an extension of your support system. Customer experience now extends beyond owned channels.

Question 6: Is Your Channel Ecosystem (content and more) Legible to AI Systems?

This is the most important question of all. AI systems extract meaning from clean, structured, machine-readable text. More importantly, AI does not work like a static search engine with a fixed ranking. These systems generate answers probabilistically. They synthesize patterns from vast amounts of content and assemble a response in real time based on context, phrasing, prior conversation, location signals, model updates, and weighting of trust signals across the web. The same question asked twice can produce slightly different answers.

It’s not  about “ranking once.”  It is about consistently showing up as a clear, credible choice.

Owned Channels

Every well-written service page, clear and concise explanation of services, third-party mention, review, and consistent description of expertise are signals that compound.  They increase the likelihood of your company being understood correctly, trusted, and included when AI assembles an answer in your target category.

Many business-critical assets are not built for extraction. Image-heavy PDFs, fragmented layouts, embedded charts, and inaccePleasssible documentation reduce the likelihood that AI will extract the right signals.

Third-Party Channels (Earned / User Generated Content)

Every third-party mention and review are signals that compound. Your off-site presence matters as much as your online presence. If your media coverage, profiles, partner pages, and customer mentions are inconsistent or hard to interpret, those signals weaken.

In practice, a clearer, more structured competitor can outperform a company with stronger expertise and better third-party validation simply because their signals are easier for AI systems to interpret and connect.

Showing up in AI answers isn’t about producing more content or hiring more vendors; it’s about strategic alignment. When SEO clarifies who you are, PR establishes earned trust, and content demonstrates expertise in ways that consistently connect back to your organization’s commercial priorities, these efforts compound rather than compete.

So, ask yourselves, are we organized in a way that makes us understandable to AI?

The AI Search Executive Mandate

This does not require the CEO to become an AI expert, nor is it a channel decision. It is an operating decision. At the executive level, this is not a marketing trend; it is a governance question. AI is changing how buyers research and validate vendors, and whether you show up in those answers is quickly becoming a board-level growth decision. The issue is not whether your team is “doing AI,” but whether your company is structurally prepared to be understood and selected within existing public,  popular AI technologies.

The mandate is straight forward: make clarity and credibility an executive standard.

Leadership must ensure their organization is structured to support consistent representation, cross-functional coordination, and long-term signal building. Executive teams that treat marketing, authority-building, and structure as one integrated system will see stronger inclusion and more efficient growth than those operating in silos.