When Manufacturer Invisibility Becomes a Retailer Risk

Google is not dead. Sales data has not suddenly become worthless. And AI is not turning everything upside down overnight. But perhaps better data is still helping us answer the wrong question too often.

Manufacturers want to know which products customers will need tomorrow. Retailers want to know which categories will grow and which suppliers can serve them. Sales data, market research, search data and AI remain essential. But much of this tells us what people have already searched for and what they have already bought. What if the customer does not yet know the product they need?

In DIY and Home Improvement, consumers are often trying to solve a problem: reduce noise in a room, connect two different floors or renovate a kitchen. Their journey may start on Google, move through YouTube, TikTok, marketplaces and reviews, and end with an AI system.

Product search becomes project-led search. That creates a commercial question: Will the manufacturers and retailers that actually have the right solution be found?

According to McKinsey research across Germany, France and the UK, 38% of consumers already use AI for product or service research or to support purchase decisions.[1] Generative AI is used for shopping by 28% of Gen Z versus 16% of Baby Boomers. AI search overviews are regularly used by 60% of Gen Z and 29% of Baby Boomers.[2] In the US, 66.6% of consumers had already used social search.[3]

Consumer intent is spreading across search, social media, video, reviews, marketplaces, communities and AI. Search is not disappearing. It is changing its form.

Reverse Retail Engineering (RRE) connects these signals into Consumer Missions: What is the customer trying to achieve? Where does friction occur? Which solutions are missing? From this, required products, services and manufacturer capabilities can be identified.

Part of this thinking is already visible at CAINZ in Japan, where elements of the offer are organised around customer problems and use cases. It is not RRE as a complete model, but it confirms the underlying approach.

In an RRE project examining skirting boards for a German DIY retailer, we first analysed consumer problems and only then asked which specialist manufacturers were digitally recognisable as relevant solution providers.

The result: of five leading specialist European skirting-board manufacturers, only one was recognisable as a relevant solution provider. And that manufacturer was not an existing supplier to the retailer.

The others may possess exactly the capabilities required. But those capabilities were not digitally recognisable within the Consumer Mission examined.

This does not only affect consumers. In a Gartner survey published in March 2026, 45 per cent of 646 B2B buyers had used AI during one of their recent purchasing or procurement processes.[5] McKinsey estimates that traditional new-supplier discovery can take around three months and more than 40 hours of a sourcing specialist's time. AI can analyse substantially larger supplier populations.[4]

A B2B manufacturer can therefore become invisible twice: to the consumer searching for a project solution and to the professional buyer searching for the manufacturer capabilities required to serve that demand. If the manufacturer's capabilities remain invisible to consumers while buyers discover alternative suppliers, digital invisibility can ultimately become the loss of a supplier position.

This shifts part of the responsibility to the manufacturer. It does not have to bypass retail and sell D2C. But it does need to think D2C.

Manufacturers need to understand which problems people are trying to solve and how they search for answers. Products, knowledge and application expertise must be digitally understandable enough for AI to connect them with a specific problem.

Had an existing supplier in the skirting-board example analysed these signals itself, it might have identified the friction before its retail customer and proposed a solution. Instead of waiting for the next product enquiry, the manufacturer can show the retailer where new demand is emerging – and strengthen its own position.

For retailers, supplier discovery changes too. The question is no longer only, “Who manufactures this product category?” but increasingly: “Which capabilities do we need to solve this Consumer Mission – and who has them?”

AI can connect project-related questions: What do I need? Which solution fits? Which product should I consider? And where can I buy it?

Product and retailer recommendations can therefore become part of the same answer. If a manufacturer's capabilities are not recognised and an alternative solution leads to another place to buy, the manufacturer may lose the order – but the retailer can lose the customer.

Manufacturers need to remain visible to defend their position with retail. Retailers need manufacturers whose solutions can be recognised where the Consumer Journey begins. AI discoverability is therefore moving from a communication issue to a question of revenue, supplier position and customer retention.

Historical sales data remains essential. Consumer Signals can add another perspective by showing which problems are emerging and where new commercial opportunities may exist.

This leaves three questions: Which Consumer Missions are emerging in my category? Is my company digitally recognisable as a solution through the capabilities it actually possesses? And would AI lead consumers and professional buyers to me today – or to somebody else? If these questions cannot be answered with confidence and evidence, part of the company's market risk may still be unknown.

That is where Reverse Retail Engineering begins.