More reasons why your degree is no longer your career

Across industries and sectors, companies are still hiring by credentials, appraisal cycles are running on familiar metrics, and career counsellors are still mapping competencies to roles. On the surface, the architecture of professional life appears unchanged.

And yet the structural conditions that made this model work are shifting, not with dramatic announcement, but through a gradual convergence of economic and technological forces that the data now makes difficult to ignore. They are producing a change in the very model of professional identity that modern careers have been built around, a shift that will require a different kind of response than reskilling programmes alone can provide.

For India, with its 1.5 million engineering graduates annually, its five-million-strong IT services workforce, and an educational architecture built almost entirely around credential accumulation, the stakes of this transition are particularly high.

The Accumulated Model of Professional Identity

For much of the post-war knowledge economy, and in India in particular since liberalisation in 1991, professional identity operated as an accumulation system. We built expertise over time, and that expertise constituted who we were. The years of domain knowledge and accumulated skill were not merely qualifications. They were identity certificates, stable and socially recognised declarations of worth. “I am a software architect.” “I am a management consultant.” Underneath each is the claim of someone who possesses a specific kind of valuable knowledge, and that possession is the basis of the professional self.

Educational systems, recruitment practices, certification structures, promotion pathways, and performance management frameworks were built around this assumption. Capability could be developed, verified, and periodically audited. The role of institutions was largely to assess what individuals possessed and how effectively they applied it. The accumulated model hardly asks about the quality of professional formation that individuals are generating from the specific conditions they are actually in, and whether they can keep regenerating it as those conditions change.

The accumulated model, the architecture of pre-formed capability verified at entry and monitored for output, is now under structural pressure from multiple directions simultaneously. The CEO of HCL Technologies put the emerging logic bluntly at a NASSCOM summit: “Generate twice the revenue with half the workforce.” The pattern is visible globally. Infogain, one of India’s established IT services firms, relaunched in April 2026 as Tenarai, repositioning from a headcount-driven services model to an enterprise AI acceleration company focused on measurable business outcomes. These are not announcements of crisis. They are announcements of a new operating logic, one in which headcount and value creation are being structurally decoupled.

GitHub Copilot research cited by McKinsey found that software developers using AI tools completed tasks 56 per cent faster than those working without them. What AI has done, quietly but structurally, is commodify knowledge, not eliminate it, but make abundant what was once scarce. When AI systems produce competent code, legal analysis, and financial modelling on demand, the scarcity that made those capabilities identity-conferring dissolves. The credential persists on paper. The professional weight it carries is shifting beneath it. This is not an argument that knowledge and skills no longer matter. Deep expertise remains essential as the foundation on which judgment is built. What is changing is that knowledge alone is no longer sufficient to constitute a durable professional identity. It is necessary. It is no longer enough.

This shift is reorganising how professional value gets assessed, and the reorganisation follows a discernible arc. In most industrial and knowledge economies, organisations assessed people by monitoring the hours worked, reports produced, procedures followed, and output volumes . When markets accelerated, this gave way to outcomes. Did the decision improve? Did the client trust deepen? Did the risk reduce? Did learning actually happen? Companies navigating fast-changing customer demands had already been moving in this direction for decades, through OKR cultures, agile performance frameworks, and periodic review cycles that replaced annual appraisals with continuous feedback. AI is now completing that shift by making activity metrics almost entirely redundant. AI is also beginning to press on outcome metrics themselves. When AI systems can optimise local outputs, outcomes, too, become partially AI-attributable rather than distinctly human.

What remains is a third register, one that organisations are reaching toward without yet having settled language for it. The distinctively human contribution is retreating into relational trust, ethical calibration, meaning-making, adaptive sensing, and developmental influence. Professionals are increasingly valued not for what they produced in a given period but for second-order effects. Whether they grew others’ capacity, whether the systems they worked within became more resilient and adaptive, whether they contributed to an ecosystem beyond their immediate role, whether the people around them developed through the encounter. This is impact assessment rather than outcome assessment, and it requires periodic and longitudinal evaluation rather than cross-sectional verification. The professional whose identity is built on the possession of credentials has no architecture for generating or sustaining value at this level of analysis. The professional whose identity continuously co-arises from engaged formation does.

From Accumulation to Enactment: Introducing Enacted Career Identity

Herminia Ibarra, one of the world’s most cited scholars of career transition, argued in 'Working Identity' that professional identity is produced through action and experimentation. This was significant for professionals who were deliberately making choices to reinvent their careers, but not for responding to continuously shifting structural conditions. In her account, old identity gives way to a liminal period, which eventually resolves into a new settled identity. The current moment demands not a strategy for navigating a transition, but a permanent mode of professional being in which formation itself, rather than any particular formed identity, becomes the stable ground.

The scholarly conversation has been moving in this direction. Recent career identity research confirms that professional identity is now understood as a perpetual, nonlinear process rather than something that settles at career entry. These transitions require navigating simultaneously between who we have been, who we aspire to be, and what our context demands of us. What this body of work has not yet produced is an account of the mechanism through which identity continuously reconstitutes itself, not through reflection or social feedback, but through practice in situ, through the quality of engagement a professional brings to conditions that keep changing.

Enacted career identity is the construct this framework introduces to name that mode of becoming — professional selfhood that continuously co-arises from the full ecology of conditions one is inhabiting, not a destination reached after a transition, but an ongoing orientation to formation itself.

Enacted career identity emerges through repeated cycles of engagement with changing conditions. Professionals encounter novel situations, interpret their significance, act within them, observe the consequences of those actions, and reorganise both their understanding of the situation and their understanding of themselves. Through this process, capability is not merely deployed but continuously reconstituted. Identity becomes less a repository of accumulated expertise and more a developmental process through which expertise, judgment, relationships, and contextual awareness are continually integrated into new forms of professional contribution. What remains stable is not a particular identity but the capacity to participate in its ongoing regeneration.

The difference between an experienced professional and one with an enacted career identity becomes sharp precisely at the moment stability ends. In familiar conditions, they may produce identical outputs; both draw on real knowledge and perform reliably. But when the context genuinely shifts, when no prior pattern applies, when AI can generate ten plausible answers but cannot tell us which one this situation actually needs, the experienced professional reaches back into their accumulation, while the enacted-career-identity professional reaches forward into the situation itself. One asks what worked before. The other asks what this situation makes possible.

Indian philosophical tradition provides a relational ontology that illuminates this process. The principle of dependent origination holds that nothing arises independently of the conditions that co-arise with it. Enacted career identity is inherently relational. Not just socially relational in the sense of networks and role models, but ecologically relational across the full field, the person-AI loop, inter-human dynamics, the internal state of the professional, and the macro-structural moment they are inhabiting. Professional identity, on this view, is not a possession we carry into contexts. It is an event, what arises at the intersection of our capabilities, our context, and the quality of our attunement to the moment. We are not our credential. We are, professionally, what we can discern and navigate in a situation that has not yet been resolved.

Why the Transition Is Psychologically Hard

Professional identities have momentum. The software engineer in Pune who has spent a decade building expertise in a particular technology stack faces something that cannot be resolved by an AI literacy course. Her skills are real. But her professional identity- her stable sense of who she is at work, what she is good for, where she belongs - was constituted not just by capability but by a whole ecology of recognition. Colleagues who relied on her expertise, clients who hired her for it, a career trajectory that rewarded its accumulation. When AI begins performing aspects of that work competently, she does not face a task reallocation challenge. She faces an identity one.

This is the lag the policy conversation misses. The macro-ecological shift can happen at a velocity that individual professional identity cannot match. The formation feels intact from the inside while its grounding is being reorganised from the outside. Identity is regenerated not through reflection alone but through participation in developmental encounters that reorganise both capability and self-understanding.

The Structural Asymmetry That Cannot Be Ignored

There is a further complexity worth naming. The capacity to inhabit enacted identity is not equally distributed. The IT professional in Bengaluru with access to diverse platforms, international clients, and a technologically rich environment has considerably more room to experiment and reconfigure than the engineering graduate in a smaller city with fewer structural options. The macro-level forces driving this transition- AI capability development, platform concentration, and geopolitical restructuring of technology services do not arrive uniformly. They land with different force depending on where we are positioned in the country’s uneven geography of professional formation.

This is why reskilling programmes, while necessary, miss the deeper point. The question is not only what individuals should learn. It is also about how colleges structure learning, how firms design developmental pathways, and how OD/HR professionals ensure that the capacity for practising enacted identity is accessible across the full range of India's professional population, not merely to those already well-positioned.

India’s comparative advantage in the emerging AI economy may not lie in frontier model development but in implementation capacity, the ability to deploy AI thoughtfully in contextually complex situations, to build human-AI collaboration that produces real outcomes for real people. That advantage is essentially the enacted identity model in practice. The professional who can navigate the intersection of technology, domain knowledge, human complexity, and shifting macro conditions is precisely what this next phase of work requires.

The knowledge worker era gave the world a developmental model that served it well for decades. The transition ahead is not a rupture. It is a recalibration, and the institutions, educators, and professionals who begin that recalibration now, before the shift fully arrives, will be better placed than those who wait for the disruption to name itself.

The views expressed are personal