Scaling AI with factory models: Time for a new approach

Building data platforms with quality data is essential to scale AI initiatives. But getting access to all the enterprise data, assessing its quality, and then streamlining it is hard. So often this becomes a multi-year endeavor. I’ve seen it happen again and again: smart teams, good intentions, big investments, and three or four years later, still no measurable return.

One reason is because building data platforms for enterprise-wide AI requires niche skills. Not only are these skills hard to find and access, but with the technology evolving every three to six months, there’s a lag in the number of people with the latest certifications.

Without the right skills, AI projects stay siloed and don’t scale across the enterprise.

Traditional AI factories aim to industrialize and automate the entire process of creating and managing AI models to create an AI production chain. The problem is they often lack the right skills, tooling, training, and delivery frameworks to get data ready quickly.

That’s why we launched Genpact’s AI Gigafactory. It’s our way of helping clients break through those barriers and realize the value they need from AI and data engineering, at speed.

Just like a traditional factory model, its focus is on scalability and repeatable tasks. But it’s also a strategic system that integrates people, tools, assets, and learning into one cohesive engine that accelerates time-to-value.

We bring together cross-functional teams of industry experts, data engineers, AI specialists, and our ecosystem partners, into what we call modular pods. These pods are tailored to the client’s industry, business goals, and technology environment so have the contextual knowledge to accelerate time-to-value from AI.

The three things business leaders tell me they appreciate are speed, access to talent, and responsible AI.

First, speed. That’s what every leader wants - faster insights, faster deployment, faster time to value. In several AI Gigafactory client engagements, we've achieved delivery timeline reductions of up to six months. We’re working closely with partners like Databricks, Snowflake, and AWS to support modern cloud-first architectures. These partnerships help us to migrate, clean, and activate client data more rapidly and effectively. Because without data, there is no AI.

Second, talent. The AI landscape changes so fast that it’s hard for even well-funded teams to stay ahead. We offer our clients a pipeline of up-to-date, specialized talent. People who not only understand the latest in AI, but who also know how to build responsibly and at scale. Our Giga Academy provides ongoing training so we maintain a pool of certified talent that can work across our partner platforms that are ready to be deployed.

Third, responsible AI. All the AI models we’re working on are built using our responsible AI frameworks so clients can implement AI in a safe, secure manner and deliver transparency and trust.

The AI Gigafactory is already delivering results. We’re working with a global energy company to scale its enterprise-wide AI ambitions, including a massive cloud migration to Databricks. Mid-project, the timeline was pulled forward by several months. Under normal circumstances, this would derail progress. But because the Gigafactory model was already in place, we tripled available talent in a matter of days and been able to keep the initiative on track and accelerate value delivery.

Scaling AI across the enterprise is hard. But by focusing on the right use cases, building a robust data foundation, and accessing talent that understands your specific needs, you can significantly improve the efficiency and predictability of your AI implementations.