There is no shortage of AI strategy decks circulating in boardrooms today. But when Kingsley Gate convened a group of senior executives in Chicago for an off-the-record breakfast discussion, the conversation moved quickly past strategy slides and into something more revealing: the organizational, cultural, and leadership realities of a transformation that no one fully understands yet.
The panel brought together C-suite leaders from professional services, commercial real estate, financial services, and diversified industrials. What was most striking was not the diversity of their industries, but the degree of alignment in the challenges they described.
This perspective captures the key themes from that conversation.
The AI strategy from three months ago is already outdated. And that might be OK.
One executive opened with a comparison that drew agreement across the table: the most effective leaders during COVID were not the ones who claimed to have the answers. They were the ones who said,
“Here is what I think today, and I will tell you when I think differently tomorrow.”
The same principle applies to AI. Several panelists challenged the assumption that not having a locked-in AI strategy means falling behind. The technology is evolving at a pace that renders any fixed strategy obsolete within months. What matters more, they argued, is a leadership posture rooted in continuous learning, structured experimentation and honest recalibration.
The data supports this. According to McKinsey’s State of AI report, 88% of organizations now use AI in at least one business function, up from 78% a year prior. Yet only about one-third have begun to scale their AI programs beyond piloting, and just 1% report having reached AI maturity. The gap between adoption and transformation remains significant.
One panelist put it in personal terms: this is the first transformation in their career where the instinct to wait and learn from others is genuinely risky. Strategic procrastination, as they described it, has served them well in prior waves of change. With AI, they argued, everyone needs to be standing up, falling down and learning at the same time.
“This is the first transformation in my career where the instinct to wait and learn from others is genuinely risky. Strategic procrastination has served us well in prior waves of change, but with AI, everyone needs to be standing up, falling down, and learning at the same time.”
Why the People Leader Should Be Calling the Shots on AI
This was perhaps the most provocative assertion of the morning, and it came from the CIO on the panel.
The argument: technologists have always relished the opportunity to present bold technology strategies to the business. Whether it was the internet, cloud computing or now AI, that pattern holds. But workforce transformation is fundamentally a people challenge, not a technology challenge. And that means the CHRO needs to be the architect of the workforce planning strategy, with the CIO and CTO following that lead.
What technology leaders need from their HR counterparts, the CIO argued, is clarity. What does the end state look like? Which roles require human judgment that is strategically important? Which roles are genuine targets for automation? Without that clarity, the request becomes a vague demand for tools with no strategic context behind it.
The parallel to offshoring is instructive. When companies offshored, they had to make deliberate decisions about which roles required proximity, institutional knowledge and judgment, and which could be relocated. AI demands the same disciplined thinking. The critical difference is that AI is not static. The workforce strategy must evolve in tandem with the technology, not get set once and left alone.
This aligns with emerging research. Deloitte’s 2026 State of AI in the Enterprise report, based on a survey of 3,235 leaders across 24 countries, found that organizations where senior leadership actively shapes AI governance achieve significantly greater business value than those that delegate the work to technical teams alone. The skills gap, not the technology itself, was cited as the single biggest barrier to integration.
The CIO’s recommendation was specific: HR needs a dedicated role focused on AI workforce transformation. Someone whose mandate is to monitor whether the models remain relevant, whether the expected ROI is materializing and whether the organizational design still makes sense as the technology matures.
Top-down accountability meets bottom-up innovation
One of the clearest patterns in the conversation was the tension between grassroots experimentation and enterprise-level accountability.
A consulting leader on the panel offered a candid admission about her own firm’s experience. They had initially tried a purely bottom-up approach: allocate funding, encourage experimentation, and see what emerges. The results were underwhelming. Without clear top-down accountability and defined ambitions, the innovation remained scattered and the outcomes fell short.
McKinsey’s research reinforces this finding. Among the roughly 5.5% of organizations classified as AI “high performers,” a consistent pattern emerges: senior leadership is deeply engaged in driving adoption, including role-modeling the use of AI. These high performers are 3.6 times more likely to describe their AI ambitions as “transformative” rather than incremental, and they are five times more likely to allocate over 20% of their digital budgets to AI.
The shift, as the panelist described it, came when leadership started driving accountability from the top while still harnessing innovation from the ground. The sweet spot lies in the middle. Leaders need to feel personally accountable for transformation, not just iteration. But the most valuable ideas about how work actually changes on a day-to-day basis will come from the people doing the work. The organizations getting this right are the ones that marry those two forces together.
Rebuilding a workforce from scratch (almost)
One CHRO shared a story that gave the room pause. Her company had consolidated five corporate offices into a single location, resulting in roughly 60% turnover across the corporate workforce. Rather than treating this as a crisis, the leadership team treated it as a once-in-a-generation opportunity.
Before filling a single role, they stepped back and asked: What does this organization need to look like two or three years from now?
They called it the “org of the future” exercise, challenging every functional leader to design their team for where the business was headed, not where it had been.
Then they changed the hiring profile. Candidates were screened for how they were already using AI in their work. Not to replicate specific tools or workflows, but to identify people who were inherently curious, already experimenting, and capable of bringing a different kind of energy to the organization. The results, even at 75% through the hiring process, were already visible: increased AI usage in daily workflows, product design, pricing analytics and sourcing. Not because of a top-down mandate, but because the talent brought in was already wired to work differently.
This approach reflects what research increasingly suggests about the nature of AI adoption. Deloitte’s enterprise survey found that companies broadened workforce access to AI tools by 50% in a single year, growing from under 40% to approximately 60% of workers now equipped with sanctioned AI tools. Yet the education and readiness gap persists. Nearly half of employees report receiving minimal or no structured AI training.
The lesson is clear: hiring for AI curiosity and fluency may be as powerful a lever as any formal training program.
Controlling for risk, not out of fear
At a company with 50,000 employees across 60 countries, consistency is an aspiration, not a reality, and the leadership team is clear-eyed about it. Rather than trying to move everyone at the same pace, they are letting the most capable and curious teams move fast, then scaling what works.
At the same time, they are building governance structures for the agentic AI era. An AI council that includes the CTO, the CHRO and compliance leadership is developing guiding principles for how the organization will treat its people as agents increasingly take over tasks, and in some cases, roles.
The framing one leader offered was precise: control for risk, but do not control out of fear. The temptation in uncertain times is to grab hold of everything and impose bureaucratic constraints. But governance born from anxiety will slow an organization down more than any technology gap.
This concern is not unfounded. Deloitte’s 2025 Emerging Technology Trends study found that while 30% of organizations are exploring agentic AI and 38% are piloting solutions, only 11% are actively using these systems in production. And of those deploying autonomous agents, only one in five report having a mature governance model in place. The governance infrastructure, in most enterprises, has not kept pace with the ambition.
In financial services, the challenge is more acute. One CIO described spending a full year building an AI governance framework. In a heavily regulated industry, that timeline is not excessive. The organization now monitors what employees input into AI platforms, assesses risk in real time and makes deliberate decisions about whether to deploy large language models or build smaller, proprietary models trained on internal data. The discipline is not about limiting innovation. It is about ensuring that innovation does not outrun the guardrails.
The conversation that matters
What stood out most from this breakfast was not any single insight. It was the quality of the conversation itself.
These are leaders who are not pretending to have it figured out. They are building new organizational capabilities while operating existing ones, and they are doing so with a level of intellectual honesty that is uncommon in most boardroom discussions about AI. The CHRO and CIO are not merely aligned on paper. They are in daily conversation, sometimes disagreeing, sometimes pushing each other, but consistently moving forward together.
That partnership, between the people who understand the technology and the people who understand the humans, may be the single most important factor in whether any of this actually works.
Kingsley Gate’s own research, conducted in collaboration with the Financial Times Group, has consistently shown that how leaders make decisions matters as much as what they decide. Organizations that assess decision-making alignment during the hiring process report significantly higher leadership satisfaction and retention. In an era where every organization is navigating AI transformation, the quality of decision-making at the top has never been more consequential.
This perspective is based on a Kingsley Gate executive breakfast panel held in Chicago on March 3, 2026. Panelist remarks have been anonymized. Kingsley Gate convenes senior leaders across global markets to explore the forces reshaping leadership, talent and organizational design.
References
McKinsey & Company, “The State of AI in 2025: Agents, Innovation, and Transformation,” November 2025. mckinsey.com
Deloitte AI Institute, “The State of AI in the Enterprise, 2026 Edition,” January 2026. deloitte.com
Deloitte, “Agentic AI Strategy,” Tech Trends 2026, December 2025. deloitte.com
McKinsey & Company, “Superagency in the Workplace: Empowering People to Unlock AI’s Full Potential at Work,” January 2025. mckinsey.com
Kingsley Gate & Financial Times Group (FT Longitude), “Decision Making and Executive Leadership” research series. kingsleygate.com
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