A significant transformation is underway in corporate India as companies across various sectors begin to deploy a new breed of artificial intelligence. These are not just simple tools but agentic AI systems – intelligent software agents capable of making decisions, executing tasks, and collaborating directly with human employees. This shift is prompting business leaders to fundamentally rethink not only their technology strategy but also how jobs are designed, skills are built, and teams are managed.
From Passive Tools to Active Collaborators
Sridhar Hariharasubramanian, senior director at Salesforce India, describes this as a structural shift. He states that the business world is witnessing a crucial transition where AI evolves from being a passive tool to becoming an active collaborator. This fundamental change alters how work is accomplished. However, Sridhar cautions that this is not merely a technology upgrade; it is a profound people transformation. As AI agents assume responsibility for routine and even complex tasks, human roles will increasingly focus on creative problem-solving, strategic thinking, and collaboration.
He highlights a surprisingly critical technical skill now often termed prompt engineering or context engineering. The quality of outcomes generated by AI is directly linked to the quality of the questions and guidance provided by humans. In an enterprise powered by agentic AI, knowing how to guide, frame, and supervise AI becomes as crucial as traditional coding skills. Sridhar emphasizes the importance of a mindset of continuously learning, unlearning, and relearning, which will define career resilience in this new age. At Salesforce, employees are actively encouraged to experiment with AI tools to become comfortable working alongside digital agents as co-workers, not viewing them as threats.
Building Workforce Adaptability at Scale
This emphasis on continuous learning is echoed by Sumati Mohan, director at Cognizant India. In response to the AI revolution, Cognizant has invested deeply in structured upskilling and cross-skilling programmes. These initiatives span beyond engineers to include consultants, sales teams, and operational staff. The company tracks these efforts through internal academies to ensure learning translates into deployable capabilities.
Cognizant has also established dedicated Agentforce labs and cohort-based learning initiatives to accelerate hands-on exposure. Sumati observes that Gen Z employees have emerged as some of the fastest adopters of agentic AI, quickly experimenting with new ways of working. Interestingly, mid-level managers, often considered the hardest group to shift, are adapting faster than anticipated. The demand from clients is clear: they want responsible, outcome-driven AI implementations. This necessitates that people are ready not just technically, but also ethically and operationally.
Adoption Spreads Beyond Digital-Native Firms
While early AI narratives focused on tech-savvy companies, the adoption is now widespread. Sanjeev Malhotra, CEO of the Nasscom Centre of Excellence for IoT and AI, provides a broader view from India's innovation ecosystem. He confirms that adoption is happening everywhere. Digital-native companies may be ahead, but even traditionally non-digital companies are now embracing agentic AI.
The Nasscom centre works with startups, large enterprises, government bodies, and SMEs. Sanjeev notes that small and medium enterprises (SMEs) show a sharp focus on return on investment (RoI). SMEs typically adopt technology when they see clear value, and they are now recognizing it in agentic AI. Applications range from automating quotations and procurement processes to handling customer queries and predictive maintenance, delivering tangible productivity gains. Collaboration between established enterprises and agile startups is further accelerating these innovations.
If proof is needed that agentic AI is moving beyond experimentation, the telecom sector serves as a prime example. Shreeya Rashinkar, head of global solutions and presales at Tech Mahindra, observes a marked shift over the past year from pilot projects to full-scale production deployments. She notes that while pilots are relatively easy, scaling is the real challenge.
Moving into production requires rigorous testing, strong data foundations, robust governance frameworks, and adherence to regional compliance standards, especially for global telecom operators. New testing and governance capabilities within platforms are helping accelerate this transition. However, Shreeya makes a crucial point: not every process needs AI, and not every AI use case delivers a positive RoI. The real work involves distinguishing between simple automation, AI-driven intelligence, and tasks that should remain human-led.
A shared theme from these industry perspectives is clear: agentic AI is not about replacing humans but about redefining work. Roles will inevitably change. Some tasks will disappear, new ones will emerge, and many existing roles will be reshaped. The future of work in India is being rewritten, with AI transitioning from a tool in the hand to a collaborative partner at the desk.
