“We spend more time chasing inventory than taking care of our patients.”
Every week, I talk to pharmacy directors and supply chain leaders at health systems across the country, and almost universally, they tell me some version of the same story. They’re smart, experienced professionals managing extraordinarily complex operations, but each of them face the same problems.
It’s a structural failure that’s costing the industry far more than most executives realize.
Drug shortages alone cost U.S. hospitals an estimated $900 million and 20 million labor hours annually, according to a June 2025 survey from Vizient. That figure only covers the direct financial and staffing burden.
It doesn’t account for additional costs brought on by more expensive alternative drugs, emergency sourcing premiums or the erosion of pharmacist capacity that happens when skilled clinicians spend hours on shortage workarounds instead of patient care.
We’re setting our pharmacists up for failure. Pharmacy teams are being asked to manage thousands of drug SKUs across dozens or hundreds of locations, each carrying expiration dates, lot numbers, regulatory controls and storage requirements. Many of those locations (IV rooms, compounding areas, hazardous drug zones, satellite pharmacies) operate in relative informational isolation. On top of this, the majority of healthcare leaders lack real-time, system-wide visibility into pharmacy inventory, and still didn’t feel prepared to manage major disruptions.
Two decades into the electronic medical record (EMR) era, most pharmacy supply chains are still running on fragmented systems, manual counts, institutional knowledge and software not written for today’s complex operational environment. When something goes wrong, teams respond the way they always have by calling around, checking spreadsheets, calling suppliers and escalating the issue.
Seventy-five percent of healthcare leaders lack full integration between EMRs, ERPs and pharmacy supply chain systems, resulting in visibility gaps that quickly escalate into operational and patient care breakdowns.
Why this problem is actually ripe for AI
Conversations around potential use cases for AI in healthcare tend to gravitate toward clinical decision support, predictive diagnostics and ambient documentation. Although these are important, the healthcare supply chain may be a more natural fit for AI, and a more urgent one.
AI performs best where a problem is well-defined, data-intensive, repetitive and operates at a scale that exceeds human tracking capacity. The supply chain hits all four. I’ve spent enough time in this industry to know that a pharmacist managing 50 drug shortages simultaneously while monitoring thousands of SKUs across multiple care settings isn’t failing, they’re just being asked to do something that no human is actually capable of doing at that level of granularity. Hundreds of locations, thousands of products, each with expiration dates, regulatory requirements and track-and-trace obligations. At the scale of how the healthcare industry operates today, this complexity is simply impossible to solve without the use of advanced automation and AI.
What practical AI looks like in pharmacy
AI is beginning to gain traction among hospital and health system leaders across the supply chain and pharmacy because it is solving problems leaders deal with every day.
Take the hospital pharmacy, where automated inventory monitoring and replenishment is the clearest example of this. When AI continuously tracks stock levels across every pharmacy location, it can flag depletion risks and trigger replenishment before a shortage materializes. RFID-enabled visibility makes this possible by capturing inventory movement in real time, so the system knows what’s where without relying on manual counts. Without this technology in place, the replenishment process becomes reliant on nurses or pharmacies being sent on a goose chase after finding empty cabinets and setting off a reactive scramble.
Demand forecasting is another area where AI’s value becomes even more measurable and valuable. Pharmacy usage patterns are shaped by seasonal variation, census changes, procedure schedules and population health trends. AI models trained on historical data can anticipate demand shifts days or weeks out. During shortage conditions, that lead time is the difference between proactive sourcing and emergency purchasing.
Compliance monitoring may be the most underappreciated application. The Drug Supply Chain Security Act (DSCSA) requires end-to-end traceability of all prescription drugs. Meeting that requirement across thousands of SKUs, multiple wholesalers and dozens of locations is practically impossible done manually or in fragmented set of solutions. But AI-assisted anomaly detection can help flag serialization gaps, lot-level discrepancies and potential diversion patterns that could be missed otherwise.
The prerequisite nobody wants to talk about
None of this works without a clean, unified data foundation. And, similar to other industries, this is where most AI initiatives in healthcare stall.
Supply chain data lives in silos across EHR systems, pharmacy information systems, ERP platforms, point of use solutions (POU), warehouse management (WMS) tools and procurement portals. When those systems don’t share a common data model, or speak the same language so-to-speak, AI has nothing to work with. What we’ve observed across health systems is that the first step for healthcare organizations adopting AI is unifying fragmented data across clinical, operational, and financial systems into a governed environment where AI can actually function That means standardizing item masters, harmonizing location data and establishing a single version of inventory truth before any machine learning begins.
Health systems that have done this work describe the shift from constant firefighting to something that resembles supply chain management.
The real promise
AI usage in healthcare faces an uphill battle, but it will earn its credibility not through headline-grabbing capabilities, but through the steady elimination of friction that drains clinical teams every day. Automating the operational use cases is where the real value lives.
When a pharmacist can stop managing shortages through phone calls and spreadsheets and start receiving automated alerts with actionable recommendations, that’s a tangible improvement. When a supply chain leader can see inventory across all care settings in real time, rather than discovering gaps after the fact, it changes how decisions get made.
The labor math is straightforward. The 20 million hours spent annually on shortage management translates to roughly 10,000 full-time-equivalent positions. Even recapturing a fraction of that capacity gives health systems the ability to redeploy clinical expertise where it belongs and upskill the workforce.
Photo: sorbetto, Getty Images
Tecsys’ vice president of healthcare market strategy, Ryan Rotar is a multifaceted health care veteran who has spent 25 years leading, developing and optimizing end-to-end supply chain operations. Ryan most recently served as the system director of ERP solutions leading HR, finance and supply chain application teams for UNC Health in North Carolina. Prior to that role, Ryan led supply chain operations as the healthcare system executive director. Ryan held positions as surgical tech and radiology systems administrator before finding his passion for supply chain innovation, advanced distribution strategies, business process redesign and staff mentorship.
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