Managing Your AI Reputation From the Outside In

We’re at a pivotal moment for artificial intelligence (AI) in business. After years at the intersection of technology, reputation and risk, we’ve seen one truth emerge: doing the right thing with AI is necessary, but not sufficient. The organizations that thrive are those that not only build strong governance frameworks (think ethical guardrails, data provenance protocols and human oversight) but also ensure the world understands their commitment.

Stakeholders, including your consumers, employees and investors, today are more sophisticated than ever. They’re not satisfied with generic claims of “AI-powered” efficiency. Instead, they’re interrogating the fairness of outcomes, the transparency of algorithms and the accountability structures behind every decision. Yet sophistication doesn’t always translate to accuracy. Public perception of AI is shaped less by internal policy documents and more by headlines and the missteps of industry peers.

When a competitor is caught misusing AI, through deploying a biased algorithm or failing a fairness audit, the spotlight doesn’t just land on them—it swings to the entire sector. If an organization hasn’t established its own narrative, it risks inheriting others’. The danger isn’t just being caught doing something wrong; it’s that silence will be interpreted as complicity.

When the Narrative Escapes Us: Lessons from the Field

Let me illustrate with two hypotheticals. First, consider a consumer credit product that launched with an algorithmic underwriting model. When users reported apparent gender-based disparities in credit limits, the company pointed to its internal fairness audits and compliance reviews. But by then, the narrative had already escaped: the product was branded as discriminatory, and the company was forced into a defensive posture, reacting to a story it didn’t write.

Or what about a logistics provider using AI to optimize delivery routes for fuel efficiency and safety? Let’s say their internal data showed a 20% reduction in accidents, but one minor incident involving a driver for the company and a beloved local mail carrier goes viral, then technical explanations about the human safety guardrails built into the software would fall flat against the emotional power of the story.

These examples underscore a critical point: even sound, defensible internal decisions can become reputational crises if we fail to manage the external communication layer.

Five Principles to Close the Perception Gap

Drawing on years of advising global organizations, here are five principles to help close the perception gap and protect your AI reputation:

1. Translate Internal AI Governance into a Public Narrative
   We can’t assume our internal policies speak for themselves. Develop a clear, accessible narrative that explains not just what you do, but why and how you do it. For example, clarify what “algorithmic transparency” means in your context and why it matters.
2. Disclose Proactively, Not Reactively
   Share information about your AI systems, their intended purposes and the safeguards in place before a crisis forces your hand. Proactive transparency builds trust and positions you as a leader, not a follower.
3. Prepare Plain-Language Explanations
   Technical accuracy is essential, but clarity is non-negotiable. Prepare explanations of your AI processes that anyone can understand and in terms that resonate with everyday stakeholders, not tech jargon.
4. Scenario-Plan for Reputation Crises
   Anticipate the stories that could be told about your AI use and prepare responses in advance. Don’t wait for a crisis to start thinking about your narrative. Scenario planning isn’t just a risk exercise; it’s a strategic advantage.
5. Make Human Oversight Visible
   Show, don’t just tell, how humans are involved in overseeing AI decisions. Make this a visible part of your public narrative. For example, highlight your “human-in-the-loop” review processes and how they safeguard against unintended outcomes.

Own Your Story Before Someone Else Does

A strong AI reputation strategy is about making the truth so visible it can’t be misrepresented. Internal governance is essential, including robust frameworks, ethical standards and technical safeguards. But that is only half the battle, the other half is owning your story, proactively and transparently, before someone else defines it for you.

As leaders in this rapidly evolving field, we have a responsibility—and an opportunity—to shape the narrative around AI. Let’s make sure we do it right, and let’s make sure the world knows it.