How To Win In The Age Of Answer Engines

1. Why answer engines are now a demand channel, not a side project

Answer engines are changing two things at once.

1. Where discovery starts
   Buyers increasingly begin with “Ask ChatGPT” instead of “Google it.” In many markets, AI overviews and chat answers already sit above traditional organic results. That means answer engines are owning the first impression.
   
   

2. How buyers behave
   Buyers now:
   
   

   • Use AI to create a shortlist of vendors and approaches

   • Validate that shortlist via human content like YouTube, review sites, and social comments

   • Hit your site much later, with more conviction and far higher intent

The result, traffic from answer engines converts faster and at a much higher rate because buyers did their homework before they ever saw your homepage. That also means:

• Traffic volume may go down.

• Pipeline and revenue from that traffic can grow dramatically.

AEO is about owning that upstream influence, so by the time buyers show up, they already trust you.

2. Step one, build a question graph, not a keyword list

Classic SEO is built on keywords. AEO is built on questions.

Answer engines take a query like “I have no idea which leads to prioritize, what are my options?” and break it into many sub questions. That process is often called “query fan out.” The model then goes hunting for answers to each sub question and synthesizes a single response.

To win, you need content that answers both:

• The main question.

• The related sub questions the model uses behind the scenes.

Here is a practical way to build that foundation.

2.1 Use a 3 x 4 grid for each product

For every product or major feature set, create a grid:

• Across the top, list buyer personas as specifically as possible:
  
  

  • Not “Marketing Manager”

  • Rather “Marketing Manager at a 200 person logistics company”
    
    

• Answer engines personalize heavily. Your strategy must match that level of context.

• Down the side, list stages of the buyer journey:
  
  

  • Awareness

  • Consideration

  • Evaluation

  • Decision

Now, in each cell, write the high intent questions that specific persona asks at that stage about that product. For example:

• Awareness, Marketing Manager, logistics:

  • “How can I generate more sales qualified leads without increasing ad spend?”
    
    

• Consideration:

  • “Best tools to prioritize B2B leads for a mid sized sales team”
    
    

• Evaluation:

  • “Tool A vs Tool B for logistics lead routing”
    
    

• Decision:

  • “Can Tool A integrate lead scoring with Salesforce and our existing routing rules?”

You now have a map of what to create, but you still need real data to fill it.

2.2 Pull questions from three places

Use all three. That overlap is where the gold is.

1. Keyword tools as a proxy
   Use SEO tools and Search Console to find “how to” and “best tool” queries. We do not yet have direct prompt data from answer engines, so search volume and phrasing remain useful signals.
   
   

2. Social and community listening
   Mine questions from:
   
   

   • Reddit and Quora

   • YouTube comments

   • X and LinkedIn comments

   • Niche communities in your industry
     
     

3. People are already asking the questions you care about in almost the exact format they type into ChatGPT.
   
   
   

4. Sales calls and support chats
   This is where the highest intent questions live. Mine:
   
   

   • Call transcripts

   • Chat logs

   • Emails to sales and support
     
     

5. If you do not have transcripts, ask your sales and customer success teams what they get asked daily.
   
   

2.3 Classify questions by intent

To prioritize and brief AI or your team, label each question:

• Awareness: unbranded “how do I” style questions

• Consideration: “best tools for”, “top solutions for”

• Evaluation: direct comparisons between options

• Decision: “Can [product] do X in Y situation?”

Now you are ready to see where you are visible and where you are invisible.

3. Find and close “visibility gaps” in answer engines

The next step is to understand where you are recommended now and where you are missing. Think of this as an audit of your AI presence.

Conceptually, you want three views per priority question:

1. Are we mentioned at all in answers across major engines?

2. Whose content is being cited to generate those answers?

3. Which sites and pages get cited repeatedly in our category?

From there, you get a simple working list:

• Questions where you appear, and want to defend or grow share

• Questions where you should appear, but do not, your visibility gaps

Your content roadmap and outreach plan should focus first on those gaps.

4. How to write AEO ready content

Here is where AEO diverges sharply from old school SEO.

You do not win by publishing “The Ultimate Guide To [Broad Topic]” anymore. AI has commoditized generic educational content. Instead, you win with hyper specific answers to hyper specific questions.

Here is the blueprint I use when we create or update a page for answer engines.

4.1 Put the answer first

The first sentence must directly answer the primary question, in clear language, with a full thought. Answer engines want quick confirmation that this passage solves the query.

Example:

“The most effective way to prioritize sales leads is to use a lead scoring system that ranks contacts based on both their fit and their engagement.”

No buildup. No story first. Answer, then explain.

4.2 Go one click deeper

After that first line, add two or three short paragraphs that:

• Define key terms

• Explain the reasoning or methodology

• Set expectations about tradeoffs

This signals completeness and credibility.

4.3 Include original data and examples

Answer engines prefer net new information they can cite. Feed them:

• Stats pulled from your CRM

• Aggregated insights from your own experiments

• Short case studies

This does not require huge survey budgets. Often the best line is, “In our own data we saw X% improvement when we changed Y.”

4.4 Add a structured FAQ tied to fan out questions

At the bottom of the page, add at least three FAQ items. For each:

• Use a clear H3 or H4 heading with the question

• Follow it with a direct one sentence answer

• Then add a brief explanation paragraph

Many of these FAQs should be the sub questions revealed when you look at query fan out type outputs. You are making it effortless for models to lift those passages.

4.5 Make the page “lazy reader friendly”

Models read in chunks, not in full narrative sweeps. That means:

• Generous use of headings

• Bullet lists instead of walls of text

• Short paragraphs

• Tables and step lists where useful

Every section should make sense even if the paragraph before it is invisible. I often use what I call the Taco Test, if you dropped this section into a new document with no context, would a smart stranger still understand it? If not, rewrite until they would.

4.6 Tie every point back to your product

This is the part almost everyone underdoes.

If you share strong educational content without connecting it to your product, you are training the model while giving it no reason to recommend you.

You want a consistent one two pattern:

• Practical advice that genuinely helps

• A clear tie back to how your product or feature solves that piece

That does not mean writing a sales pitch in every sentence. It means you:

• Name features where appropriate

• Show examples using your product

• Make it obvious which problems your product is built to solve

Aim for a product reference in every paragraph or every other paragraph. Remember, you are teaching the model what you are the “go to” choice for.

5. Authority in the AI era, why mentions beat backlinks

Old world, backlinks were the currency. New world, mentions in trusted, cited sources are what matter most.

Answer engines care primarily about:

1. Which domains they already cite frequently.

2. How those domains describe you and your competitors.

Your off site strategy should focus on three things.

5.1 Influence the pages answer engines already trust

Look at the sites that get cited repeatedly in answers for your category. Then:

• Pitch guest posts that naturally include your product and positioning

• Ask to be added to relevant “best of” or “tool roundup” articles

• Request updates to existing content where your solution is missing
  
  

You care less about whether the mention is a hyperlink and far more about:

• Is the mention positive?

• Is the description aligned with your positioning?

You are training the model on how to talk about you.

5.2 Amplify user generated proof

Review platforms and UGC rich sources are incredibly sticky in AI answers. Think:

• G2

• Capterra

• TrustRadius

• Long form YouTube reviews

Encourage customers to leave detailed, feature specific reviews. Those phrases often appear verbatim in AI descriptions.

5.3 Seed “human first” channels

Partner with:

• YouTubers in your niche

• Newsletter creators

• LinkedIn creators and industry experts

You are doing two things at once. Meeting buyers where they already learn. And putting high context, brand rich mentions in front of the models as they crawl and retrain.

The goal is simple, wherever the model looks, it keeps “bumping into” your brand, described accurately and positively.

6. Measuring AEO, a search scorecard for the AI era

We still care about classic SEO metrics like rankings and organic sessions. But they are no longer enough. For AEO, I track four extra dimensions.

1. AI visibility
   For each priority question and engine, are you recommended at all? This tells you where you exist or are invisible.

2. AI share of voice
   Of all responses that mention a solution, what percentage mention you versus competitors? And how is that trend changing over time?

3. AI citations
   How often is your content cited as the source when models answer questions? When your content is cited, you tend to get more positive and more prominent mentions.

4. AI referral demand
   Are you seeing pipeline from people who first discovered you via answer engines? This can be hard to track in analytics, so I recommend adding a simple “How did you hear about us?” survey and including “Answer engines or AI tools” as an option.
   
   
   

Visibility is the north star, but citations and referral demand tell you if that visibility is actually compounding into revenue.

7. Putting it all together

The shift from search engines to answer engines is not a minor channel tweak. It is a tectonic movement underneath how people discover, evaluate, and choose products.

If you:

• Map your personas and their questions by journey stage

• Create focused content that answers those questions and their sub questions

• Structure pages so models can lift answers with minimal effort

• Tie every insight back to your product

• And deliberately shape how trusted sites and creators talk about you

Then as answer engines take over more of the discovery journey, you will not be scrambling. You will already be the name they recommend.

Start with one product, one persona, and one 3 x 4 grid. Ship five AEO optimized pages. Influence a handful of high value external articles. Add a “How did you hear about us?” survey.

The earlier you build this muscle, the more compounding advantage you get between now and 2028.

— Author: Davood Keshavarz