The short answer
Decide around your actual service
An AI project should start with a specific decision or task, not a model. Select a measurable use case, verify data availability and rights, plan appropriate human oversight, then compare results with a baseline before expanding the scope.
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Choose an observable use case
Demand forecasting, recommendations, team assistance, customer feedback analysis and document automation do not use the same data or controls. Describe the user, the decision being supported, frequency and acceptable error.
Start with a task whose result can be compared with a baseline. A theoretical gain is not enough: measure quality, time saved, adoption, incidents and operational impact.
Assess data before the model
Inventory sources, history, granularity, seasonality, missing data and definition changes. Network forecasting, for example, requires understanding openings, closures, promotions, channels and differences between restaurants.
Determine legal basis and access rights when personal data is used. Limit data to the need, define retention periods and document the vendors or models that may receive information.
Organize control and accountability
Assign a business owner to the use case and define when a person must approve, correct or reject an output. Users should understand system limits and have a simple way to report a problematic result.
Keep a register of versions, instructions, evaluation data and incidents. Assess security, robustness, relevant bias, privacy and vendor dependency according to the use case’s risk level.
Move from experiment to product
A prototype becomes a product when it is integrated into the workflow, has indicators, an operating budget and a fallback procedure. Test across several site profiles to avoid generalizing a result specific to one restaurant or period.
Monitor data drift and quality over time. Reassess the use case after a material change to the model, sources or business process. BeezIA and BeezWA remain announced as upcoming, so their availability should not be assumed in a current project.
Compare the options
Scroll horizontally to read the full table.
| Question | Exploratory test | Operated AI product |
|---|---|---|
| Objective | Test a hypothesis | Support a task with a defined service level |
| Data | Controlled sample | Governed, monitored and documented pipeline |
| Evaluation | One-off measurement | Continuous indicators and baseline comparison |
| Accountability | Project team | Business owner, operations and incident procedure |
Your checklist before choosing
- Describe the user, task and supported decision.
- Define a baseline and measurable success criteria.
- Verify data quality, provenance, rights and representativeness.
- Plan human review, challenge and fallback mechanisms.
- Test security, privacy and use-case-specific risks.
- Document versions, evaluations, vendors and incidents.
- Monitor production quality and reassess material changes.