As consumers lean into AI search, the industry has focused on the technical “how” – tracking everything from Agentic Commerce Protocols (ACP) to ChatGPT’s latest shopping research tools. In doing so, it often misses the larger shift: conversational search, which is changing how visibility is earned.

There’s a common argument that big brands will always win in AI. I disagree. When you move beyond the “best running shoes” shorthand and look at the deep context users now provide, the playing field levels. AI is trying to match user needs to specific solutions, and it’s up to your brand to provide the details.

This article explains how conversational search changes product discovery and what ecommerce teams need to update on product detail pages (PDPs) to remain visible in AI-driven shopping experiences.

How conversational search builds on semantic search

While semantic search is critical for understanding the meaning and context of words, conversational search is the ability to maintain a back-and-forth dialogue with a user over time.

Semantic search is the foundation for conversational visibility. Think of it like a restaurant: If semantic search is the chef who knows exactly what you mean by “something light,” conversational search is the waiter who remembers that you’re ordering for dinner.

Feature	Semantic search	Conversational search
Goal	To understand intent and context	To handle a flow of questions
How it thinks	It knows “car” and “automobile” are the same thing	It knows that when you say “how much is it?”, “it” refers to the car you just mentioned
The interaction	Searching with a phrase instead of keywords	Having a chat where the computer remembers what you were asking about before
Example	Asking “What is a healthy meal?” and getting results for “nutritious recipes.”	Asking “What is a healthy meal?” followed by “give me a recipe for that.”

AI blends them together. It uses semantic understanding to decode your complex intent and conversational logic to keep the thread of the story moving. For brands, this means your content has to be clear enough for the “chef” to interpret and consistent enough for the “waiter” to follow.

What conversational search and AI discovery mean for ecommerce

I recently shared how my mom was using ChatGPT to remodel her kitchen. She didn’t start by searching for “the best cabinets.” Instead, she leveraged ChatGPT as her pseudo-designer and contractor, using AI to solve specific problems.

Product discovery happened naturally through constraint-based queries:

• “Find cabinets that fit these dimensions and match this specific wood type.”
• “Are these cabinets easy for a DIY installation?”

Her conversations were piling up, allowing her to reach multiple solutions at once. Her discovery journey was layered. When ChatGPT recommended products to complete her tasks, she simply followed up with, “Where can I buy those?”

Brands and marketers need to stop optimizing for keywords and start optimizing for tasks. Identify the specific conversations where your product becomes the solution. If your data can’t answer the “Will this fit?” or “Is this easy?” questions, you won’t be part of the final recommendation.

“Recommend products” is the top task users trust AI to handle, highlighting a clear opportunity for brands, according to Tinuiti’s 2026 AI Trends Study. (Disclosure: I am the Sr. Director of AI SEO Innovation at Tinuiti.) 

For your brand to be the one recommended, your PDPs must provide the “ground truth” details these assistants need to make a confident selection.  

Dig deeper: How to make ecommerce product pages work in an AI-first world

What to do before you start changing every PDP

Step away from the keyword research tools and stop asking for “prompt volumes.” In an AI-driven world, intent is more important than volume. Before changing a single page, you need to understand the high-intent journeys your personas are actually taking.

To identify your high-intent semantic opportunities:

• Audit your personas: Who is your buyer, and what are their non-negotiable questions? If you haven’t mapped these lately, start there.
• Bridge the team gap: Talk to your product and sales teams. They know the specific attributes and “deal-breaker” details that actually drive conversions.
• Listen to the market: Use sentiment analysis and social listening to find hidden use cases or brand problems. How are people actually using, or struggling with, your product in ways your brand team hasn’t considered?
• Map constraints, not keywords: Identify the specific constraints (size, compatibility, budget) that AI agents use to filter recommendations.

How to build PDPs for AI search with decision support

Your PDP should operate like a product knowledge document and be optimized for natural language. This helps an AI system decide whether to recommend the product for a specific situation.

Name your ideal buyer and edge cases

Content should support better decision-making. Audit your PDPs to determine whether they provide enough detail on who the product is best for – and not for. Does the page explicitly name your ideal buyer, their skill level, lifestyle constraints, and deal-breakers?

AI shopping queries often include exclusions, and clearly outlining the important parts of your user search journey will help you understand where your products fit best.

Cover compatibility and product specifications

Compatibility feels synonymous with electronics (e.g., “Will my headphones connect to this computer?”). But think beyond one-to-one compatibility and expand into lifestyle compatibility:

• Is this laptop bag waterproof enough for a 20-minute bike ride in the rain, and does it have a clip for a taillight?
• Can I fit a Kindle and a book in this purse?
• Will this detergent work with my HE washer?
• Will this carry-on suitcase fit in the overhead compartment on every airline?
• Is this “family-sized” cutting board actually small enough to fit inside a standard dishwasher?

People are searching for how products fit into their lifestyle needs. Highlight and emphasize the features that make your products compatible with their lifestyle.

Dig deeper: How to make products machine-readable for multimodal AI search

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Provide vertical-specific product guidance

Breaking down your customer search journey and listening to your customers’ concerns, either through AI sentiment analysis, social listening, or product reviews, will help you understand what you need to be specific about.

• Apparel brands should add sizing and fit guidance. Maybe you’re comparing your size 10 jeans to competitors’ sizing, or considering sizing changes based on the cut or style of your other jeans.
• Beauty or skincare brands need ingredient combination details. Is this product compatible with other common formulas? Can I layer it over a vitamin C serum?
• Toy brands could include important details for parents. Does your product need to be assembled, and how long will it take? Can they assemble it the night before Christmas?

If your biggest customer complaint is understanding when and how to use your products, you’re likely not making it easy enough for them to buy. Better defining your product attributes helps users and LLMs alike better understand your products.

Write for constraint matching instead of browsing

AI shopping discovery is driven by constraints instead of keywords. Shoppers aren’t asking for “the best laptop bag.” They’re asking for a bag that fits under an airplane seat, survives a rainy commute, and still looks professional in a meeting.

PDPs should be written to reflect that reality. Audit your product pages to see whether they answer common “Can I …?” and “Will this work if …?” questions in plain language. These details often live in reviews, FAQs, or support tickets, but rarely surface in core product copy where AI systems are most likely to pull from.

Here’s what transforming your content can look like:

Traditional PDP copy

• Laptop backpack
  • Water-resistant polyester exterior.
  • Fits laptops up to 15″.
  • Multiple interior compartments.
  • Lightweight design.
  • USB charging port.

PDP copy written for constraints

• Laptop backpack
  • Best for: Daily commuters, frequent flyers, and students who need to carry tech in unpredictable weather.
  • Not ideal for: Extended outdoor exposure or laptops larger than 15.6″.
  • Weather readiness: Water-resistant coating protects electronics during short walks or bike commutes in light rain, but is not designed for heavy downpours.
  • Travel compatibility: Fits comfortably under most airplane seats and in overhead bins on domestic flights.
  • Capacity and layout: Holds a 15-15.6″ laptop, charger, and tablet, with room for a book or light jacket – but not bulky items.
  • Lifestyle considerations: Integrated USB port supports charging on the go (power bank not included).

LLMs evaluate how well a product satisfies specific constraints in conversational queries or based on predetermined user preference information.

PDPs that clearly articulate those constraints are more likely to be selected, summarized, and recommended. This type of copy should also help your on-site customers better understand your products.

Dig deeper: Why ecommerce SEO audits fail – and what actually works in 30 days

Technical foundations still matter for ecommerce

Just because search platforms change doesn’t mean we should abandon everything we’ve learned in traditional optimization.

Technical SEO fundamentals still heavily apply in AI search:

• Can crawlers access and index your site?
• Are your product listing pages (PLPs) and PDPs clearly linked and structured?
• Do pages load quickly enough for crawlers and users?
• Is your most critical content accessible?

In conversational shopping, structured data is playing a different role than it did in traditional SEO strategies. In conversational shopping, it’s about verification. 

AI systems use your schema to validate facts before they risk reusing them in an answer. If the AI can’t verify your price, availability, or shipping details through a merchant feed or structured data, it won’t risk recommending you.

Variant clarity is just as important. When differences like size, color, or configuration aren’t clearly defined, AI systems may treat variants as separate products or merge them incorrectly. The result is inaccurate pricing, incompatible recommendations, or missed visibility.

Most importantly, structured data must match what’s visibly true on the page. When schema contradicts on-page content, AI systems avoid recommending uncertain information.

Dig deeper: How SEO leaders can explain agentic AI to ecommerce executives

Owning the digital shelf in 2026

Success on the digital shelf has moved beyond high-volume keywords. In this new era, your visibility depends on how well you satisfy the complex constraints users can provide in a single search. AI models are scanning your pages to see if you meet specific, nuanced requirements, like “gluten-free,” “easy to install,” or “fits a 30-inch window.”

The shift to conversational discovery means your product data must be ready to sustain a dialogue. The goal is simple: provide the density of information necessary for an AI to confidently transact on a user’s behalf. Those who build for these multi-layered journeys will own the future of discovery.

The expansion will cover customers in newly served states such as Idaho and Massachusetts.

Agentic AI is increasingly appearing in leadership conversations, often accompanied by big claims and unclear expectations. For SEO leaders working with ecommerce brands, this creates a familiar challenge.

Executives hear about autonomous agents, automated purchasing, and AI-led decisions, and they want to know what this really means for growth, risk, and competitiveness.

What they don’t need is more hype. They need clear explanations, grounded thinking, and practical guidance. 

This is where SEO leaders can add real value, not by predicting the future, but by helping leadership understand what is changing, what isn’t, and how to respond without overreacting. Here’s how.

Start by explaining what ‘agentic’ actually means

A useful first step is to remove the mystery from the term itself. Agentic systems don’t replace customers, they act on behalf of customers. The intent, preferences, and constraints still come from a person.

What changes is who does the work.

Discovery, comparison, filtering, and sometimes execution are handled by software that can move faster and process more information than a human can.

When speaking to executive teams, a simple framing works best:

• “We’re not losing customers, we’re adding a new decision-maker into the journey. That decision-maker is software acting as a proxy for the customer.” 

Once this is clear, the conversation becomes calmer and more practical, and the focus moves away from fear and toward preparation.

Keep expectations realistic and avoid the hype

Another important role for SEO leaders is to slow the conversation down. Agentic behavior will not arrive everywhere at the same time. Its impact will be uneven and gradual.

Some categories will see change earlier because their products are standardized and data is already well structured. Others will move more slowly because trust, complexity, or regulation makes automation harder.

This matters because leadership teams often fall into one of two traps:

1. Panic, where plans are rewritten too quickly, budgets move too fast, and teams chase futures that may still be some distance away. 
2. Dismissal, where nothing changes until performance clearly drops, and by then the response is rushed.

SEO leaders can offer a steadier view. Agentic AI accelerates trends that already exist. Personalized discovery, fewer visible clicks, and more pressure on data quality are not new problems. 

Agents simply make them more obvious. Seen this way, agentic AI becomes a reason to improve foundations, not a reason to chase novelty.

Dig deeper: Are we ready for the agentic web?

Change the conversation from rankings to eligibility

One of the most helpful shifts in executive conversations is moving away from rankings as the main outcome of SEO. In an agent-led journey, the key question isn’t “do we rank well?” but “are we eligible to be chosen at all?”

Eligibility depends on clarity, consistency, and trust. An agent needs to understand what you sell, who it is for, how much it costs, whether it is available, and how risky it is to choose you on behalf of a user. This is a strong way to connect SEO to commercial reality.

Questions worth raising include whether product information is consistent across systems, whether pricing and availability are reliable, and whether policies reduce uncertainty or create it. Framed this way, SEO becomes less about chasing traffic and more about making the business easy to select.

Explain why SEO no longer sits only in marketing

Many executives still see SEO as a marketing channel, but agentic behavior challenges that view.

Selection by an agent depends on factors that sit well beyond marketing. Data quality, technical reliability, stock accuracy, delivery performance, and payment confidence all play a role.

SEO leaders should be clear about this. This isn’t about writing more content. It’s about making sure the business is understandable, reliable, and usable by machines.

Positioned correctly, SEO becomes a connecting function that helps leadership see where gaps in systems or data could prevent the brand from being selected. This often resonates because it links SEO to risk and operational health, not just growth.

Dig deeper: How to integrate SEO into your broader marketing strategy

Be clear that discovery will change first

For most ecommerce brands, the earliest impact of agentic systems will be at the top of the funnel. Discovery becomes more conversational and more personal.

Users describe situations, needs, and constraints instead of typing short search phrases, and the agent then turns that context into actions.

This reduces the value of simply owning category head terms. If an agent knows a user’s budget, preferences, delivery expectations, and past behavior, it doesn’t behave like a first-time visitor. It behaves like a well-informed repeat customer.

This creates a reporting challenge. Some SEO work will no longer look like direct demand creation, even though it still influences outcomes. Leadership teams need to be prepared for this shift.

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Reframe consideration as filtering, not persuasion

The middle of the funnel also changes shape. Today, consideration often involves reading reviews, comparing options, and seeking reassurance.

In an agent-led journey, consideration becomes a filtering process, where the agent removes options it believes the user would reject and keeps those that fit.

This has clear implications. Generic content becomes less effective as a traffic driver because agents can generate summaries and comparisons instantly. Trust signals become structural, meaning claims need to be backed by consistent and verifiable information.

In many cases, a brand may be chosen without the user being consciously aware of it. That can be positive for conversion, but risky for long-term brand strength if recognition isn’t built elsewhere.

Dig deeper: How to align your SEO strategy with the stages of buyer intent

Set honest expectations about measurement

Executives care about measurement, and agentic AI makes this harder. As more discovery and consideration happen inside AI systems, fewer interactions leave clean attribution trails. Some impact will show up as direct traffic, and some will not be visible at all.

SEO leaders should address this early. This isn’t a failure of optimization. It reflects the limits of today’s analytics in a more mediated world.

The conversation should move toward directional signals and blended performance views, rather than precise channel attribution that no longer reflects how decisions are made.

Promote a proactive, low-risk response

The most important part of the leadership discussion is what to do next. The good news is that most sensible responses to agentic AI are low risk.

Improving product data quality, reducing inconsistencies across platforms, strengthening reliability signals, and fixing technical weaknesses all help today, regardless of how quickly agents mature.

Investing in brand demand outside search also matters. If agents handle more of the comparison work, brands that users already trust by name are more likely to be selected.

This reassures leaders that action doesn’t require dramatic change, only disciplined improvement.

Agentic AI changes the focus, not the fundamentals

For SEO leaders, agentic AI changes the focus of the role. The work shifts from optimizing pages to protecting eligibility, from chasing visibility to reducing ambiguity, and from reporting clicks to explaining influence.

This requires confidence, clear communication, and a willingness to challenge hype. Agentic AI makes SEO more strategic, not any less important.

Agentic AI should not be treated as an immediate threat or a guaranteed advantage. It’s a shift in how decisions are made.

For ecommerce brands, the winners will be those that stay calm, communicate clearly, and adapt their SEO thinking from driving clicks to earning selection.

That is the conversation SEO leaders should be having now.

Dig deeper: The future of search visibility: What 6 SEO leaders predict for 2026