The world of learning has completely changed. Today’s learning and development (L&D) leaders are no longer just supporting development; they’re helping to boost organizational agility and resilience. The rate of technological change, in particular, means that upskilling, reskilling and redeploying people in the workplace has taken on a new pace and pattern.

Deloitte’s 2025 Global Human Capital Trends research indicates that the top reason workforce technology investments have failed to deliver value is “lack of workforce skills/capabilities.” The World Economic Forum warns that 59% of theglobal workforce will need reskilling or upskilling by 2030. And LinkedIn reports that, by 2030, 70% of the skills used in most jobs will change — with artificial intelligence (AI) emerging as a catalyst.

Navigating these fast-evolving skills needs, many L&D leaders are turning to AI-powered tools that can help them keep up, particularly in global enterprise organizations.

Using AI to Spot Skills Gaps

To build an agile approach to talent management, organizations need clarity: clarity on the skills they have and the work that needs to be done. Without this, they won’t be able to spot the gaps and ensure that learning programs and reskilling initiatives are truly aligned with business needs.

Achieving this clarity involves looking at talent and work through a new, more granular lens: assessing people based on their skills and capabilities (not just their roles) and analyzing jobs at the task level (and then the skills required to perform those tasks, and proficiency needed).

Mapping this manually would be labor intensive and costly, and insights would go out of date very quickly. But AI can scan millions of data points across job descriptions, resumes, learning records and disparate human resources (HR) systems to infer and structure data about skills and tasks — and keep this information up to date dynamically.

AI isn’t just able to catalogue existing data: it can connect data across your HR ecosystem. It gives you a real-time, always-on view of workforce capability. That dual visibility into both skills supply and work demand is what makes agile workforce planning possible.

It’s the foundation for smarter workforce decisions: where to hire, the people to upskill, which capabilities to build internally and how to redeploy talent to meet evolving priorities.

Using AI for Personalized Learning and Development

AI also plays a key role in shaping how people grow and addressing organizational skills gaps as, or before, they emerge.

With rich, dynamic skills intelligence in place, organizations can go beyond generic training programs and actually deliver tailored development experiences aligned with each person’s current capabilities, their future potential and aspirations, and evolving business needs.

AI can recommend (and even generate) personalized learning content and suggest relevant career paths to make learning continuous, contextual and scalable.

Many employees feel they’re capable of far more than their current roles allow: 74% told Beamery they could take on greater responsibilities, and almost half (48%) believe they could contribute more if simply given the opportunity.

But, for many, that potential is being left untapped. More than half (55%) cite a lack of clear career progression or development plans, while 31% feel boxed in by rigid role structures that stop them from applying their skills in new ways. Nearly 8 in 10 (79%) of respondents have considered leaving their jobs.

By giving employees targeted opportunities to build skills, apply them quickly and see a clear path forward, organizations can unlock hidden potential, boost engagement and reduce costly turnover. It’s not just training but a strategy for growth, retention and resilience.

Building an Agile Workforce With AI

To make the most of AI-driven talent strategies, organizations should prioritize:

• Connected data: Ensure your talent systems (HRIS, ATS, LMS) are integrated and feed into a unified view of skills and tasks.
• A dynamic skills framework: Build or adopt a framework that reflects your industry, priorities and the way work is evolving.
• Embedded learning: Make development part of everyday work, not a separate activity. Use AI to serve relevant opportunities in real time.
• Outcomes, not just content: Track the impact of learning on role readiness, task performance and internal mobility (not just completion rates).
• Alignment with business needs: Use skills and task intelligence to inform broader workforce planning, including hiring and mobility to succession and transformation.

As organizations embrace AI to accelerate learning and workforce agility, it’s also essential to ensure that these tools are applied ethically, transparently and with human oversight. Trust is critical. Workers need to understand how their data is used, and organizations need to be confident that AI recommendations are fair and free from bias.

Responsible AI in talent management means being clear about how AI is used in learning, hiring and development decisions — and ensuring employees understand how their data is being used. While algorithms should be monitored to detect and address unintended bias, it’s important to see AI as a tool to augment not replace human judgment in the L&D space.

Organizations that get this right can reduce risk, while also building trust, improving adoption and creating a more inclusive approach to workforce transformation. They can also elevate the role of L&D professionals and free them up for more creative thinking and strategic tasks.

Learning That Fuels Strategic and Agile Workforce Planning

AI is transforming how learning is designed and delivered. Microlearning, dynamic content and AI-powered recommendations are making it easier to meet learners where they are, especially in globally distributed or hybrid teams. At the same time, upskilling is becoming a core pillar of workforce strategy.

In a survey by Microsoft, 47% of leaders said upskilling existing employees is a top workforce strategy for the next 12-18 months, pushing it ahead of “expanding team capacity with digital labor” (45%) as a priority.

It’s no longer just about training; it’s about agility. Organizations need to be able to shift talent to meet new challenges, close skills gaps faster and build resilience and flexibility into their workforce models as automation opportunities emerge.

That’s not possible without real visibility into both the skills people have and the opportunities available for them to grow. AI can ensure those insights are both precise and timely so L&D teams can focus on the human touch.