The 6 vs. 9 Problem: Why AI Projects Fail More Than Traditional Software Development
That idea stuck with me because it perfectly illustrates why AI projects fail at higher rates than traditional software development. Not because the technology is flawed, but because teams often don’t align on perspectives:
- 🔹 Data scientists focus on model accuracy.
- 🔹 Business leaders prioritize ROI.
- 🔹 Engineers worry about deployment constraints.
- 🔹 End users just want something that works seamlessly.
- 🔹 Project managers balance competing priorities and drive alignment.
If we don’t take the time to see what others see, we risk building technically brilliant AI solutions that never get adopted.
âś… Questions We Must Ask Before Building
- Are we seeing the full picture?
- Are we aligned on what success looks like?
- Are we validating perspectives regularly?
AI isn’t just about data and models—it’s about understanding context, assumptions, and real-world impact. Project managers play a critical role in ensuring AI initiatives succeed by bringing teams together and clarifying expectations.
Big thanks to Markeeva Morgan, PhD, PMP for sharing this insight—it’s a perfect reminder for AI leadership!
I’d love to hear how your team aligned on a shared perspective.
