The Engineering Manager's Role in the Age of AI

From Code Reviewer to AI Orchestrator

Hey there! As generative AI becomes a part of our daily workflows, the role of engineering managers has changed dramatically. We used to focus mainly on technical oversight, code reviews, and process management. But now, we need to become skilled orchestrators of human-AI collaboration. This shift means we have to develop new skills, rethink our leadership styles, and redefine what it means to manage an engineering team.

Back in the pre-AI days, engineering managers spent a lot of time reviewing code, resolving technical disputes, and making sure everyone followed architectural standards. These activities are still important, but now generative AI handles many routine aspects of code review and technical validation. This automation frees us up to focus on higher-level concerns like strategic direction, team dynamics, ethical considerations, and the human elements of software development that AI can't address.

The best engineering managers in 2025 have embraced this evolution, positioning themselves as orchestrators rather than controllers. We spend more time defining the boundaries within which AI operates, establishing governance frameworks, and ensuring that AI tools enhance rather than constrain human creativity. This requires a nuanced understanding of AI capabilities and limitations, as well as the ability to communicate these effectively to both technical and non-technical stakeholders.

As AI orchestrators, we also need to become adept at prompt engineering—the art of effectively communicating with AI systems to achieve desired outcomes. This skill goes beyond simple query formulation to include understanding how different prompting strategies affect AI behavior, how to combine multiple AI systems for complex tasks, and how to evaluate and refine AI outputs. Engineering managers who master these skills can significantly amplify their teams' productivity and innovation potential.

Balancing Human Creativity with AI Capabilities

One of the trickiest parts of engineering management in the AI age is finding the right balance between human creativity and AI capabilities. While generative AI excels at pattern recognition, code generation, and optimization, it lacks the contextual understanding, empathy, and innovative thinking that humans bring to the development process. The most successful engineering teams leverage the complementary strengths of both.

I've found that clearly delineating which aspects of development are best suited to humans versus AI is essential for effective team functioning. Human engineers should focus on problem definition, architectural vision, user experience design, and ethical considerations—areas where contextual understanding and value judgments are paramount. AI systems can then handle implementation details, optimization, testing, and documentation, with human oversight to ensure alignment with overall objectives.

This division of labor requires engineering managers to develop new approaches to team organization and workflow design. Traditional development methodologies like Agile and Scrum have evolved to incorporate AI collaboration, with new ceremonies focused on reviewing and refining AI outputs, and new roles emerging to specialize in human-AI integration. Sprint planning now includes explicit consideration of which tasks will be primarily AI-driven versus human-led, with appropriate time allocations for each.

The psychological aspects of this balance cannot be overlooked. Engineers may experience both empowerment and anxiety as AI systems take on more responsibilities. Some team members may fear obsolescence, while others may over-rely on AI and allow their core skills to atrophy. Effective engineering managers must address these concerns proactively, creating a culture that values human contributions while embracing AI augmentation.