Article on Building Resilient Healthcare ...

Healthcare supply chains built purely around cost minimization can't survive major disruption, according to supply chain leaders from two European health systems. In a webinar hosted by Healthcare Digital in partnership with Amazon Business, Kenny Otto, Head of Supply Chain at the NHS's Royal Papworth Hospital, and Yari Holzer, Director of Medical Technology Procurement and Investment Planning at University Hospital Tübingen, discussed how AI can help healthcare organizations build resilience in an era of geopolitical instability and global health threats.

Cost alone can't drive supply chain strategy

Otto and Holzer said a supply chain approach built purely around minimizing cost fails when major disruption hits. "The lowest price alone cannot be the primary objective anymore," Holzer said. "We have learnt that resilience and supply chain transparency are equally important." The pandemic was the clearest lesson in what happens when supply chains are optimized for price alone, with continuity, transparency and the ability to respond quickly all sacrificed in the process. Cost still matters, the panel said, but it can no longer be the sole variable. Supply continuity, visibility into where products are and the ability to anticipate risk before it becomes a shortage are equally critical to protecting patient care.

Data quality comes first

None of that is possible without accurate, integrated data. Otto warned that AI can only amplify what's already there. "It's garbage in and garbage out - AI is not going to solve your issues if your data is inaccurate," he said. Cleansing data and compiling it into a single source of truth is a good starting point. Fragmentation remains a recurring obstacle, with siloed systems, incomplete last-mile visibility and data spread across departments making it hard to see the full picture. Holzer put it simply: "Most organisations have enough data, but what they need is integrated data." AI for Healthcare training can help professionals understand how AI tools depend on clean, integrated data.

Learning from global disruptions

The pandemic, tariffs and ongoing conflict in the Middle East were all cited as key disruptors. These events exposed that many organizations lack information about their supply chain dependencies, meaning they "reacted to, rather than anticipated those disruptions," according to Holzer. Otto agreed: "Without accurate data, you are always reactive." Organizations that invested in AI and data-driven visibility tools before disruption hit were able to respond quickly, while those that had not were left scrambling.

AI adoption needs clear guiding principles

The panel stressed that AI should enhance human decision-making, not replace it. Use cases discussed included predictive analytics, supply chain optimization and better resource positioning ahead of demand spikes. Adoption only works when tied to a real business problem, backed by proper governance and supported by staff training and trust-building. Otherwise, AI risks becoming another tool nobody actually uses. Supply chain resilience and AI integration shouldn't be viewed as costs to be minimized, but as strategic investments in an organization's ability to keep delivering care. As one speaker put it: "Resilience should not be viewed as an additional cost, but as an investment in continuity of care." Getting there starts with reliable data foundations but doesn't end there. It also requires organizational culture change and giving teams the skills to work confidently alongside AI-driven tools. For supply chain teams, AI for Operations training can help build those skills.

Why this matters for healthcare professionals

For healthcare supply chain professionals, the takeaway is concrete: cost-based procurement is no longer defensible as a standalone strategy. Building resilience requires investment in data quality, AI-driven visibility tools and staff training - and organizations that delay that investment will remain reactive when the next disruption hits. Professionals who understand both the data foundations and the AI tools that build on them will be positioned to lead that shift.