Authored by Peter McKenna, founder and CEO of WSI Digital Advisors

While all the news about AI is focused on rampaging agents, a more commercially critical situation has arisen: AI increasingly sits between businesses and the people looking for them, influencing which ones get considered at all.

Instead of visiting a company website or working through a list of search results, someone may ask an AI tool to recommend a supplier, service provider, solution, or potential business partner. The answer depends on the picture AI has formed of each organisation from whatever information it can access. Some of that comes from the website, case studies and carefully worded ‘About Us’ pages. 

Much of it sits outside the business’s direct control.

If an organisation’s digital “picture” is inconsistent or incomplete, the cost is more than just a dent in your reputation. In fact, the biggest risk is that the business isn’t considered at all.

A further risk is that those at the top of the business don’t see this as their problem quickly enough. It’s easy to file it under marketing, or IT, or as ‘something the digital team is looking at.’ If that happens, it risks repeating the same pattern businesses went through with data privacy and cyber risk. Something was already shaping the organisation’s exposure long before anyone gave it a name, a budget, and a person accountable for it.

That makes AI literacy at leadership level so important. Not everyone needs to become an AI expert, but those making decisions about its impact need enough understanding to know what questions to ask. 

AI-led business discovery creates a similar challenge, and the businesses that don’t allocate internal ownership are the ones most exposed. 

The commercial cost of being misunderstood

The old model of business discovery had a human doing the comparison. Ten results led to a few research tabs being opened, and this allowed them to make a decision after weighing things up.

AI collapses that process. Ask it to recommend a product, supplier, service provider, solution, or a potential business partner, and you’re likely to see a very different list – one that is a smaller, pre-filtered selection before a human decision-maker has engaged with any of the businesses involved.

That creates a fundamentally different kind of exposure. A business is no longer simply competing to be chosen from a set of options a customer can see. It’s competing to be inside a set the customer never gets to see at all. 

A credible, well-run organisation might not appear in an AI-curated shortlist, not because it isn’t good enough, but because its digital footprint gives AI less to work with than its competitors. 

Repeated enough times, across enough decisions, a business risks disappearing from markets it never knew it was losing, without ever getting the opportunity to compete.

The starting point for leaders is recognising AI can’t stay as background noise. If AI is deciding who gets considered before your team knows there’s a decision to compete for, understanding what it says about your business belongs on the same agenda as growth, pricing, and market position.

Mind the interpretation gap

The real risk isn’t that AI occasionally describes a business incorrectly or badly. It’s that the gap between the business you are running and the business AI can see grows wide enough to impact which customers, partners or investors discover and consider you, without you intentionally influencing it.

Reputation can feel controlled the moment the website looks right. That’s only half true. Every business is going to describe itself as excellent. AI systems look for information beyond those self-authored claims.

Polished content on its own answers the wrong brief. Businesses need to understand the wider information landscape shaping how they are interpreted and whether they appear in AI-led recommendations. That means responding to reviews rather than allowing them to sit unanswered, correcting listings that are years out of date and earning credible recognition through customers, journalists, industry bodies and other relevant third parties.

The aim is not to repeat identical corporate wording across every available platform. It is to give AI enough consistent, credible evidence to understand what the business actually does, where its expertise lies and why it should be recommended when someone is looking for a business like it. Evidence is the critical element here: the more credible signals there are to support that connection, the more confidently AI can make it.

Leaders cannot control every source an AI system draws from. They can, however, identify where the overall picture is inaccurate, incomplete or outdated and decide what they can realistically do to improve it.

Practical steps that build AI visibility

Start by finding out where you stand. Ask the AI tools the questions your customers would ask: which supplier should I choose, who are the leading providers in this space, or what should I look for when choosing a business like this?

Look at which businesses are recommended, how they are described and what sources appear to inform the answer. Crucially, examine whether your own business is included, how it is positioned and where it is absent or misunderstood.

The next step is to compare that picture with reality. Does AI understand the services you provide today, or is it describing the business you were three years ago? Does it recognise the sectors in which you have genuine expertise? Can it find credible evidence to support the claims made on your website?

Businesses can then investigate the gaps, correct the information they control and strengthen independent validation where it is missing.

Someone in your organisation needs to own this, be accountable for monitoring it and recognise it cannot be treated as a one-off audit or solved through a sudden burst of new content. AI platforms, source material and customer behaviour will all continue to change. This means that businesses need a systematic process for testing how they appear when potential customers search for providers, tracing the information behind those answers and monitoring whether the gap between reality and perception is widening. 

That responsibility should sit with someone senior enough to see the whole picture, not just a single department’s slice of it. 

That also means giving the people responsible enough understanding of AI to make informed decisions about it. That means business investment through training, courses, and the development of both strategic and practical know-how of the impact of the technology.

The goal isn’t to turn business leaders into AI specialists or train them on every new tool. It is to give them a working understanding of the concepts, risks and strategic implications so they can ask better questions, challenge assumptions and guide their teams with confidence.

Marketing may shape the company’s message, IT may understand the technology and leadership may own the commercial strategy, but no one can manage this properly in isolation.

Human judgement remains essential. AI can surface patterns and produce recommendations, but it cannot decide whether its picture of the organisation reflects the business its leaders are trying to build.

Defining your business

Every leadership team already manages risks that live outside of a single department: cyber, compliance, data, reputation. AI interpretation belongs on that list.

The benefits extend beyond AI visibility. A cleaner, more consistent and credible digital presence makes the business easier to understand and trust across every audience it needs to reach. What starts with improving how AI sees the business can ultimately strengthen its reputation more broadly.

Leaders cannot control every source AI draws from, but they can put the time into understanding where its picture comes from, where it is accurate or incomplete, and what they can do to influence it.

Businesses that do will be better placed to build credibility, stay visible and remain part of the decisions that matter. Those that don’t risk being overlooked without ever knowing why.