AI conversations often start with questions about technology: platforms, governance, data, security, and implementation. But as organizations move beyond experimentation and into everyday use, a different, more human conversation emerges: How do people adapt? How do leaders build trust? How do organizations help employees develop new skills while letting go of old assumptions? And who is responsible for leading that change?

These questions sit at the center of SYPartners' ongoing Signals of the Future research, which explores how leaders are navigating the opportunities and tensions emerging from AI. One finding has surfaced consistently across conversations with technology leaders and people leaders alike: Organizations are gaining confidence in the technical dimensions of AI adoption, but expressing far greater concern about the human dimensions—reskilling, trust, leadership behaviors, communication, and culture. In other words, the technology may be advancing at speed, but people are still figuring out how to move with it.

That finding leads to a broader question: If AI transformation ultimately depends on both technology and people, are technology leaders and people leaders partnering closely enough to shape the future of work together?

To explore that question, SYPartners convened a webinar conversation between Matt Breitfelder of Apollo and George Forbes of the U.S. Armed Forces. Drawing on experiences from finance, leadership development, organizational transformation, and military training, the discussion surfaced a set of ideas that feel increasingly relevant for any executive navigating the future of work.

Watch the full conversation here, then scroll down for seven ideas that stood out:‍

‍1. AI is requiring human transformation as much as it is a technology transformation.

Technology may enable transformation, but people determine whether transformation actually takes root. Employees must learn new behaviors. Managers must rethink how work gets done. Leaders must create environments where experimentation feels safe and useful rather than threatening.

George Forbes believes that distinction is easy to underestimate.

"Technology is fantastic, it's wicked exciting, it's often complicated, but it's not complex. In the end, it's very solvable. The thing that's such a variable are the people [using it]."

Matt Breitfelder offered a similar perspective, noting that many organizations are still treating AI primarily as a technology story when the larger opportunity lies elsewhere.

"The AI revolution is only a little bit about technology. Technology is the catalyst, but this is about how humans work, and about unleashing our full potential as humans."

Their observations point toward a leadership challenge: CTOs may understand the technology. CHROs may understand the conditions that shape human behavior and adoption. Increasingly, successful AI transformation will require both perspectives operating in concert.

2. Adoption is harder than deployment.

Most technology transformations focus heavily on implementation. Success is measured by rollout schedules, platform availability, compliance, and usage metrics. AI introduces a different challenge. Access does not guarantee adoption, and adoption does not guarantee meaningful change.

Opening a tool is not the same thing as changing how work gets done. Employees need to develop judgment about when to trust AI, when to challenge it, and how to integrate it into their workflows. They need permission to experiment. Managers need to model new behaviors. Leaders need to create clarity around what effective adoption actually looks like.

Forbes sees this as one of the defining challenges of the current moment.

"Getting people to adopt new things, getting people to be early adopters, pioneers, and so forth, is the complex piece."

He argued that successful AI adoption depends not only on technology, but also on understanding the workforce itself—how people learn, adapt, and respond to change.

“The people data has been the most important element to make us successful: the data that our HR colleagues in particular possess that made it very successful for us to have high adoption rapidly.”

And particularly fluency and literacy, that matches the speed at which you're deploying things.

This is where AI transformation becomes a shared responsibility. Technology teams may be responsible for enabling AI. But enabling people to use it effectively is an entirely different discipline. Organizations that recognize the distinction are likely to move faster than those that assume deployment alone will drive change.

3. Leaders may be carrying the wrong mental model.

Many executives are approaching AI through the lens of previous waves of automation. The assumption is familiar: Technology replaces routine work first, while highly skilled work remains protected for longer. Both Breitfelder and Forbes suggested that AI may be challenging this assumption—and perhaps the way leaders think about change itself.

Breitfelder described hearing one idea that has reshaped his own thinking:

"We're using a deterministic mindset to solve a probabilistic problem."

Rather than assuming AI will follow a predictable path, he argued that leaders should prepare for multiple possible futures while remaining willing to experiment in the present. That requires abandoning familiar assumptions and developing a new mental model.

"I'm seeing a lot of people kind of lift and shift the mental model of classic tech automation that many of us grew up with."

What seems different about large language models, he argued, is that they may affect highly skilled knowledge work sooner than many leaders expect.

"What seems very different about LLMs is you're inverting that. That it'll actually impact highly skilled workers the most, and lower skilled workers might actually have more job protection in the first couple of phases of this."

He illustrated the point through an encounter with a company training robots to perform Michelin-star-level cooking. What surprised him wasn't simply the technology—it was that some of the most sophisticated aspects of cooking were being automated before more basic physical tasks.

"You're automating the most skilled part of cooking, and you can't automate the least skilled."

Whether this pattern proves universal remains to be seen. But it raises an important question for leaders: If AI isn't following the script of previous technology revolutions, are organizations preparing for the right future?

4. Innovation is increasingly emerging from the edges of organizations.

Many organizations are trying to strike a balance between governance and experimentation. Too much control can stifle innovation. Too little can create fragmentation and risk.

Forbes described a model that places trust in people closest to the work.

"What I see is my lowest skilled, my most junior people figuring out how to do their job faster and easier, because they have so many demands, and I'm empowering them."

This reflects a broader shift occurring across organizations. Some of the most valuable AI use cases are not being discovered in strategy sessions or steering committees. They are emerging from employees who understand the friction in their work and are motivated to eliminate it.

Rather than prescribing every use case from the center, Forbes advocates providing a shared platform that enables experimentation while allowing the strongest ideas to surface organically.

"[Our approach is] ‘We're gonna give you a platform. At the enterprise level, you figure stuff out, and we'll just start adopting the best of breed,' the application of Darwinism, as I call it."

The phrase is memorable because it captures something essential about the current moment. AI use cases are evolving too quickly for organizations to design every solution in advance. Increasingly, leaders must create the conditions for experimentation while remaining disciplined about governance.

For CTOs, that means building the right infrastructure and guardrails. For CHROs, it means creating a culture where experimentation is encouraged rather than feared. Forbes believes this model works because leaders shift their role from prescribing solutions to creating the conditions where the best ideas can emerge, be tested, and spread across the organization.

5. The best AI mentors may be younger than you think.

One of the most surprising themes of the discussion was the role younger employees may play in helping organizations adapt.

Traditionally, expertise flows from senior leaders to junior employees. AI is complicating that dynamic. In many organizations, younger employees are often the first to experiment with emerging tools and discover new ways of working.

Forbes believes leaders should actively seek out those perspectives.

"I think it's important for me to have mentors who are in their twenties."

Breitfelder described a similar approach at Apollo.

"We're having every senior leader in our company be reverse-mentored by AI digital natives."

This is not simply a commentary on age. It is a commentary on learning. Organizations that treat expertise as fixed may struggle to adapt. Organizations that treat learning as a continuous capability may find it easier to evolve.

Breitfelder framed the challenge this way:

"When you've reached a high level of mastery, how do you have the humility to then say, okay, well, what if it's all gonna completely change, and what if someone with fresh eyes can actually help me disrupt myself?"

The leaders best positioned for the AI era may not be those who know the most. They may be those most willing to keep learning.

6. Durable skills matter more as technology becomes disposable.

The speed of technological change raises a fundamental question: What should organizations teach people when the tools themselves are constantly changing?

Forbes offered a framework that deserves broader attention. In training fighter pilots, the military distinguishes between durable skills and perishable skills. Durable skills form the foundation. Perishable skills evolve alongside technology and changing conditions.

"We use the technique of durable and perishable skills."

Every pilot first develops enduring capabilities in communication, science, mathematics, aerodynamics, and sound judgment before learning advanced systems.

"Those things are non-negotiable."

Only after those fundamentals are mastered do pilots build expertise in the technologies they'll use every day—skills they fully expect will change over time.

That distinction has important implications beyond aviation. As AI evolves, organizations risk focusing too heavily on today's tools instead of the enduring capabilities that allow people to adapt as those tools inevitably change. AI platforms, workflows, and technical techniques are perishable skills. Critical thinking, communication, judgment, and learning agility are durable ones.

Forbes put it bluntly:

"Technology is fleeting. It's fleeting, and it's going to be replaced."

And perhaps his most important observation:

"Your cognitive ability is more valuable to us than any technology."

The organizations most prepared for AI will likely be those that intentionally develop both kinds of capability: enduring human strengths that provide a stable foundation, and technical fluency that evolves alongside new technologies.

7. Belonging and identity are prerequisites for reinvention.

The conversation ultimately arrived at a deeper question: What happens when AI begins to challenge not only how people work, but how they define themselves?

Professional identity is often built over decades. Expertise becomes part of a person's sense of value. When technology changes the nature of work, the disruption can feel deeply personal.

For Breitfelder, helping people navigate that disruption begins with something many organizations underestimate: belonging.

He argued that as work changes, organizations need to create environments where people feel respected, valued, and psychologically safe enough to keep learning.

"We need that stuff more than ever so people can feel some psychological safety as they're about to be massively disrupted."

For him, the challenge is not simply about reskilling. It is about helping people feel secure enough to experiment, remain curious, and reimagine themselves.

"I've been struggling to find the right words to describe how you root someone in feeling safe to experiment, right? And to reimagine themselves."

Forbes sees a similar dynamic in military environments, where people often become deeply attached to specific roles, tools, and ways of working.

"It's very difficult. I will admit, it's very difficult to separate the identity of the thing you've been doing."

His response is to continually reinforce that technology is temporary, while the value people create endures.

"Technology is fleeting. It's fleeting, and it's going to be replaced."

Rather than encouraging people to identify with the tools they use, he encourages them to identify with the judgment, expertise, and contribution they bring.

"Really what you put in the cells are what we care about more than the cells."

That idea may ultimately be the strongest connection between technology leadership and people leadership. AI transformation is not simply about deploying new tools. It is about helping people navigate change without losing their sense of purpose, confidence, or value.

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