AI is no longer a buzzword—it’s a boardroom priority, a product differentiator, and, in many cases, a race to relevance. From CXOs announcing bold AI transformation strategies to global enterprises rushing to build GenAI CoEs, we hear AI everywhere. Every industry explores possibilities, every leader is expected to have an AI vision, and every product claims some form of AI embedded.
But pause momentarily and ask: Do we truly have the talent to build what we’re ambitiously envisioning?
The Talent Gap Behind the AI Hype
While AI is being marketed at scale, the ground reality is stark. There’s a severe mismatch between AI ambition and AI execution capacity.
Organisations are struggling to answer fundamental questions:
Where do we find AI engineers with real-world, production-grade experience?
How do we build full-stack AI teams—from Data Engineers to ML Ops to AI Architects?
Are we allocating the right budgets and timelines to upskill internal teams or hire externally?
Can we even access market data to benchmark AI talent availability and compensation?
The gap between hype and hands-on capability is widening. Most organisations are not short of ideas or intent, they’re short of execution-ready AI talent.
The Illusion of Readiness
Hiring “AI-ready” talent is easier said than done. Many resumes today mention Python, ML, GenAI, and ChatGPT—but how many have built scalable AI models that went live? How many understand the nuances of model drift, ethical AI, or multi-modal architecture?
Moreover, the demand has skyrocketed faster than the supply can keep up. LinkedIn data shows a 70% surge in AI-related job postings in the past year. However, talent availability in core areas like LLMs, vector databases, and AI infrastructure is still alarmingly scarce.
What Needs to Change?
If we truly want to transition from talking AI to building AI, we must:
Invest in AI talent intelligence — Understand what skills are in demand, where they are available, and at what cost.
Build faster pipelines — Hire not just for today but for tomorrow. Upskilling, cross-skilling, and internal rotations should be part of every AI strategy.
Leverage domain-specific hiring platforms—Generic job boards are no longer enough. We need platforms that are purpose-built for data and AI hiring.
Redefine expectations — Every company cannot hire the same handful of AI unicorns. Building realistic, team-based AI capabilities is more scalable than chasing individual stars.
AI may be everywhere, but without the right people to build, run, and scale it, it’s just ambition on paper. The
AI revolution won’t be powered by code alone; it will be powered by people who know how to build responsibly and at scale.
Let us ensure talent isn’t the missing piece in this AI puzzle.
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