Our client, a leading advisory firm specialising in risk management, insurance, and pension benefits faced an IT system that worked, but no one knew exactly how. With highly specialised expert teams, the firm delivers comprehensive, tailored solutions that help organisations navigate complexity, manage uncertainty, and secure long-term resilience. Yet they still depended on outdated IT systems that lacked the reliability today’s challenges demand. 

As Zühlke joined the project, certain well-known patterns quickly emerged. Change was risky. Maintenance was expensive. And despite its importance, the system could not keep up with the pace of business. 

Millions of lines of legacy code, no reliable documentation, and business logic scattered through years of exception-based rules made the client’s digital landscape a black box. It was fragmented, complex, and held together by assumptions no one could trace. 
 

The client needed support in fully rebuilding and modernizing their legacy technology stack to bring clarity, speed, and scalability back to the heart of its operations.

With our long-standing business relationship, the client chose Zühlke based on our track record in successfully reconstructing intricate systems, paired with the expertise to ensure continuity, stability, operational efficiency and scalability.  
 

Here, CDM was the methodology of choice to deliver the specific capabilities required. The AI-driven analysis—powered by CDM—accelerates the delivery of digital products and services, enhancing agility, efficiency, and impact. By leveraging proven, reusable AI augmentation patterns rooted in best practices, CDM enabled rapid identification and deployment of the most suitable solutions.

Furthermore, CDM is Zühlke’s approach to building an adaptive, learning organization and turning that learning into impact, fast. In a world where AI and delivery landscapes evolve rapidly, CDM provides a practical, repeatable cycle that helps teams adopt and scale AI responsibly. It’s a closed learning loop — one that evolves with every project. And because it’s grounded in real delivery, it helps teams stay adaptive, aligned, and in control. Even as tech and business needs shift.

When the organisation started applying CDM to enable AI-augmented delivery and teams started using CDM patterns provisioned by CDP to deliver value they observed improved pace, productivity and reported work as being ‘more joyful’: AI is well-supervised, eliminating most routine work so the teams can focus on innovating much faster. Humans decide what is the right thing to do and AI does things right.

The outcome was improved productivity and speed across disciplines: AI-enabled analysis and AI-driven acceleration helped team move from task-level automation to transformative workflow improvements, and to deliver substantial productivity gains that translate into measurable business outcomes.

Here’s how the team is working with AI today:

• Business Analysts & Architects use ChatGPT to reverse-engineer undocumented legacy code and complex data structures. Business Analysts are enabled with tools like SQL-Query GPT to help them question the database in the natural language or Story-GPT to generate new user stories with the standardized formatting.
• Developers benefit from tools like GitHub Copilot, ChatGPT, and custom GPTs enriched with the team’s coding guidelines. These tools, supplemented with team context speed up development, ensure consistency, and explore unfamiliar parts of the system. Code reviews are supported by the Review Bot, catching style and quality issues earlier in the process.
• Quality Assurance (QA) plays a crucial role in the project, with AI actively supporting quality assurance throughout development.  CDM patterns combined with the power of tools like ChatGPT and GitHub Copilot are used to generate additional test cases and act as sparring partners to identify potential gaps in coverage – helping the team catch edge cases early and maintain high confidence in their deliverables. 

All in all, leveraging CDM as an underlying methodology for augmenting the software delivery lifecycle with AI, the team now:

• Works smarter, from planning to implementation
• Codes faster, with improved quality and fewer manual errors
• Automates the repeatable tasks – freeing up time for high-impact thinking

Outcome: by streamlining processes and reducing waste, the team reported ~30% efficiency gains while building a modern, maintainable platform. The increased clarity and confidence also translated into a boost in productivity.

The efficiency improvement achieved here is a strong result, especially given the extreme complexity of the client’s legacy landscape. It laid the foundation for improved productivity, a meaningful outcome that reflects the strength of the approach even in tough conditions.