Vijay Vijayasankar Explains The Rise of Agentic AI in the CAIO Connect Podcast with Sanjay Puri

Sanjay Puri & Genpact’s Vijay Vijayasankar discuss the shift from AI as an advisor to a "doer," using process intelligence to drive guaranteed business outcomes

WASHINGTON, DC, UNITED STATES, April 22, 2026 /EINPresswire.com/ -- In a compelling episode of the CAIO Connect Podcast, host Sanjay Puri sits down with Vijay Vijayasankar, the Chief Agentic AI Officer at Genpact, to dismantle the hype surrounding artificial intelligence and replace it with a pragmatic operator's blueprint. The conversation marks a pivotal shift in the industry: moving away from AI as a mere digital advisor to AI as a "doer"—a thinking agent capable of underwriting business outcomes.

The Genpact Thesis: Outcomes Over Labor
With a 30-year legacy born out of General Electric, Genpact has transitioned from traditional business process outsourcing to a $5 billion revenue powerhouse. Vijayasankar explains that their core differentiator is moving beyond "billable hours" to underwriting specific outcomes. By leveraging decades of institutional knowledge, Genpact is now productizing Agentic AI to guarantee productivity. "We can stand behind an outcome," Vijayasankar asserts, highlighting that if an agent doesn't deliver the promised value, the client shouldn't be spending on it.

Vijayasankar argues that AI projects often fail not because of the technology, but due to "enterprise debt" in processes and data. He identifies three essential buckets of knowledge required to train effective agents:
* Data: Transactional history stored in systems.
* Process Knowledge: Documented SOPs and flowcharts.
* Tacit Knowledge: What Genpact calls "fingerprints"—the uncodified wisdom residing in the brains of experienced employees.
By combining these, agents can solve the "messy middle"—those 20% of corner cases (like handwritten notes on printed invoices) that historically required human intervention.

To explain the future of work, Vijayasankar uses a "cop on the beat" analogy. Standard AI Agents are like street cops; they enforce laws and handle day-to-day enforcement within a defined jurisdiction. However, when a situation crosses borders or requires complex judgment, Special Agents—the humans—step in. This "Human-in-the-Loop" model ensures that while agents handle high-volume execution, humans retain the role of governors and judges.

Addressing the fear of "rogue" agents, Vijayasankar emphasizes the importance of restricted jurisdiction. He warns that in an enterprise context, autonomy must not mean lack of supervision. He advocates for "Symbolic AI"—deterministic if-then-else logic—to act as a circuit breaker for the probabilistic nature of Large Language Models (LLMs). This prevents agents from overspending tokens or damaging strategic vendor relationships through a lack of social nuance.
In a closing lightning round, Vijayasankar describes the current state of AI as an evolution, not a revolution. He remains skeptical of "extinction" predictions for industries like BPO, calling them "complete BS." Instead, he envisions a world where 80% of mundane tasks are offloaded to digital labor, allowing humans to constantly level up to higher-order work.

For leaders navigating this space, Vijayasankar’s message is clear, Stop measuring success by the number of AI projects in production. Instead, index on purpose—improving the top line or protecting the bottom line—and let the "thinking machines" handle the execution.

Upasana Das
Knowledge Networks
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