Building Cross-Functional Muscle for Scalable Innovation: GenAI adoption demands a mix of domain, technical, and behavioral competencies. By breaking down silos and enabling functions such as data science, operations, HR, and customer experience to co-create solutions, we unlock richer use cases and faster time-to-value. The organizations leading this change are those that embed collaboration as a core capability, not a procedural afterthought.
GenAI in 2025: From Innovation to Integration
As per a Nasscom report, GenAI is expected to generate economic value worth $2.6-$4.4 trillion annually, of which around 75 per cent is expected to be concentrated in software engineering, customer operations, product and R&D, and sales and marketing, which are core service lines of many technology service providers in India.
A key driver of this value is the rise of Agentic AI, autonomous AI entities that can independently plan, execute, and adapt tasks across workflows. Unlike traditional GenAI tools that require constant human prompting, Agentic AI systems are designed to take initiative, respond to change, and collaborate with other agents or human supervisors. Early adoption has been strong in use cases such as IT service automation, customer onboarding, and operational diagnostics.
However, human oversight remains essential. Enterprises are embedding Human-in-the-Loop (HITL) mechanisms to guide these agentic systems, particularly in regulated sectors. Collaboration evolves into co-intelligence, where humans and AI agents enhance each other’s capabilities, driving both speed and accountability.
Addressing the Talent Equation Through Collaborative Ecosystems
India’s AI talent ecosystem is growing, but the demand-supply gap remains a critical constraint. In India alone, NASSCOM estimates that the technology sector will require over one million professionals with advanced AI skills in the next 2-3 years. However, only a small percentage of engineering graduates meet these requirements. This growing talent gap can hinder the adoption of GenAI and slow down digital transformation.
To tackle the growing skills gap, collaborative training initiatives are vital. By partnering with educational institutions, governments, and tech communities, companies can create upskilling programs that equip their workforce with essential GenAI expertise. Great Learning’s ‘Upskilling Trends Report 2024-25’ highlights that 85% of professionals in India plan to invest in upskilling by FY25, with AI, data science, and machine learning leading the way. Industry giants are teaming up with academic institutions to offer certifications, internships, and hands-on learning in these fields. Such initiatives will not only bridge the skills gap but also ensure a continuous pipeline of talent ready to meet the demands of Tech 3.0
Accelerating Digital Transformation Through Collaboration
Partnerships in the Tech 3.0 era extend beyond traditional business-to-business relationships. Cross-industry collaborations are emerging as powerful drivers of innovation. For instance, alliances between healthcare providers and tech companies have led to groundbreaking AI-driven diagnostic tools, while manufacturing firms collaborating with AI startups have optimized production processes.
Collaboration allows companies to scale their digital transformation initiatives faster and more efficiently. By pooling resources and tapping into diverse talent pools, organizations can develop comprehensive solutions that would otherwise be out of reach. Furthermore, partnerships help businesses navigate the complexities of integrating GenAI into their operations, reducing the risks associated with AI adoption.
The Road Ahead: What’s Next for Tech 3.0
As Tech 3.0 gains momentum, the convergence of Agentic AI, modular digital architectures, and collaborative innovation ecosystems is reshaping how enterprises operate and scale. The organizations best positioned for this shift are those that embed GenAI into core workflows, supported by clear governance, continuous learning, and strong cross-functional collaboration.
