3 Top Things to Consider for AEO

AI search is no longer a trend to watch. ChatGPT handles over 10 million queries per day. Perplexity has attracted tens of millions of users globally. Google AI Overviews now appear on a significant share of search results pages across major markets. The question most marketing teams are sitting with is not "will this matter?" but "where do we actually start?"

Answer engine optimization (AEO) is the practice of structuring your content, authority signals, and technical foundation so that AI models surface your brand when users ask relevant questions. It sits at the intersection of traditional SEO, content strategy, and the emerging field of generative engine optimization. And while the space is moving fast, there are three core considerations that consistently determine whether a brand gets cited or gets overlooked.

1. Content Structure and Extractability

AI models don't read your website the way a human does. They extract. They pull structured, clearly formatted information that maps directly to a user's question. If your content is buried in long paragraphs, ambiguous headings, or marketing copy that talks around the answer rather than giving it, models will pass over you in favour of a source that just answers the question cleanly.

What AI models actually reward

The fundamental unit of AEO strategy is the question-answer pair. Every page on your site should identify the specific questions your audience is asking (at each stage of the funnel) and answer them directly, in the first two to three sentences of each section. Not "explore how our platform helps businesses grow" but "Lua scans your website across 13 optimisation layers and generates a 12-month execution plan."

Structure matters as much as content. Models use heading hierarchies, definition-style sentences, and schema markup to understand what your content is about and whether it's a trustworthy, extractable source. Concretely, this means:

• Use clear, question-based H2 and H3 headings where appropriate

• Lead each section with a direct answer before expanding on context

• Implement FAQ schema, HowTo schema, and structured data that signals intent to both Google and AI crawlers

• Avoid burying key claims in the middle of dense paragraphs

The counterargument worth acknowledging

Some content strategists push back here. They argue that over-structuring content makes it feel robotic and undermines brand voice. That's a fair concern, and it doesn't mean every page should read like a Wikipedia stub. The goal is clarity within your voice, not a content factory producing templated answers. The brands that get this balance right tend to win on both traditional search and AI citation.

2. Authority Signals That AI Models Actually Recognise

One of the most common misconceptions we see is that AEO is purely a content problem. Get the content right and you'll rank. That's only half the picture. Answer engine ranking factors include a layer of authority signals that AI models use to decide whose content to trust, and these go beyond keyword density or word count.

Research from McKinsey on the economic potential of generative AI points to how rapidly these models are being integrated into commercial and informational search. As that integration deepens, the models are getting better at weighting source credibility, not just content relevance.

What drives citation authority

The signals that matter most in our experience (and in the programmes we run across 40+ brands) break down into three categories:

Entity optimisation is underused

Most marketing teams haven't thought about their brand as a named entity that AI models need to recognise. But this is a significant AEO best practice that's still being missed. If models can't cleanly identify your brand, your category, your key people, and your product definitions across multiple sources, they struggle to confidently cite you. Structured data and consistent brand mentions across the web solve this more effectively than almost any content tactic.

3. Multi-Model Visibility Tracking and Execution Discipline

This is where most brands fall down, not because they don't understand the strategy, but because they don't have a system for executing it consistently or measuring whether it's working. AEO isn't a project you complete. It's a programme you run.

The measurement problem

Traditional SEO has Google Search Console. AEO doesn't have an equivalent native analytics tool yet. The growth of AI-driven search advertising and visibility (tracked across global digital advertising trends at Statista's search advertising outlook) is outpacing the measurement infrastructure most teams are working with. That means you need to build your own tracking discipline.

What this looks like in practice:

• Run structured query tests across ChatGPT, Perplexity, Claude, and Google AI Overviews on a consistent schedule

• Track citation rates for your target queries and compare against direct competitors

• Monitor which content assets are being surfaced and which aren't

• Attribute changes in citation rate to specific optimisation actions so you understand what's working

Why execution discipline is the real differentiator

The HBR analysis of generative AI's disruption to creative work raises a point that applies directly here: the organisations that build systems for working with AI early will have compounding advantages over those that treat it as a one-off project. That's exactly what we see with AEO. Brands that build a structured, week-by-week execution calendar, and actually follow it, consistently outperform brands that do a content audit, make a few changes, and then wait.

The forward-looking reality is this: as AI models are updated and refined, the weighting of different signals will shift. Brands with active tracking programmes will detect those shifts early and adapt. Brands without them won't know something changed until their competitor is consistently being cited in their place. We expect AI search to account for a growing share of discovery-stage traffic across most industries by 2026, making the tracking infrastructure you build now a genuine competitive asset.

What good execution looks like

Good execution on an AEO strategy doesn't require a huge team. It requires a system. Three to five hours per week, applied consistently to a structured plan, is enough to move visibility scores meaningfully. The problem most teams face is the plan itself: knowing which tasks to prioritise, in what order, for which AI platforms. That's the gap we built Lua to fill.

Putting It Together

The three considerations above aren't independent checkboxes. They compound. Clean, extractable content builds citation authority faster when your entity signals are already established. And tracking lets you identify which content and authority investments are actually paying off, so you're not guessing.

The brands seeing real results from answer engine optimization right now aren't necessarily the biggest or the best-resourced. They're the ones that started a structured programme early and executed it consistently. That window is still open for most industries, but it's closing faster than most marketing teams realise.

If you're evaluating where to start, the practical first step is a comprehensive assessment of where your site stands across the key AEO ranking factors: content structure, authority signals, schema coverage, entity recognition, and current citation rate. From there, a prioritised execution plan is what separates brands that make progress from those that stay stuck at diagnosis.

Frequently Asked Questions

How is answer engine optimization different from traditional SEO?

Traditional SEO focuses on ranking pages in search engine results based on signals like backlinks, keyword relevance, and page experience. Answer engine optimization is specifically concerned with whether AI models (ChatGPT, Perplexity, Google AI Overviews, Claude) cite your brand when answering user questions. While there's meaningful overlap, AEO places heavier emphasis on content extractability, entity recognition, and citation authority signals rather than click-through rankings. A page can rank well in Google and still be ignored by AI models if the content isn't structured for extraction.

How long does it take to see results from an AEO programme?

Results vary by industry, starting baseline, and execution consistency. In the programmes we run at Lua, brands with well-structured websites and some existing authority can start seeing measurable improvements in AI citation rates within 30 to 60 days. Brands starting from a weaker baseline typically see meaningful progress within 90 days of consistent implementation. The key variable is execution discipline: doing the right tasks in the right order, every week, rather than sporadic bursts of activity.

Do I need to optimize differently for each AI platform?

Yes, to a degree. ChatGPT, Perplexity, Claude, and Google AI Overviews each have different crawling behaviours, source weighting preferences, and response formats. Perplexity, for instance, places strong weight on real-time web sources and structured citations. Google AI Overviews draw heavily from Google's existing quality and authority signals. ChatGPT's training data and browsing plugin behaviour create a different set of optimisation priorities. A well-designed AEO programme accounts for these differences and includes platform-specific instructions rather than a one-size-fits-all approach.