Can AI Fix Pharma's Last-Mile Delivery Blind Spot?

Access part 1 of this interview on why pharma supply chains still run on historical demand data and how AI could close the real-time visibility gap.

In part 2 of a 3-part interview, Brent Wilhelm, Senior Vice President of Global Supply Chain, Cencora, turns to the final mile: getting pharmaceuticals from the distribution network into the hands of practitioners and patients. Even when a therapy is in stock and positioned correctly, Wilhelm notes, delivery in the US typically depends on third-party carriers like FedEx and UPS, which introduces risk beyond Cencora's direct control. "And so where there's a larger risk is having visibility into the health of those networks," he says, pointing to the need to understand whether carriers can reliably pick up and deliver shipments on schedule.

That visibility gap becomes acute during disruptions. Wilhelm cites this past winter's Winter Storm Fern as a case in point: "Winter Storm Fern this past winter was a big issue in the southeast," he says, describing how the storm affected FedEx's Memphis hub and UPS's Louisville hub for days. Shipping from the geographically closest distribution site doesn't always make sense, he explains, if that carrier lane is experiencing latency or weather-related delays, which is why Cencora is exploring AI to consume real-time carrier data and route shipments through the healthiest available network path.

Wilhelm also highlights how delivery cadence varies by site of care. Some treatment centers need shipments once daily, others multiple times a day, and turnaround expectations range from next-day to two-day fulfillment depending on the therapy and practitioner. Managing that complexity, he says, requires both a flexible distribution footprint and the operational capability to support varied delivery methods simultaneously. Cencora's network of distribution sites is built to flex to those differing customer needs.

Together, the insights underscore a theme from Wilhelm's broader conversation: that resilient pharmaceutical delivery increasingly depends on combining real-time data with AI-driven decision-making, rather than relying solely on historical shipping patterns or proximity-based routing.