Not long ago I watched a customer of an industrial technology business try to find out whether a piece of equipment he had bought four years earlier was still supported. He knew the product name. He knew roughly which part of the business made it. He spent about ten minutes on the manufacturer’s website, gave up, and phoned someone he had known for years. That contact did not know the answer either, but he knew who to ask.
Everyone in that chain was competent. The company still got the outcome it wanted. And I would guess nothing about that interaction appeared in any reporting anywhere.
For most of the time I have worked with clients in this sector, that was survivable. Industrial buying ran on relationships, technical trust and long memories. The website was a brochure. If the brochure was confusing, someone picked up the phone, and the phone worked, so no harm done.
That is the part that has changed.
Gartner surveyed 646 B2B buyers in late 2025 and found that 67% now prefer a rep-free experience, and 45% used AI during a recent purchase. In a follow-up published in May 2026, 70% said they would prefer a completely digital, self-service buying process. Forrester’s 2026 State of Business Buying puts it more bluntly: generative AI searches are now the starting point for B2B buyers.
Sit with that for a moment, because it is a bigger change than it first appears.
It does not just mean buyers are researching before they contact you – they have been doing that for many years. It means the first thing that reads your content is not a person. It is a model, summarising your category for someone who may never see the page it came from.
Google searches in the US that ended without a click reached 68% in early 2026, up from around 60% in 2024, according to SparkToro’s analysis of Similarweb clickstream data. That figure covers all searches rather than industrial B2B specifically (I’ve not found a credible equivalent measured on industrial buyers alone) which is worth saying plainly. But the direction is not really in dispute.
So, the question stops being whether a buyer can find your page and starts being whether a machine can understand your business well enough to describe it accurately. If your software documentation sits three levels under a hardware taxonomy, or if two divisions describe the same capability differently, or if half your technical content is in undated PDFs, then the model has exactly the problem your buyer has. And it resolves it the same way a buyer does, by going with whoever explained it most clearly. Sometimes that’s a competitor. Sometimes it’s a distributor. Sometimes it is a forum post from 2019.
Findability used to cost you a slow sale. It now costs you a place on the list.
There is a useful counterweight in the same Gartner data. 69% of buyers say they go back to a sales rep to validate what AI has told them, and slightly more buyers think they will be misled by generative AI (51%) than by a salesperson (49%). The relationship still matters. It just no longer gets first look, and it increasingly gets handed a set of assumptions formed somewhere you have no visibility of.
Here is where I think most of these programmes falter, including some I have been part of.
The instinct is to treat this purely as a digital estate issue. Better search, better navigation, a redesign, a content audit. All of which help, and none of which hold.
McKinsey’s 2026 B2B Pulse, based on responses from nearly 4,000 decision-makers across 13 countries, lists the top reasons buyers switch supplier. They are
Read those again. Not one of them is a website feature. All three are what an internal operating model feels like from the outside.
Buyers experience your org chart whether you intend them to or not, that’s the reality. Divisions that grew through acquisition and kept their own sites. Hardware and software owned by different P&Ls with different content standards. Service data in one system, order data in another, neither of them talking to the thing the customer logs into. The buyer does not see any of that structure. They just see a company that can’t give them a straight answer.
I used to put the fix solely in the digital team’s plan. That wasn’t strictly accurate, and it is the single most useful thing I have learned doing this work. Digital teams can rebuild the front end, but they cannot make two divisions agree on what a product is called.
You can’t tell where one part of the business ends and another begins. Brand architecture in industrial technology usually maps to acquisition history rather than to how customers buy. That is understandable, and can be invisible internally. Externally it means a buyer cannot work out who they are dealing with, which subsidiary holds the contract, or which of three similar-looking sites is the current one. What they do next is predictable. They default to a person, or a partner – or nothing at all.
Nothing is visible after the money has been spent. Order status, lead times, repair progress, warranty position. Sana Commerce’s 2025 survey of 750 B2B buyers found 73% prefer to buy online but 85% experience online-ordering frustrations linked to outdated systems and inaccurate data, and 75% would consider switching supplier as a result. Worth noting that Sana sells B2B commerce software, so read the framing accordingly, but the pattern matches what I see. Meanwhile McKinsey found 73% of buyers are now comfortable placing orders above $50,000 online, up from 59% in 2022. People will spend serious money through a screen. But they will not spend it blind.
The comparison buyers make is not with your competitors. It is with everything else in their life. An engineer who can watch a parcel move across a map cannot see where a repair worth thousands has got to – which is a reasonable expectation.
Content is organised around your products rather than the buyer’s problem. Buyers do not usually search by product family. They search by the thing that has gone wrong, or the outcome they need, or the standard they have to meet. Most industrial content estates are built the other way round, then patched with a search box that cannot bridge the gap. This is the one that has started to matter most now, because the same structure that defeats a buyer defeats a model.
I will note one thing without dwelling on it. Some of this stays unfixed not because it is technically hard but because fixing it forces a conversation about who owns the customer relationship, and that conversation involves partners and channel economics. That is a real constraint, and pretending otherwise is not useful.
Forrester puts the typical B2B purchase at 13 internal stakeholders and nine external influencers. Every one of those people can now look you up independently, in seconds, and form a view before anyone from your business is in the room. The account relationship you have spent a decade building covers perhaps two of them.
The wider picture is not encouraging. The 2026 Industry 4.0 Barometer, a study of 1,206 respondents from industrial companies run by MHP with LMU Munich, scores UK industrial digitalisation maturity at 62%, down two points year on year, with the DACH region flat at 57% against China at 72%. That index measures operational digitalisation rather than customer experience, so it is not a direct read on any of the above. But the constraints it identifies – legacy systems and fragmented data – are the same ones that stop the customer-facing work.
None of this shows up as a crisis. It shows up as longer sales cycles, more quotes that go quiet, more revenue that depends on individuals rather than on the business, and a slow drift towards whoever got the basics right.
If any of this sounds familiar, there are three checks that cost almost nothing.
If you sell into industrial or manufacturing buyers, I would be interested to know which of those three tests you would least like to run.
We are working through the data behind this properly — benchmarking how industrial technology companies actually perform against the three tests above. The report lands soon. Watch this space.
