AI Changed Everything About Reputation Except What Actually Matters

A single tweet can erase $13 billion in market cap overnight. Generative search engines are reshaping how the public discovers and judges your brand, often before a human even clicks a link. And yet, in a conversation recorded live at Cannes Lions, Burson’s Chief Innovation Officer Chad Latz made a case that the most important currency in public relations hasn’t changed at all: reputation, earned through human judgment, creativity, and trust.

Speaking with PMI CEO Pierre Le Manh on The Shift Code podcast, Latz pulled back the curtain on how one of the world’s largest strategic communications firms is navigating a landscape where algorithms now mediate public perception and where the temptation to let AI do all the thinking is both the biggest opportunity and the biggest trap.

Latz framed it through a revealing three-year arc he’s watched unfold at Cannes: the first year was “look at what AI can do,” the second was “what should we allow AI to do,” and this year the conversation has landed on “what can humans do as a result?”

Reputation is no longer a soft metric

For years, reputation lived in the squishy territory of brand sentiment surveys (reports that were outdated by the time they landed on an executive’s desk). Burson set out to change that.

About a year ago at Cannes, the firm unveiled Reputation Capital, a platform and consulting framework validated by Augmented Intelligence Labs, a University of Oxford enterprise. The system uses an ensemble of four models that blend fast-moving signals (media volatility, social chatter) with slower-moving data (what real humans actually perceive) to calculate unexpected shareholder returns directly attributable to reputation.

According to Burson’s The Global Reputation Economy, companies with strong reputations can realize as much as 4.78% in additional unexpected annual shareholder returns. Burson estimates the global “Reputation Economy” at US$7.07 trillion.

The result? Companies can now quantify their upside opportunity and downside risk across eight levers. When you walk into a boardroom and say “our reputation is worth $13 billion, and here’s what our risk is against this particular lever,” the conversation around PR shifts from nice-to-have to board-level priority.

Welcome to the zero-click world

Then there’s the question Sir Martin Sorrell has raised publicly: what happens when LLMs start shaping what people believe about your company? Latz sees that shift driving the rise of Generative Engine Optimization (GEO), where the challenge is no longer just where a brand shows up in search, but how AI systems interpret the signals that shape brand perception.

Burson has responded with a focus on GEO, helping clients think about content structure and visibility in AI-generated answers. But Latz flagged a critical flaw in the GEO-only mindset: it can overlook believability. Being visible in a generative engine doesn’t matter much if the content lacks credibility with actual stakeholder groups.

The firm’s research, published as The Credibility Paradox, analyzed thousands of reputation-related prompts across seven major AI answer platforms, covering 85 companies. The takeaway? Showing up in generative AI results is necessary, but it isn’t sufficient. Those results also need to be believable to the audiences that matter. Visibility without credibility is a hollow win, and that’s the gap Burson argues many current approaches to GEO still need to address.

Predicting how people will react in twelve seconds

Perhaps the most striking capability Latz described is Decipher, a cognitive AI tool built in partnership with a company called Limbik. Unlike generative AI, which creates content, cognitive AI predicts how different audiences are likely to think, react, or respond to a given stimulus.

Decipher evaluates two critical signals (the believability of content and its potential virality) trained on hundreds of millions of artifacts, and delivers predictions in 8 to 12 seconds, covering audience segments in over 63 countries.

For organizations managing polycrisis situations or massive infrastructure projects where community trust is fragile, that speed can be the difference between getting ahead of a narrative and being buried by one.

From brand reputation to project reputation

Le Manh asked whether the same tools Burson built for corporate reputation apply to major projects and programs — multibillion-dollar infrastructure builds, data centers, or internal transformations like an ERP overhaul, where community trust and stakeholder buy-in can make or break the outcome?

Latz’s answer was that reputation can directly affect an organization’s license to operate, regardless of sector. The same foundational approach can inform project and program decisions: understand the signals creating opportunity and risk, quantify that risk, and predict how changing conditions may affect stakeholder response. Latz described this as using data as part of a “decision architecture,” giving leaders earlier insight into potential reputation risks and a chance to adjust their approach before those risks escalate.

For anyone managing a project where public or stakeholder trust is fragile, the implication is direct: reputation management isn't just a brand-marketing discipline. It's a risk-management tool that belongs in the project manager's kit.

The project examples point to a broader leadership challenge: faster intelligence doesn’t answer what should be automated, what still requires human oversight or where expertise adds the most value. That same tension shows up inside the work itself.

Speed raises the stakes for judgment

AI makes teams faster but faster isn’t always better.

Le Manh raised a frustration many leaders now recognize: AI can accelerate drafting while leaving humans with more editing to get the work to the required standard. Latz agreed: organizations are seduced by speed, and the result is often what the industry now calls “AI slop.”

His advice? Think of AI as an accelerant for first drafts and early ideation, then expect to spend more time on strategic refinement. The question hasn’t changed: is the output high quality and impactful? Technology doesn't excuse you from answering it.

Empowerment over anxiety

Burson hit 100% AI adoption across its professional workforce last year, driven by an initiative called FutureWork. The secret? Empowerment. Through WPP’s platform, WPP Open, every team member can build their own custom agents using whichever underlying LLM delivers the best results for their task. Giving people the power to shape AI around their own work took the anxiety out of the equation.

That freedom comes with a cost-management challenge. Latz said Burson monitors how people use different agents, looks for common tasks across the organization and deploys shared global agents where it makes sense. The firm also tracks token allocations and weighs model performance against cost.

Taken together, these threads point to one idea: AI is changing how quickly organizations can detect signals, predict reactions and produce work. The leadership challenge is deciding what to do with that speed. Latz describes the shift as moving beyond “humans in the loop” toward humans at the helm — using AI to accelerate the work while keeping people responsible for judgment, quality and direction.

Tags: Artificial Intelligence | Leadership | Communication | Risk Management | Transformation

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