The 2+2 rule for successful AI roadmap that delivers results
More Pilots Don’t Mean More Results. It's time to cut the hype and build something meaningful.
A CTO of a $50mn+ company told me that his company had 12 ongoing AI pilots with different teams at different stages.
During the conversation, he admitted the success rates to be low with 8 projects failing, 2 underperforming. Only 2 showed meaningful results.
And this story is not unusual. As per MIT, 95% of AI initiatives are failing in organizations. Teams are starting their own experiments, each chasing different idea, with predictable results: scattered efforts, shallow investments, and almost non-scalable. The management eventually questions AI’s potential in delivering results.
The answer to making AI projects successful is not more pilots. It’s focus.
That’s why I use what I call a 2+2 rule:
2 long-term pilots aimed at organization wide impact (efficiency, cost, or EBITDA)
2 short-term pilots (2-3 months) focused on individual productivity
Anything more and results spread too thin. I don’t even start new pilot discussions until one of the four closes. This has enabled me to do far more successful projects.
At one of the companies, we picked two long-term problems as:
How do we grow 5x without growing the QC team linearly
How do we solve pricing for different teams
Both were transformational - tied directly to EBITDA and scale. At the same time, the short-term pilots were much narrower: improving developer productivity and boosting employee engagement.
By limiting to four, each pilot got resources, focus, and accountability. And instead of waiting to see if “one out of twelve” worked, all four moved forward with clear goals.
The companies succeeding with AI projects are the ones starting with high-impact problems and going deep before starting a new project.
In AI, Breadth is cheap. Depth is what compounds.
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