Legacy Code Mess Hampers AI Deployment as Enterprises Scramble for FDE Talent
As the AI industry continues to grow, enterprises are facing a new challenge: integrating large language models into their existing workflows.
Cleaning up the 'legacy code mess' is becoming a lucrative emerging business opportunity, as companies like CMG Financial discover that deploying Agents is not just a technical project, but also requires answering complex questions about data ownership, permissions, and historical processes.
CMG's Chief Strategy Officer Paul Akinmade had promised to deploy 100 Agents in the next phase, but encountered issues when trying to get them to access Salesforce and participate in real business processes. The company soon realized that Agents can write code and call APIs, but struggle to understand complex data and business processes accumulated over years.
June, a startup company, helped CMG find a solution by creating a 'medical record' of the enterprise system, identifying business processes, duplicate fields, and permission conflicts. This set of methods allowed CMG to see clearly where Agents are suitable for deployment and what problems must be solved before going online.
The problem encountered by CMG is not an isolated case, as enterprises integrating Agents into their workflows face a new position rapidly heating up: Forward Deployed Engineers (FDEs). According to a study by Christian & Timbers, there are only about 2,000 engineers in the United States who currently have the ability to deploy AI systems into enterprises and help customers obtain quantifiable returns.