Case Study: Enhancing Process Efficiency through Automation in an IT Multinational Company

Introduction

Serving as a Management Information (MI) Lead at a major IT multinational, I led a transformation initiative focused on improving business processes. The core mission involved analyzing data to identify automation opportunities, particularly targeting a bottleneck affecting operations teams. Today the same playbook ships AI-augmented automation — Microsoft Copilot Studio, n8n, and Claude Code for code-level workflows — but the discovery and Lean methodology stays the same.

Identifying the Problem

Initial Observations:

A 10-person team responsible for ticket quality reviews faced significant constraints. The manual process permitted evaluation of only 10-30 tickets per week, limiting data reliability. Weekly reports weren't effectively utilized during performance discussions, creating a disconnect between analysis and action.

Deep-Dive Analysis:

Investigation revealed multiple weaknesses:

• Manual processes restricted sample sizes and accuracy
• The team tracked 10 different quality metrics, creating unnecessary complexity
• No feedback loop existed to drive improvements based on findings

Developing the Solution

Building the Business Case:

Collaboration with assigned data analysts revealed that 8 out of the 10 metrics could be automated. The team employed Deming Cycle and lean management principles to guide implementation.

Key Implementation Steps:

• Validated metrics reliability
• Automated manual tasks to expand sample sizes
• Generated comprehensive reports integrated into operational performance discussions
• Freed up resources equivalent to 10 FTEs

Results and Impact

• Enhanced data accuracy through larger sample sizes
• Increased operational efficiency
• Established effective feedback mechanisms for performance improvements
• $100k+ annual run-rate saving — part of the EUR 1.1M+ aggregate documented across 14+ years

Lessons Learned

• Data-driven decision-making should precede action
• Use structured observation methods (DILO/WILO) when data gaps exist
• Engage stakeholders to ensure alignment and feasibility
• Apply continuous improvement frameworks like Deming Cycle

Conclusion

The successful automation initiative demonstrated how strategic process improvements, grounded in collaborative problem-solving and data analysis, drive organizational efficiency and support business objectives. AI tooling now accelerates each step — but the methodology, governance, and stakeholder muscle stay the differentiator.