How to adapt your marketing strategy for AI-driven buyers

A prospect joined one of my sales calls and told me he had asked ChatGPT to look up our company before the call. That was his first serious evaluation step. He did not begin on our homepage or move neatly through a nurture sequence. He asked a model to summarize us.

That moment changed the marketing question. It is no longer enough to ask whether a buyer can find your page. You also need to ask whether an answer engine can explain your company accurately, whether the evidence supports that explanation, and whether the buyer cares when they hear it.

SEO is not dead. Its job has widened.

The dramatic version of this change says SEO is dead. The useful version is more precise. Search fundamentals still matter because an answer system needs accessible, reliable material before it can retrieve or cite anything.

Google says the same SEO practices still apply to AI Overviews and AI Mode. It also says there are no extra requirements or special optimizations for those features.^[2] OpenAI says OAI-SearchBot is the crawler used to surface sites in ChatGPT search, and sites that opt out will not appear in search answers, although they may still appear as navigational links.^[3]

Keep the technical work: crawlability, indexability, canonicals, internal links, clear text, useful pages, and accurate schema. Add a second job. Your public material must make the company easy to understand, compare, verify, and recommend.

Use the Two-Reader Rewrite

Every important marketing asset now has two readers. One is a machine trying to retrieve and summarize evidence. The other is a person trying to decide whether your company deserves attention.

A page fails the model if the important answer is hidden in a video, image, private PDF, or vague slogan. It fails the buyer if it is technically tidy but says nothing a competitor could not claim.

Run the test on your homepage, product pages, comparisons, founder bio, proof pages, and five highest-value articles:

1. Can a model state what you sell, who it is for, when it is useful, and what makes it different?
2. Can it find evidence for those statements on pages you control and pages you do not?
3. Would a qualified buyer believe the evidence?
4. After understanding the facts, would that buyer know why to choose you?

If the answer breaks at step one, fix your startup positioning. If it breaks at step two, build public evidence. If it breaks at step three, improve proof. If it breaks at step four, sharpen the founder's judgment and the company's point of view.

Write a buyer decision brief before you create an asset

A content brief usually starts with a topic. A buyer decision brief starts with the choice the reader is trying to make. That change keeps the work tied to commercial reality when a model can produce endless topic variations.

Write six lines before you approve an article, video, comparison, or proof page:

1. Triggering situation. Name the event that makes the problem urgent now.
2. Current alternative. State what the buyer would do if your company did not exist.
3. Decision question. Write the exact question the buyer needs answered before moving forward.
4. Required evidence. List the facts, examples, tradeoffs, or demonstrations needed to support the answer.
5. Disqualifier. Say who should choose another path and why.
6. Next action. Give the smallest useful step after the reader understands the answer.

The disqualifier is especially useful. It stops the piece from reading like a sales page disguised as education. It also gives a model a cleaner basis for explaining fit. A serious buyer learns when your approach works and where its limits begin.

Use the brief across formats without turning each format into a duplicate. The article can carry the full decision logic. The video can show the founder's reasoning. The sales follow-up can point to the one section that answers the prospect's objection. Each asset has a job.

Make five strategic shifts

1. Move from ranking alone to recommendation readiness

A ranking tells you where a page appeared. Recommendation readiness asks whether your company belongs in the answer when a buyer describes a problem, asks for alternatives, or compares vendors.

Start with the questions that affect a real decision. Ask which approach fits a particular use case. Ask who should not use your product. Ask how you compare with the alternative your buyer already knows. Ask what implementation requires. Record whether the model names you, how it describes you, and which sources it cites.

Then improve the evidence behind the weak answer. A vague company description needs clearer language. A missing comparison needs a fair comparison page. A distorted product claim needs one canonical fact page that every public profile can point to. The narrower ChatGPT visibility guide covers that implementation work.

2. Replace the linear funnel with an evidence library

Forrester says linear funnels no longer reflect how B2B buyers discover, evaluate, purchase, and adopt solutions. Its current buyer research describes a more self-directed process in which AI speeds up research and comparison before direct engagement.^[5]

This does not mean buyers behave randomly. It means they return to different questions as risk changes. Google's original messy-middle research describes people looping between exploration and evaluation until they are ready to purchase.^[6]

Build for the loop. Give buyers enough useful material to explore the problem, narrow their options, check your reasoning, inspect tradeoffs, and confirm the decision. A founder-led evidence library might include:

• A category page that explains the problem and names the alternatives.
• A point-of-view article that says where common advice fails.
• A comparison page that states who each option suits.
• A proof page with methodology, limits, and verifiable outcomes.
• An implementation guide that shows what happens after the buyer says yes.
• Video and podcast material that lets a buyer hear how the founder thinks.

This is why content can produce customers without going viral. The library does not need everyone. It needs to answer the next question for the right buyer.

3. Change the team's unit of work from output to outcome

AI can make a draft, summarize a call, cut a transcript, resize creative, and produce more variations. More output is not a strategy. Somebody still has to decide which buyer problem matters, which claim is true, what the company should say, and whether the work changed pipeline.

Assign one owner to each business outcome. That owner can use writers, designers, agents, and software, but they remain accountable for a result such as better category understanding, stronger shortlist presence, more qualified conversations, or fewer repeated sales objections.

A small team can make this practical with an AI-assisted content system. Feed it real source material, such as customer calls, founder voice notes, product documentation, and approved proof. Use AI for research support and production. Keep strategy, factual review, and final judgment with a responsible person.

4. Replace feature piles with a defensible point of view

Features still matter. They become weak marketing when every competitor can list a similar feature or generate the same polished explanation.

A useful point of view answers four questions:

1. What is the buyer doing now?
2. Why does that approach fail in a specific situation?
3. What do you believe should replace it?
4. What evidence makes that belief credible?

The answer should appear in your positioning, your product explanation, your comparisons, and your founder content. It should also connect to a real founder story. The story explains how the belief was earned. The positioning explains why that belief matters to the buyer.

Do not manufacture a contrarian take for attention. A position is useful when it changes a buyer's decision and you can defend it with work you have done, data you own, or sources you can show.

5. Build a media capability, not a campaign habit

Campaigns have endpoints. Buyer questions do not. A founder-led company needs a repeatable way to capture what the market is asking, turn it into useful material, publish it in the right format, and learn from the response.

The raw material is already inside the business. Sales calls contain objections. Customer calls contain the words buyers use. Product reviews contain implementation lessons. Founder voice notes contain the judgment a generic article cannot reproduce.

Build one capture loop:

1. Record the recurring question.
2. Choose the one buyer decision the answer should improve.
3. Create one complete source asset in the founder's voice.
4. Turn it into the formats that help discovery and evaluation.
5. Update the asset when calls reveal a better answer.

The sales-call content workflow shows how to capture that language without inventing customer quotes. Over time, the library becomes more useful because it reflects the questions buyers actually ask.

Leon's source video ends with a useful tension. As production becomes easier, he expects marketing to become more human, not less.^[1] The company can automate the work around the founder's judgment. It should not automate the judgment itself.

Give the new system clear owners

AI blurs the old boundaries between writer, designer, analyst, and channel manager. That does not remove accountability. It makes ownership more important. A lean founder-led team needs four jobs covered, even when one person covers several of them.

This arrangement prevents a common failure. If everyone owns production, nobody owns the truth or the result. The source owner protects specificity. The evidence editor protects credibility. The distribution operator makes the material available. The outcome owner decides whether to continue, update, or stop.

Do not change the parts that still work

An AI-era plan should not discard proven marketing disciplines just because the interface changed.

• Keep positioning. A model cannot explain a company whose own team uses three different category descriptions.
• Keep brand building. Buyers still need memory, familiarity, and a reason to care before they compare features.
• Keep sales conversations. They reveal objections and decision criteria that analytics cannot explain.
• Keep technical SEO. Retrieval still depends on accessible pages, stable URLs, and accurate structure.
• Keep human review. Faster production increases the cost of publishing an unsupported claim at scale.

The strategic change is the connection between those parts. Positioning has to survive machine summarization. Brand material has to help a buyer evaluate. Sales language has to feed the content library. Measurement has to include discovery that happens before a trackable visit.

A 30-day plan for an AI-driven buyer journey

Measure visibility by discovery lane

Do not roll every signal into one vague AI visibility score. Search, answer engines, video, communities, referrals, and direct visits work differently. Track them separately, then connect them to qualified pipeline.

Google now provides a generative AI performance report for AI Overviews and AI Mode in Search Console.^[4] Use that report for Google visibility. Use referral data and a fixed prompt audit for other engines. Treat prompted audits as snapshots, because outputs can vary.

What should stay human?

The founder does not need to type every sentence or edit every clip. The founder must still supply the stance, the lived example, the judgment, and the willingness to stand behind the claim.

That division of work matters more as output gets cheaper. AI can help your team publish. It cannot decide what your company has earned the right to say.

Start with one page and the Two-Reader Rewrite. Make it retrievable. Make it understandable. Support it with evidence. Give the buyer a reason to prefer you. Measure whether the right people move closer to a conversation.