AI First, People Always: How Employers Can Harness Artificial Intelligence Without Deepening Inequality
A Wave You Can’t Out-Swim
Artificial intelligence has swept through boardrooms with the same force that the internet did a generation ago—but this time the stakes feel higher. One United Nations agency warns that up to 40 per cent of roles worldwide could face significant disruption, raising the spectre of a jobs crunch and a widening divide between nations that can leverage AI and those that cannot. Employers now stand at a crossroads: will they use automation to trim head-counts and widen profit margins, or will they invest in people so everyone rides the wave together?
At last week’s GITEX Asia in Singapore, Pedro Uria-Recio, Chief Data & AI Officer at CIMB Group, put the challenge plainly: “There is a huge wave of change, and unfortunately some people might be left behind.” His solution? Equip staff with new skills and create fresh roles tailor-made for an AI-first economy. In other words: don’t just teach existing workers how to survive—give them somewhere new to thrive.
Adaptation Beats Fortification
Not everyone agrees that “job protection” is the right mindset. Tomasz Kurczyk, Chief Information & Technology Officer at Prudential Singapore, argues that trying to hold back technological progress is like “building a sea wall against a tsunami”. Instead, he says, leaders must learn to adapt the very concept of employment: redesign job descriptions, redeploy talent swiftly, and accept that the workforce itself will become more fluid.
That thinking is already catching on. Microsoft’s 2025 Work Trend Index finds that 82 per cent of global leaders plan to deploy advanced generative-AI agents within 18 months to boost capacity, while 78 per cent expect to hire AI-specialist roles. Crucially though, nearly half (47 per cent) still see upskilling the existing workforce as top priority—a tacit acknowledgement that employees remain the beating heart of corporate value.
Bias at the Speed of Light
Even as AI promises efficiency, its darker side—algorithmic bias—looms large. Kurczyk offers a stark reminder: “The data is created by humans, so the bias isn’t a bug; it’s a feature we don’t want.” When flawed data flows into a model it can replicate discriminatory patterns at scale, amplifying social inequities faster than any hiring manager could.
Boards are now compelled to treat “AI ethics” not as a compliance checkbox but as a core business risk. Governance frameworks, transparent audit trails and diverse data-science teams are fast becoming the hallmarks of forward-thinking employers who want AI to lift their brand rather than tarnish it.
The Twin Track: Skill-Building and Job-Creation
So how does a company simultaneously embrace disruptive tech and nurture its people? Experts suggest a twin-track strategy:
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Continuous Skill Mapping
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Conduct rolling audits of emerging AI tools across departments.
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Map the competencies each tool demands and cross-reference with existing staff capabilities.
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Offer micro-credentials and modular courses that workers can complete alongside their day jobs.
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Job Innovation Labs
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Carve out small, cross-functional teams tasked with inventing roles that could not have existed pre-AI: “prompt engineers”, “ethics auditors”, “model explainability leads”.
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Pilot these roles on short, six-month horizons; scale the winners and re-skill the people in those posts for permanent positions.
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Bias Bounties
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Similar to bug-bounty programmes in cybersecurity, reward employees who uncover biased outputs in AI systems.
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Feed those findings back into model retraining cycles, turning every staff member into a quality-control agent.
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Artisanal Renaissance
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Leverage AI’s efficiency gains to reinvest in high-touch, human-centric services—bespoke craftsmanship, personalised client care, creative R&D—areas that algorithms struggle to replicate.
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Humans in the Loop—For Good
Uria-Recio sums up the emerging philosophy succinctly: “Our mindset has to be AI-first, but with humans firmly in the loop.” The phrase is more than a slogan; it is an operating model. Machines handle repeatable, high-volume tasks; people oversee, interpret and innovate. In practice that means:
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Explainable AI dashboards so non-technical staff can see why a model reached a decision.
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Escalation rights giving human reviewers final say on critical outcomes—credit approvals, medical diagnoses, hiring shortlists.
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Ethics committees comprising technologists, legal voices and frontline employees to examine unintended consequences before new models go live.
The Long View: Collaboration Over Competition
None of this unfolds in a vacuum. Universities, governments and corporations must collaborate to ensure training pathways keep pace with technology. A national curriculum tweak today can prevent a talent shortfall tomorrow. Likewise, policy-makers can incentivise responsible AI adoption through tax credits, grants and—where necessary—regulation that penalises reckless deployment.
Kurczyk remains bullish: “AI will become free for all, opening incredible opportunities worldwide.” Artisanal industries could flourish as automation relieves them of mundane tasks. Creative professions might soar as generative tools become collaborative partners rather than replacements.
The likelier risk is a short-term spike in inequality during the transition. But with proactive upskilling, transparent governance and a genuine commitment to job innovation, companies can ensure the AI revolution lifts more boats than it sinks.
Bottom Line
History shows that technology reshapes labour markets but rarely abolishes work itself. The internet displaced typists yet birthed web designers and app developers. AI will be no different—provided leaders act now to blend automation with human ingenuity.
Protecting jobs as they are is a losing battle; protecting people by equipping them for what’s next is both humane and profitable. Done right, an AI-first strategy can become the great equaliser rather than the great divider—and that is a future in which businesses, employees and society all win.
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