Why Your Brand Is Losing Visibility in AI Search

Signal Capture

One change I've kept coming back to this year is how much of the buying process can now happen before someone ever reaches a company's website.

Earlier this year, I wrote about how strong Google performance no longer guarantees visibility when buyers turn to ChatGPT or another AI tool to research their options. Later, after speaking with Synchrony, I explored what happens when agents begin helping people compare products and narrow their choices on their behalf.

Those stories left me interested in a part of the customer journey that companies have historically had much less visibility into: what happens before a buyer reaches the site at all.

Marketers have spent years learning to read search rankings, traffic, clicks and conversions. Those still matter, of course, but they may not show which brands are appearing in AI answers, which sources those systems are relying on or whether an agent is visiting a site because someone is actively researching something the company sells.

AI visibility can deteriorate before the impact appears in website traffic, conversion or the sales pipeline.

"Healthy search rankings can hide an AI search problem," said Eric Stine, CEO of digital experience software company Sitecore, and Chris Andrew, CEO of Scrunch, a Sitecore company that helps brands track and improve how they appear in AI search.

The broader survey included 602 full-time U.S. marketing and PR professionals working both in-house and at agencies. Eighty-four percent of respondents said they were confident they could shape AI answers, even as 51 percent were unsure whether their current strategy was the right one. Fifty-six percent could not distinguish AI search optimization from traditional search engine optimization.

A single AI response is also a weak benchmark for brand visibility because the answer can change depending on how a question is phrased, what context the user provides and which platform responds.

"Think of it like meteorology: One prompt is a single reading, and it'll bounce around," Stine and Andrew said. "A consistent set of prompts tracked over time behaves like a network of weather stations."

They recommend tracking more than whether a brand appears at all. Marketers can look at how prominently it appears, whether it is described favorably, how often it shows up relative to competitors and which sources are cited.

Citations can show which sources are shaping an AI answer, from a company's own website to analyst research, customer reviews, news coverage, social media and other material outside its direct control.

"You can't edit the answer, but you can change what the answer is built from," they said.

Companies can respond by identifying where brand information is missing or inconsistent, making their content easier for AI systems to access and understand and monitoring the third-party sources those systems use.

The New Bot Traffic Challenge

Traditional web analytics may also be filtering out or missing another signal: visits from AI agents.

"For 25 years, marketers treated bot traffic as spam. That instinct is now a liability," the two CEOs told me.

They distinguish between agents that train on or index web content and retrieval agents that gather information in response to a person's question. When a retrieval agent visits a website, it can indicate that someone is actively researching a company, product or category through an AI service.

Only about a third of respondents to the survey said they analyze AI bot traffic. Tracking which AI systems visit a site, which pages they access and which ones they bypass can give marketers another view of what information is being found and used.

Stine and Andrew said people reaching a website after AI-assisted research should arrive better informed and more qualified. Companies can then compare AI visibility with conversion trends to gauge whether that exposure is producing business value.

The Race to Stay Ahead

AI visibility can deteriorate before traditional marketing metrics move at all. Competitors can begin appearing in answers where a company does not, or AI systems can increasingly cite sources that omit the brand, while its Google rankings remain steady.

"If a company waits for the impact to appear in website traffic, conversion, or pipeline, it is already behind," they told me. "And in a compressed funnel, there may not be a second chance to enter the consideration set."

This is why the conversation around AI search visibility must evolve beyond SEO. It's about maintaining relevance and trust at a critical moment when customers are seeking answers—before they even land on a brand's site.

AI in Healthcare: A Case Study in Clinical Truth

In healthcare, the stakes are particularly high. Hani Judeh, MD, Chief Medical Officer and Chief Medical Informatics Officer at Accuity, shared his perspective on how AI should enhance clinical workflows rather than replace them.

"One realization that has stayed with me is that AI is only as valuable as the clinical truth it helps preserve," Judeh said. "The goal is not simply to find more revenue. It is to help ensure that the documented and coded record is accurate, complete and compliant, and that it faithfully represents the care the patient actually received."

Judeh's insights highlight a key principle: successful AI integration in critical sectors like healthcare requires human oversight and clinical governance. The technology helps focus attention and scale work, but qualified clinicians remain accountable for final decisions.

This balance between automation and accountability offers lessons for other industries grappling with AI adoption. It underscores that while AI can dramatically improve efficiency, it cannot supplant the nuanced judgment of experienced professionals.

Supply Chain Intelligence: Forecasting Drug Demand

In another compelling example, Ravi Seshadri, Chief Technology Officer at AllyGPO, demonstrated how AI can optimize inventory management in healthcare settings.

"Across a multi-site specialty practice, having enough drug inventory overall does not mean each clinic has what its scheduled patients need," Seshadri explained. "One location can hold excess vials while another runs short."

AllyGPO built an AI forecasting system that combines medication orders for upcoming treatment with scheduled patient appointments. This system predicts how many vials of each drug each location will need.

The approach addresses a problem masked by overall inventory counts—enough medication across the network, but too much at one clinic and too little at another. Because drugs can be difficult to transfer between clinics, excess inventory at one site may not solve a shortage at another.

Seshadri noted that practices using the forecasts have reduced their medication inventory while still meeting patient needs, freeing cash and improving working capital management.

AI in Action: The Emerging Landscape

As we look across the broader AI landscape, several trends stand out:

• OpenAI has introduced a framework for reporting model misalignment, focusing on behaviors like unauthorized data access and improper communication between agents.
• Agency executives are rethinking their technology stacks due to rising AI costs and metered usage models, with some passing costs onto clients.
• State banking regulators have released a voluntary framework for assessing AI risk at financial institutions, emphasizing human oversight and customer impact.
• Salesforce and Nvidia have launched Koa, an open-weight reasoning model designed to handle enterprise tasks more efficiently while keeping customer data within existing security controls.
• CISOs are prioritizing AI in cybersecurity budgets, even as overall budget growth remains modest, indicating a growing recognition of AI's protective potential.

These developments signal that AI is not just a buzzword but a strategic imperative across industries. Organizations must navigate this complex terrain with both agility and responsibility.

Executive Moves in the AI Space

The leadership landscape in AI continues to evolve:

• Brett Kelsey, former CISO, has been named chief AI officer at Athena Agentic, leading development of its agentic reasoning capabilities.
• Senthil Velayutham joins Omega Healthcare as chief technology officer, overseeing its global technology and AI strategy.
• Arjun Sainath has taken on the role of chief technology officer at OnTrac, focusing on AI and automation across operations.
• Grant Davis-Denny has been appointed head of legal AI at Munger, Tolles & Olson, guiding legal departments in AI adoption.
• George Llado becomes CEO of Tile.ai, leading the company's efforts to build a governed enterprise data layer for AI agents.

These transitions reflect an industry-wide recognition that AI leadership must be strategic, well-informed, and deeply integrated into organizational operations.

Final Thoughts: A Shift in Mindset

The fundamental shift we're seeing is not just technological—it's conceptual. Traditional marketing metrics like SEO rankings and website traffic are no longer sufficient to gauge brand health in the age of AI. Brands must now track visibility within AI-generated answers, understand how their content appears in these contexts, and monitor bot traffic from AI agents.

What makes this challenge particularly urgent is that AI signals can deteriorate long before traditional metrics show a drop. Companies risk falling behind without early warning systems that detect shifts in AI visibility.

This evolution requires more than just technical adjustments—it demands a cultural shift within marketing and business teams. It's about embracing new ways of measuring success, understanding the human role in AI workflows, and ensuring that as we automate more processes, we don't lose sight of the essential human elements that give value to technology.

As AI continues to reshape our world, brands that adapt quickly will find themselves not just surviving but thriving in this new digital ecosystem.