In today’s dynamic enterprise landscape, the shift from traditional automation to autonomous, goal-driven systems is transforming business operations. In this exclusive interview, Pravir Dahiya, CTO at Tata Teleservices, shares how organizations can leverage Agentic AI to enhance efficiency, deliver personalized customer experiences, and build resilient, future-ready enterprises, offering insights into the strategy and foresight driving AI-led digital transformation.
Q: How do you see the rise of Agentic AI, and what sets it apart from conventional AI in enterprise cases?
A: The rise of Agentic AI represents an essential evolution in enterprise automation, moving from predictive and assistive intelligence to autonomous, goal-driven systems that can proactively make decisions and execute tasks. Unlike conventional AI, which largely operates within pre-defined boundaries and relies on human intervention to act on insights, Agentic AI is self-directed and adaptive, capable of initiating actions based on evolving context and business goals. In an enterprise environment, this means transitioning from automation of workflows to automation of outcomes where systems can dynamically respond to customer behaviors, operational bottlenecks or cybersecurity threats without waiting for manual input.
At Tata Tele Business Services, we view Agentic AI as a critical enabler of intelligent automation, operational agility and digital resilience. Its enterprise relevance lies in its ability to help businesses move faster, respond smarter, and scale efficiently especially in sectors where data-driven decision-making and agility are mission-critical. However, its deployment must be grounded in strong governance, ethical design and security-first architecture to ensure trust, compliance and explainability at scale.
Q: Which major verticals are leading the adoption of Agentic AI and why?
A: We observe that verticals such as IT/ITeS, BFSI, retail, healthcare and logistics are at the forefront of adopting Agentic AI. These sectors are inherently data-intensive, customer-centric and operate in highly dynamic environments making them ideal candidates for AI systems that can act autonomously, learn continuously and drive real-time decision-making.
What unifies these early adopters is their maturity in cloud adoption, availability of clean and structured data and a strong focus on agility and innovation. As enterprise AI moves from assistive to autonomous, these verticals are leading the way in integrating Agentic AI into their digital core to drive efficiency, reduce costs, and enhance customer experiences.
Q: What cybersecurity risks or governance concerns do you foresee with Agentic AI deployments?
A: With the increasing sophistication of cyber threats, the deployment of Agentic AI, which introduces autonomous decision-making into enterprise systems, demands a proactive and deeply integrated cybersecurity strategy. At TTBS, we recognize that as AI systems gain agency, the potential risk surface expands significantly. Agentic AI systems, by design, act independently, adapt in real-time and learn continuously, which makes them both powerful and potentially vulnerable.
Key risks include unauthorized decision execution, model manipulation, data poisoning, adversarial attacks and a lack of explainability in mission-critical scenarios. This is particularly concerning for SMEs and regulated sectors like BFSI and healthcare, where ensuring compliance, transparency and data integrity is paramount.
Q: One piece of advice you’d offer to CIOs planning to integrate Agentic AI into their digital roadmap?
A: For CIOs looking to integrate Agentic AI into their digital roadmap, they must align AI autonomy with business intent before technology implementation begins. Agentic AI holds immense potential to drive intelligent automation, but its value is only realized when deployed with clear goals, governance mechanisms and cross-functional alignment.
We recommend starting with low-risk, high-impact use cases where AI agents can work alongside human teams in a controlled, monitored environment. Prioritize use cases that directly enhance customer experience, operational efficiency or cybersecurity readiness. It is also essential to establish an AI governance framework from day one, covering ethical boundaries, explainability, risk mitigation and compliance standards.
Leader Uncovered:
Education: BE, Electronics and Telecommunications from BIT Mesra Student-Industry Relations Cell
First Job: Regional Manager at Tata Telecom Limited.
𝐒𝐭𝐚𝐲 𝐢𝐧𝐟𝐨𝐫𝐦𝐞𝐝 𝐰𝐢𝐭𝐡 𝐨𝐮𝐫 𝐥𝐚𝐭𝐞𝐬𝐭 𝐮𝐩𝐝𝐚𝐭𝐞𝐬 𝐛𝐲 𝐣𝐨𝐢𝐧𝐢𝐧𝐠 𝐭𝐡𝐞 WhatsApp Channel now! 👈📲
𝑭𝒐𝒍𝒍𝒐𝒘 𝑶𝒖𝒓 𝑺𝒐𝒄𝒊𝒂𝒍 𝑴𝒆𝒅𝒊𝒂 𝑷𝒂𝒈𝒆𝐬 👉 Facebook, LinkedIn, Twitter, Instagram
