Agentic AI will shape a future where humans and machines co-create value: Zensar CTO

As artificial intelligence (AI) reshapes the global technology landscape, the next frontier—agentic AI—is redefining how enterprises operate and innovate. Pune-based Zensar Technologies, part of the RPG Group, is banking heavily on its recently launched ZenseAI platform, a modular, agentic AI framework designed to drive connected intelligence across business functions.

In an interaction with TechCircle, Narayana Prasad Shankar, Chief Technology Officer and Head of AI and Analytics at Zensar Technologies, explains how agentic AI is influencing the company’s long-term vision, technology roadmap, and how CIOs and business leaders are approaching digital transformation in an AI-driven world.

Reimagining enterprise processes

“The shift to agentic AI has fundamentally expanded Zensar’s long-term vision,” says Shankar. “We want to be an orchestrator of change, reimagining business processes and operating models for clients using AI.” Unlike traditional automation, which is rule-based, agentic AI introduces autonomous systems capable of reasoning, decision-making, and adapting to changing contexts.

Zensar has built a growing repository of intelligent agents designed to reimagine business workflows, from software delivery to operations, and is deploying them across its client base to accelerate innovation.

From automation to orchestration

Agentic AI represents a significant leap beyond deterministic automation. “It allows IT teams to automate complex workflows that require reasoning and decision-making,” says Shankar. The technology enables higher levels of self-service, productivity, and cross-functional collaboration, as agents operate across silos to route work efficiently and enhance delivery outcomes.

By embedding intelligent agents into enterprise ecosystems, Zensar believes IT will evolve from a service enabler to a strategic orchestrator of transformation, bringing technology closer to business value creation.

Building interoperability and governance

As organisations deploy multiple AI agents, interoperability and governance are becoming central to success. Shankar advocates a three-layered approach that begins with redesigning enterprise workflows, followed by the adoption of open standards such as A2A or MCP for interoperability, and building a data architecture capable of handling agentic AI across ERP, CRM, and HR systems.

“A strong governance framework is essential to ensure agents function within acceptable guardrails,” he says, adding that an agentic governance model can itself be used to oversee other automation agents.

A new C-suite dynamic

Agentic AI, much like digital transformation a decade ago, is redefining the role of technology leaders. According to Shankar, CIOs are now working more closely with COOs, CFOs, and CMOs as intelligent agents increasingly operate across business functions. “This alignment enables CxOs to focus on strategy and change management, while agentic AI handles the execution of complex workflows,” he explains.

Tackling data, compliance, and talent gaps

Zensar, like many enterprises, has encountered challenges around data quality, compliance, and the shortage of AI talent. The company has addressed data fragmentation by creating data lakes and governance frameworks through its ZenseAI.Data platform. On the talent front, Shankar says the company has adopted a structured upskilling approach, tailoring AI learning paths for engineers, architects, managers, and leaders. “This ensures we can scale AI adoption internally while keeping costs and attrition in check,” he notes.

Inside ZenseAI

ZenseAI, Zensar’s flagship platform, is built on a composable architecture with modular extensions for engineering, data, and modernisation. Enterprises can deploy specific modules to address targeted use cases while integrating seamlessly with their existing AI ecosystems.

“It provides clients flexibility to work with their preferred AI tech stacks and eliminates vendor lock-in,” says Shankar. The platform also features an extensive library of AI agents that can be customized for enterprise needs, helping clients accelerate their AI journeys.

Driving measurable business outcomes

Zensar’s agentic and generative AI systems are already delivering tangible impact across industries. Without naming any company, the CTO said that a large modernisation project for a global client achieved a 30% reduction in development cost and was completed six months ahead of schedule. In another case, a steel manufacturer implemented a computer vision model to monitor welding quality, significantly improving accuracy and reducing manual effort.

An insurance firm saw a sharp drop in false positives in claims processing with an AI-based fraud detection model, while a leading financial institution used Zensar’s AI systems to detect anomalies in foreign exchange trades. Internally, the company has used generative AI to automate hiring processes, improve employee policy support through AI bots, and analyse client feedback to strengthen project delivery.

Balancing costs with long-term value

Agentic AI deployments can be resource-intensive, but Shankar emphasizes a pragmatic, top-down approach to balance cost and value. “The major cost drivers are talent, compute, and change management—and often, the waiting cost from delayed implementation is higher than the project cost itself,” he says.

By leveraging open-source models, traditional hardware, and productivity gains from ZenseAI, Zensar has optimised its AI investments while maintaining agility in execution.

The road ahead

Over the next 12–18 months, Zensar plans to deepen its enterprise-wide AI adoption, focusing on responsible, human-centric, and scalable innovation. Key priorities include customer-centric AI experiences, ethical AI practices, continuous skill development, and driving measurable business transformation.

“As we look ahead, our goal is to make AI a foundational layer across every business process,” says Shankar. “Agentic AI is not just about automation—it’s about reimagining how enterprises think, decide, and grow.”