By Purushothaman KG, Partner and Head of Technology Transformation and AI, KPMG in India
As Agentic AI moves from experimentation to enterprise-wide adoption, organisations are discovering that the bottleneck is no longer the technology itself. Large language models, agent frameworks and orchestration platforms are becoming increasingly accessible. The challenge lies in integrating autonomous agents into complex business processes, legacy technology environments, governance structures and operating models.
The momentum behind Agentic AI is already significant. According to KPMG’s Global Tech Report 2026, 88 percent of enterprises are investing in embedding Agentic AI into their systems, while 92 percent believe that managing AI agents will become a critical organisational capability within the next five years. At the same time, Indian CEOs are preparing for the workforce implications of this shift. Nearly 81 percent are rethinking the skills required for entry-level roles, 73 percent say AI integration has prompted them to reconsider employee training, and 74 percent believe their workforce’s AI readiness will determine business success over the next three years.
These findings signal a fundamental change in how enterprises create value. Agentic AI is not simply another automation technology. Unlike earlier AI systems that generated insights or recommendations, autonomous agents can initiate actions, coordinate across functions, interact with enterprise applications and increasingly participate in decision-making processes. As a result, organisations must rethink not only technology architectures but also governance models, workforce structures and operating principles.
This evolution requires strong foundations. Enterprises must clearly define the tasks agents are allowed to perform, establish decision boundaries, determine levels of human oversight and create accountability frameworks for outcomes. We are already seeing organisations create new roles focused on supervising and managing AI agents, reflecting a shift in how enterprises view technology leadership and workforce design.
This is giving rise to a new category of talent: Forward Deployment Engineers (FDEs). Unlike traditional software engineers who primarily build technology products, FDEs operate at the intersection of technology, business operations and change management. They work alongside business teams to identify high-value opportunities, rapidly prototype solutions, integrate agents into enterprise systems and translate AI capabilities into measurable outcomes.
The emergence of FDEs reflects a broader shift taking place across the AI ecosystem. The next wave of disruption will not come from building increasingly powerful models, but from deploying them effectively within complex enterprise environments. As core AI capabilities become more accessible, competitive differentiation will depend on how quickly organisations can operationalise AI and embed it into business processes.
For Indian enterprises, this has important implications. The organisations that succeed with Agentic AI will not necessarily be those making the largest investments in AI infrastructure or foundation models. They will be the ones that build strong deployment capabilities. This requires cross-functional operating models that bring together domain experts, process owners, data engineers, AI specialists and deployment engineers who can continuously refine, govern and scale AI agents across the enterprise.
At the same time, organisations must recognise that Agentic AI amplifies existing challenges around data quality, governance and trust. While 86 percent of CEOs in India believe their organisations are well-equipped to adopt advanced technologies, 62 percent cite ethical concerns as a barrier to AI implementation, 53 percent believe their data is not sufficiently AI-ready, and 46 percent acknowledge gaps in technical skills and capabilities. These concerns become even more significant in an Agentic AI environment because agents do not simply generate outputs—they take actions, interact with enterprise systems and influence business outcomes.
The winners in the Agentic AI era will therefore not be determined by who adopts AI first, but by who deploys it most effectively. Enterprises will need a new operating model that combines trusted AI governance, enterprise-grade data foundations, human oversight and deployment excellence. More importantly, they must move beyond AI experimentation and build the organisational capabilities required to industrialise AI at scale. This is where the next competitive advantage is likely to emerge.
Just as cloud computing created demand for cloud architects and digital transformation created demand for product owners, Agentic AI is creating demand for a new generation of deployment-focused talent.
Forward Deployment Engineers, AI orchestrators and human-in-the-loop supervisors will increasingly become the connective tissue between innovation and execution.
For Indian enterprises, the question is no longer whether Agentic AI will transform business operations. The question is whether organisations can build the capabilities, talent and operating models required to deploy it responsibly, at scale and with measurable business impact.
To realise the promise of Agentic AI, enterprises should focus on these priorities:
Strengthen data foundations to ensure agents operate on trusted, governed and enterprise-grade information.
Establish clear governance and accountability frameworks that define agent responsibilities, escalation paths and human oversight mechanisms.
Move beyond experimentation by adopting a Discover–Deploy–Adopt approach. Identify high-value business opportunities (Discover), rapidly prototype and operationalise AI agents within enterprise workflows (Deploy), and drive sustained business adoption through change management, governance and continuous improvement (Adopt).
This is where Forward Deployment Engineers and cross-functional AI deployment teams become critical, serving as the bridge between AI ambition and measurable business outcomes.
In the Agentic AI era, value will no longer be determined by access to intelligence alone. It will be determined by an enterprise’s ability to operationalise that intelligence safely, autonomously and at scale. Organisations that master this deployment challenge will not only realise greater productivity and resilience, but will define the next phase of India’s AI-led competitiveness.