AI-Native product development & engineering: How AI makes your Integration, Verification, and Validation (IV&V) process faster, safer, and smarter

1. Quality as a bottleneck: Challenges in modern product development

The development of modern systems is data-intensive and complex. The share of software is growing, system boundaries are becoming blurred, and the number of interactions is increasing exponentially. Traditional IV&V methods are approaching their boundaries:

• Excessive manual effort in test preparation and execution
• Insufficient traceability
• Isolated tools and processes hinder efficient collaboration

The effort required for the IV&V process and its associated documentation can account for up to half of the entire development budget. The lack of automation leads to bottlenecks, additional work, and quality risks. Moreover, many tasks are performed redundantly for example, repeatedly creating similar test cases, test scripts, or test data for comparable functions in different projects. Artifacts are often rebuilt instead of being reused across products or projects. As a result, valuable knowledge, synergies, and efficiency gains are lost.

AI offers the opportunity to rethink this process, moving from reactive testing toward an intelligent, adaptive quality system that consolidates results, identifies connections, and makes them usable across projects.

2. The turning point: Why AI is now becoming the decisive factor

Several trends are converging today, making AI in the IV&V domain not just relevant but necessary:

• Software-defined systems are fundamentally transforming development strategies and require new approaches to IV&V.
• Agile processes promote continuous quality, and model-based methods enable early verification and simulation.
• Stricter regulatory requirements make seamless traceability across all development stages mandatory.
• AI technologies have reached a new level of maturity. If properly implemented they are powerful, trustworthy, and ready for productive use in complex development environments.

This makes the use of AI in IV&V not a future scenario, but a decisive lever for competitiveness and sustainable product quality.

3. Where AI makes a concrete impact: Five key application areas in the IV&V process

AI demonstrates its full potential throughout the IV&V process. It detects patterns, generates artifacts, learns from results, and thus increases both quality and speed. The following five application areas show where AI is already delivering measurable improvements today:

4. Approach: From data understanding to AI-based IV&V optimization

To ensure the efficient and sustainable use of AI in IV&V, NTT DATA relies on a proven, standardized process model.
With the NTT DATA Value Discovery Proof of Concept (PoC), suitable use cases can be identified and implemented within just three months including an evaluation of cost, quality, and efficiency KPIs.

By analysing existing IV&V processes, targeted prototyping of AI use cases, and integration into existing tool landscapes, a scalable solution is created to automate and increase efficiency in the testing environment.

This approach has already been successfully implemented multiple times in PoCs and has proven effective across industries.
Further information: Smart AI Agent™ Ecosystem for a Transformative Business Landscape : The Secret Sauce of Smart Product Development

5. How AI automatically generates test cases - A look into practice

The following video shows how AI makes the creation of test cases smarter, faster, and more consistent.
Using a PoC, NTT DATA demonstrates how complex technical requirements can be transformed into automatically executable test cases quickly, transparently, and consistently.

The AI reads unstructured documents, identifies logical dependencies between requirements, and generates precise test descriptions from them including test steps, expected results, and clear traceability to the original requirements.

6. Smart test management with AI-based multi-agent systems

Imagine this: instead of automating isolated tasks, an entire team of digital specialists works together to support and streamline the IV&V process.
Each of these AI agents takes on a specific role coordinated and orchestrated by the Test Manager Agent. Together, they create a connected, intelligent system that significantly increases efficiency, quality, and transparency in IV&V activities.

• The Test Manager Agent plans and manages IV&V activities. It sets priorities, allocates resources, and identifies dependencies between tasks. It also monitors progress, consolidates results, and ensures optimal collaboration among all agents.
• The Test Case Generation Agent creates concrete test cases based on current requirements and ensures they are consistent and traceable. It reacts flexibly to changes and leverages experience from ongoing and previous projects to improve coverage and efficiency.
• The Test Data Generation Agent provides the appropriate test data. It identifies gaps in existing datasets, creates realistic and representative scenarios, and thus reduces preparation effort.
• The Defect Manager Agent analyses results and log data whenever issues arise during test execution. It identifies root causes, generates detailed defect reports, prioritizes them by severity, and assigns them to the appropriate teams.

7. Conclusion: Intelligent product assurance becomes a competitive advantage

Companies that adopt AI-supported IV&V strategies benefit from:

• Faster product cycles
• Higher test coverage and quality
• Reduced costs and effort
• Improved compliance and traceability

Existing PoCs demonstrate that AI in IV&V is no longer a vision it is reality.
NTT DATA supports this transformation with proven methods, industry expertise, and scalable solutions from assessment to productive deployment.

In addition to using AI within the testing process, NTT DATA also offers AI Testing, the testing of AI systems themselves. Our experts combine domain and testing expertise with certified know-how in AI testing according to the international ISTQB^® CT-AI standard. This ensures that AI functionalities are tested in a traceable and reproducible manner.