The IT services firm strengthens its collaboration with Google Cloud to help enterprises move AI from pilot projects to production systems
Updated
February 18, 2026 8:11 PM
Google Cloud building. PHOTO: ADOBE STOCK
Enterprise interest in AI has moved quickly from experimentation to execution. Many organizations have tested generative tools, but turning those tools into systems that can run inside daily operations remains a separate challenge. Cognizant, an IT services firm, is expanding its partnership with Google Cloud to help enterprises move from AI pilots to fully deployed, production-ready systems.
Cognizant and Google Cloud are deepening their collaboration around Google’s Gemini Enterprise and Google Workspace. Cognizant is deploying these tools across its own workforce first, using them to support internal productivity and collaboration. The idea is simple: test and refine the systems internally, then package similar capabilities for clients.
The focus of the partnership is what Cognizant calls “agentic AI.” In practical terms, this refers to AI systems that can plan, act and complete tasks with limited human input. Instead of generating isolated outputs, these systems are designed to fit into business workflows and carry out structured tasks.
To make that workable at scale, Cognizant is building delivery infrastructure around the technology. The company is setting up a dedicated Gemini Enterprise Center of Excellence and formalizing an Agent Development Lifecycle. This framework covers the full process, from early design and blueprinting to validation and production rollout. The aim is to give enterprises a clearer path from the AI concept to a deployed system.
Cognizant also plans to introduce a bundled productivity offering that combines Gemini Enterprise with Google Workspace. The targeted use cases are operational rather than experimental. These include collaborative content creation, supplier communications and other workflow-heavy processes that can be standardized and automated.
Beyond productivity tools, Cognizant is integrating Gemini into its broader service platforms. Through Cognizant Ignition, enabled by Gemini, the company supports early-stage discovery and prototyping while helping clients strengthen their data foundations. Its Agent Foundry platform provides pre-configured and no-code capabilities for specific use cases such as AI-powered contact centers and intelligent order management. These tools are designed to reduce the amount of custom development required for each deployment.
Scaling is another element of the strategy. Cognizant, a multi-year Google Cloud Data Partner of the Year award winner, says it will rely on a global network of Gemini-trained specialists to deliver these systems. The company is also expanding work tied to Google Distributed Cloud and showcasing capabilities through its Google Experience Zones and Gen AI Studios.
For Google Cloud, the partnership reinforces its enterprise AI ecosystem. Cloud providers can offer models and infrastructure, but enterprise adoption often depends on service partners that can integrate tools into existing systems and manage ongoing operations. By aligning closely with Cognizant, Google strengthens its ability to move Gemini from platform capability to production deployment.
The announcement does not introduce a new AI model. Instead, it reflects a shift in emphasis. The core question is no longer whether AI tools exist, but how they are implemented, governed and scaled across large organizations. Cognizant’s expanded role suggests that execution frameworks, internal deployment and structured delivery models are becoming central to how enterprises approach AI.
In that sense, the partnership is less about new technology and more about operational maturity. It highlights how AI is moving from isolated pilots to managed systems embedded in business processes — a transition that will likely define the next phase of enterprise adoption.
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How Korea is trying to take control of its AI future.
Updated
January 13, 2026 10:56 AM
SK Telecom Headquarters in Seoul, South Korea. PHOTO: ADOBE STOCK
SK Telecom, South Korea’s largest mobile operator, has unveiled A.X K1, a hyperscale artificial intelligence model with 519 billion parameters. The model sits at the center of a government-backed effort to build advanced AI systems and domestic AI infrastructure within Korea. This comes at a time when companies in the United States and China largely dominate the development of the most powerful large language models.
Rather than framing A.X K1 as just another large language model, SK Telecom is positioning it as part of a broader push to build sovereign AI capacity from the ground up. The model is being developed as part of the Korean government’s Sovereign AI Foundation Model project, which aims to ensure that core AI systems are built, trained and operated within the country. In simple terms, the initiative focuses on reducing reliance on foreign AI platforms and cloud-based AI infrastructure, while giving Korea more control over how artificial intelligence is developed and deployed at scale.
One of the gaps this approach is trying to address is how AI knowledge flows across a national ecosystem. Today, the most powerful AI foundation models are often closed, expensive and concentrated within a small number of global technology companies. A.X K1 is designed to function as a “teacher model,” meaning it can transfer its capabilities to smaller, more specialized AI systems. This allows developers, enterprises and public institutions to build tailored AI tools without starting from scratch or depending entirely on overseas AI providers.
That distinction matters because most real-world applications of artificial intelligence do not require massive models operating independently. They require focused, reliable AI systems designed for specific use cases such as customer service, enterprise search, manufacturing automation or mobility. By anchoring those systems to a large, domestically developed foundation model, SK Telecom and its partners are aiming to create a more resilient and self-sustaining AI ecosystem.
The effort also reflects a shift in how AI is being positioned for everyday use. SK Telecom plans to connect A.X K1 to services that already reach millions of users, including its AI assistant platform A., which operates across phone calls, messaging, web services and mobile applications. The broader goal is to make advanced AI feel less like a distant research asset and more like an embedded digital infrastructure that supports daily interactions.
This approach extends beyond consumer-facing services. Members of the SKT consortium are testing how the hyperscale AI model can support industrial and enterprise applications, including manufacturing systems, game development, robotics and autonomous technologies. The underlying logic is that national competitiveness in artificial intelligence now depends not only on model performance, but on whether those models can be deployed, adapted and validated in real-world environments.
There is also a hardware dimension to the project. Operating an AI model at the 500-billion-parameter scale places heavy demands on computing infrastructure, particularly memory performance and communication between processors. A.X K1 is being used to test and validate Korea’s semiconductor and AI chip capabilities under real workloads, linking large-scale AI software development directly to domestic semiconductor innovation.
The initiative brings together technology companies, universities and research institutions, including Krafton, KAIST and Seoul National University. Each contributes specialized expertise ranging from data validation and multimodal AI research to system scalability. More than 20 institutions have already expressed interest in testing and deploying the model, reinforcing the idea that A.X K1 is being treated as shared national AI infrastructure rather than a closed commercial product.
Looking ahead, SK Telecom plans to release A.X K1 as open-source AI software, alongside APIs and portions of the training data. If fully implemented, the move could lower barriers for developers, startups and researchers across Korea’s AI ecosystem, enabling them to build on top of a large-scale foundation model without incurring the cost and complexity of developing one independently.