The benefits of AI are clear, but most businesses struggle with how to implement it at scale. Genpact’s AI Gigafactory is a first-of-its-kind AI accelerator designed to help enterprises rapidly scale AI solutions from pilot to full-scale production. “It is a smarter, faster path to AI at scale,” says Sanjeev Vohra, chief technology & innovation officer, Genpact. In this interview, he speaks to Sudhir Chowdhary on the core components of the Gigafactory and how it will contribute to Genpact’s overall
AI strategy. Excerpts:
How will Genpact’s agentic solutions benefit enterprises?
We’ve created a new category – Service-as-Agentic-Solutions. It will accelerate innovation for enterprises by fundamentally changing the way services are delivered. This delivery model leverages agentic solutions built on deep industry expertise to automate and improve business processes. We’re evolving traditional, manual service delivery with self-improving AI agents to handle complex workflows with greater efficiency, precision, and scale. We aim to move beyond legacy outsourcing models and build the next-generation, AI-powered execution machine. We’ve already launched our first agentic solution as part of our Accounts Payable suite of solutions, called- AP Capture – designed to modernise accounts payable operations. It leverages data and insights to enable faster invoice extraction with higher precision to deliver superior value.
How will the Gigafactory boost Genpact’s AI offerings in the market?
By 2026, the global IT skills shortage is projected to cost $5.5 trillion in unrealised economic growth, with 95% of tech leaders struggling to find the expertise to implement AI. Genpact Gigafactory bridges this gap, enabling businesses to stay competitive in an AI-driven world. It is designed to help organisations access specialised talent quickly and adopt AI in a way that drives scalable value. It includes thousands of pre-built industry-specific AI models and cutting-edge data and AI engineering tools, all leveraging a unique pod delivery model with cross-functional teams that bring together best-in-class expertise to accelerate time-to-value with responsible AI by design. We believe this unique combination of domain, data and AI, and skilled talent positions us as industry leaders, giving us a competitive advantage for long-term growth.
How does Genpact ensure the responsible use of AI within its agentic solutions?
We prioritise responsible AI in our agentic solutions through robust measures focused on observability, risk assessment, security, and control. Our teams act as guardians for our AI agents, implementing guardrails to monitor actions and manage risks. Our Responsible AI Institute membership and our investments in Agentic Development Lifecycle approach reinforces our commitment to best practices around ethical AI. Additionally, we embed an industry-specific semantic layer into our solutions to ensure agent decisions align with ethical standards and regulations.Businesses are also grappling with AI talent gap issues…
The AI revolution demands all of us to be nimble, invest in learning at speed, appreciate its impact on our day-to-day work – as an AI consumer and AI builder, and embrace change. At Genpact, we’re investing heavily in continuous upskilling, empowering employees to become proficient in generative AI, with 140,000+ already mastering foundational concepts. We have learning paths for AI builders (e.g., data engineers, AI scientists) and AI users from the business (e.g., financial analysts). Our internal learning platform, Genome, logged 11M+ learning hours last year, demonstrating our commitment to upskilling at scale.
We’re also collaborating with industry leaders like Databricks, Udemy, and LinkedIn Learning for cutting-edge tech certifications to ensure our talent remains at the forefront of AI innovation. Applied AI certification programs with MIT, Berkeley, and other top schools further enhance our senior leaders’ expertise. This dual focus on internal development and external partnerships is how we’re closing the AI talent gap and building a data-ready future.