@vasuman: There are only 3 buckets of value that matter for AI implementations. Time/cost savings: Is AI going to replace the wor…
Summary
The article discusses three key value buckets for AI implementations: time/cost savings, revenue uplift, and risk reduction, emphasizing the importance of aligning priorities and KPIs to avoid wasting resources.
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Cached at: 07/15/26, 07:58 PM
There are only 3 buckets of value that matter for AI implementations.
Time/cost savings: Is AI going to replace the work humans do, cheaper or faster?
Revenue uplift: Is AI going to increase the amount of money I make, after accounting for token spend?
Risk reduction: Will I be more accurate as a result of AI doing, or checking, the work my team does?
It’s incredibly important to walk teams through this value capture exercise, allowing both sides to mutually agree on priorities, KPIs, and definition of success.
The reason why AI keeps failing is because giving everyone in the org access to Claude Code and burning through $10M in tokens in 3 months feels productive until you realize there was no time/cost saving, no revenue uplift, and no risk reduction.
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