How Genpact is building an autonomous enterprise: Vidya Rao, CITO, on scaling AI from within 

Less than 20% of enterprises have managed to scale AI beyond pilots. Legacy systems, fragmented data, and siloed experimentation continue to stall progress, even as investment in generative AI accelerates.

For Genpact, this statistic is not surprising—and it is precisely why the company chose to turn the lens inward first.

“As CIO and transformation leader, my mandate is not to run experiments,” says Vidya Rao, Chief Information & Transformation Officer at Genpact. “It’s to build an enterprise that can operate with intelligence at scale, every day, across functions.”

That ambition has shaped Genpact’s journey toward what Rao calls an autonomous enterprise—one where AI, data, and digital workflows are deeply integrated, reducing manual intervention while strengthening human judgment.

Fixing the real bottleneck: data before AI
Rao is clear that Genpact’s early challenges had little to do with algorithms or models.

“Our biggest constraint was never a lack of AI tools,” she says. “It was data scattered across systems, dashboards, and spreadsheets. Without fixing that, no amount of AI would deliver consistent outcomes.”

The company’s first architectural shift focused on consolidating fragmented data estates into a unified, AI-ready foundation using Microsoft Fabric. Beyond modernization, the goal was to eliminate manual enrichment and provide AI systems with clean, contextual inputs.

“When you give teams a single, trusted data backbone, everything changes,” Rao explains. “You move from weeks of preparation to real-time intelligence. That’s when AI becomes usable, not theoretical.”

Fabric’s native LLM-powered capabilities now automate tasks such as classification, sentiment analysis, and entity extraction—dramatically reducing engineering effort and accelerating deployment.

Democratizing AI without losing control
The second shift addressed a common enterprise tension: speed versus governance.

To scale AI responsibly, Genpact introduced AgentBuilder, a low-code/no-code platform designed to let business teams build and deploy AI agents directly within workflows.

“We didn’t want AI to live only with specialists,” Rao says. “At the same time, we couldn’t afford uncontrolled experimentation. AgentBuilder gave us both—speed and structure.”

By standardizing on reusable APIs, accelerators, and monitoring frameworks, Genpact reduced dependency on niche skills while ensuring enterprise-grade reliability.

“The real breakthrough,” she adds, “was giving teams one place to design, deploy, and monitor agents. That consistency is what makes scale possible.”

Moving toward agentic ecosystems
Beyond individual use cases, Genpact is preparing for a future where multiple AI agents collaborate across systems. Rao describes this as a shift from automation to orchestration.

“We’re exploring Modular Coordination Protocols because the next phase of AI isn’t about single agents,” she says. “It’s about agents that can communicate, coordinate, and adapt across workflows.”

This foundation enables dynamic, interoperable AI systems—critical for enterprises aiming to move beyond point solutions toward autonomous operations.

Preventing AI sprawl by design
As AI adoption grows, Rao warns that AI sprawl is one of the most underestimated risks.

“When employees are faced with multiple assistants and conflicting insights, confidence drops very quickly,” she says. “Adoption doesn’t fail because AI doesn’t work—it fails because the experience is fragmented.”

Genpact addressed this with Scout, its unified interface for AI interactions. Scout acts as a single gateway, orchestrating multiple agents and models behind the scenes.

“Users don’t need to know which agent or model is running,” Rao explains. “They just need a consistent, intuitive experience.”

This design philosophy extends to Genpact’s ERP modernization and its use of Workday as a system of engagement, ensuring AI capabilities are embedded into core workflows rather than bolted on.

When AI disappears into the flow of work
The impact of this approach becomes visible in tools like MyTwin, Genpact’s AI-powered work companion.
“Before MyTwin, leaders spent hours reconstructing context—emails, meetings, open actions,” Rao says. “Now that context comes to them.”

MyTwin surfaces risks, pending actions, and delegation opportunities by analyzing a user’s digital footprint across collaboration, CRM, and service platforms.

“The success of MyTwin reinforced an important lesson,” she adds. “AI works best when it fades into the background and amplifies decision-making, rather than demanding attention.”

In finance, Genpact’s agentic AP Suite has delivered faster billing cycles, better cashflow visibility, and more resilient supplier relationships—driven by agents that operate with deep domain context.

Governance as an enabler, not a brake
A key reason these initiatives scaled is Genpact’s approach to governance. Rather than treating it as compliance overhead, the company built governance into the lifecycle of every agent.

“We treat governance like a product,” Rao says. “Our Agent Development Life Cycle includes checkpoints for privacy, risk, explainability, and ROI.”

This structure allows teams to move quickly while giving leadership confidence to scale pilots enterprise-wide.
“Speed without trust doesn’t scale,” she notes. “And trust comes from transparency and accountability.”

Client Zero: learning before selling
Central to Genpact’s strategy is its Client Zero model—using its own operations as the proving ground for AI.

“Being Client Zero forces honesty,” Rao says. “If something doesn’t work internally, it won’t work for clients.”
The approach has paid off. Genpact scaled from 20 pilot agents to more than 100 in production, now handling over five million interactions annually for more than 100,000 active users. Billing cycles are faster, HR analytics are real-time, and IT incident resolution has improved significantly.

“Perhaps the biggest shift,” Rao adds, “is cultural. AI is no longer a project—it’s part of how work happens for 140,000 people.”

Looking ahead: from pilots to autonomy
As enterprises look toward 2026, Rao sees two trends reshaping the AI landscape: large-scale agent orchestration and decentralized compute.

“These trends move intelligence closer to where work happens,” she says. “That’s how enterprises become more resilient, adaptive, and autonomous.”

Genpact is investing heavily in talent to support this shift, with over two million hours of AI learning completed across the organization.

“Our goal isn’t to deploy more AI,” Rao concludes. “It’s to build systems that think with the business—systems that learn, adapt, and scale responsibly.”