Switching to AI: IT Managers

Why this works

Managers understand delivery, stakeholder needs, and team enablement — critical for leading AI projects and productization.

Key skills to develop

• AI literacy: basics of ML, model capabilities and limitations
• Project management for AI: MLOps lifecycle, data governance
• Vendor evaluation and cost modeling for AI services

How to transition (practical steps)

1. Take short AI-for-managers courses (strategy & use cases)
2. Run a small AI pilot — pick a low-risk internal use case
3. Partner with data scientists to iterate and measure impact
4. Learn basics of prompt engineering and API-based model integration
5. Apply for roles like AI Program Manager, ML Product Owner, or Head of AI Ops

Leadership tips

• Start with measurable KPIs (time saved, error reduction)
• Document ROI and risk mitigation
• Invest in upskilling programs for your team