IBM and Google Cloud launch AI practice to support enterprise agent deployments – EdTech Innovation Hub

Home AI IBM and Google Cloud launch AI practice to support enterprise agent deployments – EdTech Innovation Hub
IBM and Google Cloud launch AI practice to support enterprise agent deployments – EdTech Innovation Hub

IBM and Google Cloud have launched a new global practice focused on enterprise AI, hybrid cloud modernization, and Gemini-based agent deployments
IBM and Google Cloud have launched a new global Google Cloud Practice to help enterprises deploy AI systems, modernize core infrastructure, and manage technology across hybrid cloud environments.
The practice brings together thousands of Google Cloud-certified IBM consultants, forward-deployed engineers, IBM Consulting Advantage, and Google Cloud’s Gemini Enterprise Agent Platform. The companies say the partnership represents a multi-billion-dollar opportunity in Google Cloud services.
IBM Consulting Advantage is IBM’s AI-powered delivery platform for designing, building, and deploying AI solutions using agents and industry workflows. Through the partnership, it will now be expanded with industry-specific agents optimized for Gemini Enterprise.
IBM is developing a portfolio of industry-specific AI agents for banking, government, retail, telecommunications, energy, security, insurance, and life sciences.
The agents are designed to support workflow automation, decision-making, and autonomous operations using Gemini models. IBM consultants will also be able to design, build, and govern enterprise-grade AI agents directly on Google Cloud.
The partnership combines pre-built assets, reusable agents, and transformation methods from IBM with Google Cloud’s agent runtime, governance controls, and enterprise safety features.
Mohamad Ali, Senior Vice President and Head of IBM Consulting, says: “Enterprises are facing one of the most complex modernization cycles in decades. By expanding our work with Google Cloud, we’re giving clients a clearer and more reliable path to scale AI across their business, combining deep industry expertise, hybrid-cloud modernization, and an AI-first delivery platform.”
The new practice will focus on production-ready AI and data, industry-specific solutions, cybersecurity operations, hybrid cloud modernization, AI-powered workflows, operational resilience, and governance.
This includes using Gemini Enterprise Agent Platform and BigQuery to support AI and data foundations, as well as Confluent for real-time data streaming and governance in sectors including aerospace, financial services, government, healthcare, and telecommunications.
IBM will also work on interface patterns and solutions that connect enterprise data into Gemini. These interfaces can be adapted to client architectures, with the goal of helping organizations scale Gemini-based AI systems across existing technology environments.
The companies also said Red Hat OpenShift is now available directly in the Google Cloud Console. IBM will integrate Gemini with watsonx Orchestrate for decision automation and agent intelligence, and with watsonx.data to support insight generation for applications.
Kevin Ichhpurani, President, Global Partner Ecosystem at Google Cloud, says: “This partnership significantly expands the pool of expert Google Cloud consultants in the market to meet surging demand for AI. By combining Google’s agentic infrastructure with IBM’s deep industry expertise and proven delivery frameworks, we are ensuring joint customers can move beyond pilots to deploy and govern production-grade AI agents across their entire cloud environment.”
IBM and Google Cloud have already worked together on migration and modernization projects, including with Airbus.
In that project, IBM consultants and Google Cloud helped transition two aerospace businesses into independent operations in under 18 months. The work involved updating more than 100 critical systems across engineering, manufacturing, customer service, and other regulated functions.
In a LinkedIn post announcing the partnership, Ali said AI success required “more than models” and depended on platforms, assets, governance, and the ability to run AI where client data is held.
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