US-based IT services firm Cognizant expects an AI-driven system that analyses employee and customer interactions to generate as much as $1 billion in incremental business pipeline by the end of this year, after already creating about $200 million in opportunities.
Speaking at the company’s AI forum last week, CEO Ravi Kumar S said the initiative, built around what Cognizant calls “context engineering”, mines emails, meetings, chats, contracts and other enterprise data to uncover potential sales opportunities that would otherwise remain scattered across the organisation.
“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. “By the end of the year we think this is going to be $1 billion pipeline.”
Broader shift in how enterprises are deploying AI
The initiative reflects a broader shift in how enterprises are deploying AI. While code generation has emerged as one of the most widely adopted AI applications in the technology industry, companies are increasingly exploring how the technology can help create new revenue opportunities rather than merely improve productivity.
Cognizant has been developing the platform since 2024 in partnership with Workfabric AI, a startup founded by Rohan Murthy. The system analyses information generated across customer-facing functions, including sales, delivery, support and finance teams, to identify signals that may point to future business opportunities.
During a demonstration, Murthy said much of the intelligence generated through customer interactions never finds its way into traditional customer relationship management systems, which typically rely on information entered by sales teams.
“The intelligence about what is happening when you interface with that customer, what you’re learning about that customer is not captured in any system of record,” he said.
According to Cognizant, the platform creates AI-powered digital representations of customer accounts and continuously analyses interactions to identify gaps or emerging requirements. In one example shown during the demonstration, the system detected signals suggesting a customer’s quality assurance staffing levels were under scrutiny and recommended that a salesperson pitch Cognizant’s QA optimisation offering.
Kumar said the approach could help the company identify and address customer challenges before clients formally seek assistance, potentially creating new business opportunities while strengthening customer relationships.
Both Kumar and Murthy said the platform operates with privacy guardrails designed to protect employee and customer information.
Beyond sales, Cognizant is also using context engineering internally to improve workforce deployment. Kumar said the system can identify employees working on similar assignments across the organisation and recommend suitable candidates for projects based on their experience and work context, rather than relying solely on information captured in resumes or skills databases.
Murthy said the use case was significant because it demonstrated AI’s ability to generate net new business opportunities, an area where enterprises are still searching for measurable returns from their AI investments.