AI Case Studies in Practice: Delivering a Complex Software Analysis Under Severe Time Constraints

Explore More AI Use Cases

Each scenario reflects real-world pressure points where applied AI delivers measurable impact across engineering workflows.

Case 1: Handling a Late Architecture Change Before Release
Case 2: When Secure Communication Breaks. Resolving a Critical Java Issue
Case 3: Delivering a Complex Software Analysis Under Severe Time Constraints
Case 4: Understanding and Optimizing a Legacy System Without Documentation

‍

Business Context

In many projects, the analysis and design phase is what determines how fast the project can move forward. If analysis takes weeks, the entire project is delayed. If analysis is delivered quickly and in sufficient quality, decision-making and development can start much earlier.

In this case, the client needed to make decisions about further system development.

Without the ability to prepare a high-quality analysis quickly, the project would have been delayed or decisions would have been made with insufficient information.

From a business perspective, this means:

• faster decision-making
• faster project start
• reduced risk of wrong architectural decisions
• better planning of future development

AI in this case did not just save engineering time — it accelerated decision-making.

Situation

• The client required a detailed analysis and extension plan for a system they had been developing since 2019.
  The available time was reduced from 7 MD to less than 2 MD.

Why it was hard

• The analysis had to include:
• Functional requirements
• Non-functional requirements
• Security considerations
• Configuration
• Testing strategy
• Integration with surrounding systems
• Future scalability
• A comparable output would normally require approximately 12 MD.

How we used AI

• The process was iterative and guided by engineers:
• The input and requirements were refined multiple times
• The plan was generated and reviewed
• Examples of expected system behavior were added
• The plan was decomposed into smaller parts
• The final document was generated in structured form
• After approval, the plan was used to pre-generate code
• Testing and tuning were also supported by AI

Outcome

• Standard approach: ~12 MD
• With AI-assisted engineering: ~80–85% faster delivery of the analysis
• Document was approved by the client with minimal comments
• The plan was directly used for implementation

Delivery Impact – What this means for software delivery

• Analysis and design phases often determine the overall project timeline.
  AI made it possible to deliver decision-ready documentation in a fraction of the usual time.
  This allowed the client to move forward faster and shortened the overall project timeline.

What made the difference

• Structured context
• Engineering experience
• Iterative approach
• Validation and testing
• Domain knowledge

AI made it possible to deliver complex analysis quickly, but only because the process was properly guided by experienced engineers.