For years, one of the foundations of India's technology and GCC growth story has been its access to a large pool of engineering talent. But the equation is becoming more complicated.
As GCCs take on mandates spanning AI, semiconductor engineering, product development and deep tech, organisations are looking beyond whether candidates possess the right qualifications. Increasingly, the question is whether talent can translate knowledge into work, navigate ambiguity, learn continuously and contribute in increasingly complex business environments.
That distinction framed a recent roundtable hosted by People Matters in partnership with IIIT Dharwad, titled From Talent Consumer to Talent Architects: Rethinking GCC and Partnership. The conversation brought industry and academia together around a fundamental question: if the capabilities organisations need are changing faster than traditional talent pipelines can respond, can companies remain simply consumers of talent?
The emerging answer was clear. The talent challenge can no longer belong exclusively to either academia or employers. Building future-ready talent will require both to become active participants in shaping it.
Is access to talent still enough?
One of the sharpest distinctions to emerge from the discussion was between available talent and deployable talent.
Technical proficiency remains critical, but organisations increasingly need people who can apply those capabilities to real problems quickly. At the same time, the shelf life of individual technical skills is shrinking. A particular tool or technology learned today may change considerably by the time a student enters the workforce.
That shifts the emphasis from simply teaching the technologies currently in demand to developing the capacity to keep learning as technology changes.
AI illustrates the shift particularly well. Rather than treating AI as a specialist capability confined to particular roles, participants argued that it is increasingly becoming a foundational capability across work. For academia, therefore, staying relevant cannot mean continuously adding new technologies to an already crowded curriculum. It also means helping students become better at learning, adapting and applying new knowledge.
And for industry, the implications are equally significant. Organisations cannot expect academia to anticipate every emerging skill requirement independently. If companies want talent with greater relevance to changing business contexts, they may need to participate much earlier in shaping that capability.
What happens when technical skill is not the bottleneck?
Interestingly, the discussion suggested that the harder gap may not always be technical.
Today's students have extraordinary access to technology, online learning, competitions, hackathons and global communities. Many enter organisations with technical exposure that previous generations acquired only after joining the workforce.
Yet participants pointed to another set of capabilities that can determine whether that knowledge translates into sustained performance: focus, discipline, resilience, communication, adaptability and comfort with ambiguity.
Curiosity, for example, is unquestionably valuable. But leaders also described a tendency among some early-career talent to want to move quickly from one emerging technology to another without spending enough time developing depth. Knowing GenAI can create the urge to move immediately into machine learning, followed by the next new technology.
The challenge for organisations and universities is therefore not to reduce that curiosity, but to help young talent direct it.
This is where mentorship emerged as an important bridge. Industry practitioners can help students understand their strengths, explore possible career pathways and develop depth around a smaller set of meaningful capabilities rather than attempting to master everything simultaneously.
Corporate readiness, in this sense, is not a finishing course delivered just before placement. It is exposure to how work actually happens: deadlines, teamwork, stakeholder expectations, ambiguity, accountability and persistence when a problem does not have an immediate answer.
Can the internship model go far enough?
If practical experience is essential, the traditional short internship may also need a rethink.
One model discussed during the roundtable was a deeper 12-month immersion, where students spend a significant period working on industry problems while receiving academic credit for the experience. IIIT Dharwad shared that it is already experimenting with longer experiential pathways alongside flexible curricula, industry-led learning and professors of practice.
The underlying idea is bigger than extending the duration of an internship.
Industry exposure becomes most valuable when it is integrated into learning rather than sitting outside it. Students should be able to encounter a real problem, discover what their classroom knowledge cannot yet solve, return to learning with better questions and then apply that knowledge again.That creates a loop between theory, application and reflection.
For employers, such models could also change early-career hiring. Instead of assessing candidates almost entirely through interviews, tests and academic credentials, organisations could observe how students solve problems, collaborate and learn over a sustained period.
The relationship begins to shift from recruiting finished talent to helping validate and develop capability before hiring even begins.
Who teaches the context that technology cannot?
There is another layer to employability that generic technology training cannot easily provide: domain context.
A technologist solving a problem in financial services operates within a very different environment from someone building for healthcare, insurance or cybersecurity. Understanding regulatory constraints, customers, risk and business processes can often determine whether technically strong solutions are useful in practice.
This creates an opportunity for industry experts to participate not merely as guest lecturers, but as mentors, professors of practice, project sponsors and collaborators on real problem statements.
Alumni networks can play a similar role. Alumni working inside organisations carry both an understanding of the academic environment and first-hand knowledge of how industry requirements are evolving. Used strategically, they can become a continuous feedback mechanism between campuses and workplaces rather than simply a network activated during placements.
From placement partnership to capability partnership
Perhaps the most important shift discussed was therefore one of mindset.
For decades, the university-company relationship has often become most visible at the end of the student journey: companies arrive on campus, assess candidates and make offers.
But if skills are evolving faster, that relationship needs to start considerably earlier.
Industry can bring emerging problem statements, mentors, domain knowledge and exposure to real working environments. Academia can provide strong foundations, research capability, experimentation and the flexibility for students to explore new disciplines. Students, meanwhile, need opportunities to repeatedly apply what they learn rather than waiting until employment to discover what the workplace demands. That creates something more consequential than a placement pipeline. It creates a capability partnership.
And as India's GCCs take on increasingly sophisticated global mandates, that distinction may matter. The next chapter of the talent story will depend not simply on how many skilled people organisations can access, but on how deliberately industry and academia can build the capabilities that neither can create as effectively on its own.