Employee churn, broadening skills gaps, and remote learning and development (L&D). These are just some of the challenges facing HR teams during a period of unprecedented workplace uncertainty.

According to the Chartered Institute of Personnel and Development (CIPD), the number of employees with a tenure of less than 12 months rose to a high of 18% in 2022 and still sits at 16% in 2024. If that level of employee churn wasn’t challenging enough, employees now value work-life balance, career advancement, and personal skills development more than ever, with more than one-third describing these aspects of their employment as “make or break” when weighing up their future.
 

Factor in the widening skills gap that organizations are now facing, with around 9 in 10 saying they have or expect to have a skills gap in the coming years, and the big HR challenge facing businesses becomes readily apparent.

Like countless other industries, this has led HR teams and HR tech providers to turn to tools like artificial intelligence. Given the sudden boom in popularity of technologies like generative AI and large language models (LLMs), it’s only natural that these avenues should be explored, and their potential to tip the scales back in the favor of HR teams is becoming increasingly notable.

AI might one day be able to streamline recruitment, help personalize and deliver employee training, and perhaps even predict and identify skills gaps within an organization before they emerge.

According to a 2024 survey, more than 80% of companies plan to invest more in AI as part of their HR technology stack. However, while it’s very easy to paint a picture of AI as an incoming silver bullet, it’s important to remember that the “human” element in human resources remains – and will likely always be – vital.

AI can augment, enhance, and even automate, but when it comes to context-based decision-making, the verification of skills, and the recognition of talent, human hands are still irreplaceable.

What AI Can Do…

While the subject of AI may have gone stratospheric in recent years, it’s important to note that it’s not just a passing trend. It’s a tool and one that has been around for quite some time. Like any tool, it’s only useful if it's deployed and utilized in the right way. As businesses transition to digital-first learning, they will naturally have more opportunities to gather and make sense of data.

While AI’s most exciting selling point at the moment is the use of generative tools like ChatGPT to create something out of nothing, the real benefit of AI lies in its ability to process vast volumes of data, spot patterns, and give us an advantage when it comes to making decisions.

Let’s apply this to the real world. By using a learning management system (LMS) that leverages AI, L&D leaders can get much deeper insights into how effective their training programs are. Things like completion rates, certifications, and potential sticking points can be monitored en masse, allowing for a form of continuous improvement that, up until recently, wasn’t possible without painstaking manual processes.

The learning experience itself can also be positively impacted by AI. Training managers don’t have the time to cater to individual learning preferences or create tailored learning pathways based on an employee’s current level of knowledge. That’s why one-size-fits-all training workshops and courses have become standard.

But while it might be easier to cast the net wide with a training workshop and make sure everybody gets the same level of training, not everybody will benefit from it. How much of that is wasted? What’s the return on investment? It’s too hard to gauge. With AI, training can be fine-tuned to individual employees or groups of employees, so that it complements their experience instead of taking up time that’s better spent elsewhere. If a particular knowledge or skill gap is identified, relevant training content or individual modules can be automatically recommended.

These are all examples of how AI can be successfully integrated into HR, optimizing learning and development, providing more avenues for establishing ROI, and ensuring that skill-based needs are met. But none of these are examples of AI working in isolation; each scenario requires that human hands are there to guide it and verify its impact.

Supporting the Bridge Requires Human Hands

AI is impressive, but it lacks one thing that only humans possess: context. For instance, AI can suggest that an employee has acquired a particular skill based on a completed training module or previous experience, but it lacks the nuanced understanding needed to validate these skills effectively.

Human intervention is crucial for assessing how well an employee can apply their knowledge in practical, on-the-job situations. This validation process often requires a level of judgment and contextual awareness that AI, despite its advancements, cannot replicate. The same is true for employee training. For example, AI can analyze feedback and performance data to continuously refine and adapt training modules, providing a tailored learning experience that keeps employees engaged and motivated. However, the human element remains crucial in curating these programs and validating the real-world application of acquired skills.

In other words, AI can scope out the problem and recommend solutions, but it’s down to human hands to guide those solutions and ensure they align with company goals and aspirations. The difference is that those who leverage AI will have a distinct advantage in parsing data and gathering insight into potential solutions and outcomes.

We’re not witnessing the start of an AI revolution but rather the start of a human-AI collaboration. This collaboration has the potential to foster more dynamic recruitment and learning environments, which, in turn, will pave the way for more meaningful and impactful people development.

Author Bio

Ramesh Ramani is the CEO of ExpertusONE.

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