What is AI product development?

The product is the system around the model

A model can generate text, interpret an image, classify information or choose between tools. A product turns those capabilities into something people rely on. Users need a coherent workflow around the capability, and operators need to know how the system behaves when inputs are incomplete, tools fail or the model is uncertain.

What usually sits around the model?

• Deterministic application logic for permissions, eligibility, calculations and safety gates.
• Context and retrieval so the model works from the right information for the task.
• Tools and integrations when the system needs to read or change external state.
• Evaluation against representative tasks, difficult cases and regressions.
• Observability so failures can be detected and investigated.
• Human factors that determine when users should rely on, review or override the system.

When should AI be used?

Use AI where interpretation, language, synthesis or uncertain reasoning is the source of value. Keep deterministic functions deterministic where the correct behaviour can be defined explicitly. A strong architecture uses both.

What is different about agentic AI?

Agentic systems add the ability to choose and sequence actions. The ability suits multi-step work across changing state, and it brings more failure modes with it. Tool permissions, state management, recovery, evaluation and boundaries become core parts of the product architecture.

How should an AI MVP be scoped?

The first release should test the hardest assumption: whether the AI improves the target task enough to justify its cost, latency and operational complexity. In practice, that means a narrow workflow, explicit evaluation criteria and a deliberately limited toolset.

Digitalis applies this discipline from its origins in clinical software, where evaluation and governance are part of the product from the first release.