Asking better questions.
Paul Reeves
Two decades leading fleet, transportation, and business operations — now bringing applied AI and machine learning to how institutions actually run
EnterThe Perspective
More than two decades leading operations — fleets, transportation, parking, and the budgets, teams, and systems behind them. The kind of work measured in cost-per-mile, uptime, and P&L, not slideware.
That operational core is the foundation of everything here: at Emory University, directing a 585-vehicle fleet across five locations, cutting total cost-per-mile nearly in half, and — earlier, leading campus parking — rebuilding visitor revenue from $2.1M to $5.1M. This is leadership learned inside the work, where the numbers are real and the consequences land on Monday.
The InquisitiveCynic is what that experience does next: applying disciplined skepticism, applied AI, and machine learning to make institutions run better. The cynic part means nothing gets accepted on reputation alone. The inquisitive part means the questions keep coming regardless.
Explore the focus areas →The Work
Two decades running fleet, transportation, and parking operations for large institutions — P&L ownership, multimillion-dollar budgets, enterprise systems, and the teams that make them work. Leadership measured in outcomes, not org charts.
Turning telematics, maintenance, and utilization data into decisions — predictive maintenance, fleet right-sizing, and cost-per-mile analysis. The data was always there; the discipline is in asking it the right questions.
A practitioner's study of how large language models, retrieval-augmented generation, and ML actually perform inside institutional operations — not the hype, the practice. What works, what does not, and what the difference costs an organization.
The Dispatch
Observations from the intersection of data, operations, and applied intelligence.
Cutting cost-per-mile nearly in half, right-sizing a 664-vehicle fleet, and the operational lessons that no management book quite prepares you for — the wins, the friction, and the institutional realities that only show up on the ground.
Read the note →On the gap between data collection and data comprehension in institutional fleet management, and what changes when you finally close it.
Read the note →Parking looks like a capacity business. It almost never is. A method for finding the revenue that's already there — pricing, utilization, and the quiet lever nobody watches: leakage.
Read the note →A practitioner's account of bringing language models into daily operations — the wins, the friction, and the institutional realities that no benchmark ever tests for.
Read the note →Leadership does not clock out. Lessons on people, logistics, and service — drawn from the volunteer work that shaped how I run a team.
ForthcomingThe Introduction
If you work in operations, institutional management, or the applied study of AI and machine learning — and you believe the best questions come from inside the work, not above it — this is an open invitation. Whether you're building a team, untangling an operations problem, or just want to compare notes, the door is open.