3 Ways AI in Learning and Development Is Solving HR’s Biggest Challenges

3 Challenges in Employee Learning and Development – and How AI Helps Solve Them

Learning and development has never been more important – or more complex. Employees want meaningful growth opportunities, while businesses expect training to deliver measurable impact. Yet major obstacles still stand in the way. Below, we look at three of the biggest challenges holding L&D back today – and how using AI in human resources can help turn each of them into opportunities.

Lack of time for training

A lack of time is one of the biggest barriers – not only for HR and L&D teams, but also for employees themselves. Despite a high appetite for learning, employees often struggle to find time and relevant opportunities to develop new skills – the average employee can only dedicate about 1% of their work week (just 24 minutes) to formal learning. In KPMG’s research on workplace learning experiences, almost half of employees said the most important improvement would be having more “time to focus on training.” Additionally, access to the right content is an issue – 40% of employees want more relevant learning content, and only 22% say they can find appropriate learning resources internally within five minutes. When training is hard to find or not immediately applicable, employees often turn to external sources: over 60% report using outside online platforms or social media to learn new work skills, which can lead to some quality and consistency challenges.

When employees don’t have time to learn, skill gaps widen and business agility suffers. An overwhelming majority of organizations report serious skills shortages. According to McKinsey, 87% of companies either already face a skills gap or expect to develop one within a few years. Alarmingly, organizations report that only half of their employees have completed training that effectively bridges skill gaps.

Similarly, a lack of time is also the top obstacle L&D professionals themselves cite when it comes to developing learning programs. HR teams often juggle multiple priorities, leaving minimal bandwidth to design meaningful, role-specific training content. As LinkedIn’s 2025 Workplace Learning Report notes, managers, employees, and talent teams are “all stretched too thin” to go beyond daily work and focus on longer-term development. A lack of time and resources is holding back employee development in many organizations.

The lack of time doesn’t just mean less training – it raises the bar for what that training must deliver. Most employees don’t have the capacity for lengthy, broad-based training programs, they require learning that is specific, efficient, and delivered at the moment of need. In other words, every minute counts. This is where HR automation and AI tools, like AI Course Creators, can make a transformative difference: they are designed to accelerate the entire training process. Work that once took instructional designers weeks can now be done in days or hours with an AI course creation tool, and updating or refreshing content later is also far easier. Just as importantly, AI course builders can produce content in formats (short videos, brief modules, quick quizzes) which align with the 24-min-per-week reality of modern learners. And because AI content curation speeds up production, it also makes it possible to personalize training quickly for different roles, teams, or skill levels, ensuring the content is both relevant and impactful.

Aligning learning goals with business goals

L&D functions are under pressure to directly support business objectives, illustrated by LinkedIn’s 2024 global survey of 1,636 L&D/HR professionals, where the #1 priority identified for L&D was “aligning learning programs to business goals”. There is a recognition that training must target the skills and behaviours that drive key business metrics. Nearly 9 in 10 L&D professionals (87%) surveyed by LinkedIn said they can demonstrate business value by helping employees gain skills to move into different internal roles, linking learning outcomes to internal talent pipelines and organizational agility.

Although most executives are supportive of learning and development – 90% of global leaders say they plan to maintain or increase their investment in L&D, particularly in upskilling and reskilling – L&D teams still face the same pressure as any other function: proving their impact. A 2024 HR/L&D trends survey showed that HR and L&D professionals rank “budget and resource constraints” among their top five concerns. This means that even with funding available, L&D leaders need to allocate it carefully, focusing on high-impact initiatives and clearly demonstrating value if they want that support to continue.

With greater strategic focus comes a demand for measurable ROI on learning investments. Executives expect learning initiatives to pay off quickly – about two-thirds of companies anticipate a return on investment from skills training within one year (through outcomes like improved productivity, internal mobility, or employee engagement). However, proving L&D’s impact remains difficult. A recent Boston Consulting Group study noted that “failure to achieve expected ROI or impact” is consistently among the top three challenges executives cite with L&D programs and vendors. HR leaders also face pressure to justify L&D investments in terms of workforce productivity and business results. This puts a spotlight on analytics – from basic training uptake metrics to more advanced measures linking learning to performance – to validate that L&D efforts are moving the needle.

Advanced learning analytics (often powered by AI and machine learning) can help L&D move from activity metrics (like course completions) to outcome metrics (like skill growth and job impact). When done well, learning analytics can answer critical questions that business and learning leaders care about: Are we building the right skills needed to deliver on business objectives? Where should we invest more or pull back? How do we anticipate learners’ needs in a changing environment? AI can crunch large data sets to uncover these insights in ways that traditional methods could not. As LinkedIn Learning’s 2024 report notes, L&D leaders are now advised to “lean into analytics” and cultivate data literacy so they can measure specific business improvements tied to learning (e.g. gains in employee productivity, performance, or retention).

Beyond dashboards, using AI in learning and development can provide predictive insights into skill needs. Advanced analytics might analyse industry trends, company strategy changes, and employee competencies to forecast what new skills will be in demand. This allows proactive upskilling. AI tools for HR can even suggest which employees could be reskilled for evolving roles, by identifying adjacent skills or learning behaviours that indicate high aptitude for a new domain. In sum, analytics powered by AI ensure that learning programs are not operating in a vacuum – they are tightly coupled with the organization’s talent needs and future business direction.

Engaging, motivating, and retaining employees

To engage white-collar employees in continuous learning, linking development to their personal career goals is key. LinkedIn’s research shows that employees’ number one reason for wanting to spend more time on learning is “to advance their careers.” Employees who set clear career goals engage with learning 4× more than those without goals. This means HR and L&D teams must frame learning opportunities as pathways to career progression.

Employee development is also increasingly seen as a strategic lever to attract and retain top talent. With global turnover intent high, companies are anxious about retention. Notably, 90% of organizations are concerned about employee retention, and providing learning opportunities has emerged as the #1 retention strategy companies are deploying. Workers are more likely to stay with employers that invest in their growth. Employees without access to upskilling feel far less prepared for future changes and are more inclined to leave, but organizations that invest in career development see tangible retention benefits – a LinkedIn analysis found a +57% retention advantage in strong learning cultures.

One of AI’s biggest contributions that can raise both employee motivation and engagement is making individualized learning at scale finally feasible. Adaptive learning platforms use AI to tailor content, pacing, and difficulty to each learner’s needs, and some are even capable of creating gamified learning experiences. For example, if an employee aims for a team leader role or needs better communication skills, there are AI-powered learning platforms that can identify the optimal personalized learning paths and resources to get there. Individualised learning also means learners aren’t wasting time on what they already know but instead focus on areas that move them forward. This level of personalization directly increases motivation, because employees see training linked to their goals and career growth rather than generic compliance. The impact on employee satisfaction and retention is equally strong – when people feel that learning is tailored, purposeful, and time well spent, they’re far more likely to participate, complete courses, and stay with an organization that invests in their development.