AI Case Studies in Practice: Evaluation of a Delayed Modernization Program

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In real projects, engineering problems rarely come in a clean or predictable form. They appear late, affect multiple systems and often put delivery under pressure. AI can help in these situations, but not on its own.

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Business Context

A major client modernization program repeatedly missed milestone dates, changed scope several times, and showed signs of budget overrun. The program affected dozens of enterprise systems, included retirement of legacy platforms, new system development, master-data restructuring, and cloud transition.

A fast, evidence-based assessment was needed for stakeholder decision making.

Situation

We had one month for the assessment, with the first two weeks partially affected by limited availability within the customer project team.

Why it was hard

Inputs came from many sources: notes, Confluence and JIRA exports, and workshops with investors, project management, business owners, architects, integration teams, infrastructure teams, developers, and security experts. The resulting dataset was large, heterogeneous, and sometimes contradictory.

How we used AI

We used AI in three focused ways after collecting and structuring source materials:

Information analysis and fact extraction
Drafting findings and identifying hidden risks
Scoring predefined hypotheses by mapping supporting and contradicting evidence

To facilitate the analysis of source materials and improve navigation, we created an AI-powered knowledge base.

Outcome

Assessment delivered on time despite reduced effective assessment window
Higher-quality findings with stronger evidence and argumentation
Clearer risk profile for budget and timeline decisions

Delivery Impact - What this means for software delivery

Without AI-assisted analysis, the assessment would likely have been less precise, less defensible, and significantly slower. In this case, AI improved both decision quality and delivery speed under severe time pressure.

What made the difference

Consolidating heterogeneous and sometimes contradictory source materials
Creating an AI-supported knowledge base for easier orientation
Extracting facts from notes, Confluence, JIRA and workshop records
Testing predefined hypotheses against supporting and contradicting evidence
Using AI outputs as analytical support, not as a replacement for expert judgment

AI did not replace consulting and engineering judgment. It amplified it when combined with disciplined analysis.

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
Case 5: Evaluation of a Delayed Modernization Program
Case 6: Remediating Post-Migration Data Inconsistency in Production
Case 7: Delivery Based on an AI-Assisted Specification

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