🚀 How to think about AI implementation in an organization#
This article proposes a framework for Chief Data and AI Officers: wanting AI is not enough; you also have to think about productivity, cost, time, and real impact.
What stands out#
- 📈 Separates autonomous and augmented productivity
- 💸 Considers implementation and opportunity cost
- ⏳ Analyzes time-to-value
- 🧩 Applies to data, engineering, and operations teams
The takeaway is useful: AI often doesn’t cut costs immediately, but it increases productivity and organizational speed.
🪄 Quick explanation#
Think of AI as hiring an extra team.
It’s not enough to bring it in: you also have to integrate it, measure the value it adds, and see how long it takes to pay off.
👉 The key is to think in terms of return, not just excitement.
More information at the link 👇
Also published on LinkedIn.
