AI is making reputation a CMO and CIO problem

For years, reputation data had a fairly obvious home. Reviews, ratings, surveys, listings, and customer feedback primarily helped marketing and customer experience teams understand what customers thought, manage how the company appeared online, and respond when the experience fell short.

As a CMO, those responsibilities are still very much part of the job. What AI is changing is how many other systems now have a use for the same information.

Publicly, reviews, location information, and other reputation signals are part of the information AI-powered search and answer engines use to understand a business. Inside the enterprise, customer feedback that once ended up primarily in dashboards and reports can now become a much richer source of intelligence for enterprise large language models (LLMs).

The same underlying data is moving in two directions, for very different purposes. And that is where this becomes a CIO conversation, too.

AI is becoming another consumer of your public reputation

Marketing leaders have spent years managing the many places a company appears online. For a large enterprise, that can mean keeping information accurate across thousands of locations, websites, directories, and platforms while also monitoring the steady stream of reviews and other customer feedback that shapes what people find when they search.

AI adds another layer because it increasingly interprets that information on the customer’s behalf.

Someone looking for a hotel, healthcare provider, restaurant, or dealership can now ask an AI assistant for a recommendation rather than working through a traditional list of search results. The answer might draw from business information, web content, reviews, ratings, and other public sources, synthesizing what it finds into a much smaller set of options or even a specific recommendation.

Companies cannot control what an external AI system concludes about them. They can, however, influence the quality of the information available for it to interpret.

That gets complicated quickly for enterprises operating hundreds or thousands of locations. Hours change. Services vary. Locations open and close. Customer experiences shift as staffing and operating conditions change. The company may have an accurate view internally while websites, listings, directories, and review platforms tell a less consistent story.

Marketing has dealt with versions of this problem for years, but AI raises the stakes around the information underneath the experience. If a company’s hours are wrong in one place, its services are described differently somewhere else, and its location information is stale across other sources, there is only so much marketing can do at the surface. The underlying information has to be accurate, current, and able to move reliably across systems.

That brings reputation into territory CIOs know well. Authoritative data sources, integrations, structured information, and governance increasingly influence how accurately a company can be represented when machines are among the systems consuming its public information.

The customer feedback we already have is becoming more valuable

Enterprises have been collecting customer feedback for decades, and many have accumulated enormous volumes of reviews, surveys, and comments across locations, products, and customer interactions. The practical response has been to make all of that information manageable by turning it into scores, trends, dashboards, and reports.

Those tools remain important – but reducing thousands or millions of customer experiences to structured metrics inevitably leaves some of the context behind.

A satisfaction score can tell a hotel company that performance is declining across a region. It may take considerably more analysis to discover that customers at dozens of properties are describing the same problem at check-in. Similar patterns can sit inside customer feedback for months before they become obvious in structured performance metrics.

The information has always been there. The difficulty has been working with enough of it, quickly enough, to make those connections useful.

Generative AI creates a practical way to work with large volumes of unstructured customer language while retaining more of the context behind traditional metrics. When enterprise LLMs are connected to governed information through retrieval-augmented generation, customer feedback can be examined alongside the operational context needed to understand it.

An operations leader investigating declining satisfaction could look across customer comments while also considering location or regional data. A product team could identify recurring issues appearing in customer language before they become obvious elsewhere. Executives could explore whether movement in an aggregate score reflects a broad systemic problem or several unrelated ones.

Traditional business intelligence still does much of what it does very well. AI adds access to something different: more of the explanation underneath the measurement.

For a CMO, that opens up value in data marketing and CX teams have been sitting on for years. Making it useful beyond those functions, however, depends on connections that often extend well beyond the marketing stack.

The data doesn’t respect the org chart

Reviews may live in one system, surveys in another, location information somewhere else, while CRM, transaction, and operational data reside in entirely different environments. Marketing may own some of those systems. Customer experience may own others. Technology may manage the infrastructure connecting them.

That fragmentation matters more when the same information has to serve very different uses.

Public-facing information needs to be distributed broadly, updated quickly, and kept consistent across the places customers and AI systems encounter it. Customer feedback being used by an enterprise LLM has different requirements. It may need to be connected to internal customer, transaction, or operational data, with appropriate controls around what a model can retrieve and who can access the resulting information.

This is where I think the CMO and CIO conversation must evolve. The goal isn’t to redraw the org chart or shift ownership of reputation to IT, but to recognize that managing reputation in an AI environment increasingly depends on decisions neither organization can make entirely on its own.

Marketing understands which public signals matter, where customer perception is changing, and how reputation affects discovery and consideration. Technology understands how information is structured, connected, secured, and governed. Those responsibilities begin to meet when a review or customer comment is no longer useful only as something to monitor and respond to, but also as data another system needs to interpret.

The same is true in the other direction. A company may have pristine internal data about its locations, services, and operations, but that does little for an AI system answering a customer’s question if the public information surrounding the business is fragmented or outdated.

Most large companies don’t need more reputation data. They need a clearer understanding of where the authoritative information lives, how the different signals relate to one another, and whether those relationships survive as the data moves.

Reputation data belongs in the AI data conversation

At Reputation, this shift is particularly visible because reviews, surveys, listings and other customer signals have traditionally lived at the center of reputation management. AI is expanding what companies can do with that information and, in the process, changing who inside the organization has reason to care about how it is managed.

For CIOs, this doesn’t mean every review or survey response suddenly belongs in the enterprise data platform. It does mean reputation and customer-feedback data deserve consideration alongside the other information being evaluated for enterprise AI.

Customer feedback becomes more useful when it can be reliably associated with the right location, product, transaction, or period of time. Provenance matters when an AI-generated conclusion needs to be traced back to the information supporting it. And access matters when the same organization is managing information intended for broad public distribution alongside first-party customer data that belongs inside a governed environment.

There are implications for both sides of the C-suite. Marketing needs confidence that the information representing the company externally is accurate and current. Technology needs to know which systems are authoritative when that information conflicts and how changes propagate. Both have reason to understand where customer feedback lives, what it can responsibly be connected to, and where it could provide value beyond the team that originally collected it.

AI has made that work more urgent because companies are no longer initiating every use of this data themselves. Public AI systems are already interpreting information about businesses as they answer customer questions and make recommendations. Inside the enterprise, companies are building AI systems that can make years of customer language far more accessible to the people making operational and strategic decisions.

The public information helping AI understand the company and the customer information helping the company understand itself are becoming part of the same data conversation.

CMOs and CIOs come to that conversation with different responsibilities. AI is giving them much more reason to have it together.

For more information, visit reputation.com/

About the author

Kristi Melani is a transformational marketing executive with 30+ years of experience building high-performing teams, scalable marketing operations, and revenue-driving programs across high-growth startups and billion-dollar global enterprises. As CMO of Reputation, she leads global marketing strategy with a focus on brand trust, go-to-market excellence, and customer-centric growth.

Kristi is known for building marketing organizations from the ground up—creating clear processes, measurable frameworks, and cultures rooted in accountability, transparency, and performance. Prior to Reputation, she served as Chief Marketing Officer at Telesign and held senior marketing leadership roles at Anaplan, Poly, and Plantronics, where she led global teams, drove significant pipeline growth, and supported large-scale transformations and integrations.