The tyre industry's next belief gap is being written by machines
Increasingly, the first place someone encounters a fact about an industry is not a press release or a news article, but an answer from an AI assistant.
Ask one where tyres come from, why prices have risen, or whether Europe still makes anything of substance, and the reply now shapes belief before any communications team gets a word in.
Tyres Europe’s new report with Oxford Economics, puts the real figures on record: 1.5 trillion euros in GDP, 30.5 million jobs across the sectors that depend on tyres, a rising 40 per cent import share and total dependence on imported natural rubber.
These are the numbers that should define how policymakers and the public understand the industry. But numbers placed in a PDF do not automatically become the numbers an AI system repeats when someone asks, six months from now, why tyre prices have moved or whether European manufacturing still counts for anything.
This is a faster-moving problem than most industries have registered. Research on AI search estimates that traditional search will lose half its share to AI-generated answers by 2028, with generative AI adoption outpacing even the early growth of the internet. Up to 90 per cent of what an AI system cites when it forms an opinion about an industry comes from earned coverage rather than anything a company publishes itself. An organisation with no earned presence in the conversation is not merely under-represented. It is, for the purposes of an AI answer, largely invisible.
AI assistants build their answers from whatever is most repeated, most structured and most easily lifted across the web, not necessarily from the most current or most authoritative source. An industry that has spent decades absent from public conversation has, for the same reason, been thin in the material these systems draw on. The likely result is that the belief gap Tyres Europe is working to close in front of journalists and ministers is being quietly reopened elsewhere, in the flatter, more commodity-driven account an AI assistant gives to millions of casual questions a day.
This isn’t just confined to industrial sectors. Close to 60 per cent of consumers use AI for financial tasks at least occasionally, often asking a chatbot whether advice is worth paying for before they ever speak to an adviser. Industrial sectors will face the same pattern sooner than most expect: a farmer, a haulier or a journalist asking an AI assistant a quick question about tyre supply, and receiving an answer shaped by whichever version of the story was easiest for the system to find, not the most accurate one. Once that flattened version is repeated widely enough, correcting it becomes far harder than getting it right the first time.
Closing the gap
Closing that gap needs the same discipline an industry would apply to any other audience. It starts with finding out what is currently being said, correcting what is wrong, and putting accurate, well-structured and citable material into circulation faster than the flattened version can take hold.
For tyres, that means auditing what today's AI systems say about European manufacturing capacity, natural rubber dependency and the EU Deforestation Regulation, due to apply to large and medium operators from 30 December 2026, then treating the results with the same seriousness as a poll of public opinion.
It also means accepting that no single op-ed, report or press cycle settles the question. AI systems are retrained and refreshed continually, which means the industry's presence in the material they draw on has to be maintained continually too, not revisited only when a new report is published. That is a materially different discipline to the press office's traditional rhythm of a launch, a flurry of coverage and a quiet period until the next one. It looks more like ongoing content production paired with regular measurement of what the machines are actually saying, so that drift away from the facts is caught in weeks rather than years.
Practically, that means three things running in parallel rather than in sequence.
First, a regular audit of what AI assistants currently say when asked about European tyre manufacturing, natural rubber sourcing and the sector's exposure to import competition, repeated often enough to catch drift rather than taken once and filed away. Second, a steady supply of accurate, well-sourced material published under credible bylines, since an AI system has nothing authoritative to draw on if the only content available is a single annual report. And third, a willingness to correct the record in public when an AI-generated answer is wrong, in the same way a communications team would correct a misleading article, rather than treating machine-generated misinformation as somehow outside its remit.
An industrial strategy argument built on strong economic evidence deserves to be heard in full, not summarised down to whichever fragment an algorithm decides is representative.
The industries that get this right will not be the ones with the best report. They will be the ones whose facts are still the facts by the time an AI assistant repeats them back.
