By Upali Dasgupta

Brand discovery used to follow a clear path. People would read articles, compare reviews, check social chatter and influencers, maybe even visit a competitor’s site. Each step took time and effort as they pieced together their view of a brand.

Now AI curates that process for them.

Imagine forming an opinion about someone not through direct conversation or scattered clues, but via a neat summary of their digital footprint. That summary doesn’t just introduce them, it quietly shapes your lasting impression of them.

In 2026, brands increasingly meet us this way: through their AI-curated version. Tools like ChatGPT, Gemini and Google’s AI Overviews are starting to guide the entire process of discovery, research and comparison, often before anyone visits a website. ChatGPT now drives over 85 percent of chatbot-to-website referrals, according to a recent report.  

This change feels subtle at first. We used to optimise for search engines. Now it’s about Generative Engine Optimisation (GEO) – how large language models (LLMs) synthesise and represent your brand narrative. As people lean on AI as a primary source of truth, what these systems ‘know’ about your brand starts driving real decisions.

The new laws of digital attraction: Marketing to machines

Brands continue to invest in trust as though it exists only in human relationships. The reality is, credibility is both emotional and algorithmic. AI assistants ingest signals from everywhere – media, reviews, forums and influencers. They don’t limit themselves to scanning your homepage. Every touchpoint shapes how you’re represented. AI compresses your reputation into a working summary that decides what matters, and that summary goes on to frame research, consideration and comparison.

Marketers now need smarter strategies and tools to keep pace. It’s time to rethink how we track, measure and invest across digital channels, not just to stay visible, but to ensure that what’s found truly reflects who the brand is today.

A strong brand shows up consistently across every touchpoint. It’s about grounding every touchpoint in clear facts, credible sources and messaging that feels aligned and authentic. Earned media takes on a bigger role in this mix. It not only builds trust and credibility with people, but also helps train the systems that influence how brands are seen and understood online.

But with that shift comes a new reputational risk. When AI surfaces outdated messaging, biased commentary or inaccurate third-party data, those errors can amplify quickly, often before a brand even realises it.

Yet, many marketing and communications teams still aren’t fully equipped to navigate this change. The same report that found ChatGPT was the major driver of chatbot-to-website referrals, also found that 40 percent of senior leaders don’t have a clear understanding of what their PR team actually does, while 28 percent of PR professionals struggle to prove their value internally. At a time when trust is increasingly built in spaces outside of a brand’s direct control, that disconnection creates a dangerous reputational blind spot.

Many teams still rely on volume and reach as their main success metrics. But today’s visibility hinges on accurate representation rather than reach. The future of measurement will focus on how stories shape perception and influence decisions, a truer reflection of what brands actually stand for.

Authenticity: the signal that speaks to both humans and machines

If there’s one constant in all this, it’s authenticity. And ironically, that’s the very signal AI is learning to prioritise.

LLMs are trained on peer reviews, community commentary and real conversations, the kind of content that can’t be scripted or staged. People, too, look for the same thing: information that feels honest and consistent.

When a brand’s story, tone and behaviour don’t line up, or when promises lack proof, AI doesn’t overlook it, instead it learns from it. These misrepresentations get baked into how your brand is described, compared and recommended by both people and algorithms alike.

From overload to insight: media intelligence for the algorithmic age

So where do brands begin? By closing the visibility gap. As AI becomes part of how information is discovered and interpreted, understanding how a brand appears within those systems is becoming a core element of communications strategy.

Modern media intelligence platforms already help teams track news in real time, monitor sentiment and uncover emerging narratives. The next horizon is extending that awareness into generative AI and understanding how earned coverage and public conversation shape what these systems say about a brand.

Some platforms are starting to make this possible by linking media data, sentiment signals and outputs from LLMs. With this view, marketing and PR teams can spot bias early, address blind spots, and manage reputation before issues gain traction.

This marks a new chapter in media intelligence. Just as we once measured share of voice, we’ll soon measure share of algorithmic presence: how often, how accurately, and in what context a brand appears when AI provides answers.

Customer journeys may still close with emotion or loyalty, but they increasingly start with a single prompt. Staying credible means understanding how those prompts get shaped and what they surface. In the AI era, brand perception becomes an ongoing conversation – one brands can join or watch unfold without them. 

Upali Dasgupta is the senior marketing director for APAC at Meltwater

Read more: Why ecommerce brands should revisit their content generation frameworks