Value stream economics, not unit cost, drives effective AI adoption.
Quantify outcomes, not just input costs.
Investment in AI capabilities must be evaluated against the total cost of delivery, including human effort, cycle time, and risk, not solely the cost of compute. An incomplete solution, however cheap its components, introduces substantial hidden costs in human labor and delay.
Model selection is a function of task complexity and desired outcome. Simpler models suffice for well-defined, low-context tasks, while frontier models are necessary for ambiguous, high-context work where human intervention is costly.
The true cost of a technical solution is the aggregate of all resources expended to achieve the desired outcome, not merely the most visible line item. Ignoring indirect costs leads to suboptimal resource allocation.
Technology economics evolve; today's expensive capability becomes tomorrow's commodity. Prioritizing current unit cost savings over the capability gains of frontier technologies leads to a competitive lag.
The first question for any AI program: what is the total cost of delivering the completed work, and what is its value?
Value stream economics, not unit cost, drives effective AI adoption.
Quantify outcomes, not just input costs.
Investment in AI capabilities must be evaluated against the total cost of delivery, including human effort, cycle time, and risk, not solely the cost of compute. An incomplete solution, however cheap its components, introduces substantial hidden costs in human labor and delay.
Model selection is a function of task complexity and desired outcome. Simpler models suffice for well-defined, low-context tasks, while frontier models are necessary for ambiguous, high-context work where human intervention is costly.
The true cost of a technical solution is the aggregate of all resources expended to achieve the desired outcome, not merely the most visible line item. Ignoring indirect costs leads to suboptimal resource allocation.
Technology economics evolve; today's expensive capability becomes tomorrow's commodity. Prioritizing current unit cost savings over the capability gains of frontier technologies leads to a competitive lag.
The first question for any AI program: what is the total cost of delivering the completed work, and what is its value?
After 20 years in software development, Norman is both a hands-on leader and defining the new age of AI SDLC for some of the biggest brands in the world — and exploring it with the builders. He writes here about things he is hearing and seeing. All posts are his personal points of view and do not reflect any employer or any customer he has ever had contact with.
The views and opinions expressed in this article are the author’s own and do not represent the positions of any employer, client, or affiliated organization.
