By Mr. Milind Shah, Managing Director, Randstad Digital – India
India’s AI journey has entered a more mature phase. For the last couple of years, the narrative has been around adoption; improving existing capabilities, investing in infrastructure, experimenting with use cases, and figuring out where AI can deliver real value. The next challenge is more complex. It is about building the leadership capacity to turn AI investments into sustained business outcomes.
One particularly telling sign of this transition is the time taken to fill an AI Manager role in India, which has gone up significantly. This is not just a change in hiring timelines but a familiar pattern in periods of major technological change. Demand initially grows faster than the market can develop specialised capability. Over time, however, the constraint moves further up the value chain. It is no longer enough to have people who understand the technology. Businesses need leaders who can decide where to use AI, reinvent the workplace, navigate the risks, and leverage the investments in technology to grow the business. That is where India’s AI journey is now heading.
The leadership question is becoming an enterprise question
AI leadership which was once largely viewed as a technology mandate is changing rapidly now. AI is touching on product development, customer experience, supply chains, financial planning, hacking and cybersecurity, human resources and strategic decision-making. As AI spreads throughout the firm, responsibility for its consequences can no longer be delegated to a single silo, such as the technology function.
The next generation of AI executives, therefore, must be multifunctional. They need to understand not only technology but also capital allocation, operating models, customer behaviour, organisational design and risk. This is an important distinction between having an AI leadership role and having the broader business leadership required to turn AI into measurable value. The market does not need more chief AI officers; it needs executives who can unlock value from AI.
The scale of the underlying technological change makes that requirement even more urgent. India accounts for 20.5% of global AI technology job postings. Hiring demand for AI-skilled developers in India has grown by over 660% since 2021, while specialized AI positions remain undersubscribed. The implication, therefore, is that India has developed significant depth in its technology talent. The challenge, therefore, is to build the leadership layer that will be able to leverage that depth.
The bottleneck is moving from adoption to integration
The first phase of AI adoption rewarded organisations that were willing to experiment. The next phase will reward organisations that can integrate. This requires a fundamentally different leadership capability.
An AI leader has to address questions concerning the economic impact of AI on the business, the automation and re-engineering of business processes, the capabilities that have to be retained, how human governance has to be integrated into AI, and how the business has to evaluate the improvement of productivity through AI. These questions sit at the intersection of technology and business strategy. The growing demand for AI integration roles reflects this transition.
Globally, demand for AI Solutions Lead positions has increased 226% over the past year. Senior AI leadership roles in key markets are also experiencing significant vacancy rates. For Indian businesses, this suggests that leadership scarcity could become a more significant constraint than technical talent scarcity as AI adoption moves deeper into the enterprise.
Hiring alone cannot solve the problem
There is a temptation to address emerging capability gaps primarily through external hiring. That approach will have limits. If every organisation is fighting for the same small pool of expert AI leaders, recruiting will take longer, pay will increase, and businesses will be at the mercy of a smaller group of individuals than they can rely on. The best way for businesses to develop leadership is from within.
India has a deep base of engineering, technology, product, data and transformation professionals. Many of these leaders already understand how large organisations operate, how technology is deployed at scale and how complex teams are managed. What they may need is structured exposure to AI and the opportunity to apply it to strategic business problems. This makes internal mobility an increasingly important component of AI strategy.
An engineering leader with strong systems expertise can develop into an AI transformation leader. A product leader can build the technical fluency to lead AI enabled product strategy. The objective is not to turn every existing leader into an AI specialist but to expand the pool of leaders capable of making sound decisions in an AI enabled enterprise.
The skills architecture of leadership is changing
This also changes the criteria for organisations to identify future leaders in the field of AI. While technical expertise will continue to be valued, it will increasingly be complemented by strategic thinking, adaptability, critical analysis, commercial awareness and leadership skills in navigating ambiguities.
The labour market is already signalling this shift. Recent analysis shows that demand for emotional intelligence has risen 173%, creativity 168% and problem solving 35% as AI takes on more routine forms of work. At the same time, professionals with verified AI credentials are advancing into senior roles significantly faster.
This suggests that the future of management is likely to be defined by a different set of competencies, ones that go beyond mere technological proficiency and tap into distinctly human skills such as judgment and decision-making.
The best AI leaders are not going to be people who know the most about new developments in artificial intelligence. They will be managers who know how to ask the best questions and challenge assumptions, who have the judgment to make hard decisions, and who can rally their organizations to embrace change.
Executive upskilling needs to become business infrastructure
This is where the traditional approach to executive learning needs to evolve.
AI cannot be approached through the occasional training session and one-time certification program. The technology is evolving too quickly, and its implications too far-reaching. Leaders require ongoing exposure to emerging applications. For boards and executive teams, this should be treated as a capability infrastructure issue rather than simply an L&D issue.
The question is no longer how much an organisation spends on AI training. It is whether its workforce is learning fast enough to convert AI investment into business value.
The next competitive advantage will be leadership velocity
The increase in fill time for AI Manager hiring should therefore be read as an early indicator of where the market is heading. The competition will increasingly be about leadership velocity. This means how quickly organisations can identify potential leaders, build AI fluency, move people into strategic roles and equip them to lead transformation.
This will require a more deliberate connection between workforce planning and business strategy. AI talent cannot be treated as a specialist hiring requirement that sits separately from the organisation’s broader leadership pipeline. It also means rethinking career pathways. The traditional corporate ladder is already under pressure, with 72% of employers globally describing the conventional career ladder as outdated.
AI will accelerate that change. Leadership paths will become more fluid, with individuals moving between technology, product, data, operations and business domains as their skills evolve.
India’s opportunity is to build, not simply hire, AI leadership
India has already demonstrated that it can build technology talent at considerable scale. The next phase of its AI competitiveness will depend on whether it can build leadership capability at the same pace. That requires three shifts. First, organisations need to broaden the definition of an AI leader beyond traditional AI specialists and identify transferable leadership potential across technology and business functions.
Secondly, internal mobility within the corporation and upskilling of its executives need to be prioritized as key components of the AI-driven work force development strategy. High potential leaders should be given the needed opportunities to acquire new skills required for leading AI initiated business transformations before the market creates such positions.
Thirdly, the executive upskilling process needs to be continuous and should not be viewed as a one-time event, but rather as an ongoing support of business priorities. The objective should ultimately be bigger than filling AI roles. It should be about creating organisations capable of continuously absorbing technological change. The rise in AI leadership hiring time is therefore not simply a sign of a tighter talent market. It is a marker of India’s AI maturity.
The first phase was about building the capability to adopt AI. The next will be about building the leadership capacity to scale it. The organisations that move fastest will not necessarily be those that hire the most AI leaders. They will be those that build the strongest systems for creating them.
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