Hospital logistics have grown increasingly complex as health systems add new technologies and resources that operate independently. The challenge is no longer automating individual tasks, but intelligently orchestrating people, systems and equipment across hospital operations. For years, the healthcare industry has viewed hospital logistics as a robotics problem. Amir Nardimon, founder and CEO of deliverz.ai, argues it is actually an operational intelligence problem.
"Hospitals have invested in robots, automated pharmacies, pneumatic tube systems, transport teams and countless digital platforms," said Nardimon. "Each performs a specific function well, but they largely operate independently. The missing piece is an intelligence layer that understands the entire hospital and continuously coordinates all of those resources toward the same operational objective."
deliverz.ai has developed an AI-powered orchestration platform for end-to-end healthcare logistics automation. Nardimon calls the approach hospital operations intelligence, describing it as a physical AI platform that sits above existing infrastructure. It continuously decides which resource - a robot, transporter, nurse or another available asset - should perform each task based on urgency, location, workload, predicted demand and operational priorities.
Physical AI versus generative AI
Generative AI helps people create and process information. Physical AI helps organizations execute work in the physical world. Nardimon explained that hospitals generate thousands of physical decisions daily: moving chemotherapy medications, transporting laboratory specimens, relocating equipment, assigning patient transport, dispatching staff and balancing workloads across departments. Most of these decisions remain manual, reactive and disconnected.
Technology can process thousands of operational events daily and learn what Nardimon calls a hospital's "operational pulse." It can recognize recurring patterns, predict demand before it occurs, identify bottlenecks, anticipate congestion and reallocate resources proactively. "Healthcare leaders still underestimate how significant this shift will become," he said. "They often view AI as a tool that assists clinicians with information. We see AI becoming the operational nerve system of the hospital."
Why hospital logistics resists automation
Unlike in a warehouse, priorities change constantly in hospitals. A routine specimen suddenly becomes urgent. An elevator becomes unavailable. An emergency patient changes transportation priorities. Staff availability shifts throughout the day. Clinical workflows evolve continuously.
"The challenge has never been getting a robot from point A to point B," Nardimon said. "The challenge is deciding, in real time, what should move next, who or what should move it, and how that decision affects every other department in the hospital." Large-scale automation becomes practical when hospitals stop thinking in terms of individual technologies and begin thinking in terms of orchestration. Hospitals do not need to replace their investments - they need to get those investments to work together.
Measuring outcomes, not robot counts
Success should not be measured by how many robots are deployed or how many deliveries are completed. Nardimon pointed to more meaningful markers: Can patients begin treatment sooner? Can nurses spend more time with patients instead of transporting medications or specimens? Can hospitals absorb increasing demand without proportionally increasing staffing? Can laboratory turnaround times improve? Can operating rooms avoid delays?
At Sheba Medical Center, Israel's largest hospital, reducing chemotherapy delivery times from approximately 40 minutes to 14 minutes was not simply a logistics achievement. "It meant patients could begin treatment earlier, pharmacy operations became more predictable, nursing workflows became more efficient, and valuable clinical time was returned to caregivers," Nardimon said. "Our broader objective isn't faster deliveries. It's enabling hospitals to operate with greater predictability, resilience and efficiency while improving the experience for both caregivers and patients."
Lessons from Sheba's expansion
Sheba initially focused on optimizing one workflow - chemotherapy delivery. Once the platform was deployed, the real value extended beyond the robot or that individual workflow. As the system processed thousands of daily tasks, it learned recurring operational patterns, demand fluctuations, congestion points, departmental behaviors and resource utilization. That operational knowledge allowed continuous improvement of decisions across the hospital.
Sheba is now expanding beyond medication delivery into laboratory specimens, biopsies, patient transport and broader workforce coordination. The same intelligence layer applies because the challenge is not moving a specific item - it is optimizing how the entire hospital functions. For operations leaders building expertise in this shift, AI for Operations Courses address the coordination challenges that platforms like deliverz.ai are designed to solve.
"Healthcare has already digitized patient information - the next decade will be about digitizing hospital operations," Nardimon said. "Hospital operations intelligence will become as fundamental to hospital performance as electronic health records became to clinical documentation. The organizations that learn to orchestrate every physical resource - people, robots, equipment and infrastructure - as one intelligent system will be the ones best positioned to address workforce shortages, financial pressures and growing patient demand."
Why this matters for healthcare operations leaders
The shift from automating individual tasks to orchestrating entire hospital systems changes what operations teams need to measure and manage. Nardimon's framework suggests that deploying more robots or point solutions without an intelligence layer simply adds complexity. The measurable outcomes - reduced treatment wait times, returned clinical hours, predictable patient transport - become the actual KPIs. Operations leaders evaluating automation investments should ask whether a proposed system coordinates existing resources or just adds another siloed tool to the stack. For those building the strategic skills to lead these integrations, AI Operations Leadership Courses focus on the orchestration mindset that hospital systems now require.
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