HCLTech has developed a dedicated Gemini Enterprise Business Unit to help enterprises build and deploy industry-specific AI solutions on Google Cloud’s Gemini Enterprise platform. The company said the unit is the first of its kind from a global system integrator and is aimed at moving customers from fragmented manual workflows to more automated decision-making. The new unit will focus on measurable business outcomes such as lower unplanned downtime, stronger fraud detection, and better diagnostic accuracy.
In March, the company said it was expanding its collaboration with Google Cloud to accelerate enterprise adoption of agentic AI, using Gemini Enterprise and Gemini models to build custom AI agents for global clients. That earlier deal also set a target of supporting more than 2,000 GenAI-led customer engagements and expanding HCLTech’s Google Cloud-certified workforce from 12,000 to more than 35,000 within three years.
Rather than treating AI as a broad consulting layer, HCLTech is organizing a dedicated business unit around Google’s enterprise agent platform. Google Cloud highlighted HCLTech in its partner ecosystem announcement, saying the company is launching the unit to accelerate the development and adoption of industry-specific solutions built on Gemini Enterprise.
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What the new Gemini Enterprise Business Unit is designed to do
HCLTech said the Gemini Enterprise Business Unit is meant to help enterprises unlock the value of generative AI and agentic AI in a more structured way. The focus is on moving from manual, fragmented processes to unified, automated decision-making. It suggests HCLTech is not positioning this as a demo-driven AI practice, but as a delivery layer for production use cases in sectors where workflows are complex and expensive to modernize.
The unit will support industry-specific solutions built on Google Gemini Enterprise, which is Google’s enterprise AI platform for building, governing, and scaling agents.
Google describes Gemini Enterprise as a unified environment that connects data, workflows, people, governance, and security, so enterprises can build and manage agentic systems in one place.
Google Cloud said earlier this year that the first wave of AI had been stuck in silos, and that Gemini Enterprise was designed as a front door for AI in the workplace. Reuters reported this week that Google is now placing AI agents at the center of its enterprise monetization strategy, while also reaffirming a plan to spend $175 billion to $185 billion on computing infrastructure in 2026, with more than half of that tied to cloud machine learning.
Compared with HCLTech’s March collaboration announcement, the April launch is more focused and more commercial. The March update covered a wider framework: custom AI agents, Google Workspace integration, security collaboration, AI Force integration, a growing agentic center of excellence, Gemini Experience Zones, and an expanded certified workforce pipeline. The April launch narrows that into a standalone business unit dedicated to Gemini Enterprise and industry-specific delivery.
Google Cloud’s own partner ecosystem post places HCLTech in a wider group of firms building around Gemini Enterprise, alongside Accenture, Capgemini, Cognizant, Deloitte, Infosys, PwC, TCS, and others. That comparison matters because it shows HCLTech is part of a broader channel strategy from Google, but with a more specific emphasis on industry solutions and a dedicated operating unit.
The most concrete numbers in HCLTech’s collaboration with Google Cloud are the ones from its March announcement: more than 2,000 GenAI-led customer engagements and a plan to grow Google Cloud-certified talent from 12,000 to over 35,000 within three years. Those figures show the company is not starting from zero; it is trying to organize an existing delivery base around a more focused agentic AI model.
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Google added another data point this week when it said Gemini Enterprise is being positioned as the main platform for enterprise AI work, with governance and security controls built in. That framing matters for large companies, where AI adoption usually stalls on compliance, control, and integration problems rather than model quality alone.
