Is AI in Design the Magic Tonic for All That Ails Us?

Estimated reading time: 1 minute

In electronic systems design, engineering teams are pressured by management to adopt AI wherever possible to reduce design cycle time and cost. Engineers look at AI with a wary eye: Will it consistently return accurate results? Will it take longer to set up, train, run, and verify than doing it manually? Will it replace me? Yes, there is a transition with AI, but it’s no different than any other form of automation. It must be extensively validated and integrated into the existing design process to enable maximum efficiencies.

Where Do We See the Biggest Success?

AI is being applied to every stage of the PCB design process, but the biggest successes are with smaller tasks. The splashy stories about full design synthesis are substantiated only by software prototypes, often on exceedingly simple designs. Building a proof of concept for an AI application is fairly easy, but making it production-ready requires identifying what “accurate” looks like, making the solution self-verifying, and making it work across a broad spectrum of design types. Production-grade AI must be accurate, verifiable, usable, and generalized across all designs, and robust enough to handle all corner cases.

Challenges in Deployment

Model Training

AI needs models, and with bigger models comes greater accuracy. Some AI tools can build their models automatically on the fly; others must be trained in advance. Models built by Google, OpenAI, Meta, Microsoft, and Anthropic provide a huge jumpstart to the process, but these models must be fine-tuned to address a task, such as searching through a specific set of documents. These models are constantly being updated, and they must be selected based on the target task. Model management can be painful.

To continue reading this article, which originally appeared in the February 2026 edition of I-Connect007 Magazine, click here.