From Tribal Knowledge to Trusted AI Knowledge Base

Energy companies run on hundreds of custom business-critical applications - many built decades ago, under-documented, and hard to maintain. The result: engineering teams spend precious time keeping the lights on instead of building new products that business units urgently need. Attrition makes the problem worse: when experienced developers leave, their tribal knowledge often goes with them.
 

In this session, Sharad Agrawal, Co-Founder and COO of Adapts and former product leader at Microsoft in the Energy industry, will show how AI can unlock that trapped knowledge and give operators a clear path to modernize without disrupting core operations. By transforming undocumented legacy code into a trusted AI knowledge base, leaders can reduce onboarding time for new engineers, mitigate attrition risk, and give business units confidence that technology can keep pace with evolving processes.
 

The outcome: engineering capacity shifts from maintenance to innovation. Business leaders gain the assurance that the applications running critical energy operations are stable, well-understood, and ready for modernization when the time is right.
 

1. The challenge: Energy operators depend on hundreds of legacy apps that are difficult to maintain and risky to change.

2. The business impact: Delays in modernization stall innovation, increase attrition risk, and slow delivery of new business capabilities.

3. The opportunity: AI-driven knowledge bases de-risk legacy portfolios, shorten engineer ramp-up, and free teams to build the products the business needs.