Dmitry Zaytsev, Founder of growth and career game platform Dandelion Civilization takes a look at the impact of AI Talent Twins
Recruitment has relied for decades on a simple idea. Past experience is the best predictor of future contribution. That logic no longer holds when the pace of change outstrips the pace of careers. It was a workable assumption when careers changed slowly. That assumption is now breaking down.
The latest Labour Market Outlook shows that AI use inside UK workplaces has grown sharply and that many employers expect the biggest impact on junior and clerical roles. These findings highlight a growing tension inside the hiring process. If early experience is shrinking while work complexity is rising, recruiters cannot rely on CVs alone. They need a clear view of potential, not just a record of the past.
In other industries this shift has already happened. Engineering teams use digital twins to simulate how aircraft, energy systems or logistics networks will behave before they make changes. Experts describe digital twins as virtual models built to test scenarios and predict outcomes with high accuracy.
Recruitment is starting to follow the same pattern with what some HR teams now call AI talent twins.
What a talent twin represents
An AI talent twin is not a replica of a person. It is a model that reflects how someone learns, adapts and makes decisions. These signals come from structured simulations, scenario based assessments and short problem solving tasks that reveal patterns in behaviour. They show learning velocity and decision style in a way that CVs cannot.
This idea aligns with broader findings in HR analytics research. A recent framework shows how predictive models built from high quality behavioural data can support decisions across selection and development and can help organisations identify patterns that traditional hiring methods fail to capture.
The shift is simple. Instead of asking if a candidate fits a job description today, recruiters can begin to test how someone might progress inside a role over time.
Recruitment is already moving in this direction
Recruitment teams are already moving toward predictive thinking even if they do not call it that. A recent interview with Google’s VP of Recruiting shows how AI tools now help recruiters rehearse conversations, learn new contexts and identify promising candidates faster. These tools reduce repetition and let recruiters spend more time on judgement.
The predictive era will take that further. Talent twins use behavioural signals to test how different conditions might influence performance or progression. This follows the same logic as digital twins in engineering, where scenario testing reveals outcomes long before any real world change takes place.
Josh Bersin’s work points in the same direction. His recent article describes how digital twins of employees now help organisations access knowledge without interrupting people. These models allow teams to query a colleague’s history, insights or relationships and receive answers in real time based on a detailed digital profile.
Recruitment can apply the same principle by modelling how a candidate’s potential unfolds over time rather than judging them only at a single moment.
Why ethics must stay central
Any predictive system carries risk. If a talent twin learns from biased data it will repeat old patterns with more efficiency. Dr Emily Yarrow’s work at Newcastle University highlights this risk clearly. Her analysis shows how algorithmic systems trained on historical hiring data can amplify inequality. She reviews the well known example of Amazon’s early hiring model that downgraded female applicants because the training data reflected male dominant historical patterns.
The UK Department for Science, Innovation and Technology now provides guidance that stresses transparency, bias audits and human involvement for any material hiring decision. This guidance is practical. It explains how organisations can assess risk and implement safeguards across procurement, deployment and review.
Talent twins must follow this path. They cannot be black boxes. They must be transparent in design and grounded in behavioural inputs that candidates can influence. Without this they would repeat rather than repair bias.
What this means for recruiters
A talent twin should not replace judgement. It should provide context that helps recruiters make better decisions.
Three shifts will follow:
First, recruiters will gain clearer visibility of potential among candidates who may lack traditional experience. This matters because early career roles are under pressure and many capable people are filtered out by rigid criteria.
Second, hiring decisions will be supported by models that simulate development pathways rather than relying solely on intuition. This helps companies place people where they can grow instead of guessing based on past job titles.
Third, recruiters will be able to challenge outdated selection habits with evidence. If a model shows a candidate has strong learning signals or stability indicators, it becomes easier to counter narrow definitions of fit.
Research by McKinsey shows that employees are already using AI tools at scale and that the real advantage appears when organisations combine these tools with human judgement rather than attempting to automate decisions.
Recruitment fits this pattern. Predictive tools can expand the recruiter’s field of vision while leaving the final decision in human hands.
A shift from snapshots to trajectories
Recruitment has been a snapshot for too long. A CV is a past tense document. A job description is a static view of a moving target. A talent twin is a trajectory. It helps organisations see where someone is heading rather than where they have been.
This shift matters for candidates who have been overlooked by traditional methods. It matters for organisations that want to build adaptable teams. It matters for recruiters who want clearer evidence to support their decisions.
Predictive recruitment will not remove uncertainty but it will reduce reliance on guesswork. When used with care, AI talent twins help organisations recognise early potential and create fairer opportunities for people whose growth outpaces their credentials.
