AI looks cheap in a pilot. That’s the problem.
Pilots lie. They are controlled experiments focused on the happy path. Clean data. Limited scope. Minimal integration. Low volume. The economics look attractive because the variables are contained.
Production is the opposite.
Once AI moves into production, costs start to climb. Integration, data preparation and cleaning, governance and compliance, monitoring, tuning, and human oversight.
This is where the real effort sits. It’s also where most organisations run into trouble.
Usage-based cost changes the game
Unlike traditional software, AI costs scale with usage. More decisions. More interactions. More processing. Higher cost.
Agent-based models amplify this.
What looks like a single task often involves orchestration, multiple model calls, retries and validation. A single workflow can multiply cost in ways that aren’t obvious upfront.
The spiral
There’s a pattern emerging. Use AI to solve a problem. Add more AI to control the first AI. Add human oversight on top. Each layer adds cost.
At that point, the question shifts. It’s no longer “does it work?”. It becomes “is it value for money?”.
Very few organisations can answer cost per decision, process or outcome. Suppliers opaque pricing doesn’t help either.
Without that clarity, scaling becomes guesswork.
AI isn’t expensive because of the models. It’s expensive because of everything around them.
If any of this resonates, come and talk to us at Simplify Consulting.
Chris Moore
Head of Solution Architecture
