Choose a specific use case
A conversational assistant needs clear boundaries and a way to handle unsuitable answers. Image analysis needs representative examples and measurable acceptance criteria. Recommendations need relevant data and an evaluation method. Confirm that AI is useful for the problem rather than adding it simply as a product label.
Compare hosted services and custom work
A hosted API can reduce initial model-development effort, but usage charges and data-handling requirements remain. A custom model requires appropriate data, specialist work and ongoing evaluation. Include integration, safeguards, logging and fallback behaviour in the estimate. Test the difficult cases, not only a successful demonstration.
Budget for ongoing evaluation
Monitor quality, latency and cost as usage changes. Set spending limits and decide what happens when the service is unavailable. Keep users informed about the feature’s limitations where that affects their decisions. Product success depends on reliable behaviour in the full workflow, including the moments when the AI output is incomplete or incorrect.