AI is not creating your brand, it is interpreting what already exists

AI is rapidly reshaping how brands are discovered and understood, but most organisations are still early in proving its commercial impact. This think piece explores why testing at scale, strengthening brand inputs, and focusing on reputation signals are becoming critical for marketers navigating this shift.

The gap between narrative and reality

There is a growing disconnect in marketing conversations about AI. While it is positioned as a transformative force, the reality inside most organisations is far less advanced. There is no shortage of propositions, platforms and promises, but there is limited, consistent proof of commercial impact. Unless organisations are actively testing, and doing so at scale, most decisions are still being made on assumption. Small-scale pilots or isolated use cases rarely provide enough evidence to inform meaningful strategic change. As a result, AI risks becoming another layer of activity rather than a driver of advantage.

AI as the new layer between brand and decision

What is becoming clearer is not just what AI can do, but where it sits. Increasingly, it acts as an interface between customer intent and brand consideration. Rather than directing users to a set of options, it interprets, summarises and recommends. This has a fundamental implication. Brands are no longer only competing for attention, they are competing for accurate representation. Large language models do not create brand narratives from scratch. They assemble them from existing signals - media coverage, expert commentary, corporate messaging and third-party validation. In this sense, AI is acting as a curator of reputation.

Why reputational anchors matter

This is where the concept of “reputational anchors” becomes important. These are the signals that inform how a brand is understood externally and, increasingly, how it is interpreted by AI systems. If those inputs are inconsistent or weak, the outputs will reflect that. Unlike traditional channels, this is not something that can be easily corrected through paid spend. Visibility is earned through presence in credible, authoritative sources. 

For an international FMCG organisation, this has shifted focus towards strengthening narrative consistency across markets, increasing the visibility of senior voices, and ensuring that key messages are clearly reflected in trusted environments - not as a standalone AI initiative, but as part of broader commercial priorities.

Why testing at scale changes the conversation

Measurement remains one of the biggest challenges. Traditional metrics such as impressions or reach do not capture how a brand is represented within AI-generated outputs. However, when organisations begin to test systematically and at scale - running large prompt sets across different models, markets and scenarios - patterns start to emerge. Brands with stronger, more consistent external signals appear more frequently and more accurately. They are also more likely to be associated with authority and trust. Without this level of testing, it is difficult to separate perception from reality.

Where investment is really going

Despite the attention on AI tools, investment is not primarily flowing into platforms. Instead, it is shifting towards the quality of inputs. This includes clearer messaging, stronger content, more credible media presence and better integration between brand, communications and data. At the same time, there is caution. Many organisations are deliberately avoiding over-automation, particularly in content, where it risks reducing distinctiveness. There is also increasing scrutiny of platforms that promise transformation without clear evidence of impact.