Software teams are under increasing pressure to deliver innovation faster while maintaining the safety, security, and reliability demanded by critical embedded systems.
At the same time, generative AI is rapidly changing how software is developed. Developers can now generate code, unit tests, and documentation in seconds, accelerating productivity and helping teams move faster than ever before.
But when software is responsible for human safety, mission-critical operation, or regulatory compliance, speed alone isn’t enough.
How do you know AI-generated code is correct?
How do you identify hidden defects, security vulnerabilities, or standards violations?
And how can organizations confidently adopt AI-assisted development without increasing risk?
Join us for a practical discussion on how modern verification practices can help organizations realize the benefits of AI while maintaining engineering discipline and software integrity. We’ll explore:
- The opportunities, risks, and limitations of AI-generated software in embedded and safety-critical environments
- How static analysis, unit testing, structural code coverage, and continuous testing help validate AI-generated code
- Best practices for integrating AI-assisted development into CI/CD pipelines while maintaining traceability, accountability, and human oversight
- Emerging industry trends shaping the future of AI-assisted software engineering
Whether you’re evaluating AI adoption or already incorporating AI into your development workflow, you’ll leave with practical guidance for balancing productivity gains with the verification rigor required to build software that is safe, secure, reliable, and trustworthy.