1.⁠ ⁠Lightning-Fast Prototyping: AI can generate firmware snippets, netlists or schematic outlines in seconds—slashing your R&D cycle and speeding products to market. For example, I use AI to get quick ideas to execute a prototype. AI won't do things 100% for us, but surely reduce time and the effort it takes on reworking on elements
2.⁠ ⁠Early Error Detection: With AI-driven test-vector creation and “rubber-duck” debugging prompts, you catch edge-case bugs before hardware spins, cutting rework costs. I use this a lot, use chunks of code to find out possible flaws. you can simply tell AI your coding standard document, and it can ensure that your developers don't move away from standard protocols.
3.⁠ ⁠Automated Documentation & Compliance: Hassle-free generation of user manuals, test reports and traceability matrices ensures consistency, audit readiness and frees engineers from busywork. I make quick excel sheets of test cases, or write descriptions on how my system works, and it is able to give detailed info on the test cases. The more data we share, the better it tells.
4.⁠ ⁠Focus on Innovation & ROI: By offloading repetitive tasks—code boilerplate, DRC checks, data analysis—your team can tackle high-value work, boosting productivity, cutting expenses, and gaining competitive edge. Common thing I use now.