By: Manish Wadhwa
Every CHRO in India today faces the same arithmetic problem. The World Economic Forum's Future of Jobs Report 2025 estimates that around 39% of workers' core skills will be transformed or become outdated by 2030. Meanwhile, demand for AI-fluent talent has exploded across every function - engineering, sales, finance, HR, operations - while the supply of people who can genuinely work with AI remains thin and expensive. Organisations that try to solve this purely through hiring will find themselves in a bidding war they cannot win, acquiring talent at a premium that walks out the door the moment a better offer arrives.
The more durable answer is the harder one: build AI readiness from within. Your existing workforce already holds the one asset no lateral hire brings on day one — deep context about your business, customers, and problems. AI capability can be taught; context takes years. The organisations that lead the next decade will layer the first onto the second, deliberately and at scale.
From answers to options: what AI actually changes
Any rightful problem needs context: the situation itself, the environment and ecosystem around it, and the constraints within which a solution must operate. Traditionally, a professional facing a complex decision spent days assembling this picture — gathering data, mapping stakeholders, listing approaches, weighing trade-offs. Much of that effort went into generating options, not choosing between them.
This is precisely the stage AI compresses. An individual who knows how to brief AI well can now surface the full set of permutations and combinations, each option laid out with its pros, cons, and dependencies in minutes rather than weeks. The coverage is holistic in a way no single mind, working alone, could match.
But notice what remains untouched: the judgement call. AI can enumerate paths; it cannot know which trade-off your organisation can live with, which stakeholder relationship is fragile, which risk your culture will absorb. The scarce skill in an AI-enabled workplace is therefore not prompting or tool fluency — those are learnable in weeks. It is judgement: framing the right problem, evaluating machine-generated options against real-world context, and owning the decision. An AI-ready workforce does not outsource thinking to AI; it lets AI do the exhaustive groundwork so humans spend their energy where humans are irreplaceable.
Learning that finds the learner
The second shift is in how people grow. Every professional receives improvement signals from all sides — appraisal feedback, manager conversations, peer input, engagement surveys, customer escalations, their own sense of falling short. Historically, most of this signal died on arrival: the individual either lacked awareness of which gap actually mattered or lacked a practical route from feedback to capability.
AI changes both halves. First, it sharpens awareness and prioritisation: an individual can take scattered feedback and ask, in effect, “Which of these gaps has the greatest impact on my role, and in what order should I address them?” That triage of problem, impact, and priority is the step most development plans skip, and the step AI handles well.
Second, AI personalises the learning itself. Pace, timing, and medium can finally bend to the learner rather than the other way around — worked examples at 6 a.m. for one person, conversational Q&A on a commute for another, real-time correction on a live task for a third. Increasingly, this learning arrives at the point of need, embedded in the flow of work rather than scheduled weeks later in a training calendar. Because the learner chooses the pace and format, completion stops being a compliance metric and starts reflecting genuine pull. Development becomes continuous, self-directed, and tied to real gaps — a standing capability, not an annual event.
The organisation's job: enable, evidence, evangelise
None of this scales on individual initiative alone. If AI readiness is left to the enthusiastic ten percent, that is exactly where it will stay. The organisation must make three commitments.
Enable through resources responsibly: Give people legitimate, secure access to capable AI tools, clear guardrails on data and confidentiality, and time to experiment. An organisation that blocks AI tools while preaching AI readiness is asking its people to learn swimming on dry land. But enablement must be paired with responsible use: a human decision-maker retained for any outcome affecting an individual's role, remuneration, or standing, and enough transparency for employees to understand how AI-derived insight informs decisions about them and what it will never be used for. Trust in the guardrails is what makes adoption safe enough to be enthusiastic.
Evidence the impact: Adoption follows proof, not exhortation. When a team uses AI to cut a reporting cycle from five days to one, capture that story and circulate it with numbers attached. Internal evidence from a colleague two desks away persuades far more than any external case study.
Evangelise through a culture of sharing: The fastest-learning organisations treat AI know-how as a commons, not a personal advantage. Communities of practice, internal demos, prompt libraries signal that sharing what you learn is rewarded. And leaders must be visibly engaged in their own learning: employees are unlikely to admit a skills gap in an environment where the most senior person appears to have none. Culture, more than technology, is where AI transformations quietly succeed or fail.
The workforce we should be building
Future-proofing talent is not about predicting which tools will dominate in 2030. It is about building two compounding capabilities: the judgement to use machine-generated options wisely, and the learning agility to keep re-skilling as the technology shifts. Organisations that treat AI readiness as a one-off training rollout will be retraining the same people within a year.
Those that build it as judgement, continuous learning, and trusted governance, together, will find their people adapting to whatever comes next largely on their own. Both capabilities are built, not bought. That is what AI-ready actually means, and it begins within.
About the Author: Manish Wadhwa is the Chief Human Resources Officer at CarDekho Group, where he leads the Company's people strategy across India and Southeast Asia. He has been instrumental in strengthening leadership capabilities, developing talent, and supporting the Company's growth across multiple businesses and geographies. With over two decades of experience in human resources and organizational transformation, Manish has established deep expertise in talent strategy, leadership development, and business partnering. He continues to play a key role in shaping CarDekho Group's people strategy and enabling its long-term growth.