From AI Exploration to Execution for a Global Aviation Logistics Organization
The Challenge
The client, a global aviation and baggage claim logistics company serving airlines and airports worldwide, recognized the urgency of adopting AI to improve operations and remain competitive. While leadership was aligned on the importance of AI, they lacked clarity on where to begin, how to structure ownership, and which use cases would deliver the greatest value.
Their organization spans multiple business units, including marketing, HR, finance, IT, construction, simulation, and core operations, each with distinct workflows, tools, and data practices. However, there was limited visibility into how data flowed across teams, what technologies were in use, and whether current systems were scalable or even supported long term.
The immediate challenge was a lack of structure. The leadership team needed a clear roadmap: governance ownership, prioritization criteria, actionable use cases, and a phased implementation approach that balanced impact, feasibility, and cost consciousness. Without this foundation, AI adoption risked becoming fragmented, reactive, or misaligned with strategic goals.
Our Approach
- Two-Day Executive Workshop: Facilitated alignment around AI vision, decision criteria, and strategic priorities.
- Functional Discovery Sessions (7): Conducted structured interviews across leadership and operational teams to document workflows, tools, and data flows.
- Process & Use Case Documentation: Identified 89 business processes and evaluated 14 high-potential AI use cases.
- Prioritization Framework: Developed transparent criteria to assess value, feasibility, risk, and implementation readiness.
- 12-Month Roadmap: Delivered a phased execution plan with governance, ownership, and change management considerations.
- Executive Advisory: Provided CEO-level insight on leading AI transformation from the top down.
Value Delivered
- 89 business processes documented by owning department with key data-processing details
- 7 functional discovery sessions with BNP leadership and operators
- 14 high-potential AI use cases rigorously assessed for value, feasibility, and risk
- 5 prioritization criteria established to evaluate use cases consistently and transparently
- 1 unified AI vision and practical roadmap to guide investment and execution
- Clear AI direction tied to growth
- Investment focused on high-impact opportunities
- Reduced risk of fragmented pilots and ad hoc experimentation
- Shorter operational/project cycle times
- Reinvested time into revenue-generating activities
- Faster time-to-value from concept to pilot to scale
