Enterprise automotive ยท AI search
Building an AI-search baseline across a global automotive portfolio
A repeatable measurement framework for understanding how multiple brands appear, compete and are represented across generative search.
The challenge was consistency, not another visibility score
A global automotive group needed to understand how its brands and products appeared across AI-powered discovery. The scale made one-off prompt checks almost useless: each market, brand and customer journey could produce a different story.
The useful question was not simply whether a brand appeared. It was how accurately it was represented, when it entered a recommendation set, which competitors appeared beside it and what sources seemed to support the answer.
I designed a framework that could survive contact with a large organisation
I helped turn an emerging discipline into a documented research programme: agreed journeys, a controlled prompt sample, consistent capture conditions and separate measures for presence, recommendation, citation and message accuracy.
The work drew together specialist AI-search tooling, manual investigation and traditional technical and content evidence. That combination mattered because automated scores can reveal a pattern, but rarely explain what a team should do next.
- Comparable prompt and journey sets across brands
- Citation and source-pattern analysis
- Entity, product and competitor representation checks
- Prioritised technical, content and authority recommendations
The output had to work for specialists and decision-makers
Findings were translated into detailed working decks and concise stakeholder narratives. Each recommendation connected an observed problem to the team able to act on it, with the evidence and limitations left visible.
That made the baseline more than a report. It became an operating model the organisation could rerun after changes, compare across brands and refine as AI-search behaviour and measurement tools developed.
What the work changed
The programme gave the account a common language for a fast-moving subject and a defensible starting point for investment. Instead of chasing screenshots, teams could see where visibility problems originated and which changes were worth testing first.
