Test Strategy
For AI-enabled applications, agree acceptable behaviour, evaluation datasets, scoring criteria and escalation points. Record the limitations of automated scoring and the human review needed for consequential outcomes. Explore testing AI systems.
A good test strategy gives the project a clear view of what will be tested, how it will be tested, who is responsible and what evidence is needed before release.
We help define test strategies for:
- D365 implementations and upgrades
- ERP and CRM programmes
- Digital platforms and web applications
- SaaS products
- Integration-heavy projects
- Data migration projects
- Release and regression testing
- Post-go-live support and hypercare
A practical strategy should cover scope, risk, test phases, responsibilities, entry and exit criteria, reporting, governance and quality gates.
For D365 programmes, this often includes SIT, UAT, regression testing, integration testing, security role testing, data migration testing, release testing and Microsoft service update testing.