A few months ago, a global manufacturer proudly announced it was “investing in AI talent.” Within weeks, an entire layer of new roles appeared across the organisation – AI Strategist, AI Innovation Lead, Head of Generative Programmes. Six months later, most of those hires were still waiting for meaningful work.
The data systems hadn’t changed. The decision processes hadn’t changed. The incentives certainly hadn’t changed.
It wasn’t a shortage of talent. It was a process problem dressed up as a hiring strategy.
The Mirage of the “AI Talent Gap”
The narrative that there’s a global shortage of AI expertise is convenient. It justifies recruitment budgets and sounds suitably urgent in board papers. But the data suggest otherwise.
McKinsey’s 2024 State of AI research found that although over 90 percent of companies are investing in AI, only about 1 percent have achieved mature, enterprise-wide deployment—meaning AI is fully integrated into workflows and delivering measurable business impact. The obstacle isn’t capability; it’s how companies are organised to use it.
Data remains fragmented, decision-making is slow, and governance is ambiguous. Drop a handful of machine-learning specialists into that environment, and they soon become spectators rather than catalysts.
Before asking “Who do we need to hire?”, leadership teams should be asking “What do we need to redesign so AI can actually function here?”
Capability Is a Process, Not a Payroll Line
The organisations making meaningful progress with AI – in financial services, healthcare, and manufacturing – share one characteristic: they redesigned their processes before scaling their teams.
That redesign tends to unfold in three practical stages:
Re-map decision flows
Traditional corporate hierarchies were built for risk avoidance, not for data velocity. AI requires shorter loops, greater transparency in accountability, and decisions that can keep pace with the data feeding them.
Reconstruct data access
Too many firms still rely on ad-hoc requests and manual workarounds. A genuinely AI-ready enterprise creates standardised, auditable data pathways so teams can build and test without endless permission chains.
Recalibrate incentives
If your performance metrics only reward efficiency, no one will experiment. AI thrives in cultures that value learning and iteration. That shift starts in how success is measured, not in who’s hired.
Once these foundations are in place, upskilling and recruitment become multipliers. Without them, they’re decoration.
The Reskilling Mirage
“Reskilling” has become the fashionable response to the AI shift. Yet retraining people for a broken process creates better-trained frustration.
Process-led capability building works in reverse: redesign workflows first, then train people to operate within them.
One pharmaceutical company did precisely that. Rather than teaching its scientists to code, it began by mapping every delay in its R&D cycle – every review, every hand-off, every duplicated approval. Only then did it introduce AI tools for data analysis and simulation. Training followed naturally because the new process demanded it.
Reskilling without redesign is like teaching drivers new dashboard technology while leaving potholes in the road.
Redefining What “AI Work” Actually Means
The next phase of AI maturity won’t be about new job titles. It will be about re-engineered processes that make existing jobs smarter.
Think of:
- A buyer comparing suppliers through predictive dashboards rather than spreadsheets.
- A compliance officer reviews algorithm-flagged anomalies rather than manually sampling files.
- A product engineer tests 30 virtual prototypes before commissioning a single physical model.
None of these roles has “AI” in the title, yet each depends on a process redesigned for intelligence.
What Leadership Looks Like Now
Leading AI transformation isn’t about chasing talent; it’s about creating context.
When employees understand why the organisation is investing in AI and how it links to commercial goals, the appetite for learning emerges naturally. The most forward-thinking executives I’ve met this year don’t talk about “AI transformation” at all – they talk about redesigning the machinery of decision-making.
That means:
- Clarifying ownership – who governs data use and model deployment.
- Simplifying structure – removing procedural bottlenecks that suffocate experimentation.
- Aligning speed and governance – ensuring approvals don’t take longer than the projects themselves.
These may sound operational, but they are profoundly strategic. Process is where ambition becomes execution.
The Real Competitive Edge
Over time, access to AI tools will level out. Everyone will have similar models and platforms. What will separate leaders from laggards is the architecture of work – how seamlessly human and machine intelligence interact through well-designed processes.
The AI hiring frenzy will fade. What endures is the ability to operationalize intelligence.
A Final Thought
Before signing off on your next AI recruitment plan, pick one core business process – say, customer onboarding, product testing, or claims review – and trace it end-to-end on a whiteboard.
If that journey still depends on emails, spreadsheets, and approvals layered four deep, your issue isn’t talent. Its design.
The companies that grasp this early won’t just adopt AI. They’ll re-engineer how intelligence moves through their business – and that, not another round of hiring, is what will set them apart.
Would you like to explore how your IP or R&D processes could be re-engineered for intelligent performance?
Schedule a meeting with our IP and R&D experts, and let’s turn AI potential into measurable capability.
