Ipsos’s latest Reputation Council research points to a tension for corporate communications: AI is becoming routine in content work, but governance and judgment are not keeping pace. The firm’s 2026 Reputation Council findings draw on conversations with around 200 senior communications and corporate affairs leaders across global organizations.
The implications go beyond faster drafting. Senior communicators increasingly see large language models as an intermediary between organizations and the people trying to understand them. That puts reputation, credible third-party coverage and AI-mediated discovery on the same strategic agenda.
Table of contents
Jump to each section:
- AI adoption is outpacing policy readiness
- Large language models become reputation intermediaries
- Strategic silence creates a different visibility problem
- What marketers should know about AI and reputation
AI adoption is outpacing policy readiness
76% of council members report using AI meaningfully in 2026, versus 43% in 2025, according to Ipsos.
That 33-percentage-point change describes AI use among the council’s participating senior communications professionals, not adoption across all marketers or businesses. It nevertheless shows how quickly the tools have become relevant to the work of managing corporate messages.
12% believe their organization’s existing ethical policies are sufficient for AI adoption, according to Ipsos.
Production capacity is therefore growing much faster than confidence in the policies that govern its use. For brand teams, the practical tension is between the ability to publish more and the responsibility to ensure that what gets published is accurate, authorized and appropriate.
A team can ask a model to draft a response to criticism. That does not answer the harder question: whether the organization should respond, who needs to hear from it, or what evidence it can offer. The bottleneck may move from writing a message to making a defensible decision.
Large language models become reputation intermediaries
One of the report’s most relevant observations for marketers is that large language models are increasingly standing between companies and their audiences. People may encounter a machine-generated synthesis of a company’s reputation before visiting a brand website or reading a press release.
That changes the job of corporate communications. An organization can control its newsroom and owned copy, but it cannot directly control how outside sources are summarized in an AI answer. Its reputation depends partly on the evidence, coverage and third-party context available to that intermediary.
For brand marketers, AI visibility should therefore be considered alongside earned-media quality and factual consistency. Optimizing a page for discovery cannot replace the broader task of building independent, verifiable information about a company.
The strategic shift is not simply from search engines to chatbots. It is from managing the messages a company publishes to understanding the explanations audiences may receive about it.
Strategic silence creates a different visibility problem
The same research shows that communications leaders are becoming more selective about public statements, particularly on potentially divisive topics.
84% consider political factors when deciding whether their organization should speak about an issue, according to Ipsos.61% prefer to remain quiet on potentially divisive issues even when their organization could speak authentically, according to Ipsos.35% said their organization had stayed silent over the previous year on an issue it would once have addressed publicly, according to Ipsos.
Ipsos calls this approach strategic silence. It can be a deliberate choice not to amplify a controversy, rather than evidence that a company lacks a position. Milorad Ajder, Ipsos’s global service line leader for corporate reputation, described the challenge as knowing which issues to engage with and helping organizations make decisions under uncertainty.
Yet there is a complication in an AI-mediated information environment. Not publishing a new statement does not stop an issue from being discussed or summarized by others. A company may reduce the risks of speaking while still needing to understand what information is circulating about it.
The distinction for marketers is between avoiding unnecessary commentary and leaving important factual gaps. Silence can be a communications decision, but it is not a substitute for monitoring reputation or correcting material inaccuracies through appropriate channels.
What marketers should know about AI and reputation
The report suggests that the future communications role will be defined less by message volume and more by sound interpretation. These implications are especially relevant to teams responsible for brand reputation, PR, executive visibility and AI search.
Treat AI output as a draft, not a decision. Set explicit human accountability for high-stakes statements, source verification and final approval. The report’s policy-readiness finding makes this more than a technical workflow concern.
Connect PR and AI visibility. Review how a brand is described by AI tools as well as how it appears in traditional news, search and owned channels. Separate what can be corrected on owned pages from what requires better independent evidence.
Assess when silence is appropriate. An issue-management process should specify the relevant audiences, factual obligations and likely consequences of speaking or not speaking. More content is not automatically better communication.
Prioritize quality of evidence over output volume. Reliable company facts, transparent sourcing and credible earned coverage are useful whether stakeholders learn about a brand through a journalist, search result or AI assistant.
These are editorial implications of the council’s findings, not practices shown by the study to produce a measured commercial return. The council reflects senior corporate communications perspectives rather than a representative sample of the entire marketing profession.
As AI increases the supply of messages, differentiation may come from something harder to automate: knowing what can be substantiated, what deserves attention and when the organization should speak. The teams that manage those choices well may find that reputation strategy and AI discovery are becoming inseparable.
