Start with a measurable workflow problem
Useful AI in SaaS improves a defined workflow with measurable quality, latency, and fallback behavior. It should not exist merely because a model can generate text. The product must decide what context the model may see, what output it may influence, how quality is evaluated, and when a human or deterministic path takes control.
Design the AI boundary before choosing a model
- Define one user problem and a baseline metric before integrating a model.
- Keep authorization and irreversible business actions outside free-form model output.
- Ground answers in trusted tenant-scoped data when factual accuracy matters.
- Evaluate representative examples offline and monitor quality and fallback rates in production.
- Design explicit “I cannot determine this” and human-review paths.
From user intent to controlled AI assistance
The model proposes or summarizes inside a policy boundary; deterministic application code owns permissions and state changes.
Controlled AI assistance inside SaaS
The model proposes or summarizes inside a policy boundary; deterministic application code owns permissions and state changes.
User task
Adding AI to support-ticket triage
AI features that look impressive but fail users
AI feature checklist
- Write the workflow goal and baseline metric.
- Define data, privacy, authorization, and action boundaries.
- Create a representative evaluation set before release.
- Validate model output against controlled schemas or vocabularies.
- Monitor fallback, error, latency, cost, and quality indicators.