Experience as Asset or Expense: What Indian IT Can Learn from Manufacturing
In large sections of Indian IT services, Experience as Asset or Expense, the answer is becoming increasingly visible. So too is the unease among professionals who recognize that constant upskilling alone may no longer guarantee security in a system where economics, more than expertise, determines longevity.
There is a revealing contrast between how manufacturing industries and large sections of the Indian IT services sector treat experience. In one, long tenure is considered an operational advantage. In the other, it increasingly becomes a financial liability. The distinction is not accidental, nor is it purely technological. It reflects the deeper economics of how each industry is structured, what each business model rewards, and how organisations define value over time.
A manufacturing engineer with twenty-five years inside an automotive plant is typically regarded as indispensable. Their value lies not merely in technical credentials but in accumulated operational judgment. They understand production behaviour under stress, equipment failure patterns, material tolerances, supplier inconsistencies, and the subtle indicators that separate routine variation from an impending crisis. Their knowledge is not theoretical. It has been built across years of direct exposure to systems operating at scale. Organisations compensate such expertise generously because replacing it is neither easy nor inexpensive. Retirement in manufacturing is often ceremonial because experience itself is treated as a strategic asset.
The experience of many senior professionals in large Indian IT services firms is markedly different. Engineers with two decades of service frequently find themselves vulnerable during restructuring cycles. The language surrounding such transitions is familiar: “technology evolution,” “learning agility,” “adaptability,” or “future readiness.” Beneath these phrases often lies a more fundamental issue — cost. Senior employees become expensive relative to fresh graduates who can be trained quickly and deployed at lower billing rates. The problem is not necessarily that experienced engineers have become less capable. The problem is that the prevailing economics of large-scale IT services increasingly reward cost efficiency over institutional depth.
This difference exposes an uncomfortable irony. Manufacturing, commonly described as labour-intensive, frequently treats experienced employees as repositories of institutional knowledge. Large parts of the IT services industry, despite being branded as “knowledge-driven,” increasingly treat accumulated experience as financially inconvenient.
The explanation most commonly offered for this phenomenon is technological change. Software frameworks evolve rapidly. Programming languages lose relevance. Cloud-native architectures replace legacy systems. AI-assisted development changes workflows. It is true that technology cycles in software move faster than those in traditional manufacturing. Some technical skills do depreciate over time. An engineer who stopped learning decades ago will inevitably struggle in a modern engineering environment.
But technological change alone does not explain the structural anxiety surrounding tenure in Indian IT services. The deeper driver is the industry’s business model. Large IT services firms fundamentally operate on utilisation, billing efficiency, and labour arbitrage. Clients typically own the product vision, technology stack, and strategic direction. The service provider supplies engineering capacity against those requirements. In such a system, engineers risk becoming interchangeable units of delivery rather than long-term custodians of institutional knowledge.
That distinction matters enormously.
In manufacturing, continuity directly affects quality. Knowledge about historical production failures, supplier relationships, process trade-offs, safety risks, and operational exceptions cannot easily be recreated through documentation alone. Institutional memory prevents organisations from repeatedly making expensive mistakes. The business itself depends on continuity because the company owns the product, the reputation, and the customer experience. If institutional knowledge erodes, the company itself bears the consequences.
In contrast, many IT services engagements prioritise execution efficiency over long-term continuity. Projects may shift technologies within two or three years. Clients often seek lower delivery costs while expecting rapid adaptability. Under these conditions, a younger engineer trained quickly in the required framework may appear economically more attractive than a senior engineer with broader systems knowledge but significantly higher compensation expectations. If both will eventually need retraining anyway, the cheaper resource becomes easier to justify financially.
This creates a structural bias against long careers.
The language used during restructuring often masks this reality. Organisations rarely frame layoffs as cost optimisation exercises. Instead, they invoke concepts such as “learning agility” and “adaptability.” These ideas are difficult to measure objectively and therefore convenient to deploy selectively. Certainly, some experienced professionals do become rigid or resistant to change. But many senior engineers who have successfully navigated multiple technological shifts across decades possess stronger adaptive capabilities than younger employees who have experienced only a narrow slice of the technology lifecycle.
The critical point is that the debate is frequently less about capability than about economics.
An engineer with twenty years of experience carries costs beyond salary alone. They also carry expectations regarding role, influence, autonomy, and career progression. In delivery environments driven heavily by pricing pressure, those expectations may conflict with business models optimised around scalable labour deployment. The result is a subtle but persistent devaluation of tenure itself.
What gets lost in this process is not immediately visible.
Institutional memory weakens first. Teams begin rediscovering solutions to problems that had already been solved years earlier because the people who understood the original context have exited. Technical debt accumulates because the architects who understood earlier trade-offs are no longer present to guide future decisions. Mentorship becomes increasingly shallow when the most experienced engineer in a team has only seven or eight years of exposure.
More importantly, organisations gradually lose engineering judgment.
Experienced engineers contribute differently from junior developers. Their value often lies less in coding speed and more in pattern recognition, systems thinking, architectural foresight, and risk anticipation. They understand how design decisions interact across systems, where shortcuts become dangerous, and which compromises create long-term fragility. These capabilities emerge only after prolonged exposure to complexity under real operational constraints.
This becomes particularly significant in the era of artificial intelligence.
AI coding assistants are already changing software development economics. Many organisations assume that AI-enhanced junior engineers will become sufficiently productive to replace large sections of experienced engineering talent at lower cost. Superficially, the logic appears compelling. If code generation becomes automated, why pay premium salaries for senior developers?
But this assumption misunderstands where senior engineering value increasingly resides.
As AI reduces the mechanical difficulty of writing code, differentiation shifts toward judgment, architecture, system reliability, security awareness, scalability decisions, and ambiguity management. AI can generate functional code snippets rapidly. It is far less capable of understanding organisational context, long-term maintainability, conflicting business constraints, or hidden operational dependencies. In many ways, automation increases the importance of human judgment precisely because execution becomes easier.
The challenge is that judgment is difficult to quantify inside delivery models built around measurable productivity metrics. Billable hours, velocity, ticket closure rates, and utilisation percentages are easy to track. Institutional wisdom is not. As a result, organisations may systematically undervalue the very capabilities that become most critical in complex engineering environments.
Not all technology companies operate this way. Product firms, semiconductor companies, deep-tech organisations, infrastructure platforms, and research-intensive businesses often place enormous value on specialised expertise and long-term continuity. Engineers with rare capabilities in distributed systems, cybersecurity, AI infrastructure, chip design, or systems architecture continue to command significant strategic importance.
The issue is particularly pronounced within large-scale labour-arbitrage-driven IT services models.
This distinction matters because India increasingly aspires to move beyond outsourced execution toward innovation-led technology leadership. The national technology narrative now includes intellectual property creation, global products, AI leadership, deep-tech ecosystems, and platform engineering. Yet ecosystems that systematically cycle out experienced professionals every decade may struggle to build precisely the institutional depth required for such ambitions.
Innovation ecosystems require continuity. They require mentorship structures where expertise compounds across generations of engineers. They require environments where institutional memory is preserved rather than periodically reset. The most sophisticated technology companies in the world did not emerge solely from abundant junior talent. They emerged from decades of accumulated engineering judgment layered over time.
Manufacturing industries demonstrate that technological evolution does not automatically require the devaluation of experience. Manufacturing itself has transformed dramatically through robotics, automation, advanced analytics, supply-chain digitisation, and AI-enabled production systems. Yet experienced professionals remain valuable because continuity itself is recognised as strategically important.
The fundamental issue, therefore, is not whether technology changes too quickly for long careers. It is whether organisations are designed to treat accumulated knowledge as an appreciating asset or as a rising expense.
In large sections of Indian IT services, the answer is becoming increasingly visible. So too is the unease among professionals who recognise that constant upskilling alone may no longer guarantee security in a system where economics, more than expertise, determines longevity. For further insights into the evolving workplace paradigm, visit
