What shapes the cost of an AI product?
The model is only one part of an AI product. Most of the real work sits around it: the workflow, interface, data, integrations, safeguards and the moments when a person needs control.
Begin with the workflow, not the model
A useful AI product starts with a repeatable job. Mapping inputs, decisions, exceptions and outputs reveals whether AI should generate, classify, retrieve, summarise, converse or simply stay out of the way.
The main cost drivers
Complexity grows when the product needs several data sources, real-time interaction, detailed permissions or high-confidence review paths.
- •Number of workflows and user roles
- •Quality and availability of source data
- •Real-time versus background processing
- •Existing systems that need to connect
- •Review, escalation and audit requirements
- •Interface and operational tooling
Prototype the uncertain part first
The highest-risk assumption may be model quality, response speed, data access or user trust. A focused prototype should test that assumption before the surrounding platform expands.
Budget for operation as well as build
AI products have ongoing usage, monitoring and improvement considerations. A clear scope should identify what the team will need to review, update and control after launch.