AI is useful when it helps a team move from ambiguity to a concrete technical path. In my workflow, tools such as GitHub Copilot CLI, Codex CLI and Google Gravity-style exploration are part of discovery, implementation planning, documentation and review preparation.
The value is not replacing engineering judgment. The value is shortening the time between a problem, a few possible solutions and a validated implementation. AI can inspect code, draft options, generate refactors and prepare tests, but the final decision still depends on architecture, product context, maintainability and team ownership.
What changes for a Tech Lead
For a Tech Lead, this changes the pace of collaboration. It becomes easier to create technical proposals, explain trade-offs, prepare migration plans and help engineers understand a codebase faster.
A good AI-assisted workflow still needs clear standards: small changes, explicit assumptions, local validation, review discipline, security awareness and a team culture where generated code is treated as a draft.