AI Assessment for IT Operations

The challenge

The team saw potential across IT operations, from CMDB accuracy and completeness to dependency mapping. What they lacked was a shared, honest read on which of those ideas were worth funding, and in what order. Everyone had an opinion. Nobody had a ranked list.

The approach

• Moderated a structured, pragmatic ideation process, bringing in relevant AI use cases and best practices as a subject-matter expert.
• Analyzed the baseline, challenges, and pain points from a business, organizational, and strategic angle.
• Built a shared vision and target picture for AI in IT operations that the team agreed on.
• Structured, feasibility-checked, and prioritized the use cases by value and complexity.
• Selected one prioritized use case as the basis for a possible MVP, with a roadmap to get there.

The outcome

The team walked out with a ranked backlog, a shared vision, and one clear MVP candidate to build next. Decisions that used to stall in debate now had a rationale behind them.

Client names are generalized. The work is real; the labels are not.