Reflecting on AI's real impact on engineering leadership
In her recent post, “RDEL #99: How has AI impacted engineering leadership in 2025?”, Lizzie Matusov shares some grounded findings from the 2025 LeadDev Engineering Leadership Report. The data paints a picture that is less about revolutionary transformation and more about a gradual, and sometimes challenging, integration of AI into engineering workflows.
Key findings from the report
The survey of over 600 engineering leaders reveals:
- Productivity gains are modest: 60% of leaders said AI has not significantly boosted team productivity yet.
- Headcount is stable: 54% do not expect AI to reduce their team size this year.
- Adoption is focused: The most common use cases are coding assistants for generation, refactoring, and documentation.
- Leaders are concerned: Over half (51%) believe AI will have a negative long-term impact on the industry, citing code maintainability and the effect on junior developers.
The overall message is that while adoption is widespread, the promised productivity revolution has not yet materialised.
A practical path forward
These findings align with my own experience. In the early days, AI and LLM projects often felt like a technology searching for a use case. It was a classic example of a solution looking for a problem, which I discussed in my post on assessing new technology with Readiness Levels.
What gives me confidence for the future, however, is the emergence of standards that help with corporate adoption. Protocols like MCP are a great step forward, providing a common language for integrating AI with existing tools. This helps to reframe the technology; instead of being a goal in itself, AI becomes a practical tool to help realise business capabilities more effectively.
For AI to deliver real value, there must be a clear roadmap for its implementation and practical usage. This is not just about buying new tools; it is about organisational change. A well-defined plan is the first practical step to getting people on board and ensuring the company invests its time and money in the right things. Without that clarity, we risk staying in a cycle of experimentation without ever achieving meaningful results.
