AI Transformation Case Studies

These case studies are anonymized composites drawn from common patterns observed across AI transformation journeys. Company names, specific metrics, and identifying details have been changed. The patterns, challenges, decisions, and lessons are representative of real organizational experiences. Each case study follows the same structure: starting conditions, approach taken, challenges encountered, outcomes observed, and lessons learned.

A mid-size B2B SaaS company (engineering team of approximately 80) with no AI capability. The VP of Engineering was tasked with exploring AI after the board identified it as a competitive risk. No dedicated AI budget, no ML experience on the team, and a data infrastructure that had been built for analytics rather than ML.

The team selected a narrow first project: using LLMs to auto-categorize incoming support tickets, a task that was consuming significant support team time and had clear success criteria. A small team (two senior engineers and one product manager) was formed with 50% time allocation. They used a commercial LLM API rather than training a custom model, reducing time-to-production to 6 weeks. After the pilot demonstrated measurable impact on support ticket routing accuracy, the team was given dedicated headcount and budget to expand to additional use cases.