Corporate Affairs Says They're Falling Behind on AI. The Reputational Consequences Are Already Here.

A recent BCG study of more than 200 Corporate Affairs and Communications leaders at Fortune 1000 and Forbes Global 2000 companies surfaces a tension that should concern anyone leading a reputation function. Seventy-four percent of Corporate Affairs leaders say they believe in AI's potential and payoff. Only 31% report meaningful progress scaling it beyond pilots.

Meanwhile, BCG's data shows that 72% of CEOs have taken personal ownership of AI strategy and half believe their jobs depend on getting it right. Corporate Affairs teams that stay in pilot mode while their CEOs accelerate will find their influence inside the organization under pressure. There is, however, a faster path to demonstrating AI competence than most lagging teams realize, and it starts outside the organization, not inside it.

A Gap Is Opening Up, and It Has Consequences

What separates the leading 31% from the lagging 68% isn't enthusiasm for AI. The gap is organizational. Leaders have done the operating model work: they've redesigned workflows, established governance, and built the decision rights that allow AI to be embedded rather than bolted on. Lagging teams are still running pilots and waiting for someone else to define the path.

The numbers show how quickly the divide compounds. Among leading teams, 71% have upskilled at least a quarter of their workforce, compared with 22% among lagging teams. Leaders are 2.3 times more likely to commit at least 10% of their functional budget to AI. Deep or systematic workflow integration exists in roughly one in ten teams, all within the leading group. Among lagging organizations, that figure is zero. Meanwhile, more than 60% of all leaders plan to allocate less than 10% of their functional budget to AI in 2026, and 14% plan no investment at all. In other words, the leaders are compounding an organizational advantage while the majority of the profession stands still.

The consequence goes beyond falling behind peers. BCG warns that CCOs who don't keep pace risk missing opportunities to enhance the function's productivity, impact, and innovation. Over time, this may affect their credibility and influence within the organization. Corporate Affairs teams are often the internal voice on how the organization should communicate about AI transformation. When those teams are behind HR, Finance, and Marketing in their own adoption, a credibility gap can emerge with the CEOs who are doubling down. The same BCG research finds 72% of CEOs have taken personal accountability for AI across their organizations.

The instinct when facing that gap is to look inward: rebuild workflows, pilot tools, establish governance, redesign the operating model. That's the right long-term answer. But it takes time and organizational infrastructure most lagging teams don't yet have. There's a faster path, and it starts not with what your team does, but with what AI is doing to your company's reputation right now.

Start With the Stakeholder Your Competitors Aren't Measuring

The credibility gap with leadership doesn't require a full operating model transformation to close. It requires Corporate Affairs teams to demonstrate they're thinking about AI strategically, and the fastest way to do that is to turn attention to what AI is already doing to the company's reputation externally.

Generative AI platforms are now functioning as a primary research tool for the stakeholders Corporate Affairs teams are responsible for influencing. Customers, prospective employees, policymakers, and investors are consulting AI to form views about companies, and those views are landing without the company having any input into them. Measuring what AI says about your organization is something a team can act on now, without waiting for workflow transformation. It produces insight that's directly relevant to the C-suite, demonstrates strategic AI thinking, and addresses a reputation exposure that most competitors haven't yet identified.

What AI Is Actually Saying

Understanding what AI says about your company requires going directly to the source. RepTrak's AI as a Stakeholder offering does exactly that: systematically asking RepTrak's proven Reputation, Driver, and Factor questions of multiple leading AI platforms, and integrating the results into the RepTrak Model so they sit alongside your human stakeholder data. One client using RepTrak to measure reputation across multiple stakeholder groups, including AI, found a pattern that communications leaders need to understand.

AI perception scores were significantly more negative than informed public scores overall. For the bank with the largest gap, AI assigned a reputation score of 43.6 (rated "Weak") against a human score of 66.3 (rated "Average"), a difference of 22.7 points. At the driver level, the gap is specific: Conduct showed the largest negative divergence, where AI scores lagged human scores by nearly 30 points. Products and Services followed the same pattern. Innovation, by contrast, showed a small positive gap, with AI marginally more likely than humans to rate these banks as innovative.

The explanation is structural. AI synthesizes from the information that's most available and most consistently present in its training data. Innovation is well-documented in press releases, analyst coverage, and product announcements. Conduct (ethics, fairness, transparency, accountability) is documented primarily in adverse coverage, regulatory filings, and complaint records. AI surfaces what's loudest and most consistent in the record. For most companies, that means the Conduct narrative is thinner than the Innovation narrative, and AI scores reflect it.

This is reputation exposure that traditional measurement won't catch, because it lives in a gap between what your stakeholders believe and what they'd tell a survey. Stakeholders using AI aren't always aware they're doing it. That means the view AI has formed about your company may be influencing decisions (hiring, procurement, investment, advocacy) without appearing in your standard tracking data.

Getting the Upstream Work Right

Corporate Affairs at its best has always been a stakeholder intelligence function. The communications work is downstream of that understanding. Knowing what stakeholders actually think — their concerns, their views on your leadership, their perception of your conduct and workforce practices — is what makes every subsequent communications decision more accurate and more credible.

Whether that intelligence-gathering is done manually today or via AI-assisted workflows tomorrow is, in the long run, a secondary question. The more immediate question is whether the intelligence itself is complete. For most Corporate Affairs teams, it isn't, because AI is now forming and distributing views about your company, across every audience that matters, and most teams aren't measuring it.

Adding AI to your stakeholder measurement set is the upstream work. In practice, that means running your company through the questions your stakeholders are already asking AI, across ChatGPT, Gemini, Perplexity, and others, and comparing what comes back against what your human tracking data shows. It doesn't require rebuilding how the function operates. It requires treating AI with the same systematic rigor you'd apply to any other influential voice shaping stakeholder perception.

For teams looking to demonstrate AI competence quickly, this is the right starting point. It repositions a Corporate Affairs function from a team managing communications to a team generating intelligence about the environment leadership is operating in, and that repositioning is available right now, without waiting for workflows to be rebuilt.

That opportunity is available to anyone willing to look at a stakeholder they've been ignoring.