A common mistake in AI search discussions is to treat organic visibility measurement and paid media performance as separate conversations.

They are increasingly connected, like other channels are too. 

Share of Model can show how your brand is being surfaced across AI models. That is valuable on its own. But the commercial upside grows when those insights are connected directly to media execution, especially in Google’s PMax where granular query and audience intent can be difficult to optimise for in detail.

In simple terms, Share of Model helps brands move from AI visibility diagnostics into practical campaign optimisation. This matters because many brands are still running PMax with broad campaign structures and generic inputs that are not fully aligned to the demand language and recommendation logic emerging in AI search. The result is often decent delivery but missed efficiency and growth potential.

The opportunity is that Share of Model tangibly helps to simplify the action.

When a category analysis is set up in Share of Model, the platform gives a broad view of your brand and competitor performance across AI discovery, including metrics such as share of voice, mention rate and average position, along with the strengths and weaknesses indicated by that data.

Share of Model then connects this AI visibility picture to your PMax activity in the background and generates practical optimisation recommendations. Specifically it can recommend search themes to add, keep, or remove from existing campaigns.

That means teams do not need to manually interpret prompt clusters and translate them into campaign actions from scratch. The platform is doing the analysis and surfacing usable inputs in a much more straightforward way. This is what makes the workflow incredibly powerful. It reduces the gap between insight and execution.

Instead of a long strategy exercise, teams can move quickly: connect campaigns, review the optimisation inputs, apply a focused test and measure the result.

The best way to use this in practice is simple.

Start by connecting the relevant PMax campaigns to your existing analyses and reviewing the recommended optimisation inputs from Share of Model. Then run a controlled test on one or two campaigns first to measure uplift before making wider changes.

This gives your team a clean way to validate impact with limited risk. If performance improves, the next step is to expand the approach across more PMax campaigns.

Done well, this can improve both reach and performance by enriching campaigns with search themes that are better aligned to how users are actually discovering and evaluating products in AI environments. It works well now but just imagine the optimisation power when ads go full tilt in Gemini surfaces too. 

By linking AI search diagnostics directly to campaign optimisation, Share of Model makes it easier to operationalise what the data is showing.

This is just one way that organic AI visibility work starts to become a powerful growth system rather than just perceived as a vanity side project.

Find a Share of Model partner like Lmo7 to power up your PMax ads today.

Author - Stephen Honight, founder of The Lmo7 Agency.