Reimagining the Test Pyramid in AI Era 

Modernising the Test Pyramid for AI-Driven Enterprises

The traditional Test Pyramid remains a valuable framework for test automation, but AI demands a significant upgrade. Unlike conventional software, AI systems learn, adapt, and sometimes fail in unpredictable ways. That’s why Sogeti is introducing a horizontal AI testing layer—a cross-cutting approach that spans all levels of the stack, ensuring robust validation from code to customer.

Evolving the Test Pyramid: From Code to Confidence

Layer	Focus	Strategic Value	AI Layer Interaction
Unit	Code-level validation	Fast feedback, safe refactoring	Validates feature engineering and data preprocessing. Detects early data quality issues.
Integration	API & service orchestration	Early defect detection, contract assurance	Tests model APIs and data pipelines. Monitors schema mismatches and data drift.
UI	End-to-end user journeys	Business-critical validation, user trust	Validates AI-driven interfaces like chatbots and recommendations. Ensures fairness.
AI Layer	Model & data integrity	Trustworthy AI, fairness, explainability	Ensures data quality, model behaviour, bias audits, and continuous validation.

How the AI Layer Strengthens Each Level

Why AI Testing Is Essential for Business Success

AI introduces unique risks that traditional testing cannot detect:

To mitigate these risks, Sogeti focuses on:

1. Data Quality & Drift Detection
2. Model Behaviour & Explainability
3. Bias & Fairness Audits
4. Continuous Validation in MLOps Pipelines

Tools That Power Responsible AI Testing

Layer	Tools	AI Considerations
Unit	JUnit, pytest, xUnit	Validate feature engineering logic
Integration	Postman, WireMock, MLflow, REST-assured	Test model APIs and schema contracts
UI	Selenium, Cypress, Playwright	Validate AI-driven UIs like chatbots and recommendations
AI	WhyLogs, Alibi, Fiddler, AIF360	Monitor fairness, drift, and explainability

Best Practices for AI-Ready Quality Engineering

• Shift Left: Integrate AI testing early in the development lifecycle.
• Automate with Purpose: Focus on business-critical paths and data integrity.
• Use the VOICE Model: Validate outcomes, not just outputs.
• Measure What Matters: Align KPIs with customer trust and ROI.
• Think Holistically: Combine deterministic and probabilistic testing methods.

Introducing the VOICE Model for AI Quality Assurance

The VOICE Model is a comprehensive framework for building high-performing, ethical, and compliant AI systems:

Component	Focus Area
V – Validity	Ensure decisions are based on accurate, relevant data and logic.
O – Outcomes	Evaluate the real-world impact of AI predictions and actions.
I – Integrity	Monitor for data drift, bias, and model degradation.
C – Compliance	Align with the EU AI Act and internal governance standards.
E – Explainability	Make AI decisions transparent and interpretable for all stakeholders.

Quality Engineering: A Competitive Advantage in the Age of AI

At Sogeti, we believe quality is a growth driver. The modern Test Pyramid is your blueprint for building intelligent systems that are not only fast and functional, but also fair, explainable, and future-ready. Whether you’re modernising QA or scaling AI, our goal is the same: engineer confidence, layer by layer.

🔍 Explore Sogeti’s Gen AI Amplifier or contact us to learn how we can help your organisation build responsible, scalable AI solutions.