Around half the people in the agency's core roles were 50 or over and heading for retirement, and when a veteran division head or regulatory engineer left, decades of hard-won know-how left with them, undocumented. There was no plan for who would step up, and no way to see which younger staff were ready to.
The public sector was staring down a demographic cliff. Roughly half of the agency's people in core roles were 50 or over and approaching retirement, and it was experiencing a genuine crisis of institutional-knowledge loss: when the long-serving experts left, they took decades of understanding that lived in no system anywhere.
There was no orderly way to move that knowledge to the next person. When a veteran retired, the handover was rushed or never happened, and the role was left thinner than before, which showed up directly as a weaker service to the public.
The agency also had no clear map of its own talent. Management could not easily see which of the more junior staff were ready and suited for the complex senior roles, so it defaulted to long, expensive external recruitment instead of drawing on the people it already had.
See who is about to retire, move their knowledge to a named successor, and look inside the organisation first when a role opens.
Built on dynamic skills mapping, the system automatically identifies veteran employees inside a retirement window and flags the key roles at risk of falling empty, so the organisation sees the gap forming years ahead instead of discovering it the week someone leaves.
An internal knowledge network connects departing experts to the employees marked as their successors, so decades of understanding is documented, recorded and turned into shared material inside the learning function rather than walking out the door unrecorded.
For each pairing the system generates a focused handover and training plan, giving the successor a structured, long-lead overlap with the person they are replacing, so the transfer happens over months of real preparation instead of a rushed final fortnight.
When a senior role opens, the matching engine first scans the entire nationwide workforce and recommends internal candidates who have completed the development tracks the role needs, surfacing ready people long before anyone drafts an external job posting.
The engine surfaces and ranks internal candidates; it does not appoint them. A human panel makes the final decision on every promotion, so the AI widens and speeds the shortlist while the judgement about who leads stays with people.
Younger employees gain transparency into the routes open to them and what each one requires, which turns a career in the service from an opaque waiting game into a set of visible, adaptive paths they can actively work towards.
Continuity held through the retirements, and most of the senior roles were filled from within.
The agency built a strong, stable talent pipeline: 85% of the leadership and core roles filled in the last year were internal promotions, with a large saving on external recruitment cost, as ready successors stepped up instead of the organisation starting each search from scratch outside.
Because successors went through a structured, long-lead handover before the previous holder retired, the vacuum in critical roles shrank dramatically and services kept running through the transitions. Younger staff, given visible and adaptive career paths, were markedly more motivated to stay and grow inside the service.
Experience that lives only in one person's head is one resignation away from being lost. The value here is spotting the exit early, moving the knowledge to a named successor over a real overlap, and matching from within first, with a human panel deciding. It is the same skills-matching discipline behind our talent-matching work, turned toward promoting and preparing the people already inside the organisation rather than screening candidates from outside it.
Keyword screening rejected strong candidates and time-to-fill hit 70 days. Semantic skill-matching, blind screening and predictive ranking cut time-to-fill 45% to 38 days, lifted the interview-to-offer ratio 35%, and improved diversity 20%.
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