What does it mean to upskill a workforce for AI? Is it about helping employees understand the language, tools, and risks? Equipping people to embed AI into daily workflows? Rewiring your organization through AI-enabled domain transformation that changes how work gets done?
In truth, it’s all the above. But order, emphasis, and leadership approach matter enormously. Companies that treat upskilling as a training rollout miss the larger point: It is a change management effort.
Learning and development is more important than ever in times of change—helping to build individual skills as well as an organization’s ability to bounce back and bounce forward. As organizations roll out AI, those that approach it as a holistic change journey can accelerate adoption, unlock innovation, and build trust with their workforce, all while strengthening their competitive position.
The dimensions of upskilling and reskilling
AI upskilling and reskilling unfold along three interconnected dimensions:
- AI literacy: Building a shared baseline of fluency across the organization, reducing fear, increasing transparency, and giving employees the confidence to experiment.
- AI adoption: Embedding tools and behaviors into core workflows by redesigning roles, processes, and incentives so AI becomes part of how work gets done.
- AI domain transformation: Developing domain-specific use cases that extend competitive advantage, often upskilling technical and functional experts to reimagine what is possible.
According to the latest McKinsey Global Survey on AI, 78 percent of respondents say their organizations use AI in at least one business function. Most companies spend disproportionately on literacy because it is visible and easy to measure. Fewer lean into adoption, which is messier and requires leadership courage. Only a handful are consistently connecting upskilling and reskilling to innovation, where real performance gains lie.
From courses to capabilities
Evidence suggests that training alone rarely drives sustained behavior change. In a study of M365 Copilot adoption behaviors by Riya Sahni and Lydia B. Chilton, nine in 10 participants acknowledged that formal training would be useful. Yet, seven in 10 ignored onboarding videos, instead relying on experiential learning (i.e., trial and error) and social learning (i.e., peer discussions).
Employee sentiment is another reason framing matters. Indeed’s 2024 special report, “Tomorrow’s World: The Workforce and Workplace of the Future,” found that 75 percent of U.S. workers expect their roles to shift due to AI in the next five years, but only 45 percent have received recent upskilling—a gap that training alone cannot bridge.
Recent initiatives show that large-scale reskilling can become a catalyst and competitive advantage—even reducing recruitment time and cost with AI-powered reskilling pathways and by directly connecting candidates to job opportunities.
When reskilling is designed as a talent and change journey, not a standalone training program, it can unlock adoption and trust. Leaders who frame AI adoption as a journey of shared growth can turn what feels like a threat into a source of loyalty. Also, by using AI to embed learning in AI tools and workflows, companies can continue to break down the distinction between working and learning in ways that were previously not possible. Embedding upskilling in the flow of work and linking it to visible career pathways transforms the narrative. Individuals don’t just learn skills to remain employed in the future; they see a future for themselves in an AI-enabled organization.
The leadership and talent challenge
Most employees can learn the basics of prompting or the terminology of generative models in a few hours. The hard part is changing how leaders and teams think, decide, and collaborate in an AI-enabled environment. That requires a talent strategy anchored in behavior change.
Our research on large-scale transformations shows that lasting adoption happens when employees know what to do differently and also believe in why it matters, feel supported by leadership, and see reinforcement in the systems around them.
Consider a company that ran an AI literacy course for employees. Everyone left knowing what generative AI can do. But a month later, adoption was minimal because workflows, incentives, and frontline leadership behaviors remained unchanged.
Contrast that with another company that introduced AI assistants directly into the flow of work, trained supervisors to model adoption, redesigned performance metrics to reward experimentation (e.g., ongoing online learning hours, use case development, efficiency KPIs), and created peer-led support communities. Literacy and adoption rose together because the organization treated upskilling as a holistic change journey.
Leaders make AI real
Reframing upskilling and reskilling for AI as a change journey puts leadership at the center. Four implications stand out:
- Leaders must go first. Employees will take their cues from how leaders talk about and use AI in their own work. Modeling adoption, whether through small signals in daily routines or bold moves in strategic decision making, is essential.
- Culture must be reshaped, not just skills. Upskilling succeeds when organizations create psychological safety for experimentation and failure. In AI adoption, that means rewarding curiosity and iterative learning over polished perfection.
- Incentives and systems must align. If employees are trained on AI but still measured against old KPIs, adoption will stall. Leadership must ensure that performance management, career progression, and recognition mechanisms reinforce new behaviors.
- Change is sustained through reinforcement. Research on McKinsey’s Influence Model highlights the importance of role modeling, fostering understanding, building conviction, and formal reinforcement. These elements apply directly to AI adoption: Training sparks awareness, but reinforcement will sustain change.
Some organizations are already moving in this direction. For example, a leading consumer packaged goods company created a customized capability-building program tailored to executives, equipping leadership with the necessary language and skills to role model AI and drive cross-business collaboration.
Another company, a telecom industry leader, laid the foundation for enterprise-wide adoption of AI by establishing a gen AI hub and engaging senior leadership through bespoke sessions. It rolled out tailored training, leveraged ongoing re-engagement strategies, and tracked success using multiple metrics.
McKinsey & Company took a similar approach to drive organization-wide adoption of its gen AI platform, Lilli. By tapping leadership to tell the change story, promoting new capabilities and workflows, developing learning content, delivering hands-on support, and more—in short, treating upskilling as a change journey—the Firm deployed the platform to 30K users and reduced time to insights by 20 percent.
The common thread: Each organization treated AI upskilling as a leadership-led transformation, not an HR-led training rollout.
By combining AI literacy, AI adoption, and ultimately AI-driven domain transformation into a coherent journey, and by embedding reskilling directly into the flow of work, leaders can move their organizations from awareness to confidence, from hesitation to momentum, and from incremental adoption to transformative impact.
Agentic organization
Capture enterprise-wide value from AI and agentic technologies
This blog post is part of a People and Organization Blog series that explores how organizations will be transformed by agentic AI. Follow us on LinkedIn and keep an eye on the blog for our latest insights and how these technologies will shape organizations today and tomorrow.