AI has closed the gap between idea and execution. A non-coder can launch an app, a recruiter can surface candidates with the skills they need in seconds, and a teacher can build a custom lesson plan during recess. And employees aren’t waiting for formal programs, they’re building AI agents to handle routine tasks, creating learning plans, and solving problems on their own.
For many businesses, the question isn’t whether people will use AI. With so many tools available, the real question is whether companies can create the conditions for employees to do it safely, effectively, and at scale.
The companies succeeding aren’t just deploying tools. They’re building a specific capability in their workforce: agency. With agency, a professional can control their own destiny and learn the skills and utilize the tools needed in this moment. Agency thrives on autonomy, so leaders must create environments where empowerment is supported and teams can create in ways previously unimaginable.
We’re in the midst of a generational technological leap, but it’s just as much a human leap. Scaling this capability requires partnership between technology leaders providing secure, connected tools and people leaders creating environments for learning. That’s why 92% of CHROs say AI is accelerating the integration of HR and technology functions. Some companies, like Moderna, have combined these functions under one leader. Others are testing new models.
But the org chart matters less than the partnership itself—one that empowers individuals to learn, leaders to experiment, and organizations to adapt. Here’s what we’ve seen work.
Moving employees from doers to directors
For decades, organizational hierarchies have determined who makes decisions and who executes them. The higher up you go, the more deciding you do. AI is changing that dynamic. This new technology will require everyone in the organization to direct work—whether you’re high up or early in your career, your job is to decide what matters, steer AI to do the work, and validate the results.
What’s important here is judgment, which includes things like quality, perspective, and taste—the ability to determine what problem to solve, how to solve it, what to optimize for, and what quality bar to hold. These capabilities are traditionally not where companies invest their corporate learning resources. But as AI handles more execution, taste becomes an appreciating asset, among the few skills that grow more valuable over time.
Teaching these skills at scale requires a deliberate approach: pairing experienced employees with junior talent so they can understand what good output looks like in practice, creating onboarding programs focused on decision making, or building opportunities to learn the difference between acceptable and exceptional AI outputs. At LinkedIn, we offer coaching to every employee—from interns to the C-suite—as support through constant change. Coaching provides a safe space to work on uniquely human challenges: difficult conversations, building confidence, or developing judgment when there’s no clear answer. And the results speak for themselves. 98% of participants report increased confidence and clarity, and 86% apply coaching insights directly to their work, driving 5–8% measurable performance improvements. It’s proven to be a strategic investment, not just a perk.
Another great example of this in action is KPMG’s new early career program focused on human qualities such as critical thinking, data analysis, and drawing conclusions rather than technical know-how. Training like this is how you move employees from doers to directors, shaping AI, guiding models, and establishing standards for great quality work rather than just completing tasks.
Of course, none of this human development happens in a vacuum. It depends on a partnership between teams and tech infrastructure that employees can rely on–responsible AI principles, secure‑by‑design systems, and infrastructure that connects tools to the right data. For us, that means teams across engineering, legal, and security collaborate early to spot risks and set boundaries. These foundations encode trust, signaling to employees the right guardrails are in place so they can exercise agency confidently. The key is getting the basics right: clear data ownership, strong protections, and thoughtful review of new AI use cases.
The result is employees who feel safe experimenting and confident moving ideas into production.
Building leaders who create agency in others
Managers are the frontline stewards of any big organizational change. The best leaders right now are sharpening their technical competence with the tools their teams are using, because you can’t coach what you don’t understand. From there, they can model intentional use and create psychological safety and space for experimentation, while focusing on what machines can’t replicate: managing energy, coaching, and facilitating collaboration.
The shift shows up in small moments. When a strong leader notices a team member using AI to optimize their workflow, they don’t just acknowledge it – they share it with the rest of the team, hold that person up as an example, and reinforce that trying new things is valued. They create a culture where solving problems independently is encouraged, not just permitted.
At LinkedIn, we put on dedicated leadership labs for our senior leaders tied to business priorities, along with ongoing community learning groups– cohorts of senior leaders across different business lines who come together monthly to build relationships and address real-time challenges together. We’ve also created AI tooling bootcamps specifically for engineering managers with the goal of building fluency with emerging tools first so they can help their teams experiment at the edge of what’s possible.
And our employee resource groups create excellent opportunities for mentorship. For example, our Women In Technology (WIT) community runs a year-long program called “WIT Invest,” designed to strengthen their leadership skills through mentoring circles, upskilling events and leadership panel discussions. Last year, as a result of this initiative, 63% of the community felt more confident in making themselves more visible and navigating uncertainty.
Other companies are making similar investments. Coca-Cola is growing managers into coaches through rigorous leadership assessments and cohort-based development, seeing notable upticks in how employees rate their managers and overall satisfaction.
What’s different about this technological shift is its universality. Every manager—regardless of function—is navigating the same fundamental challenge of building agency in their teams. A marketing manager and an engineering manager may have vastly different day-to-day work, but they need the same leadership capabilities: recognizing quality, encouraging upskilling, and developing judgment in others.
Inspiring a culture shift at scale
The hardest part of building agency isn’t the technology or training; it’s the culture shift. That shift takes hold when you reimagine the structures of daily work: what gets measured, who is involved in what training, what gets rewarded, and where leaders invest their attention.
IBM is a great example, redesigning their performance management process to include assessments on AI skills and behaviors like curiosity alongside business outcomes, signaling to every employee that these capabilities matter as much as hitting quarterly targets. Lumen took a similar approach, weighting what employees do (performance against goals) equally with how they do it (living cultural behaviors).
One of our big bets this year to encourage every team to use AI at LinkedIn was the decision to open up Hack Week to all employees, not just our R&D teams, providing everyone with the chance to roll up their sleeves and play with AI over five days. By doing this we had almost 3,500 employees participate and over one thousand hacks submitted with over 50% being first time hackers and over 20% of participants outside of R&D. The hands-on time builds skills and develops habits for responsible, impactful innovation.
We also use our bi-weekly all company meeting to consistently spotlight genuine AI breakthroughs from employees at all levels across the company. The goal isn’t to wait until each use case is a perfectly polished case study, but to showcase the little wins we can all learn from.
While using AI requires substantial infrastructure investment: token costs, GPU capacity, and computational resources that scale with usage. Leaders should view AI tooling as an employee benefit—if you give people inadequate tools, you undermine their success and fail to get full value from the talent you’ve hired. These costs belong in the same category as headcount, healthcare, and retirement—core operating expenses that enable the business to function. Without adequate infrastructure budget, teams have nowhere to experiment, learn, or develop the fluency this moment requires.
The pattern is consistent. Pair infrastructure with human development, make learning visible, and create space to experiment. Design learning like a product—relevant, personalized, and valuable—not a mandate. Treat employees like customers with choice. When these elements align, agency compounds.
Agency is the advantage
Technical AI capabilities will commoditize. Every company will have access to similar tools and models. What won’t commoditize is how well your people can wield them.
The future belongs to organizations that build agency at every level: individuals who take initiative with confidence, leaders who build those capabilities in others, and culture that reinforces both. Technology creates possibility, but people create results. That requires a people strategy powered by technology—with tech and talent leaders at the table, building in lockstep from day one.
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