For manufacturers with complex product ranges, helping customers find the right product has never been straightforward.
The information they need might be spread across product pages, technical datasheets, internal systems, and distributor websites, with sales or technical teams stepping in when the available information isn’t enough.
AI search now adds another dimension to that problem. Customers can describe their requirements in detail and ask AI to identify suitable options, rather than starting with a product category and working through the available choices themselves.
Google’s 2026 AI Search Playbook, produced with Marketing Week, says the average query in AI Mode is three times longer than a traditional search query. Those longer searches give AI more context about what someone is looking for, including their priorities and constraints.
For manufacturers, the implications go beyond search visibility. As AI becomes more involved in interpreting requirements and identifying relevant products, the quality of the product information available to AI becomes part of the buying experience.
Think about how someone might traditionally search for an industrial pump, component, machine, or piece of equipment. They might enter a product name or category, visit several websites, download technical information, and gradually narrow down their options.
AI allows them to begin with the requirement itself. They can describe an application, operating conditions, required specifications, and other constraints in a single query, then ask for products that could meet them.
Google describes searches of this kind as ‘containing several layers of intent at once’. Someone can be researching, evaluating, and preparing to buy within the same interaction, with AI interpreting the underlying requirement, rather than relying solely on individual keywords.
This creates an opportunity for manufacturers whose products meet specific or technical requirements, while placing greater demands on the information available about those products. The more detailed the customer’s question becomes, the more information AI may need to establish which products are relevant.
Your product information is rarely in one place
Manufacturers usually have plenty of information about their products. Making that information consistent and usable across every channel is hard.
Your website may contain the headline specifications, with more detail in a downloadable datasheet. Product data might sit in an ERP or PIM, while distributors hold their own copies of descriptions and specifications. Application knowledge may live with sales or technical teams, and availability information might only become clear once someone makes an enquiry.
Customers may have to move between web pages, downloadable documents, distributor websites, and your own teams to piece together the information they need.
If someone asks AI to identify a product that meets several requirements, it needs enough reliable information to connect those requirements with products that could meet them. Missing attributes, contradictory specifications, or outdated third-party information can make that connection harder, and your product may not appear in the results.
Google highlights product feeds, imagery, structured data, reviews, and consistent brand information among the signals that can help AI understand when your brand or product is relevant. It also recommends checking that product specifications, pricing, and brand details are consistent across both owned and third-party platforms.
For manufacturers with large or complicated product ranges, the quality of your underlying product information has a direct bearing on how effectively AI can interpret what you sell.
Would AI reach the same conclusion as your sales team?
An experienced salesperson or technical specialist often understands much more about a product than your website. Give them a detailed customer requirement and they can interpret it, understand which specifications matter, identify products suitable for particular applications, and explain where apparently similar models differ.
Could your website perform the same?
Google argues that visibility in AI search increasingly depends on giving its systems enough evidence to recognise when a brand is relevant, credible, and useful. As searches become more detailed, manufacturers need to consider whether the information surrounding their products provides enough context to make those connections.
Improving product information involves more than re-writing product descriptions. Your website can only work with the information available to it, and how that information is stored and managed affects what customers can find and do.
Where is the definitive product record held? How are products categorised? Which attributes are stored consistently? How are updates passed to the website? How are technical documents managed? How does information reach distributors and other third parties?
AI relies on the same information to understand what you sell and when a product might be relevant to a customer's requirements.
What sits behind the website is just as important as what customers see. Navigation, design, and content all depend on the taxonomy, product data, information architecture, and integrations supporting them.
Google’s playbook places considerable emphasis on these foundations, including accurate and consistent product information, structured data and product feeds, and accessible information that AI search can use when assessing relevance.
For a manufacturer with thousands of SKUs, multiple variants, and several routes to market, getting those foundations right can be a substantial piece of work.
AI will need more than product specifications
The Google report also looks beyond today’s AI search experience towards agentic AI - systems capable of searching, comparing, and acting on a customer’s behalf.
For commerce, Google describes emerging experiences in which AI could move further through the buying process, supported by real-time inventory, accurate pricing, and connected check-out infrastructure. Structured data covering pricing, availability, and fulfilment, sits alongside content providing expertise, reviews, and credibility. Product feeds, APIs, payments, and fulfilment systems all form part of that picture.
Businesses are encouraged to make product, pricing, availability, and fulfilment information accessible, prepare the necessary feeds and APIs, connect the systems agents need and test the journey to identify where an agent can successfully discover, evaluate and transact.
How quickly AI agents become part of manufacturing buying journeys remains to be seen. But they will depend on the same things customers already need today – accurate product data, availability, pricing, technical content, and the connections between them.
Start with the questions your customers are already asking
What information do they need to choose between different options? Which questions repeatedly end up with sales or technical teams because customers can’t answer them online? Where does that information come from, and how consistently is it maintained across the website, technical documentation, and other channels?
The knowledge held by people inside your business is particularly important. Sales and technical teams may routinely explain applications, compatibility, product differences or limitations that aren’t represented properly online. Capturing more of that knowledge can improve the experience for customers today while providing richer information for AI systems to work with.
The same applies to the structure of your product information. Specifications and attributes need to be accurate, relationships between products need to make sense, and information needs to remain consistent as it moves between internal systems, websites, and third parties.
Customers can ask increasingly detailed questions about what they need, while AI is becoming more capable of interpreting those requirements and evaluating the available options. How useful its answers are will depend partly on the product information it can access. That puts greater importance on making your product knowledge accurate, structured, and accessible wherever customers, or AI, are looking for it.
How well does your product information work today?
If you have a complex product range, it’s worth understanding how easily customers can find, understand, and compare your products, and where they run into problems. That means looking across the whole experience - what customers are trying to achieve, how they search and navigate, the product information available to them, how that information is structured and managed, and the technology and systems supporting it.
At DotCentric, we bring together customer research, UX, content, and technology to understand how people find and evaluate products, and where the digital experience gets in their way. We help identify gaps in product journeys and information, understand what customers actually need from the experience, and work out what needs to change across your website and the systems behind it.
If you want to understand where gaps in your product information or digital experience are making it harder for customers, and AI, to find and choose your products, talk to us.
