AI code assistants enable more capabilities that go beyond code generation and completion. They are collaborative assistants that improve developers’ efficiency by stimulating brainstorming and increasing code quality enhancements which empower developers to continuously upskill and build proficiency across programming frameworks. The enablers offered by AI code assistants lead to increased job satisfaction and retention, thereby saving the costs associated with turnover.

“Software engineering leaders must determine ROI and build a business case as they scale their rollouts of AI code assistants,” said Philip Walsh, Sr Principal Analyst at Gartner. “However, traditional ROI frameworks steer engineering leaders toward metrics centered on cost reduction. This narrow perspective fails to capture the full value of AI code assistants.”

Reframing ROI Conversations Is Critical to Capture the Full Value of AI Code Assistants
Traditional ROI frameworks fail to capture the full value of AI code assistants. To build an effective value story extending beyond traditional ROI metrics, software engineering leaders must reframe the ROI conversation from cost reduction to value generations (see Figure 1).