From 6 Months of Manual Test Design to 3 Weeks of Structured Readiness

Industry

Imaging, Printing & Technology Solutions

Platforms & Technologies

• Microsoft Dynamics 365 Finance & Operations (D365 F&O)
• Microsoft Dynamics 365 Customer Engagement (CRM)
• Enterprise Data Warehouse
• Microsoft Power BI (Reporting & Analytics)
• ServiceNow (IT Service Management)

The Intervention

Scaling Testing with Intelligence, Not Headcount

Testpoint introduced its ground breaking Contextual AI-led testing engagement model.

Rather than increasing resources or compressing scope, Testpoint scaled intelligence.

The Contextual AI platform:

1. Ingested all relevant documentation functional, technical, integration, compliance, and migration artefacts.
2. Interpreted them as structured business logic frameworks.
3. Automatically decomposed end-to-end processes into testable components.
4. Generated detailed SIT and UAT-ready test scenarios, including:
   • Positive and negative cases
   • Boundary conditions
   • Integration dependency paths
   • Security and workflow validation
   • Compliance and statutory controls
   • Data reconciliation checks

Compliance and governance were embedded from inception not added later.

Testing shifted from manual interpretation to intelligence-driven execution readiness.

Building Testing Maturity and an Automation-Ready Quality Framework

While reducing test design effort was a significant outcome, Testpoint’s broader objective was to improve the organisation’s overall testing maturity and establish a sustainable quality engineering capability.

Traditional testing within the program was largely document-driven, heavily dependent on SME knowledge, and lacked the consistency required to support scalable automation. Through the adoption of Contextual AI, Testpoint transformed testing from a manual activity into a structured, traceable, and repeatable quality process.

Using Vansah’s Contextual AI capability, Testpoint analysed business requirements, process flows, integrations, and business rules to generate high-quality test scenarios aligned to real operational workflows. This enabled:

• Consistent end-to-end coverage across business-critical processes
• Identification of coverage gaps and risk areas early in the lifecycle
• Full traceability between requirements, risks, and test assets
• Standardised test design practices across multiple workstreams
• A measurable improvement in test governance and quality maturity

Rather than simply generating more test cases, the focus was on generating the right test cases—ensuring business-critical paths, exception scenarios, integrations, and compliance requirements were comprehensively covered.

Automation Proof of Concept

To validate the long-term value of the approach, Testpoint also conducted an automation proof of concept (POC) using the Contextual AI-generated test assets.

Because the test cases were created using structured business logic and consistent coverage models, they were inherently automation-ready. Testpoint selected representative high-value business scenarios and successfully demonstrated how the AI-generated assets could be converted into automated regression tests with minimal rework.

The POC confirmed that:

• Test assets generated through Contextual AI could be directly leveraged for automation.
• Automation effort could be significantly reduced due to improved test quality and consistency.
• Regression coverage could scale alongside future program releases.
• Quality assurance could evolve from a project activity into a reusable enterprise capability.

This provided the client with a clear roadmap for future automation adoption while ensuring that immediate delivery objectives were achieved.

The result was not only a faster testing cycle but a more mature, sustainable quality engineering capability capable of supporting ongoing ERP transformation and future releases.

The Results

88% Efficiency Improvement

Manual test design effort was reduced from an estimated six months to just three weeks, including setup, workshops, refinement, and approval cycles.

Accelerated Test Readiness

The program moved into System Integration Testing and User Acceptance Testing significantly earlier than planned, without compromising coverage.

Reduced SME Dependency

Business stakeholders validated structured AI-generated outputs instead of designing scenarios from scratch dramatically lowering time demands.

Stronger Compliance & Governance Assurance

Regulatory requirements, segregation-of-duties controls, workflow validations, and statutory tax processing were embedded directly into structured test logic.

Improved Data Migration Accuracy

Master data, balances, and reconciliation processes were formally validated within generated test scenarios, reducing post-go-live exposure.

Increased Executive Confidence

Traceability from requirement to configuration to test case improved transparency, audit readiness, and stakeholder assurance.

Strategic Impact

By automating and structuring test design through Contextual AI, the organisation:

• Eliminated a major delivery bottleneck
• Reduced testing preparation cost
• Strengthened integration risk management
• Improved compliance validation
• Enhanced delivery predictability

Quality assurance evolved from a schedule constraint into a strategic enabler of digital transformation.

Scaling ERP Assurance with Contextual AI for Testing

For complex ERP modernisation programs, the greatest risk is not technology failure it is inadequate validation at scale. Through the adoption of Contextual AI, this global imaging provider fundamentally redefined its testing model.

What once required six months of manual effort was delivered in three weeks with broader coverage, stronger governance, and higher executive confidence.

Testing became not just faster but smarter.

Speak to Testpoint about scaling testing with intelligence, not headcount.