AI Drives Convergence Of Technology And Operations Talent

AI Drives Convergence Of Technology And Operations Talent

BPM providers are bringing domain, technology and operations skills together as AI changes the way teams are built and deployed

Task-based roles are giving way to broader jobs that combine domain knowledge, technology and problem-solving skills as companies rethink how work is organised. More than 60 per cent of organisations in India reported a visible shift from task-based roles towards roles requiring problem-solving and creativity, according to a Deloitte report.

For business process management (BPM) providers, the change is reshaping how delivery teams are structured, with process, technology and operations capabilities being brought together to work on complex client problems.

One such model is the Forward Deployment Unit (FDU), where domain experts, engineers and operations teams work as a single unit across solution design, technology deployment and the management of the process after it goes live.

Speaking at the Nasscom BPM Confluence & Awards 2026, Sanjay Kukreja, CTO, eClerx, said a FDU is built around multiple competencies rather than a single specialist. That can include a process or domain expert, an engineer responsible for developing and deploying the technology, and people responsible for testing and evaluation.

The model also extends beyond implementation. Kukreja said the team remains involved after a solution goes live, helping manage the transition from design and specifications to ongoing operations.

For BPM companies, the FDU model does not necessarily represent an entirely new organisational structure. Instead, AI is adding another capability to teams that already combine process, operations and design expertise.

Amit Vohra, CTO, TP, added that the objective in operations is ultimately to move a client's metrics rather than simply deploy a technology solution. “For us, it is making sure that day in and day out, understanding of the problem continues to change and then putting a solution out there that continues to run,” he said.

An FDU-style structure can combine engineering, analytics, experience and knowledge management with core operations and frontline teams. The model allows technology to remain connected to the people operating the process and receiving its outcomes.

Vohra said the underlying approach has existed in some form for years, but the capabilities available to those teams have changed as data, computing power and technology have become more accessible.

The broader hiring market reflects that movement towards implementation. A Quess Corp analysis of 3.5 lakh job postings in 2026 estimated India's AI workforce at about 9.2 lakh professionals, of whom 6.63 lakh were in AI-embedded roles rather than core AI roles. The report also found that hiring demand was shifting from experimentation towards deploying, integrating, and scaling AI across business workflows.

That transition is also changing the role of managers who have traditionally overseen people and processes. Sameer Bapat, COO, Cognizant IOA, said the company uses its own approach rather than necessarily adopting the FDU terminology. Its model brings together three broad areas: domain, technology, and operations. Existing operations talent is being developed through additional technology and AI capabilities.

“Our people are great at managing other people and what they may not be able to do is managing AI,” Bapat said. That requires a different set of capabilities, including understanding how AI systems perform, monitoring their behaviour and responding when automated processes produce exceptions.

The need for this hybrid capability is likely to increase as AI moves further into enterprise workflows. Deloitte's 2026 State of AI in the Enterprise report found that 40 per cent of Indian respondents reported significant or full AI usage across their organisations, compared with about 28 per cent globally. At-scale adoption was particularly strong in strategy and operations, where 56 per cent of respondents reported deployment.

Human involvement remains important as AI takes on more process work. Bapat said operations teams must learn from exceptions and use them to improve AI systems, expanding their role beyond running processes to managing how AI performs them.

The emergence of FDU-style structures therefore reflects a wider change in BPM delivery. As AI takes over more execution, providers need teams that can connect technology with the domain context and operational knowledge required to make those systems work in real-world environments.

Kukreja said the teams become particularly valuable when client specifications are changing, multiple systems need to be integrated or sensitive data is involved. He also linked their importance to the industry's movement towards outcome-based models, where providers are increasingly accountable for what a process delivers rather than simply how many people are assigned to it.

The result is a talent model in which the boundaries between technology implementation and operations are becoming less distinct. The competitive question for BPM companies is not how many people can execute a process, but how effectively different kinds of talent can work together to redesign, run, and continuously improve that process in an AI-enabled environment.

BW Reporters The author is a Trainee Correspondent at BW Businessworld. She is a postgraduate with a passion for research and storytelling. Curious by nature, she enjoys writing and exploring new ideas and perspectives.