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Are Companies Paying Enough Attention to AI Risks? · C-Suite Perspectives

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Steve Odland: Welcome to C-Suite Perspectives, a signature
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series by The Conference Board.
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I'm Steve Odland from The Conference Board and the host of this podcast
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series, and in today's conversation, we're going to talk about how
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businesses are grappling with AI risk.
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Joining me today is Dr. Andrew Jones, principal researcher
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at The Conference Board in our Governance & Sustainability Center.
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Andrew, welcome.
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Andrew Jones: Thanks so much, Steve.
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Great to be back with you again.
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Steve Odland: Andrew, you've written a new report, and it focuses on the S&P
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500, so the larger firms, but talk to us about the scope of what you looked at
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for AI disclosure, AI risk, and so forth.
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What does AI mean in this context?
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Andrew Jones: Yeah, sure.
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Happy to, Steve.
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And yeah, the Governance & Sustainability Center at The Conference Board put
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out some new research recently in a brand new report on how, as you said,
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S&P 500 companies are describing AI-related risks in the risk factors
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section of their annual SEC findings.
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That's their Form 10-K.
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We've looked back over the last few years, starting in 2023 all the way up to 2025.
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So, it's capturing the period where AI is quickly mainstreamed and
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been adopted by a lot of companies.
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And for those who don't know, the Form 10-K is, the risk factor
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section of it is a standardized, legally required disclosure meant to
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investors to material business risks.
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So a risk that a reasonable investor might consider important when making decisions.
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So looking at these risk disclosures can be a really valuable lens
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on how companies, how boards, how executives are thinking about risks,
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thinking about what's material, thinking about what's ahead, and how
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they're adapting their governance.
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And AI is obviously a really interesting area because of how quickly it's
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exploded in the last few years.
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It's quite a timely and quite an innovative study.
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No one else has really done this kind of work yet, and I think it
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only offers a window into how big companies are integrating AI into
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their frameworks, into their enterprise risk management frameworks, into
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their governance processes, and how it's becoming a key responsibility
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for both the C-Suite and the board.
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Steve Odland: So this study looked at the S&P 500 companies and their disclosures,
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what they've written about it.
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And so the scope of AI is whatever it was for that company that
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they deemed to be material right.
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Andrew Jones: That's right.
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It's a really good point that we talk about AI sometimes in this broad
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way, obviously under the umbrella of AI, of artificial intelligence.
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There's a lot of different technologies, different processes,
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different capabilities, right?
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Generative AI has obviously captured a lot of attention in the last few years,
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and some companies might have been disclosing about generative AI and those
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kind of models and what they mean, but also talk about other technologies, too,
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such as machine learning, deep learning, computer vision, predictive analytics.
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There's a lot of technologies here that together mark a new era of, I guess a new
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era of almost cognitive infrastructure, where AI is being embedded across
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functions and products and decisions.
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And companies are grappling with what that means, both internally for their
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workforce with operations, but also externally for their products and
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how they engage with their customers.
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Steve Odland: So basically, this isn't a laundry list of everything
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that they're doing in AI.
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This is essentially a disclosure, a legal disclosure, essentially, or a
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governance disclosure that informs investors and external parties what
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the risks are around their AI use.
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So it's not meant to be a catalog of anything, but tell us, what are the
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legal requirements for AI disclosure?
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Andrew Jones: Obviously, we've looked at Form 10-K filings, right?
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So this is a standardized, legally required annual report.
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And within that, companies are obliged, US public companies are obligated by the SEC,
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the Securities and Exchange Commission to disclose their material risks.
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So these are risks that a reasonable investor would consider important
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when making investment decisions.
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So in other words, meaning it could significantly affect a company's financial
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performance, operations, or reputation.
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Different companies and different industries obviously will be disclosing
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different kinds of things there.
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There might be some commonalities, but every company has its own sort
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of, I guess, its own risk exposure, its own sort of material issues.
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And AI is obviously starting to bubble up into that conversation, whereas
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companies integrate AI, whether it's into their operations or among their
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workforce, or entering into their products, it's raising new questions
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about risk, in terms of risks, whether AI systems fail or whether they misinform,
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or whether they perpetuate bias.
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And also the broader sort of sphere of risks attached to
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AI, related to regulations and litigation and investments.
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So there's a long list of things here, right?
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There's a broad universe of risk, but companies are obligated to disclose
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what they think is a material risk.
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And looking at these disclosures, therefore.
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we can see a lot of things, including, as you said, how
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companies perhaps are thinking about.
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Investor expectations, thinking about future legal exposure, thinking
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about how boards are approaching governance and oversight of AI.
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So it's a broad, it's one lens of many, but it is an important lens
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and an important window, I think.
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onto how companies are thinking about AI, and not just thinking
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about the opportunities of AI, but also the risks that can
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potentially materialize from that.
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Steve Odland: Now your report talks about the surge here in disclosures
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just since '23—'23, '24, '25.
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Talk about the percentage of companies now disclosing some sort of material
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risk for AI and how that's changed.
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And then, what does that mean about the rapid pace of AI adoption?
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Andrew Jones: Yeah, it's a really interesting finding.
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So we found as recently as 2023—we're talking two years ago, the 2023
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filing year—only 12% of the S&P 500 referred to AI at all in their risk.
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So, had at least one material, AI-related risk disclosure.
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This year, it's 72%.
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That's a rapid increase in a very short space of time.
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I think it just sort of really illustrates and underscores how AI has so quickly
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shifted from being something that's new, something that's shiny, something
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that's niche, something that's perhaps in is pilot stages, to really starting
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to be embedded in the enterprise.
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And companies across industries are starting to act on it and deploy it and
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invest it in and bring it into their compliance, into their operations,
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into their brand, into their products.
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So I think it just really illustrates how quickly that has happened.
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And perhaps also at the same time, given that we're talking here about
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risks, also about shows that we're at an important moment where boards and
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executives are starting to think more about, it's not just about opportunities.
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It's about the challenges associated with this now.
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And how do we govern and have oversight of AI in a way that is effective
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and efficient and responsible.
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So very, very quick surge, Steve, in AI.
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I think just illustrates that AI, it's on the table now.
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It's table stakes.
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It's in every conversation.
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And this is now a material risk for many companies.
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Steve Odland: And so as you look across the Fortune 500, the S&P 500, there's
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a lot of different sectors represented.
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Do you see any trends across sectors?
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Andrew Jones: We do.
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So when we break down data on S&P 500, we tend to slice into the 11 sectors
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that come under the GICS standard.
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So every public company is assigned to one of these 11 sectors.
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And the truth is, first of all, all sectors are disclosing on AI
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risk, as you can imagine, given how many we just spoke of are doing it.
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But this recent surge in disclosure is really concentrated in
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about four or five industries.
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And these are financials, health care, industrials, IT, so information
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technology, and consumer discretionary.
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So these are quite different kind of sets.
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There's obviously a lot of different companies under those five.
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But we feel that perhaps the reason why is they maybe share two traits,
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one of which is high data intensity.
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The second is direct human or regulatory exposure.
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So as AI is becoming embedded into decisions, these risks around bias and
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reliability and privacy and oversight have become financially material.
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So it's a broad thing, but it is interesting how it's a handful
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of sectors that have really led this charge so far, at least from
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a risk disclosure perspective.
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Steve Odland: It's interesting because I can't imagine any company not having
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some sort of AI deployment, whether it's just simply a large language
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model or use of ChatGPT for enhanced search or a little bit of Gen AI.
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But we talked about the surge, but the flip side is, you got 30% who are not
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disclosing anything as a material risk.
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What does that say?
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Andrew Jones: It's a really good point, yeah.
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But it's still, while 72% is a big number, there's still a significant number that
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aren't saying anything yet about AI risk.
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That's not to say they won't be soon.
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We attribute this to a few things.
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I think one, it might just be simply disclosures haven't
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caught up with practice yet.
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It'd be interesting to see what this number looks like next year.
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Some companies may also be still perhaps folding AI implicitly
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into other disclosures.
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So perhaps they talk about cybersecurity.
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They don't quite foreground the AI angle yet, but maybe that's coming.
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But we do expect this number to continue to grow, Steve.
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I think it will become just commonplace to acknowledge at least one, if not more,
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material AI risks in the years to come.
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Steve Odland: So if you think about, your point was that there are people
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who are, companies that are heavily tech-oriented or heavily regulated
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are the ones that lean a little bit more to the disclosure side.
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So you can see that with the banks that are heavily regulated.
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And so therefore, the disclosures are around trying to make sure
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that they don't get sued, they're following the laws on these things.
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And if your business is IT or AI-related, of course, your whole
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business model's predicated on it.
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What are the most common categories of AI risk that you see, now
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that we've looked at the sectors?
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Andrew Jones: We grouped every single AI risk disclosure into broad categories.
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These are broad categories that themselves have many subcategories, but we found
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three broad themes really dominated across the disclosures, and that was
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reputational risk, cybersecurity.
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and thirdly, legal and regulatory, and particularly regulatory.
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So these are broad categories, and we can go into what comes under them
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and what doesn't come under them.
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But yeah, these are really where companies' focus is trained, where
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they're really looking right now.
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Reputational risk, in particular, is the most common we found.
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About 40% of all S&P 500 firms disclose something relating to reputation this
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year, and that might encompass bias, it might encompass misinformation,
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might encompass privacy lapses.
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And so on.
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But other categories, very important, too.
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Steve Odland: Now, what kind of regulatory risk did you see disclosed here?
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I guess regulatory risk is always an issue in any company that follows regulations.
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And if you read the risk factors in these reports, you always see factors
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related to whatever industry they're in, that's tied to regulatory risk.
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So is a bunch of this just that there're new regulations and so therefore,
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companies feel obligated to do that?
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I'm trying to get to, how much of this is just process-oriented,
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regulatory-oriented, and how much of this is that the world has
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changed and we really have a more risky situation because of it?
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Andrew Jones: It's a really great question.
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I think on the one hand, you are right that there's always going to
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be an element of that, that there's always new regulations, right?
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There's always diverging regulations, there's always complexity and
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uncertainty around regulations.
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Thinking, for example, in the G&S Center, The Conference Board,
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we do a lot of work on ESG and sustainability disclosure regulations.
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And Steve, that is a huge sort of minefield and a rapidly evolving field.
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But I think when it comes to AI, there is something new, as well.
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And actually most of the risk disclosures that refer to regulations
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on AI typically refer to the challenges of uncertainty and the divergences
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between jurisdictions, and how difficult it is for firms to plan their AI
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deployments amid diverging frameworks.
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And particularly, the key divergence—particularly US terms and US
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multinationals—is between the EU, where you're seeing the EU AI Act starting
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to come in, it's really going to take force in the next couple of years, has
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quite big obligations on companies.
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And the US, which is a very different sort of approach where there isn't really
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a sort of unified federal law, but there is state-level regulations emerging,
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which is creating a kind of patchwork, and companies are struggling to navigate.
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So I think that's the really challenging thing about AI that,
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on the one hand, companies are in a rapid race to invest, to deploy, to
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integrate into their business, into their operations, into their products.
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But there's such uncertainty as to how to navigate these different regimes.
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And particularly, the US-EU divergence, that is probably only going to
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continue to grow in the years to come.
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Steve Odland: Yeah.
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It's a good point.
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A lot of these companies in the S&P 500 are multinational, and
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therefore it's not just a US issue.
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And so you've got different countries regulating in different fashions, and yet
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a company needs to act pretty consistently across all of their operations.
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So that's a challenge.
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But even in the US, these, as you said, these multi-state regulatory schemes are
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very difficult for companies to navigate.
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And it feels like these states should get together or the federal government
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should step in and homogenize these rules.
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Andrew Jones: You're so right, Steve, it's a real challenge where the EU
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effectively has created the world's first big comprehensive binding AI
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law, and it has all these different aspects and risk-tiered systems and
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all these disclosure obligations.
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Whereas, yeah, the US is different, and the federal government and the
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executive branch has very much emphasized innovation and infrastructure and
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global competitiveness and trying to have a light-touch regulatory approach.
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But yet the states, it's very different and different state laws.
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Some are very focused on specific issues, whether it's bias in
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employment or deepfake disclosure laws, but others are looking quite
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ambitious in their own right.
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I'm thinking, particularly, emerging regulatory frameworks in
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places like Colorado, California, Texas, very very ambitious, and
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have different kind of approaches and different levels of emphasis.
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And it's going to be difficult for companies to stay ahead of all of this
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in the next few years in a way that still allows them to be nimble and
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agile and compete and invest and deploy.
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So it's definitely, I think you can see that in the risk disclosures.
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As recently as a year or two ago, you didn't really see
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many disclosures about this.
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Now it's quickly emerging as a material issue.
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So I think it is definitely top of mind for boards and for C-Suites.
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Steve Odland: We're talking about AI risk in companies and disclosure.
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We're going to take a short break and be right back.
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Welcome back to C-Suite Perspectives.
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I'm your host, Steve Odland from The Conference Board, and I'm joined today
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by Dr. Andrew Jones, principal researcher in the Governance & Sustainability
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Center at The Conference Board.
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OK, Andrew, we're talking about your new paper, which our listeners can
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access on our website, TCB, and just click on the Governance & Sustainability
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Center, and they'll find your paper.
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It's a really interesting thing.
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Cybersecurity risks have been disclosed for a very long time, decades and
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decades, as digital transformation has come into the company since the 1970s.
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And I don't know, this sort of feels like just the next
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wave of digital transformation.
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AI is, at its root, it's software, right?
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It does then introduce a whole other layer of cybersecurity risk, too.
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Andrew Jones: It really does.
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And I think some of the cybersecurity risks being collected by
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companies are very interesting.
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And as I mentioned earlier in the podcast, cybersecurity risk was actually
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the second-most-disclosed category.
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About 20% of all S&P 500 firms both this year and last year said
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something about AI specifically, not just talking about cybersecurity in
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broad terms, AI specifically, and what it means for cybersecurity.
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And I think this is interesting, actually, most of those disclosures talk in general
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terms about how AI is effectively just massively complicating the whole
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issue of cyber risk and cybersecurity.
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The AI itself is a force multiplier that increases the scale, speed, and
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unpredictability of cyberattacks.
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And I think people, hostile actors can use AI to really just create
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much more sophisticated attacks, find those sort of surfaces to get in.
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At the same time also, the company's use of AI is itself inviting new
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kinds of challenges if hostile actors are able to, for example,
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inject prompts into their own system.
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So it's a real big concern, and I think this is something that's going to be, we
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expect this to grow more in visibility as we see high-profile incidents.
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And AI just, it just really complicates what was already a very challenging area.
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So a very interesting field, Steve, and something we'll be keeping a close eye on.
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Steve Odland: The Conference Board just released its latest CEO Confidence
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Index for the fourth quarter, and we asked about AI and adoption.
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And we asked specifically of CEOs what their organization's primary
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driver was for AI investment.
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And it came back that the number one driver for investment in AI was to drive
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cost reduction and operational efficiency.
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Number two was new product and service innovation.
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Three was customer experience enhancement.
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So, as you think about then risk disclosure, everything you've
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talked about follows that.
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The risks of, there's always the cybersecurity risk and people abusing
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AI tools, but the risk factors follow along these priorities for AI investment.
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Andrew Jones: They do.
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It's a really good point.
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I think it does, and I think it shows how, as companies scale up
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investment in those priorities, it also just creates or enhances
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or intensifies the risks, as well.
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And something we've heard a lot in our own engagements with chief legal
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officers, with heads of AI, with board directors, is how companies might
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benefit from differentiating between different types of AI and different
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types of uses from a risk perspective.
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So for example, if you have a consumer-facing AI product, perhaps
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you put a bit more emphasis and a bit more controls on that from a
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risk management perspective than you do from a workforce-facing product.
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Because obviously, the reputational implications of a failing,
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misinforming customers, or perpetuating bias is enormous.
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And companies I think are waking up to how AI does both intensify certain
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risks that are already there—thinking, for example, cybersecurity—and
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also maybe fundamentally change or introduce new risks at the same time.
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Steve Odland: It's interesting, we talked about the cybersecurity and
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regulatory risks, but early on, you mentioned reputational risks.
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What do you mean by that?
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Andrew Jones: So we found this is the most frequently disclosed AI category, right?
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And the truth is it's a broad category.
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It encompasses a lot of stuff.
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But reputational risk, I think just reflects how, when we talk about
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reputational risk, we're talking about how brand trust or stakeholder confidence
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can be impacted by AI performance.
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And we found a lot of things that came under the umbrella of reputation.
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Perhaps I'll just touch on a few that were the most disclosed.
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Interestingly, Steve, often when people think of reputation or harm, they might
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think of privacy and data protection issues, or generative AI creating
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hallucinations or perpetuating bias.
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And those are in there, and we find companies are disclosing
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more on those kinds of issues.
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But actually the most commonly disclosed reputational AI risk
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related to the potential damage from overpromising or failed AI deployments
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that do not meet expectations.
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So in other words, the risk that AI might not deliver the
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promise that we hope it does.
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So I think it's just really interesting how companies are
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looking at reputation and AI from a very much a multidimensional lens.
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They're thinking about reputation in terms of consumers and stakeholders, but they're
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also thinking about the reputational harm that may ensue if AI doesn't work out.
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Steve Odland: Well there's so many pieces that AI impacts.
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If you're talking about generative AI, you know this,
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you've mentioned hallucinations.
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But even beyond that, you have AI producing content or
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design or creating things.
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You don't know if they're violating somebody else's IP.
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You don't know whether this stuff is right or wrong a lot of times.
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All of these things go to people trusting the products and services of a company.
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And so this is part and parcel of the reputational risk.
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Andrew Jones: It's a really good point.
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Yeah.
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And it's extremely difficult to keep up, right?
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It's difficult to keep up from a corporate perspective, from
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a risk management perspective, from a governance perspective.
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And I think it just illustrates how, as AI uses mainstream, and it's
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incorporated into all sort of corners of the business and the enterprise,
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AI risk is also cutting into all corners of the enterprise, too.
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And it's cutting across legal, financial, operational, and ethical dimensions.
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And it just raises huge governance challenges.
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And I think that's perhaps the key to all of this.
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That if firms are going to harness the potential of AI while avoiding
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or mitigating these kind of potential risks, some of which have potentially
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huge material impacts, it will require very effective and very
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agile governance and oversight.
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Steve Odland: Another place that reputational risks
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creep in is in recruiting.
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And a lot of human resource departments have implemented AI tools in order to sort
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through the enormous volume of resumes and applications that people get for jobs.
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And so in other words, think about it as you just program the screening
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of this thing, and they're finding sometimes that these tools are screening
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out certain classes or certain types of people, maybe on demographic data and
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so forth, and can be discriminatory.
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So that's another place where reputational risk creeps in and the
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need to have, human beings really govern the implementation of AI.
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Andrew Jones: Really important point, Steve.
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I think people are waking up to the fact that AI can often, I guess
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it can perpetuate certain biases or could, as you said, perhaps exclude
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certain groups, certain categories, particularly demographic categories,
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perhaps because the training data itself it's trained on has gaps in
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it, or even the way it's processed.
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And that can then take on and perpetuate already existing
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disproportionate impacts and biases.
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It's a really important point.
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And these kind of issues, if they come to light or they lead
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to discriminatory outcomes, can be reputationally catastrophic.
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And it's worth pointing out that the lit there is litigation
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emerging on, for example, the use of AI in workforce hiring practices.
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Companies need to be very careful here, for sure.
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Steve Odland: And it cuts both ways, because you also have the government,
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clamping down on DEI-related and quota-related training and recruiting.
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And so therefore, either way, if it's a positive bias, negative
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bias it can be risky, as well.
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We're talking about disclosures in your report mostly.
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The concept of disclosures come out of a company's ERM, enterprise risk
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management planning, which usually sits with the audit committee.
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Sometimes there's a risk committee in financial services and other industries,
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but it's usually the audit committee.
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But that whole ERM process needs to be rethought in order to include all of these
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kinds of AI risks, because the risk factor reporting comes out of that process.
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Andrew Jones: Agreed.
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And we've done some interesting sort of early work on in the Governance
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& Sustainability Center on where companies and specific boards are
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starting to sit AI responsibility.
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And to your point, Steve, yeah, we're often finding the audit committee
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is where it is for the reasons you said, I think—the oversight of risk
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or the oversight of risk management.
23:44
But clearly there is, I think to your point, there is a huge need for evolution
23:48
here to refresh and even rethink some of the risk exposures of companies.
23:54
And also think about how you govern all of that, right?
23:57
And how you govern across functions, as well.
23:59
And I think the more effective approaches here are those that really
24:02
do bring in from across the company.
24:03
I'm thinking, obviously, risk and compliance, technology, but
24:07
also legal, communications, ESG.
24:10
There needs to be a lot of voices here to ensure that AI risks one, aren't ongoing
24:15
undetected, and two, are being managed and mitigated in an effective and agile way.
24:20
Steve Odland: So just wrapping up here, as you look ahead, what are
24:23
some areas of AI risks that you think will become more prominent and
24:30
companies need to be thinking about just in case they have blind spots?
24:33
Andrew Jones: So it's a really great question, and I think you already
24:35
mentioned one of these, Steve, which is around the workforce and labor
24:38
relations, and what the scaling and integration of air might mean.
24:41
We all know the automation in AI can impact unemployment patterns,
24:46
can widen skill gaps, can lead to changes within workforce composition.
24:51
and that does raise risks, right?
24:53
And we didn't really find any companies really disclosing on these issues yet.
24:57
But I think the labor and retraining costs and reputational elements could
25:01
become material in the years ahead.
25:03
And in addition to that, we'd also perhaps point out two other things,
25:06
one of which is the environmental and sustainability dimensions here.
25:10
No risk disclosures to talk about this, but we know training and running
25:14
large AI models can consume a lot of energy, can consume a lot of water.
25:19
AI's resource intensity might start attracting more investor
25:23
attention in the years to come.
25:24
And we might see, particularly certain companies like the hyperscalers,
25:27
the tech companies, the data center providers, may be putting
25:30
a bit more emphasis on that area.
25:32
And one other thing I'd point to that I think is across the board and
25:35
something that a lot of companies still need to grapple with is
25:38
the next generation of AI, right?
25:40
And particularly agentic AI, that is a lot more autonomous, has
25:45
a lot less human involvement.
25:47
And I think the unpredictability and lack of clear human control
25:51
could trigger a whole range of new liability and governance challenges.
25:55
So there's a lot that companies should be keeping on their radar here.
25:58
Steve Odland: Yeah.
25:58
And if you look at the stock market today, we are in a bit of an AI bubble.
26:03
It reminds me of 1999 and the dot-com era bubble where the market got frothy.
26:10
And a key risk here is that these AI companies, or companies that have
26:15
talked about implementation of AI or their business model revolves
26:19
around it, there's risks that they're overpromising to their investors.
26:23
And you know, no line continues at a steep up, up, up.
26:28
And so there are all sorts of market risks and valuation risks about.
26:33
And any loan and debt covenants that revolve around that.
26:37
So lots of financial things that companies need to think about, as well.
26:41
Andrew Jones: Really great point, Steve.
26:42
I completely agree, and I'd add as well that those market risks
26:45
are also obviously intertwined with geopolitical risks, as well.
26:48
At the same time, we're seeing AI at the global level, it has almost become
26:52
a sort of proxy for economic power.
26:54
And, the US, China, EU are all pursuing quite a different approach to AI, but
27:00
obviously the end goal is clear to position as a leader in the space.
27:03
So that complicates all of the market and industry-level risks, as well.
27:07
So it's a really fascinating area, and companies need to stay ahead and
27:11
need to look ahead and need to be closely monitoring the environment.
27:14
Steve Odland: OK, we'll leave it there.
27:16
Dr. Andrew Jones, thanks for being with us today.
27:19
Andrew Jones: Great to be with you, Steve.
27:20
Always a pleasure.
27:21
Steve Odland: And thanks to all of you for listening to C-Suite Perspectives.
27:25
I'm Steve Odland, and this series has been brought to you by The Conference Board.