This is the sixth article in our 10-part series on the key questions posed by CHROs and senior HR leaders about AI implementation. The series explores the implications for work, talent, trust, ethics, regulation and human connection. Each article stands alone, and together they build a practical view of what responsible, commercially useful AI adoption requires. In the interests of transparency and openness the data sources for all ten articles are provided at the end of each article.
Key points:
- Technology can help, but it won’t solve the talent crisis alone.
- The core challenge is expertise drain, as experienced workers retire and take institutional knowledge with them.
- Traditional mentoring is under pressure, with many employees lacking the coaching needed to build core skills.
- AI can capture and share knowledge, helping organisations turn individual expertise into collective intelligence.
- HR adoption of AI is accelerating, especially in recruitment and talent acquisition.
- AI can speed up hiring, from job descriptions and candidate screening to scheduling and candidate engagement.
- The risk is over-reliance. Technology can scale talent processes, but cannot replace human judgement, mentorship and capability-building.
The largest proportion of the global workforce in history is reaching retirement age. This is precipitating an accelerated and severe "expertise drain" across virtually all major industries. This mass exodus of tenured professionals is actively draining organisations of their most experienced, deeply knowledgeable employees.
According to Gartner, simultaneously to the ageing workforce is rapid technological disruption is upending the traditional relationship between expert mentors and novice employees. The pace of change is so rapid that traditional apprenticeship and work shadowing models are breaking down, creating a fractured environment where six in 10 workers currently report that they lack the hands-on coaching and foundational mentorship required to effectively master their core job skills. This dual threat, the loss of experienced personnel and the inability to train their replacements rapidly, poses a critical threat to long-term corporate sustainability.
Faced with this urgent, intensifying expertise gap, organisations are turning to artificial intelligence as a primary mechanism of "collective intelligence". Organisations are developing technology supported capabilities designed to seamlessly capture, synthesise, and distribute deeply embedded institutional knowledge, ensuring that critical information flows from departing experts to novice employees who desperately need rapid upskilling…or so they hope.
According to HRToday, driven by the necessity to overcome talent shortages, AI adoption in HR surged dramatically in 2024, reaching 65 percent—an astonishing 25 percentage point increase from the prior year. Currently, 64 percent of HR departments actively utilise algorithmic systems specifically to optimise talent acquisition, making it the highest priority application over learning and development (43 percent) and performance management (25 percent). The applications deployed within the talent acquisition lifecycle are extensive and effective at driving operational scale. Enterprises leverage natural language processors to draft legally compliant job descriptions tailored to specific key performance indicators demanded by the business. Machine learning algorithms are aggressively deployed to screen millions of data points across global talent pools, analysing resumes to shortlist ideal candidates with greater consistency and exponential speed compared to human recruiters. Furthermore, predictive tools are utilised to analyse competitor talent landscapes, actively alerting recruiters to highly desirable passive candidates based on their current employer and expected market movement. The deployment of virtual assistants for continuous candidate engagement, the automation of complex interview scheduling, and the utilisation of AI-powered psychometric testing have dramatically streamlined the recruitment funnel, significantly enhancing the overall candidate experience while drastically reducing the time-to-hire metrics.
This is great news, however candidates are also using AI to help them draft applications to align their application better to the job description. One cannot help but wonder whether true diversity of thought and approach can make it through the AI filters? How many amazingly talented people have you missed simply because the AI analysis rejected them on the basis of a failure to use the right terminology or language? Human judgement remains a critical part of the recruitment process. We write about this some more in Day 8’s article.
Questions you may want to think about:
- Are we actively using AI as a tool for 'collective intelligence' to capture and transfer the institutional knowledge of our retiring experts, or are we overly focused on using it just for recruitment?
- As candidates increasingly use AI to optimize their resumes for our AI filters, how do we ensure our screening algorithms aren't accidentally locking out non-traditional, highly talented individuals and stifling diversity of thought?
- While AI is successfully optimizing our operational metrics like time-to-hire, what specific human-led frameworks are we building to solve the coaching deficit for the six in 10 workers who lack hands-on mentorship?