Reimagine learning and development for the AI age

March 16, 2026In the AI age, reskilling and upskilling will be required on a scale that hasn’t been seen before. By 2030, 59 percent of the world’s workforce will require training, according to the World Economic Forum’s Future of Jobs Report 2025. Additionally, as automation begins to scale to tasks historically completed by junior and entry-level roles, organizations should rethink approaches to early career development. Learning and development (L&D) will become part of the engine of organizational performance, adaptability, and resilience—not just a support function.

Companies will need a new shape of talent to unlock the potential of agentic AI. As workflows evolve to include incremental opportunities from agentic AI, organizations should prepare managers to lead teams that include both humans and AI agents. Companies will also need to rethink how to attract and retain the best and brightest people, and maintain institutional knowledge, whilst addressing future skill gaps.

In this environment, a paradigm shift in thinking about L&D will be required. Fast. Development will no longer be adjacent to work but rather a core part of work itself, and every employee will need opportunities to build skills continuously. In a world of constant change, learning organizations will win.

Supporting an enterprise-wide learning capability

Organizations should make six shifts to reimagine L&D for supercharged performance, adaptability, and resilience in the AI era:

1. Align business, talent, and technology leaders around the skills that drive enterprise value.

   Challenge: AI will reshape activities across most occupations. Therefore, organizations must redefine the capabilities they need—and the underlying skills that enable them (e.g., the capability of predictive maintenance at scale and data analysis, sensor design, and predictive modeling skills to enable it). However, organizations often begin with broad and complicated skill lists that reflect departmental priorities rather than enterprise value. Without a unified direction, skilling efforts become fragmented and may fail to shift outcomes meaningfully.

   Solution: Reimagine the role of L&D as a strategic integrator and partner in workforce planning. Create a joint leadership forum across business, HR, and technology to determine which capabilities are needed and which skills will unlock value as work changes. Focus on what matters given enterprise strategy to ensure that investments focus on building capabilities that will materially improve performance, adaptability, and resilience.

2. Prepare managers and leaders to champion AI fluency and integration into work.

   Challenge: While AI can augment a wide range of tasks, its impact depends heavily on how work is redesigned and how people learn to collaborate with intelligent systems. As a result, the nature of managerial work is shifting from supervising people to orchestrating systems in which people, AI agents, and robots collaborate.

   Solution: Reorient leadership development toward AI fluency and human-AI collaboration as core managerial capabilities. Equip managers to understand where AI can augment work, how to redesign roles and decision rights, and how to coach teams operating alongside agents and automation. Prioritize the development of durable “meta-skills” such as problem solving, adaptability, and ethical judgment, alongside foundational AI literacy for all leaders.

3. Deliver personalized, experience-rich learning at scale to build AI-era skills at pace.

   Challenge: Traditional learning remains too generic. Different groups need different learning experiences: frontline employees will adopt AI tools to lift productivity, managers to lead human-agentic teams, domain leaders to redesign workflows and embed decision rights and ethical guardrails. In many cases, AI is “under the hood” and the learning needs to focus more on ways of working than tech fluency. In these cases, in particular, experiential learning in the employee context will be essential.

   Solution: Use AI as an accelerator to go beyond course-based learning toward a system where development is continuous, contextual, and embedded in daily workflows. AI-enabled platforms already personalize content by role, skill gap, and learning history, helping drive uptake at scale. Many organizations are using AI coaching tools embedded within providers (e.g., Udemy, DataCamp) to guide learners in real time. The next wave, enabled by emerging standards such as the Model Context Protocol, will allow employees to ask an AI assistant for help and receive in-the-flow-of-work support drawn from learning and knowledge sources, tailored to role, geography, and access rights.

4. Pivot from episodic learning to development in the flow of work at the pace of business change.

   Challenge: Many organizations still treat learning as a periodic initiative rather than an ongoing requirement. Against the pace of technological and market change, episodic learning quickly becomes out of date, leaving workforce skills lagging business need.

   Solution: Adopt a continuous skilling rhythm. Use short, targeted skill sprints tied to quarterly business priorities, embed learning moments into transformation programs, and model learning visibly at senior levels. Reinforce learning through performance management and career progression so that continuous skilling becomes an organizational habit in the flow of work, not an intervention.

5. Measure L&D capability building with the same rigor as financial, operational, and people performance.

   Challenge: Most organizations still measure L&D through activity metrics such as hours trained. Yet, such measures rarely correlate with business performance or capability uplift. Productivity can decline by up to 22 percent when employees lack the right skills. Without meaningful measures, executives struggle to justify investment or sustain momentum.

   Solution: Measure what matters. Track skill acquisition and deployment, such as time to proficiency, internal redeployment rates, and the contribution of skill uplift to productivity or customer outcomes. Clearly communicate the value of L&D—including cost avoidance—and integrate these metrics into existing business dashboards so leaders can link L&D investments to financial, operational, and people outcomes. Top-performing organizations that rigorously focus on human capital development are 4.2 times more likely to outperform peers financially, reinforcing why skilling metrics must sit alongside core business dashboards.

6. Redesign mobility offerings and early career pathways to match a more agile, skill-driven future.

   Challenge: Career progression models built on stable ladders no longer match how work evolves. Automation is scaling to many of the routine tasks that once provided early-career learning experiences. At the same time, employees expect fluid movement across teams and domains, but most organizations lack the systems and career options to support this shift.

   Solution: Design for mobility and early career development explicitly. Provide structured rotations; project-based learning merged with real, everyday work; and opportunities to practice judgment together with more tenured employees. Surface internal opportunities transparently and encourage employees to move toward roles that match emerging strengths. Establish apprenticeship programs that take full advantage of increasing fluidity and learning while working.

Building L&D momentum

Making these shifts requires conviction and disciplined execution. Most organizations will not solve everything at once, but they can begin building momentum quickly. The most successful early movers take a practical, test-and-learn approach.

Here are actions to get started within the next 90 days:

1. Pilot AI-enabled learning in a priority area. Identify a function or group where work is changing fastest and where learning can unlock immediate value. Experiment with tools that provide real-time guidance, surface tailored learning content, and support employees as they complete day-to-day tasks. Beginning with a defined cohort helps focus resources, demonstrate impact, and build organizational belief.
2. Role model change from the top. Senior leaders should make their own learning visible, reinforce expectations for continuous skilling in performance conversations, and celebrate teams that demonstrate adaptability. When leaders participate, learning becomes part of the culture rather than an initiative.
3. Embed simple, meaningful measures of progress. Track 2-3 indicators that matter to the business, such as time to proficiency in a key skill, redeployment into priority roles, or improvements in frontline productivity or customer outcomes. Use these insights to refine and scale what works.

Organizations will not win in the age of agentic AI by technology alone. They will win by building workforces that learn continuously, adapt quickly, and grow into the opportunities ahead.

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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.