Microsoft - From discovery to influence: AEO A guide to and GEO

AI discovery didn’t arrive as a feature update. It arrived as a reallocation of power. Quietly, then all at once, the work that humans used to do manually—researching, comparing, synthesizing, deciding—collapsed into a layer of systems that now sit between intent and action. What most people still call “search” is no longer a destination. It’s an invisible reasoning process that runs before the user ever touches a screen. And the uncomfortable truth for brands is that this process does not care what you say about yourself. It only cares what the rest of the world appears to agree on, consistently, precisely, and in a form it can use.

This is why so much of the current conversation around SEO, AEO, and GEO feels slightly off. Too tactical. Too obsessed with mechanics. Too focused on outputs instead of incentives. Because what’s actually changed is not how content is formatted, but how influence is earned. We’ve moved from a discovery economy, where being findable was enough, to an influence economy, where being selected is everything. And selection is governed by a very different logic.

The Microsoft paper frames this shift in retail terms, but the implication is universal. AI assistants, AI browsers, and AI agents are not separate channels. They are overlapping capabilities drawing from the same underlying inputs: crawled web data, structured feeds, real-time site information, and prior knowledge. The system reasons across all of it at once. It decomposes a question into intent, context, constraints, and tradeoffs, then assembles an answer that minimizes risk and maximizes usefulness. That answer is the moment of truth. If you are not present there, you are not present at all. 

This is the point where traditional marketing instincts fail. Brands are used to thinking in terms of persuasion, messaging, and funnel stages. AI systems are not persuaded. They are calibrated. They don’t respond to adjectives. They respond to attributes. They don’t care how confident your copy sounds. They care whether your claims survive comparison against other sources, other products, other experiences. Influence in this environment is not asserted. It is inferred.

The easiest way to understand what’s happening is to stop thinking about brands and start thinking about skills. In a skills-based labor market, generalist signals collapse. Everyone claims them. They stop differentiating. What matters instead are specific, contextual capabilities that can be demonstrated and validated. AI discovery applies the same rule at scale. Broad brand narratives are oversupplied. Specific, machine-readable, evidence-backed signals are scarce. Scarcity wins.

This is why the shift from SEO to AEO and GEO is not a rebrand. SEO was built for a world where clicks were the currency. You optimized to be retrieved. AEO optimizes to be understood. GEO optimizes to be trusted and reused. Together, they operate in a world where the “click” is often an afterthought. The decision has already been made upstream, inside the conversation, inside the summary, inside the recommendation that never required a visit.

In that world, your catalog is not just a catalog. Your site architecture is not just navigation. Your product descriptions are not just marketing copy. Every one of these becomes a data surface that AI systems reason over. Every inconsistency becomes friction. Every vague claim becomes noise. Every missing attribute becomes an opportunity for a competitor to be selected instead.

This is why Microsoft’s framing around feeds, crawled data, and live site data matters more than it initially appears. Crawled data establishes baseline understanding: what category you’re in, how you’re talked about, what reputation precedes you. Feeds and APIs supply precision: prices, availability, specifications, differentiators, freshness. Live site data confirms reality: does the thing actually exist, does it work, can it be bought, does the experience match the promise. AI systems don’t privilege one of these in isolation. They reconcile all three. If they don’t agree, trust decays.

Trust, in this context, is not an emotional concept. It’s a statistical one. The more often the same facts appear, in the same form, across independent sources, the more confidently the system can act. That’s why AI agents can recommend a product, explain why it’s a good fit, check inventory, apply a promotion, and complete a purchase without ever asking the brand for permission. The system isn’t loyal. It’s probabilistic.

This is also why competition is shifting from discovery to influence. Discovery is cheap now. AI can find almost anything. Influence is expensive because it requires coherence across systems you don’t control. It requires discipline. It requires treating data quality, structure, and integrity as strategic assets, not implementation details.

Most organizations underestimate how radical this is. They think they need more content. What they actually need is less ambiguity. They need to decide what they are optimizing to be the best answer for, and then remove everything that muddies that signal. AI systems do not reward breadth. They reward clarity.

Consider how an assistant reasons through a recommendation. It doesn’t ask, “Which brand has the best campaign?” It asks, “Which option satisfies this intent, under these constraints, with the least downside?” That requires understanding use cases, tradeoffs, context, and outcomes. Brands that surface this information explicitly—through structured attributes, clear descriptions, comparison data, reviews, and verified signals—make the system’s job easier. Brands that hide it behind slogans force the system to look elsewhere.

This is where AEO and GEO quietly become operational disciplines, not marketing tactics. They touch product, engineering, merchandising, analytics, support, and compliance. They force alignment between what is promised and what is delivered. They punish exaggeration. They reward boring accuracy. In an AI-mediated environment, being boring and correct beats being exciting and vague every time.

There’s also a deeper implication most people miss. As AI agents become capable of acting, not just advising, the cost of being misunderstood increases dramatically. If an agent fills a cart, applies a discount, calculates shipping, and completes a purchase, any discrepancy between feed data and live reality becomes a failure point. Visibility without operational integrity doesn’t just waste spend. It breaks transactions. Influence now extends all the way into execution.

This is why the real competitive advantage in AI discovery is not growth hacks or clever prompts. It’s infrastructural. It’s the ability to present a unified, trustworthy version of reality to machines that are constantly cross-checking you against the rest of the web. That requires treating your digital presence as a single system, not a collection of channels.

Brands that do this well will feel like they’re slowing down at first. They’ll publish less. They’ll focus more. They’ll invest in data hygiene, schema, feeds, and consistency. Meanwhile, competitors will flood the zone with AI-generated content and wonder why nothing sticks. Over time, the divergence becomes obvious. One group is repeatedly selected. The other is perpetually summarized out of existence.

This is the part that makes people uncomfortable: AI discovery rewards restraint. It rewards saying no. It rewards deciding what not to be. Generalists fade because generalism creates uncertainty. Specialists win because they reduce it.

The irony is that this is not new. It’s how expertise has always worked. AI just enforces it at scale.

So when people ask how to “win” in AEO or GEO, the answer is disappointingly unsexy. Make your data accurate. Make your content specific. Make your claims verifiable. Make your structure legible. Make your presence consistent across places you don’t own. And above all, decide what problem you are actually solving better than anyone else, then teach the machine that answer so clearly it has no reason to doubt it.

This is not about chasing algorithms. It’s about aligning with incentives. AI systems are incentivized to be useful and not wrong. If you help them do that, they will keep coming back to you. If you don’t, they won’t even remember you were an option.

That’s the shift from discovery to influence. And it’s already happened.

We keep talking about AI search as if it’s a future event, something we still have time to prepare for, but the truth is the behavior change already happened and the infrastructure followed. Half of the decision-making work consumers used to do manually is now being offloaded to systems that don’t browse the way humans do. They reason. They reconcile. They select. And they do it before the brand ever gets a chance to perform.

What that means in practice is that visibility is no longer something you win at the moment of interaction. You either exist in the system’s mental model, or you don’t. And that mental model is built from data, structure, repetition, and trust signals accumulated over time.

This is why the old obsession with rankings feels increasingly hollow. Rankings were a proxy for attention. AI doesn’t need proxies. It goes straight to probability. Which option is most likely to satisfy this request without creating a problem? That’s the question being answered thousands of times a day on behalf of users who never see the deliberation.

In that environment, your job as a brand is not to be loud. It’s to be legible.

Legible means your products are described in a way that mirrors how people actually ask questions. It means your attributes are explicit instead of implied. It means your reviews are accessible, structured, and credible. It means your prices, availability, and promotions are synchronized everywhere they appear. It means your site tells the same truth to machines that it tells to humans.

This is what Answer Engine Optimization really is. It’s not about gaming responses. It’s about removing friction from understanding. Generative Engine Optimization builds on that by layering authority and credibility, not through self-assertion, but through corroboration. When third parties say the same things about you that you say about yourself, the system listens.

The brands that figure this out early stop worrying about traffic volatility. They stop chasing every platform update. They focus instead on becoming the default reference for a specific set of intents. Over time, they notice something interesting: they don’t have to fight as hard for attention anymore. The system brings them into the conversation automatically.

That’s the real prize. Not clicks. Not impressions. Influence.

Influence in an AI-mediated world is quiet. It doesn’t announce itself. It just shows up, again and again, as the answer that makes the most sense. And once you’re there, it’s very hard to dislodge you.

Most brands will miss this window because they’re still optimizing for the wrong outcome. They’re polishing the résumé when the market has already moved to skills. They’re perfecting the homepage when the decision is made in the summary. They’re arguing about channels when the real game is coherence.

The ones that adapt will look obvious in hindsight. They’ll feel inevitable. People will say they were always strong. They weren’t. They were just aligned earlier.

That’s the opportunity. And it’s still open.

Jason Wade is a systems architect focused on how AI models discover, interpret, and recommend businesses. He is the founder of NinjaAI.com, an AI Visibility consultancy specializing in Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), and entity authority engineering.

With over 20 years in digital marketing and online systems, Jason works at the intersection of search, structured data, and AI reasoning. His approach is not about rankings or traffic tricks, but about training AI systems to correctly classify entities, trust their information, and cite them as authoritative sources.

He advises service businesses, law firms, healthcare providers, and local operators on building durable visibility in a world where answers are generated, not searched. Jason is also the author of AI Visibility: How to Win in the Age of Search, Chat, and Smart Customers and hosts the AI Visibility Podcast.