Engineering management is undergoing a transformation that rivals any in recent history. The rise of artificial intelligence, especially large language models (LLMs), is changing not just how we build products, but how teams learn, grow, and collaborate. As a senior engineering manager, embracing this shift and guiding your team through it is more essential than ever.
AI-Driven Transformation in Action
At Apple, I helped launch a Sales Education and Enablement platform that delivered targeted content to empower sales professionals. The integration of an AI chat feature into the mobile app marked a turning point, user engagement soared, adoption spread rapidly, and revenue growth followed. Direct feedback from the field revealed that AI-powered insights boosted customer satisfaction and made interactions more meaningful. This wasn’t just about speed; it was about elevating the whole sales experience.
Pragmatic AI: Strategy and Execution
Success with AI starts with clear objectives and a pragmatic approach. Not every problem calls for a complex AI solution. When my team rolled out new capabilities, we always started with a pilot, a small group of users, fast feedback, and quick iterations. Once we saw impact, it scaled regionally and then globally. This phased approach allowed us to minimize risk and keep teams engaged.
Just as importantly, we observed that over-engineering could quickly erode returns. In tech, complexity often creeps in unnoticed. We stayed disciplined: only deploying AI where it drove measurable business results, not just because it was possible.
Maintaining Reliability While Accelerating Innovation
Rapid deployment can be a double-edged sword. Even small errors from AI systems can have outsized consequences in enterprise environments. My management philosophy: build solid context layers, shape clear boundaries for AI agents, and never sacrifice reliability for short-term speed. If a system’s response had the potential to impact business outcomes, it underwent extra review, human judgment remains irreplaceable.
Overcoming the Learning Curve
The steep pace of AI development can unsettle even seasoned engineers. Early on, some worried: “Is AI making my role obsolete?” My response was to promote experimentation, invest in continuous training, and foster psychological safety. By attending workshops, leveraging courses, and hosting “failure retrospectives,” we made learning central to our culture. Our most resilient engineers weren’t the ones who already knew AI, they were the ones who could adapt, learn, and apply new tools thoughtfully.
Rethinking Mentorship and Career Development
Automation shifts the skills engineers need. Recognizing that, I moved mentorship away from repetitive tasks to system design, problem-solving, and cross-functional collaboration. We encouraged ownership of projects and opened new roles for those who excelled at human-AI teamwork and ethical reasoning.
A practical upgrade for our team was adopting NotebookLM. It made onboarding new hires easier, sped up documentation, and helped share expertise using podcasts and searchable knowledge capsules. This tool multiplied how fast new hires got up to speed and made ongoing learning as seamless as possible.
Shaping Culture Amid Change
None of these changes succeed without the right culture. For my teams, fostering psychological safety, celebrating experimentation, and encouraging open feedback were cornerstones. We recognized mistakes as part of growth and curated an environment where trying new things with AI was expected. That culture was our best antidote to uncertainty and fear.
What’s Actually Changing Day to Day?
· Engineers now routinely pair with AI agents, not just colleagues, for problem-solving.
· Code reviews involve deeper scrutiny, especially where logic is generated by machines.
· Senior staff operate more as “editors” and “sense-makers” than ever before.
· Team workflows are evolving, and speed must be matched by thoughtful judgment.
Principles for AI Transformation That Work
· Always start with “why” before “how”
· Pilot small, scale only when validated
· Be relentless about simplicity, complexity kills margins
· Make learning resources (like NotebookLM) accessible for fast onboarding
· Reward adaptability, collaboration, and ethical judgment, not just output
· Invest in culture: trust, safety, and growth mindset are your strongest levers
Final Thoughts
The revolution is here. Senior engineering managers play a pivotal role, bridging ambitious AI adoption with the practical needs of their teams and stakeholders. Combining strategic discipline, investment in people, and a culture built for change, you can ensure your team is not just ready for the AI era, but thriving within it.
