This article by Kaila Yates, co-host of the FMI podcast and Founder, Two Jacks Comms first appeared on LinkedIn here.

AI is already explaining your business. The question is what it’s saying.

There is a growing disconnect between what companies present and what systems learn.

As more of us think about AI-driven search, the conversation is still largely focused on how we show up. How do we rank, optimise, and make sure we are included in the answer. That’s valid, but it’s not the constraint. What matters more is what gets understood once you are surfaced in that answer.

Because AI is not just pointing people towards your brand in the way traditional search does. It is interpreting, summarising and presenting a view on your behalf. That view is then repeated – consistently, at scale – to anyone asking similar questions, including other systems. This is already visible in how AI tools summarise vendors, compare options and recommend suppliers – often before a buyer has engaged directly. That changes the dynamic.

Your brand is no longer shaped primarily by what you choose to say, but by what can be assembled from the signals around you. Product structure, pricing logic, delivery evidence, sales language, analyst commentary, peer reviews, forums and community discussion all contribute. There is a growing disconnect between what companies present and what systems actually learn.

Most enterprise organisations are not underinvesting. They are investing across brand, sales enablement, product and delivery. The issue is where that investment lands. It doesn’t always resolve into something that can be consistently understood from the outside. What looks coherent internally can still appear fragmented, partial or unclear when those signals are interpreted together.

Take product.

In many technology-led businesses, the narrative leans heavily towards the product itself – the platform, the architecture, the innovation. This is particularly true in product-led or engineering-led environments, or in organisations built around a small number of core products rather than a clearly structured portfolio. Internally, that logic is sound.

From the outside, it can make it harder to see where the value sits, how it compares, and what a buyer should expect to gain. A sales team can bridge that gap in conversation. An AI model will simplify it, often reducing it to a category label or a partial comparison that lacks the depth you would want.

The same applies to evidence.

In enterprise B2B markets, the strongest work is often the least visible. Governments and large corporates rarely endorse in a way that is easy to reuse. What appears publicly can feel selective or incomplete, even when the underlying delivery is substantial. Again, not a lack of substance. But a thinner and more fragmented set of signals than expected. And then there is the broader set of signals that sit beyond formal messaging.

Sales and marketing alignment is usually stronger than it is given credit for, particularly around targeting and priority accounts. In larger organisations, messaging is often tightly managed. But what the market sees extends far beyond that. Recruitment adverts signal capability and focus. Employee reviews indicate culture and delivery reality. Customer communications and investor updates hint at implementation timelines and complexity. Partner ecosystems and integrations suggest where value is created – or where it is dependent on others.

Individually, these signals make sense. Collectively, they are what AI systems use to form a coherent view.

In most organisations, these signals have never needed to align this tightly before. This is where AI search starts to diverge from traditional search. Search engines indexed and ranked content. AI systems interpret and synthesise across sources to produce an answer. That means inconsistencies are not just discovered, they are resolved – often by simplifying. And that simplification tends to remove detail, nuance and context. It favours what is easiest to explain and compare, not necessarily what is most accurate or most advantageous.

None of this is new. What has changed is how it is interpreted.

This is a moment where organisations are no longer deciding what to reveal. They are, whether they intend to or not, shaping what can be inferred. AI will take what is available, fill in the gaps, and construct a version of your business that it can explain to others.

That version will be reused, influencing how you are compared, trusted, shortlisted, priced and ultimately chosen – and how confident buyers feel making that decision – often before you have any direct interaction.

If the underlying signals are clear, consistent and sufficiently detailed, that works in your favour. If they are not, the system will still produce an answer. It will just be a simplified one – thinner, less differentiated, and often shaped by whatever signals are easiest to access or reconcile.

That is where the risk sits. Because once that version is established, shifting it is not immediate. It takes time, consistency and deliberate effort, but it is within the control of the organisation to prioritise and resolve.

Which brings a more practical implication.

This is not something that is fixed with a campaign, dedicated resource or a content programme. It requires clarity and alignment across the organisation – product, sales, marketing, delivery, and the signals that sit around them, including talent and culture.

In practice, that means making deliberate choices about what must be consistent, where precision matters, and how those signals are maintained over time. Not because the capability isn’t there – most CMOs are already working across these areas – but because it hasn’t needed to be resolved in quite this deliberate way before.

The immediate question may be how you show up in AI search.

The more important question is whether what shows up is something you would recognise – and want repeated.

About the Author: Kaila Yates is an Interim Chief Marketing Officer working with enterprise B2B technology and financial services businesses on marketing transformation and commercial performance. She is also a NED and Chair, bringing board-level perspective to growth, governance and performance. www.twojackscomms.com