Every CMO knows the feeling. A new technology emerges that promises (or threatens) to reshape your marketing strategy. But the path forward isn’t clear. The metrics aren’t standardised. The benefits are still largely theoretical.

AI search is the latest disruption demanding attention, nagging you to do something.

Perhaps the safe move is to wait. Marketers have been burned before. Remember when Comscore predicted that voice search would account for 50% of all online searches by 2020? All the ensuing investment in voice search optimisation never delivered the hoped-for return when the prediction failed to materialise.

Virtual reality? NFTs? We’ve seen a few technologies fail to make as big a splash on marketing as the initial hype suggested.

So, let others test the waters and take the risks, then implement a strategy once the methodologies and likely returns are proven.

But with AI search, this wait-and-see strategy risks compounding competitive disadvantage over time rather than avoiding wasted effort.

The AI adoption curve

Devised by Everett Rogers in 1962, the Diffusion of Innovations curve is a handy framework for understanding how new technologies spread through society.

The first 2.5% of the market are innovators. They’re followed by early adopters (13.5%). Once adoption approaches roughly 15–20%, technologies typically begin crossing into the early majority — the phase where behaviours shift at scale.

That transition is where competitive advantage is redistributed.

AI search appears to be evolving slightly differently to Roger’s model. Recent research from Datos and SparkToro shows that Instead of replacing existing search behaviour outright, it is increasingly integrating into it – becoming a decision layer within discovery rather than a wholesale substitute.

That transition is still where competitive advantage is redistributed – not because usage suddenly explodes, but because default information sources become entrenched.

So where does AI sit today?

According to OpenAI’s most recent published usage data, ChatGPT reached approximately 700 million weekly active users in mid-2025. Independent estimates since then suggest that number has continued to grow.

Against a global internet population of more than five billion users, that places ChatGPT alone well into double-digit penetration – and that’s before accounting for other AI systems such as Google’s AI-powered search experiences, Gemini, Claude, and Perplexity.

Not all of those users are replacing traditional search. In fact, the Datos data shows that traditional search remains stable and dominant while AI usage grows alongside it. And not all AI interactions are search driven. But at this scale, AI-assisted information discovery is no longer experimental. It is embedded in everyday workflows across consumer and professional contexts.

But historically, once technologies reach this level of ubiquity, behavioural acceleration tends to follow – and more importantly, ranking logic begins to solidify before usage looks “mainstream” in analytics.

For CMOs, that distinction matters.

Because by the time adoption is unquestionably mainstream, the strategic advantage will already belong to brands that invested during the transition phase – not after it.

The analysis paralysis problem

In large organisations, CMOs are expected to be the experts in the room when it comes to effective marketing strategy, working with extremely precise KPIs. But that becomes a problem when proven methodologies for AI optimisation don’t yet exist; never mind precise KPIs.

This creates a dangerous paradox. The larger the organisation, the higher the stakes; creating a greater demand for data-driven certainty.

Some reasonably stable AI search metrics are beginning to emerge, such as AI-driven referral traffic, frequency of AI crawler activity, and brand mentions within AI-generated responses.

More advanced teams are beginning to track “share-of-answer” visibility across priority prompts, brand inclusion rates versus competitors, and patterns of AI-assisted conversion journeys.

However, these KPIs alone won’t reveal the full story. AI systems evolve rapidly, and measurement frameworks will continue to mature.

Even when companies do commit to action, another trap awaits: analysis paralysis. It’s easy to get stuck researching and debating the “right” approach instead of actually implementing anything.

The solution? Start small, test incrementally, and adapt quickly. There is no data to light the way ahead except for what you can generate yourself by simply getting on with it.

The businesses that will dominate tomorrow’s search landscape aren’t waiting for perfect KPIs or standardised methodologies. They’re experimenting now, learning from failures, gathering valuable data, and building the expertise that will matter when AI search reaches full maturity.

What does investing in AI search look like?

The good news is that optimising for AI search doesn’t require massive budgets or entirely new skillsets. You don’t need to invest in new page layouts or templates. You don’t need to send your dev team off on expensive courses.

The investment is in relevant, high-quality content that provides genuine value to the right audience. If that advice sounds anti-climactic, almost mundane, that might be because the best SEOs and content marketers have been shouting this message for at least a decade, long before the arrival of AI search.

Many AI systems now retrieve and synthesise live web content when forming answers, which makes clarity, authority and structured expertise increasingly important.

The shift isn’t just where people search – it’s how they ask. Queries are becoming longer and more explicit, meaning authoritative explanation increasingly beats keyword coverage.

Those brands already investing in original, well-researched, in-depth content are well placed to capitalise on AI search without having to adapt or optimise much at all.

The businesses likely to struggle are those relying on outdated SEO tactics, churning out mountains of listicles and other thin content while aggressively acquiring backlinks. Those tactics will become increasingly ineffective as AI systems become more sophisticated in sniffing out the best sources to cite in generated answers.

If your content strategy is more about quantity than quality, more about keywords than thought leadership, then now is the time to change course.

The defensive dimension

There is also a defensive risk that many brands are underestimating.

If you are not actively shaping the content AI systems rely on, those systems may default to competitor positioning, outdated third-party sources, or incomplete descriptions when representing your brand.

AI systems are probabilistic and imperfect. They synthesise from available sources. If your authoritative voice is absent or unclear, the narrative may be shaped elsewhere.

And because AI answers increasingly satisfy informational intent directly, your brand may never enter the click journey at all.

In that sense, investing in AI visibility is not only an offensive growth strategy – it is increasingly a brand governance and reputation management strategy.

The time to act is now

We’re at a pivotal moment. AI search is moving rapidly from experimentation to embedded behaviour – not by replacing search overnight, but by influencing decisions earlier in the discovery process.

This phase, likely the next six to 12 months, represents one of the most significant opportunities to build advantage before AI visibility becomes table stakes.

Don’t wait for someone else to solve all the problems and hand you a bunch of best practices. By the time something has become a best practice there’s very little competitive advantage left to be gained from it. Your strategy will be lagging months or even years behind the competition.

The competitive edge, on the other hand, comes from a willingness to get out in front of the pack, to pioneer the practices that might eventually crystalise into tomorrow’s best practices for others to follow.

In AI search, advantage doesn’t arrive when adoption spikes. It locks in while behaviour is still stabilising.

Pioneers shape the landscape while followers inherit it. It’s time to choose.

Not sure where to start? Get in touch.