Epitome Global CEO Kevin Chan shares insights on why traditional recruitment and psychometric testing fall short, and how whole-person profiling surfaces the talent conventional screening misses.
Artificial intelligence is collapsing the lifespan of skills, leaving many organisations to discover that the candidate who has done the job before is no longer the safest hire. Epitome Global has spent nearly a decade building psychometric infrastructure to address that shift, drawing on more than a million career profiles to assess people as individuals rather than CVs.
Kevin Chan explains why conventional hiring overlooks capable people, what employers should prioritise when skills change faster than job descriptions, and how governments, educators and employers might finally share a common language on talent.
Interview Excerpts:
Why do you believe traditional hiring methods and psychometric assessments are no longer sufficient in today’s AI-driven workforce landscape?
Most organisations built hiring around one assumption: the best predictor of future performance is past performance. Hire someone who has done the job before, with the right credentials. Such logic made sense when roles stayed stable for a decade, but it actively misleads now. Conventional psychometric tests rarely measure what matters. A questionnaire might flag a broad personality type, yet cannot tell you whether someone will adapt when AI restructures their role in eighteen months. Watching large reskilling programmes shifted our thinking. The people who thrived were not the highest scorers, but those whose cognitive style and motivations suited learning under uncertainty.
How can workforce intelligence and whole-person profiling help organisations identify talent that may be overlooked by conventional recruitment processes?
Conventional hiring excels at finding people who have already done the job. Yet it is almost useless for finding people who could do the job given the chance. In workforce transitions, internal mobility and any role where AI is collapsing the lifespan of skills, “has already done it” is the wrong filter. The internal candidate ready to step up, the motivated career-changer, the returner with the right cognitive profile: CV screening eliminates them on the first pass. Whole-person profiling adds what a résumé cannot show. We cross-reference each profile against 1.3 million career profiles and 330,000 skill-occupation records.
With AI reshaping jobs and skills requirements, what capabilities should employers prioritise when building future-ready workforces? Let me push back. The moment you ask which capabilities to prioritise, organisations reach for a fresh list, AI literacy, adaptability, critical thinking, then keep hiring to a checklist already out of date by publication. The real question is whether your organisation can identify people who will keep acquiring capabilities as requirements change. Still, three things help. Build internal assessment infrastructure rather than trusting third-party credentials; one oil and gas firm cut hiring from nine months to two weeks this way.
“Treat cognitive style and learning orientation as first-class criteria. Build a feedback loop between assessment data and on-the-job outcomes.”
How can governments, educational institutions, and employers work together to address employability challenges and workforce reintegration?
Employability problems are really data problems. Three parties hold partial, incompatible pictures of the same workforce. Governments hold qualification records, training institutions hold completion data, employers hold performance and tenure data, yet none of it joins up. Progress comes when a shared data layer sits across all three. Drawing on the same whole-person baseline, a government agency, a training provider and an employer can finally ask whether someone is ready for a pathway or needs a different intervention first. Employability should also be continuous, not event-triggered. Reintegration support that begins only at unemployment arrives too late. Resilience beats safety nets.
Looking ahead, what will the future of skills-based hiring and workforce optimisation look like over the next five years?
Skills-based hiring has a structural flaw that will soon become obvious: skills degrade faster than job architectures can be rebuilt. Define a role as a skills taxonomy now, and that taxonomy is already partial next year. The durable layer, cognitive style, motivational structure and working preferences, changes over years rather than months. Workforce intelligence will increasingly treat it as the baseline, with skills profiling as one input. Expect three shifts. Complete human capital data infrastructure becomes a procurement standard. Workforce data is treated as a strategic asset, audited and tracked like financial data. The line between hiring assessment and workforce planning dissolves.
Source: Tahawul Tech