Cognizant has generated approximately $200 million in incremental sales pipeline by analysing employee emails, meetings, chats and other customer-related interactions, according to Chief Executive Officer Ravi Kumar.
Speaking at the company's AI Forum last week, Kumar said the IT services firm has used an AI-driven approach known as context engineering to uncover business opportunities hidden within information generated across its workforce.
The initiative analyses signals created by employees working with clients across sales, delivery, support, finance and other functions, helping the company identify opportunities that may not emerge through traditional sales processes.
"At this point of time, we roughly have $200 million of pipeline generated incrementally through this extraordinary effort of doing a sprawl on the systems, emails, meeting, chats, everything else and generating it," Kumar said.
He added that Cognizant expects the figure to reach $1 billion by the end of 2026.
Mining workplace knowledge for growth
The programme forms part of Cognizant's broader effort to build what Kumar described as organisational context for AI systems.
Rather than focusing solely on automation, the company is attempting to capture knowledge generated during everyday interactions between employees and customers. The goal is to convert fragmented information into actionable business intelligence.
According to Kumar, enterprises have historically relied on software engineers to automate predictable processes. In the AI era, organisations increasingly need to structure business context and institutional knowledge so AI systems can support decision-making more effectively.
The initiative is being developed with Workfabric, a startup co-founded by Rohan Murty, the son of Infosys founder Narayana Murthy.
How context engineering works
Cognizant said the platform creates digital representations of customer accounts by combining information generated across multiple business functions.
The system draws signals from:
- Sales teams
- Delivery operations
- Customer support functions
- Finance teams
- Internal meetings, emails and chats
By analysing these interactions collectively, the platform seeks to identify commercial opportunities, emerging customer needs and potential project risks.
In one example shared during the event, the system detected that a customer was facing pressure to reduce engineering costs while closely reviewing quality assurance spending. Based on those signals, it recommended that Cognizant's sales teams propose a quality assurance optimisation offering tailored to the client's situation.
The company said the technology can also flag issues before they escalate by identifying warning signs from conversations and interactions occurring across global account teams.
Expanding beyond sales use cases
Cognizant has also begun applying the technology to workforce deployment and talent management.
Kumar said the company can use the platform to identify employees with relevant project experience based on actual work performed, rather than relying only on resumes, certifications or skills databases.
The approach could help organisations match talent to projects using a broader set of operational signals captured across the business.
AI's next frontier for IT services firms
The announcement highlights how large IT services companies are moving beyond the initial productivity gains associated with generative AI and exploring ways to generate revenue from enterprise-wide AI deployments.
Instead of limiting AI applications to coding assistance or process automation, companies are increasingly seeking to use AI to uncover business opportunities, improve customer engagement and strengthen decision-making.
The development also reflects a wider trend across the technology sector. Reuters recently reported that Meta has begun deploying software on employee computers to capture workplace activity such as mouse movements, clicks and keystrokes as part of efforts to train AI agents capable of carrying out workplace tasks.
For Cognizant, the focus is on turning the knowledge generated through millions of employee interactions into a strategic business asset. If the company reaches its target of $1 billion in incremental pipeline by year-end, it could become one of the most closely watched examples of how AI is being used to create commercial value beyond productivity improvements.