As a CTO, the AI boom brings new terminology that's both exciting and overwhelming. When it's your job to explain these complex ideas to your leadership team or board, who aren't in the tech trenches daily, the challenge grows.

AI change management has turned the engineering world on its head. Unlike previous technology transitions, AI adoption fundamentally impacts how teams work, think, and contribute value. That means it requires a different approach—one that recognizes the sophisticated nature of engineering talent and the importance of bringing people along the journey thoughtfully.

AI is reshaping team dynamics and collaboration patterns. Code reviews and pair programming are becoming more critical as teams need to collectively evaluate AI-generated changes. Non-coding applications provide immediate value with minimal risk—from building dashboards to generating better PRs and documentation.

In an era where AI systems increasingly shape our interactions with the world, the need for trustworthy AI has never been more critical. In this article, we will delve into the complexities of designing AI systems that are not only functional but also ethically sound, transparent, and secure. We explore six core elements of trustworthy AI—truthfulness, safety, fairness, robustness, privacy, and machine ethics—providing a pragmatic framework of assessment to counter some of the top known ethical issues with generative AI specifically.

Leading the engineering discipline at Able, I've had the privilege of working with a team that is both exceptional and naturally curious. Experimentation is in our DNA—we’ve always been early adopters, thoughtfully evaluating new tools and technologies before integrating them into our stack.