If you’re an OG reader, you’ll remember how Memo’s Katrina Dene told us the playbook era of PR was over, and that data was about to become non-negotiable. Three years later, data is table stakes, and Memo is now part of Signal AI, an enterprise risk intelligence platform used across the Fortune 500. A tool that started out counting media mentions now predicts business risk before it becomes a headline… and that’s not just Signal AI’s trajectory, it’s our industry’s.
CEO David Benigson’s view is clear: his customers aren’t just comms pros or risk specialists. They’re business leaders who need decision intelligence built on real signals. That reframe from “how many people saw this” to “what does this actually mean for the business” is where our industry should aspire to be. The best in our industry are already there.
What I like about David’s point of view is that he doesn’t fixate on AI as magic. It’s a means to gather and analyze data, and provide real proof for our counsel.
I have a theory: in a post-Trump, post-Musk climate, a lot of CEOs believe their brands are durable enough to weather reputational storms. They see those two weather everything. So they deprioritize traditional comms risk. What are your thoughts?
My experience has actually been the opposite.
The importance of reputation management has increased dramatically over the last decade. Almost every CEO has now lived through some combination of geopolitical disruption, social activism, supply chain shocks, cyber incidents, AI-related challenges, regulatory scrutiny, executive controversies, or major reputational events. As a result, leadership teams are more aware than ever of the value of actively managing trust and reputation.
You can see this reflected in organizational structures. The Chief Communications Officer has become significantly more influential, and we’ve seen the rise of the Chief Corporate Affairs Officer role, often reporting directly to the CEO rather than sitting under marketing. That shift reflects the reality that reputation today is not a marketing issue; it’s a business issue.
What has changed is that the world has become extraordinarily noisy. Every day brings a new controversy. As a result, some leaders have become conditioned to believe that many issues will disappear quickly. In some cases they’re right, but we also see issues become sustained reputational headwinds.
The challenge is distinguishing between noise and signal. The organizations that outperform are not the ones trying to react to every headline. They’re the ones that understand which issues have the potential to affect customers, employees, regulators, investors and policymakers and which are simply transient moments in an increasingly crowded information environment.
Give me a concrete example of a client using your platform to predict something that media reports or traditional monitoring would have missed.
One example comes from a leading international spirits and beverage company that uses Signal AI to monitor its top enterprise risks.
The company identified severe drought conditions in Mexico and emerging regulatory mandates around water usage as potential threats to agave production and long-term supply continuity. While elements of the story were visible in trade publications and local reporting, the signals were fragmented across sustainability discussions, regional media, regulatory consultations and legislative developments. No single source presented the full picture.
At the same time, the company’s Enterprise Risk team had become leaner and no longer had the capacity to manually track every regulatory and environmental development globally. Using Signal AI, they moved from a traditional biannual risk review process to a real-time intelligence model. The platform continuously monitored water stress indicators, regulatory developments, agricultural policy changes and stakeholder sentiment across multiple markets.
The result was that they were able to develop contingency planning significantly earlier, strengthen supply chain resilience planning, and prepare for anticipated regulatory changes before they became immediate operational challenges.
Another example comes from a global luxury automotive manufacturer.
The company faced a combination of emerging threats, including executive deepfake attempts, data security concerns, semiconductor supply constraints and geopolitical regulatory developments that could affect production forecasts. Traditional monitoring tended to focus on major headlines after they emerged. Instead, the company established an early-warning operating model using Signal AI to track semiconductor supply-chain stability, monitor regulatory actions and identify emerging external risks before they became business disruptions.
The value wasn’t predicting a specific headline. It was identifying converging signals early enough for the business to make decisions before the market fully appreciated the risk. That’s increasingly what predictive intelligence means: connecting weak signals across multiple domains before they become obvious.
Signal AI just launched a product measuring how often brands get cited in AI-generated answers. But I’m skeptical; AEO is so new. How do you actually verify that a citation Signal AI is flagging is real?
The skepticism is entirely reasonable. We are in the early days of Answer Engine Optimization (AEO) or Generative Engine Optimization (GEO) and ensuring data integrity is paramount. To answer directly: We verify citations by capturing and analyzing the actual, live outputs of these models at scale.
When we systematically test prompts across different LLMs, geographies, and use cases, we capture the exact underlying response and its accompanying attributions. LLMs derive their answers from specific source data, and we can directly cross-reference the model’s claims against the source material in the cited link. If a model cites a source, the footprint is there, and we capture it.
With our recent acquisition of Memo, we’re moving the industry beyond ‘was I mentioned?’ to ‘how do I action this?’ Historically, PR looked at coverage volume and total potential reach. With Memo, we’ve evolved to measuring actual readership, showing exactly which articles people are reading. Now, we are entering a third wave: AI influence.
So, for the first time, communications leaders can identify high-value outlets through Memo’s readership data with our AI citation tracking. They can see exactly which media outlets and specific articles are driving the LLM responses. They can finally pinpoint which content has the highest ‘algorithmic authority’ and focus their attention on the most influential outlets for your business. And if an LLM is spitting out inaccurate data or hallucinated citations about your brand, you can trace it back to the source and correct the record at the root level, ensuring future training data is accurate.
The ultimate goal isn’t just to see if you were mentioned. It’s understanding how to ensure your brand is cited, recommended, and trusted by the AI systems people use to make decisions.
For a comms leader, this provides a clear, data-driven signal that breaks through the noise, allowing them to shift from passive monitoring to actively shaping how AI perceives their brand — and with readership data they can further tailor who they focus on.
How often do you discover that what a client thought was a reputation problem is actually a business problem? And when that happens, what’s your advice for your client?
Very often. One of the biggest shifts we’re seeing is that organizations are moving beyond treating reputation as a communications metric and increasingly viewing it as a business performance metric.
Historically, companies might have measured media coverage and sentiment in isolation. Today, the most sophisticated organizations want to understand how reputation connects to outcomes such as sales performance, customer retention, employee engagement, net promoter scores (NPS) and employee net promoter scores (eNPS), regulatory relationships and outcomes, investor confidence and ultimately shareholder value.
When you look at reputation through that lens, it becomes clear that many apparent communications problems originate elsewhere.
A negative narrative about customer service may actually be an operational problem. A trust issue with regulators may reflect governance challenges. Employee criticism may reveal cultural or leadership issues rather than messaging failures.
Don’t ask how to communicate your way out of the problem. Ask what business reality is creating the perception in the first place.
The most effective communications leaders today act almost like management consultants. They use external intelligence to anticipate issues, identify root causes and help the organization make better decisions—not simply craft better messages and manage reactive communications.
That’s where the combination of intelligence and outcome data becomes so powerful. When you can connect reputation signals to business metrics, you move the conversation from anecdotes to evidence.
What did you wish I had asked?
Whether we’re approaching a point where communications, corporate affairs and risk functions become one of the most strategically important intelligence functions inside the enterprise.
For decades, these teams were largely responsible for understanding what happened yesterday.
Today, they’re increasingly expected to identify what happens next.
The organizations we work with are no longer asking for monitoring tools. They’re asking for early-warning systems. They want to understand emerging risks, stakeholder expectations, regulatory shifts, geopolitical developments and AI-driven changes before they affect the business.
That’s a fundamentally different role. The future of communications isn’t reporting on reputation. It’s helping leadership teams make better decisions amid uncertainty and a crushing amount of information and data. That’s the most significant shift happening in corporate affairs today.