Cognizant and Gilead Sciences are extending their technology relationship for another five years, with the expanded agreement putting greater emphasis on DevOps modernization and selected uses of agentic AI. The work builds on more than a decade of collaboration across Gilead’s IT infrastructure, applications, platforms and analytics, as the biopharmaceutical company looks to apply AI more practically across its technology operations.
Gilead takes a measured approach to agentic AI
Gilead Sciences is expanding its use of artificial intelligence within its technology organization, but the company is taking a targeted approach rather than positioning AI as a wholesale replacement for existing systems.
The biopharmaceutical company has extended its relationship with Cognizant for five years, adding support for DevOps capabilities and selected applications of agentic AI.
The agreement builds on more than a decade of work between the companies covering elements of Gilead’s global IT infrastructure, platforms, applications and advanced analytics. Under the new arrangement, Cognizant will combine its life sciences and technology capabilities with DevOps and AI tools to support specific technology programs.
The announcement is notable less for the size of the AI deployment than for its positioning. Gilead is describing agentic AI as a practical technology that can help simplify processes and improve program execution rather than as a standalone transformation initiative.
Agentic AI systems differ from conventional generative AI tools because they are designed to perform multi-step tasks with a degree of autonomy. In an enterprise technology environment, that can include assisting with software development, coordinating workflows, analyzing information, monitoring processes or supporting operational decisions.
For organizations operating in highly regulated industries such as pharmaceuticals, however, deploying such systems requires additional controls around data, security, traceability and human oversight.
DevOps provides a foundation for enterprise AI
The combination of DevOps and agentic AI is also significant.
DevOps practices bring together software development and IT operations through automation, continuous integration and delivery, testing, monitoring and standardized workflows. Those capabilities can provide an operational foundation for introducing AI agents into software and technology processes.
Instead of deploying an AI agent into an isolated environment, enterprises can incorporate AI-assisted workflows into established development and operations processes where activities can be monitored and governed.
For Gilead, Cognizant says the expanded work will focus on selected programs where the technology can help simplify processes, improve delivery and enable teams to execute priority initiatives more efficiently.
The companies have not disclosed specific agentic AI tools, models or production use cases under the agreement. That makes it difficult to quantify the expected productivity gains, and those details will ultimately determine how significant the deployment becomes.
Still, the structure reflects a broader shift in enterprise AI adoption.
Organizations are increasingly moving beyond AI experimentation and looking for ways to embed generative AI and autonomous capabilities into existing business and technology workflows. According to McKinsey’s 2025 global AI research, 88% of organizations reported using AI in at least one business function, although many companies remain in the process of scaling deployments beyond individual experiments.
The next stage is therefore less about proving that AI can generate useful output and more about determining where it can safely perform repeatable work.
Life sciences raises the stakes for responsible AI
Gilead’s industry also makes governance particularly important.
Technology systems supporting pharmaceutical research, development, manufacturing and commercial operations can handle sensitive information and interact with highly regulated processes. An AI system that can independently execute multiple steps therefore requires stronger controls than a general-purpose productivity assistant.
Gilead’s emphasis on “practical” and “responsible” applications suggests that the initial focus will be on bounded technology programs rather than unrestricted autonomous decision-making.
That approach mirrors how many enterprises are approaching agentic AI. Human oversight remains important for workflows where an incorrect action could affect compliance, data integrity, security or business-critical operations.
Cognizant’s role also reflects the growing market for services that combine AI implementation with existing enterprise technology expertise. Large IT services companies are increasingly positioning themselves as integrators between foundation models, enterprise applications, cloud platforms and industry-specific workflows.
Microsoft, Google, Amazon and Salesforce are similarly building agentic AI capabilities into their enterprise ecosystems, while technology services providers are helping organizations connect those capabilities with existing infrastructure and processes.
Enterprise AI moves from experimentation to execution
The Cognizant-Gilead agreement illustrates a broader transition in enterprise AI.
The early wave of generative AI adoption focused heavily on assistants, content generation and conversational interfaces. Agentic AI expands the proposition by allowing systems to take actions across workflows rather than simply generate information.
That creates a larger opportunity—but also a larger governance challenge.
For Gilead, the immediate objective appears to be improving technology delivery and operational efficiency while maintaining the controls required in a highly regulated environment. Extending an established technology partnership gives the company a way to introduce those capabilities within infrastructure and processes it already understands.
The five-year agreement also indicates that enterprise AI adoption is increasingly being viewed as a long-term operating capability rather than a short-lived technology experiment.
Whether agentic AI ultimately delivers substantial productivity improvements will depend on execution: the quality of the underlying data, integration with enterprise systems, security controls, model reliability and the ability to keep humans accountable for consequential decisions.
For now, Gilead’s strategy is deliberately narrow. Rather than betting on autonomous AI everywhere, the company is testing where agentic capabilities can deliver measurable value within its existing technology environment.
Market Landscape
The enterprise AI market is moving toward workflow-level automation, with agentic AI emerging as a layer above traditional generative AI assistants. Software development, IT operations, customer service, analytics and business-process management are among the areas where AI agents can potentially perform multi-step tasks.
For life sciences organizations, adoption is likely to remain more controlled because AI systems operate alongside regulated processes and sensitive information.
The competitive landscape includes Microsoft, Google, Amazon and Salesforce, alongside IT services companies such as Cognizant that help enterprises integrate AI capabilities into existing technology environments. DevOps, cloud infrastructure, data governance and AI security are becoming increasingly interconnected parts of the enterprise AI stack.
Top Insights
- Cognizant and Gilead extended their technology relationship for five years, adding selected DevOps and agentic AI capabilities.
- Gilead is emphasizing practical, responsible AI applications rather than describing a broad autonomous transformation across the organization.
- DevOps automation can provide an operational foundation for integrating AI agents into software development and technology operations.
- Life sciences organizations face additional governance requirements when deploying autonomous AI around sensitive and regulated workflows.
- Enterprise AI is shifting from experimentation toward workflow integration, measurable productivity and controlled execution.
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