Most developers have experimented with AI tools like GitHub Copilot or ChatGPT for writing code or catching bugs. But enterprise-wide adoption across the full software development lifecycle is still far from the norm. High upfront costs, murky ROI, integration complexity, and genuine concerns about AI-generated code quality are keeping many organizations on the fence.
This blog by Damco's VP of Technology is written for CTOs and engineering leaders navigating exactly that tension. It maps AI capabilities across each SDLC stage, from smarter requirement analysis and automated test generation to real-time production monitoring, and shows what measurable impact looks like at each step.
It also addresses the trust gap honestly: 45% of U.S. tech leaders report AI code reliability issues, and only 31% of developers say AI tools noticeably improve their output. The blog then lays out a practical three-phased roadmap to close that gap, backed by a real-world Healthtech case study where a new feature shipped in six days, down from two weeks.
If your SDLC is running on legacy processes and you are wondering where AI actually fits, this is a good starting point.
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