Hey, have you thought about how the latest generative AI could slash operational costs while boosting product development speed? Let’s crunch the numbers.

Sure thing. **Cost‑cutting levers:** 1. **Dev time** – AI‑generated code snippets reduce manual coding by 30–40%, saving roughly $200k/year for a mid‑size team. 2. **QA automation** – AI‑driven test suites cut manual testing hours by 50%, cutting QA spend by $120k annually. 3. **Training data** – Using open‑source model weights cuts GPU training costs by ~70%, saving ~$80k. 4. **Documentation** – Auto‑generated docs reduce dev time on docs by 60%, saving $60k. **Lean roadmap (12‑week sprint):** - **Week 1–2**: Select models (OpenAI Codex, GPT‑4, Diffusion) and set up infrastructure (GPU pool, API key budget). - **Week 3–4**: Pilot AI code generation in a low‑risk module; track time savings and bug rates. - **Week 5–6**: Roll out AI‑driven unit tests; integrate with CI/CD. - **Week 7–8**: Implement AI‑assisted design docs; train team on prompt engineering. - **Week 9–10**: Deploy generative art engine for UI assets; benchmark production speed. - **Week 11–12**: Consolidate metrics, refine prompts, lock in cost model, and set quarterly KPI review. Track metrics: dev hours saved, defect density, time‑to‑market, and ROI. That should give you a clear, data‑driven push forward.

Got it—let's hit the ground running, lock in the metrics, and iterate fast. Stay on target and keep those numbers clean.