Tech Mahindra Scales Agentic AI with Gemini Enterprise

For many enterprises, artificial intelligence has lingered in pilot mode, impressive in demos but uneven in day-to-day business impact. The next phase of AI adoption is less about experimentation and more about operationalisation. Tech Mahindra’s latest move with Google Cloud signals that shift clearly.

The IT services major is expanding its use of Gemini Enterprise, powered by Google’s Gemini 2.5 multimodal models, to help enterprises design, deploy, and scale Agentic AI across core business functions. The collaboration reflects a growing recognition that enterprise AI must be secure, governed, and tightly integrated with existing data and cloud ecosystems to deliver sustained outcomes.

Rather than positioning AI as an add-on, Tech Mahindra is aligning Gemini Enterprise with its implementation frameworks and engineering talent to embed AI deeper into enterprise workflows.

From AI Experiments to Enterprise Systems

Enterprises today face a common challenge: moving from isolated AI use cases to systems that can reason, act, and adapt across business environments. Agentic AI, which combines autonomous decision-making with contextual awareness, promises that leap but introduces new complexity around governance, safety, and integration.

Tech Mahindra is positioning Gemini Enterprise as a platform layer that supports this transition. The models enable enterprises to build specialised AI agents, apply governance guardrails, and integrate them with Google Cloud services such as Vertex AI, BigQuery, and data-to-AI pipelines.

This approach reflects a broader industry shift. AI value increasingly depends not on model capability alone, but on how seamlessly intelligence connects to enterprise data, security policies, and operational systems.

Implementation, Not Just Intelligence

As a Google Cloud Premier Partner, Tech Mahindra brings implementation scale to the collaboration. Its “AI Delivered Right” strategy emphasises embedding AI into business processes rather than running parallel innovation tracks.

The strategy is structured around four pillars: Transformation Delivered, Productivity Delivered, Innovation Delivered, and Assurance Delivered. Together, these pillars aim to ensure that AI adoption aligns with enterprise goals, regulatory expectations, and operational realities.

“As enterprises evolve from AI experimentation to AI-driven business transformation, they require secure, scalable, and enterprise-ready intelligence that can operate across diverse business environments,” said Amol Phadke, Chief Transformation Officer, Tech Mahindra. “Through our collaboration with Google Cloud and Gemini Enterprise, we are equipping organisations to embed AI into core operations with confidence, enabling intelligent, connected, and secure ecosystems powered by next-generation agentic AI.”

Why Agentic AI Matters Now

Agentic AI changes how enterprises think about automation. Instead of static workflows, AI agents can reason, coordinate, and act across systems with minimal human intervention. That capability opens opportunities in areas such as operations optimisation, customer engagement, and decision support, but only if deployed responsibly.

Google Cloud sees system integrators like Tech Mahindra as critical to bridging that gap between model innovation and real-world enterprise use.

“Agentic AI offers a powerful opportunity to reshape business models and unlock greater efficiency across every enterprise,” said Victor Morales, Vice President, Global Systems Integrator Partnerships, Google Cloud. “By combining their deep industry expertise with Google Cloud’s leading AI solutions, Tech Mahindra helps businesses meet this opportunity and deploy powerful solutions to solve complex business challenges.”

Building for Scale and Trust

To support enterprise-scale deployment, Tech Mahindra has expanded its Centers of Excellence, developed ready-to-deploy Agentic AI solutions, and built a certified talent pool skilled in Google Cloud AI technologies. The focus is not speed alone, but repeatability and trust.

For enterprises navigating regulatory scrutiny, data privacy requirements, and rising expectations from customers and employees, this emphasis on governance and assurance could prove decisive.

The collaboration underscores a broader market reality: the AI leaders of the next decade will not be defined by who experiments fastest, but by who can operationalise intelligence safely, consistently, and at scale.