Most AI initiatives don’t fail because the technology doesn’t work. They fail because organizations commit to building before they understand what to build — or whether the data, the governance, and the organizational alignment actually support it.
Komon addresses the question that comes before any of that: what AI should this organization pursue, why, and under what conditions?
The Scenario
Pacific BioLogistics operates across the US-Japan corridor — pharmaceutical cold chain, multiple legacy systems, bilingual operations teams, growing regulatory pressure on both sides. Every department has a different version of what the company’s problems are.
Running Komon on the PBL organizational profile focused on Inventory operations returns five ranked AI opportunities, each with a readiness assessment, identified data gaps, and the process changes that need to happen before any AI is built. The top recommendation: a cross-system shipment and inventory visibility assistant that bridges the gap between NetSuite, vendor spreadsheets, and the Kobe operations team.
What It Delivers
Each recommendation includes a business rationale, an AI justification, the risks, the data that must exist for the initiative to succeed, a human review checkpoint, and the non-AI process work that should precede any build.
The output is a prioritized roadmap grounded in organizational reality — not a list of things AI can theoretically do.