In the News

A significant amount of Genpact's current agentic AI customers are running on outcome-based models, and most new customers are signing onto outcome-based AI for agentic processes.

AI strategy and workforce strategy now go hand in hand, and supply chain organizations that invest in both will move faster and turn AI into a sustained competitive advantage.

Genpact's Steve Prewitt on why the real risk in AI-driven decision-making isn't the model; it's the foundation underneath it.

AI can handle very complex tasks when the objective is clear, the data is reliable, the process is defined, and the outcomes are measurable. 

The real test of AI is not whether it can perform in a controlled pilot, but whether it can operate in the last mile of the business: the complex, exception-heavy layer where work actually gets done, decisions are made, and outcomes are measured.

The success of an agentic solution is less about the intelligence of the models and more about the quality of guardrails put in place.

Organizations need high-quality well-governed data to enable and trust AI agents to make real-time decisions autonomously. 

Many guardrails can be programmed, but it also needs human guardrails. Autonomous should not mean ungoverned. 

The ROI that’s being measured now is really on margins, on growth, on speed, on risk reduction, and converting all of that into some sort of business value

After two years of agentic AI pilots, proofs of concept, and experimentation, enterprises are entering a more demanding phase of adoption.

For CFOs, the question is no longer whether AI is entering the enterprise, but whether the growing investment behind it is translating into measurable business value.

The next phase of AI maturity will be defined not by how much companies spend but by how effectively they measure outcomes.

While agentic AI can significantly enhance execution, human judgment, accountability and governance remain critical to enterprise adoption at scale.

According to Genpact Global Agentic AI Officer Vijay Vijayasankar, the institutions that lead India’s next phase of financial services growth will be those deploying AI in ways their customers trust and their regulators can inspect.

According to Genpact Global Data & AI Leader Matt Arellano, enterprises need to figure out how to manage the identities of AI agents, control access to data, create audit trails, and filter prompts and content.

Siloed patient and claims data and disconnected clinical systems limit what artificial intelligence can deliver, according to a new report from Genpact and HFS Research.

Organizations are being held back by four interconnected forms of enterprise debt: process debt, data debt, technology debt, and talent debt.

Resolving enterprise debts can unlock approximately 8% faster annual revenue growth and 16% annual cost reduction, according to a study released by Genpact and HFS Research.

According to a new report from Genpact in partnership with HFS Research, 85% of companies admit that enterprise debt is “actively limiting” the value they can extract from AI by creating an unstable foundation for such projects to succeed.

As enterprises struggle to scale AI, Genpact is leaning on its decades of process work to become the foundation for agentic operations.

Rather than spending most of their time writing code, developers increasingly focus on defining intent, capturing requirements through specs, designing system behavior, and resolving complex scenarios that fall outside established patterns.

The Iran war has upended corporate insurance. According to Genpact Global Head of Agentic Insurance Solutions Yasir Andrabi, the attribution problem makes it worse.

Ajay Vasal, Senior Vice President, Data & AI Services, Genpact walks through Genpact's own AI-first transformation journey, why outdated productivity metrics are obscuring true AI ROI, and what it will take for enterprises and IT firms to shift from labour arbitrage to intelligence arbitrage in the agentic era.

According to Genpact Global Agentic AI Officer Vijay Vijaysankar, the shift to AI-driven autonomous operations is less about isolating AI as a revenue line and more about embedding AI into the fabric of how work gets done.