AI Now Helps Run Software Teams — But Bosses Still Do the Babysitting
AI speeds up coding, but managers say it creates new work: checking and chasing.
A new exploratory case study asked software project managers at a large, multi-project software organization how they perceive changes in their work and learning demands associated with LLM-based automation. The findings: LLM-based automation influenced planning, estimation, coordination, monitoring, and governance activities rather than introducing new formal management practices. Participants described LLMs as becoming embedded in everyday project work, producing uneven effects on productivity, increasing the need for review and validation, and reducing visibility into task execution. Those effects contributed to greater reliance on managerial judgment and coordination. Learning demands were perceived as experiential and incremental, centered on understanding LLM capabilities and limitations, critically assessing generated artifacts, and guiding responsible use within teams.
- AI tools are being absorbed into everyday project work, not replacing how teams are managed
- Managers ended up doing more checking and validating, because it's harder to see how AI-generated work got done
- The skill that matters most now is judgment: knowing when to trust AI output and when to push back
Why It Matters
If AI joins your workplace, expect more checking, less clarity, and managers who matter more — not less.