When Sridhar Vembu, the founder of Chennai-based software firm Zoho, recently cautioned that rapid AI adoption could eventually create a purchasing power problem by reducing entry-level hiring, the response was predictable. The conversation quickly collapsed into the familiar debate over whether AI will replace software engineers and eliminate jobs.
Yet that framing understates the significance of his intervention. Vembu's remarks are not merely about employment. They are about how organizations and governments are making decisions on one of the most consequential technologies of our time.
The question is no longer whether companies should invest in AI. They should. Artificial intelligence has the potential to transform productivity, improve customer experience, accelerate innovation, and create entirely new business models. The real question is whether these investments are being evaluated with the same strategic discipline that organizations apply to other major capital decisions.
Unlike a factory, an acquisition, or a research and development program, an AI investment is often celebrated simply because it exists. Success is measured by lower costs, fewer employees, and higher productivity. Those are undoubtedly important outcomes, but they are also incomplete. As AI becomes central to corporate strategy, organizations need a broader framework for evaluating what these investments actually create—and what they may unintentionally erode.
AI investments need better success metrics
Every significant corporate investment is judged through multiple lenses. A new manufacturing plant is expected to improve capacity, strengthen supply chains, and generate future growth. Research and development expenditure is assessed not only by immediate returns but by its ability to create intellectual property and long-term competitive advantage. Human capital investments are justified because they build organizational capability over time. AI deserves the same treatment.
Today, boards often justify AI expenditure through familiar financial metrics: productivity gains, cost savings, operating margins, and shareholder returns. While these are legitimate objectives, they represent only part of the value AI can generate. A narrow focus on efficiency risks encouraging organizations to deploy AI primarily as a cost-cutting tool rather than as a capability-building technology.
Boards should therefore ask broader questions. Has AI improved decision-making? Has it enabled employees to innovate faster? Has customer experience improved? Has it strengthened organizational learning? Has it created new products, services, or business models? Most importantly, has it enhanced the firm's long-term competitive capability rather than merely improving the next quarter's earnings?
The companies that derive the greatest value from AI are unlikely to be those that simply automate existing work. They will be those that redesign how work is performed and how value is created. AI should therefore be viewed as a strategic investment in organizational capability, not merely an exercise in operational efficiency. This is particularly relevant in India, where the productivity gains from technology have often been uneven across sectors.
The apprentice gap: Who builds tomorrow's experts?
Perhaps the least discussed consequence of AI adoption is its effect on how expertise is developed. Every knowledge profession relies on apprenticeship. Doctors begin with routine clinical work before making life-and-death decisions independently. Lawyers spend years researching and drafting before arguing landmark cases. Academics build expertise through years of research before becoming recognized scholars. Software engineers similarly learn by debugging code, fixing errors, reviewing systems, and gradually assuming greater responsibility.
These routine tasks are often portrayed as repetitive work that AI can easily automate. But they also constitute the training ground where professional judgment is formed. If AI increasingly performs entry-level work while organizations fail to redesign how expertise is cultivated, companies may inadvertently weaken the pipeline that produces future architects, engineering managers, product leaders, and chief technology officers. The savings realized today may come at the cost of organizational capability tomorrow.
This is not simply a labor-market issue. It is a knowledge-management challenge. Businesses have always understood the importance of succession planning for leadership. AI demands a similar conversation about succession planning for expertise. Organizations must ask not only how AI replaces tasks, but also how future professionals will acquire the judgment, intuition, and contextual understanding that cannot be downloaded from a model or generated through a prompt.
The AI race needs strategic discipline, not herd behavior
There is little doubt that AI represents a transformative technological shift. Yet history also reminds us that transformative technologies often create waves of imitation alongside genuine innovation. Organizations sometimes adopt new technologies because they solve real business problems; they also adopt them because competitors, consultants, and investors expect them to. AI risks creating a similar dynamic, as seen in the contradictions that refuse to clear in current AI investing.
Today, announcing an AI initiative often signals that a company is technologically progressive. But signaling should not be mistaken for strategy. The real question is whether AI is genuinely transforming workflows or merely being inserted into existing processes to satisfy market expectations. Boards should therefore apply the same rigor to AI investments that they would apply to any other strategic decision. Which workflow is being improved? Which customer problem is being solved? What measurable capability has been created? Would the investment still make sense if competitors were not making similar announcements?
These questions are particularly important for India. Much of the global AI conversation is shaped by economies facing aging populations and high labor costs, where replacing labor is a primary driver. India, by contrast, has a young and growing workforce, and its competitive advantage has long rested on a deep pool of skilled engineers and professionals. If Indian firms rush to automate entry-level roles without rethinking how expertise is built, they risk undermining the very ecosystem that has made the country a global technology hub. The same logic applies across Asia, from Japan's robotics-heavy industries to Southeast Asia's emerging digital economies, where the race for technological leadership is intensifying.
Ultimately, the AI investment debate should move beyond productivity. The real measure of success is whether these investments strengthen an organization's ability to learn, innovate, and compete over the long term. That requires a disciplined, strategic approach—one that values capability as much as cost savings, and that protects the apprenticeship pipeline that builds tomorrow's experts. Only then can AI deliver on its promise without eroding the foundations of future growth.
