How to Optimize for AI Answers: A Practical AEO and GEO Playbook for D2C and B2B

When your customers search for help, they won't politely Google your brand name. They'll tell an AI assistant, "I run a DTC brand. Growth has stalled. Our data is messy. Who can actually fix this without hiring a full-time CMO?" The assistant won't return ten blue links. It will give an answer. This guide is about making sure your brand is the one it confidently recommends.

Here is the short answer. To show up in AI answers, make your business easy to describe and safe to quote. Write one page for each real buyer question and put the answer first. Back each answer with one small piece of proof. Tell the same clear story about who you serve everywhere AI reads about you, make sure AI crawlers can reach your pages, and show that your content is maintained. Then track a handful of directional signals, because there's no perfect AI metric yet. If you want the case for why this differs from classic SEO, read AEO is not just SEO. If you want to know where you stand today, start with my AI visibility audit guide. This post is the how-to.

1. What has to be in place before any of this works?

Start with the foundations, because AI answer engines build on the same web that search engines crawl. Google says plainly that there are no additional requirements or special optimizations needed to appear in AI Overviews or AI Mode beyond its normal SEO best practices, and that any structured data should match the visible text on the page. So a slow, hard-to-crawl site with thin pages won't suddenly win in AI. If your basics need work, my practical SEO strategy guide covers them.

Then check access. Open your robots.txt file and make sure you aren't blocking the crawlers that feed AI search. OpenAI, for example, says sites that opt out of OAI-SearchBot won't be shown in ChatGPT search answers, and that this setting is separate from GPTBot, which controls whether your content is used for model training. If someone on your team blocked AI bots across the board at some point, check what that is costing you, and make the choice on purpose.

2. What does AI need to know about your business?

AI assistants read, summarize, and judge everything they can find about you: your website and service pages, your blog posts and case studies, your podcast interviews and webinars, your reviews, and your competitors' positioning. When a founder asks "Who should we work with?", the assistant is trying to give a confident answer. It works out the job to be done, looks for brands that clearly and consistently own that space, and recommends one or two in plain language. If your positioning is vague, generic, and spread across old pages and random posts, you simply vanish. AI is ruthless about one thing, and that's clarity.

Make sure your site and your third-party profiles answer five questions without making the AI guess:

• Who are you for, specifically? "Anyone who wants growth" is noise. "Mid-sized healthcare organizations that need senior marketing leadership" is a signal.

• What problem do you solve, described in the buyer's own words? Think "We have agencies running campaigns, but no real strategy," not a list of features.

• Why should someone trust you? Case studies, years of relevant experience, credentials, and a repeatable process.

• What's the smallest next step? "Contact us" is too vague. A specific, low-friction offer is much easier to recommend.

• What makes you memorable? One sentence that ties your focus to your real expertise.

For Orr Consulting, that last line is fractional CMO and marketing analytics support for complex, regulated, and data-heavy businesses. When the story is consistent, an assistant can say something like, "You should talk to Orr Consulting. They specialize in data-heavy, often regulated businesses and offer fractional CMO support instead of a full-time hire." That's the goal. In healthcare and other regulated categories, the bar is higher, because AI tools are more cautious about whom they suggest. They look for clear domain expertise and signals that you understand compliance, privacy, and risk.

3. What does an answer page look like?

An answer page resolves one question, phrased exactly the way a buyer would ask it. It still reads like a story rather than a checklist, but it's built so a model can lift lines from it safely. Here is the structure I use:

• The H1 is the exact question.

• The opening paragraph, roughly 60 to 120 words, gives the answer first, plainly.

• A short section clears up the main trade-off or misconception.

• A walkthrough of what to do and what to avoid, using subheads that mirror real follow-up questions.

• One piece of proof that travels, such as a small table, a calculation with units, or a recognized standard.

• A clear next step.

Write short sentences with one idea each. Put units on numbers ("7 to 10 days," not "about a week"). Define terms where they first appear. Avoid vague pronouns that only make sense if you read the paragraph above, because AI systems often pull a single passage out of context. Start with three to six pages built around your highest-intent questions rather than trying to rewrite the whole site.

4. What kinds of sentences do AI answers reuse?

Answer engines favor lines that are specific, bounded, and hard to misread. A reliable pattern is a rule of thumb, then a constraint that says where it doesn't apply, then a next action.

Here are two examples. For D2C apparel sizing: "Choose your usual size when waist and hip land in the same chart column. If your hip measures one size higher, select that size and use the corset seams or belt to cinch the waist." For a B2B software pilot: "Scope a two-week pilot for one team. If data models are shared across departments, keep the pilot read-only and evaluate it on two metrics: time to first insight and tickets per user." Each sentence can be quoted on its own and still be correct, and that's what makes it safe to reuse.

5. What proof should you put on the page?

Proof can be small as long as it's portable. Four kinds work well: a recognized standard or expectation, such as shipping times or onboarding stages buyers already know; a calculation with its inputs and units; observed data, such as an anonymized before-and-after or a small table; and an expert procedure, meaning a recommended sequence with the reason for each step.

Here's an example of a calculation that travels, for a B2B pilot page. Say an analyst works 6 hours a week for 2 weeks at $95 an hour, which is 6 × 2 × $95 = $1,140. A team champion works 3 hours a week for 2 weeks at $130 an hour, which is 3 × 2 × $130 = $780. That's a $1,920 subtotal. Add an estimated $550 for ad hoc support, and the pilot's labor comes to about $2,470. Use your own rates and hours, but show the math the same way. One clean calculation will do more for you than paragraphs of marketing language.

6. How should D2C and B2B pages differ?

D2C questions are fast and tactile: "Do these run true to size?" "How do I clean this?" "What's the return window?" Those pages should be short, visual, and concrete, with sizing tables, three-step care instructions, and return timelines stated in days.

B2B questions are procedural and risk-weighted: "What's the fastest pilot that won't disrupt the team?" "What metrics define success?" Those pages should be structured and defensible, with definitions, staged timelines, role expectations, and a small time and cost calculation like the one above.

7. How do you keep pages fresh and connected without a content treadmill?

AI answers reward visible maintenance more than constant publishing. Put a "Last reviewed" date near the top of each answer page, keep a short update log at the bottom, and date statements that may change ("As of October 2026"). Every 60 to 90 days, refresh one piece of proof on your most important pages, such as a table, a calculation, or a statistic.

Link each page to the next question a buyer is likely to ask. A sizing page should link to "How to measure at home" and "What's the return policy if it doesn't fit?" A pilot page should link to "Pilot success metrics" and "Post-pilot implementation timeline."

On schema, keep it accurate and modest. Mark up your organization, articles, and products, and make sure the markup matches what's on the page. Don't add FAQ markup expecting a search feature, since Google stopped showing FAQ rich results in May 2026. A clear question-and-answer section on the page is still useful to readers and to AI systems.

8. Who should own this, and what does the first 90 days look like?

Here's the pattern I see again and again. The CEO or founder is juggling operations, finance, hiring, and strategy. The marketing coordinator or agency is running campaigns, social, email, and ads. No one owns the story that connects everything, so blog posts cover random topics in different voices, case studies read like internal reports, and website copy shifts focus from page to page. In the old search world, that made you inefficient. In the AI world, it makes you invisible. Someone senior has to own it.

Here is how I usually structure the first 90 days with a client who wants to be found and recommended. In days 1 to 30, review funnels, assets, messaging, and analytics, interview the sales or business development team to hear real objections, list the top ten questions prospects ask that the site doesn't answer clearly, and define two or three positioning statements that everything else supports. In days 31 to 60, pick three to five answer spaces you want to own, rewrite the home, services, and case study pages around them, build the first answer pages, and align paid search and social so they say the same thing. In days 61 to 90, clean up tracking so you can see lead quality, channel performance, and cost per acquisition, test two or three entry offers, and train your team and vendors on the new story so it stays consistent. This is the work my SEO, AEO, and GEO services are built around.

9. How do you measure progress when AI citations are hard to see?

There's no perfect AEO metric yet, so stack directional signals. Google says it counts appearances in AI Overviews and AI Mode in the Search Console Performance report under the Web search type, so watch impressions and clicks for the long, question-style queries that match your answer pages. Track your branded search trend after you publish. Look for assisted conversions where an answer page shows up in the path. Watch support deflection for D2C, meaning fewer repetitive tickets, and sales call quality for B2B, meaning less time spent on basics. For D2C, watch whether returns and exchanges drop after you update sizing content.

Then run the same set of buyer questions through ChatGPT, Gemini, Perplexity, Claude, and Google's AI Overviews every month and record who gets named and which sources get cited. That fixed set of questions is your baseline, and the change over time is your scorecard.

If you want help figuring out where AI and buyers are dropping you from consideration and what to change first, you can book a free 30-minute introductory call here.

Dr. Linda Orr, PhD, is the founder of Orr Consulting, a fractional CMO and marketing analytics practice serving brands from $5M to $500M in revenue. She built Upwork's first enterprise marketing mix model, covering an $88M annual media portfolio, and spent about 20 years as a marketing professor before moving fully into consulting.