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AI in the C-Suite: Ambition, Anxiety, and Work to Be Done

Spencer Stuart survey on AI and functional leadership
September 2026
| 5 min read

AI is transforming how businesses operate, connect with customers and deliver value. But what impact is that having on talent, teams and ways of working?

To answer that question and more, Spencer Stuart recently surveyed more than 500 functional leaders — including the heads of IT, HR, legal, finance, marketing and many more.

The findings from our survey shine a light on an interesting moment for business leaders: excitement about the possibilities, with some trepidation about what it means for workforces. This article looks at the important themes that emerge from the survey.

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Respondents were asked to rate their AI maturity on a five-point scale, and the average self-rating was right in the middle: 2.9. Only 28% of respondents assessed themselves as a 4 or 5 (leader), while 33% self-assessed as a 1 or 2 (laggard), a sign that for all the excitement about the promise of AI, many leaders are still feeling their way through a new world.

AI implementation is broad but not necessarily deep. While 82% of respondents say their AI usage has gone past the piloting stage, there was an awareness among many that work remains to be done to capture AI’s full value.

“Our workforce has a high degree of interest in gaining efficiencies,” said one respondent. “However, they’re busy with day-to-day work. It’s hard to find time to focus on implementing AI.”

At a functional level, technology leaders and CHROs were much likelier to rate their organizations’ AI maturity highly, especially compared with communications, operations and finance leads. At the industry level, respondents from financial services (36%) and legal and professional services (38%) were the likeliest to self-rate their organizations’ AI abilities as a 4 or 5. Meanwhile, industrial (50%) and healthcare (45%) were the likeliest to self-rate as laggards (1 or 2).

AI’s potential impact on the size of the workforce has captured headlines in the past few years, but our survey finds that the headcount impact has been more muted. For now, workforces are more commonly being reshaped by AI, rather than shrunk by it.

Most respondents report that AI’s impact has been felt most in changing roles, workflows and expectations rather than replacing people. Common shifts include automating repetitive work, reducing demand for junior or transactional roles, expanding analytics and product capabilities, and helping existing employees become more AI-fluent.

Only 16% of respondents reported having reduced headcount in the past 12 months due to AI. And while a higher percentage (40%) expect AI to lead to headcount cuts in the next one to two years, 49% say they plan to keep headcount steady while shifting toward AI, and 8% plan to increase headcount to add AI capabilities.

40%

of respondents expect to reduce headcount due to AI in the next 1-2 years

51%

of respondents have not been given an explicit AI-related efficiency target

2.9

Average AI maturity self-rating (on a 1-to-5 scale)

One of the most interesting findings is related to who is currently in charge of organizational AI efforts. While the CIO, CTO or other similar tech-focused leaders are typically in charge, it’s hardly the dominant answer.

A top IT executive (the CIO or CTO) was the most common answer (42%), followed by the CEO (15%) or a dedicated AI team or center of excellence (12%). A long tail of alternate choices made up the final one-quarter of responses.

Respondents from companies where the CEO personally leads AI efforts are twice as likely to rate their function as a leader in AI ability than those led by technology chiefs.

Interestingly, among companies where the CEO personally leads AI, respondents were more than twice as likely to rate their function as a leader in AI ability than those led by technology chiefs — 50% compared with 22%.

One likely reason for this is that strong central leadership can drive the full-scale, end-to-end transformation needed to fully capture AI’s potential. Other reasons are related to the non-technological imperatives central to any transformation: linking investment to a business case; seeking new generations of leaders; supporting employees’ technology adoption; and the need for a clear narrative.

“Leading change” was the most-cited critical leadership profile in the AI era (34%), followed by strategic thinking (18%) and driving results (17%). Respondents say the hardest part is helping people use AI better: defining use cases, building confidence, training employees, changing mindsets and embedding AI into everyday work.

In other words, the best AI leader is not necessarily the strongest technologist, but rather the leader who can drive behavioral, organizational and workforce change. Can they align people around a common vision and overcome resistance and uncertainty? Can they drive the creation of new operating models and a reskilled workforce? Can they translate AI investment into business outcomes?

“Leaders have to personally engage with AI in order to lead the change,” one respondent said.

More than half of respondents (51%) report that they either have not been given a defined AI-related efficiency target, or that they are feeling pressure to show savings but have not been given a defined target. Interestingly, those without a defined target are significantly likelier than other respondents to be planning to increase headcount in the next year or two, or to be keeping headcount steady while shifting capabilities.

There were some differences among industries when it comes to efficiency targets. In the technology/media/telecommunications industry, which often operates in highly digital environments and has typically been an early adopter of AI, 63% of respondents say they are expected to show specific efficiency improvements, and another 13% face undefined pressure to show savings. Contrast that with the healthcare/life sciences sector, which faces greater regulatory requirements, patient-safety considerations and complex implementation. Almost half (48%) of healthcare/life sciences respondents say they are not facing pressure to use AI to improve efficiency, and another 16% report pressure for savings but no defined target.

Overall, respondents who self-rated as a leader (4 or 5) were nearly twice as likely than laggards to have a defined efficiency target.

Survey respondents said that AI cannot scale effectively without clean data, clear guardrails, strong governance, data protection and enterprise-ready architecture. Several warned that poor data or weak controls can turn AI into a faster path to bad answers, risky outputs or the exposure of sensitive information.

The speed of AI adoption by employees and of innovation by AI companies is far outstripping the ability of any traditional corporate governance structures to thoughtfully mitigate risk.”

Among companies that self-rate as “leaders” in AI, one-quarter (25%) say that they have no barriers (compared with 10% among all respondents), and 13% say team resistance/fear of job loss is the top barrier (compared with 9% overall), suggesting that human challenges become more visible as technical friction eases.

“The speed of AI adoption by employees and of innovation by AI companies is far outstripping the ability of any traditional corporate governance structures to thoughtfully mitigate risk,” said one respondent.

A breakdown of the data shows how the challenges differ by industry. For example, 29% of consumer/retail respondents said that their data infrastructure not being ready was the top barrier to scaling AI in their functions, significantly higher than any other industry. In technology/media/telecom, almost one-quarter of respondents said team resistance was the top barrier, while the lack of a clear playbook was the most common answer in the healthcare/life sciences and industrial sectors.

The need for human interaction with AI — expert review, context, prompting, validation and judgment — was a dominant theme among our survey respondents. Some lamented that so much focus has been put on AI as an efficiency play, rather than as a way to make existing teams operate better.

In comment after comment, respondents said that they had found AI to be most effective when it was seen as a thought partner, an assistant or a force multiplier, rather than as a replacement for strategy, experience, critical thinking, customer understanding or responsible decision-making.

“The ability to discern the answers AI provides is built upon wisdom and experience doing the work you are asking AI to complete,” one respondent said. “With a workforce who are being raised in this environment, how will this wisdom be generated?”

• • •

AI has evolved from a technology challenge to a primary leadership challenge. Organizations across industries are moving into the workforce transformation phase — not smaller teams, but rather teams with the skills to thrive amid today’s rapid technological change.

Those pulling ahead are distinguished by leadership teams that are redesigning work, building new capabilities, aligning their people around change and translating AI investment into business outcomes. AI is here. The challenge now is leading through the change that AI is creating.

About the Survey

During the final quarter of 2025 and the first half of 2026, Spencer Stuart conducted three surveys of functional leaders about AI at their companies and within their functions. Combined, 545 leaders completed the survey, which was a combination of multiple-choice and open-field-response questions. The surveyed functional leaders included CFOs, CHROs, CMOs, chief information officers, chief technology/engineering officers, chief information security officers, general counsel/chief legal officers, chief communications officers, chief revenue/commercial/sales officers, chief product officers, chief supply chain/operations officers, chief strategy/transformation officers and chief investment officers. Almost half (48%) of the respondents were from public companies, and 25% were from private equity-backed companies.