From Insight to Execution: How AI Is Strengthening Healthcare Supply Chains
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Marlin Doner, VP of Product Management
Tuesday, May 5, 2026
Marlin Doner, VP of Product Management
Healthcare supply chain leaders are navigating an environment defined by disruption, cost pressures, uncertainty and increasing operational complexity. Forecasting demand, responding to backorders and controlling expenses require faster decisions supported by better information.
But many organizations still rely on manual analysis and disconnected systems, making it harder to understand what is happening across the supply chain in real-time.
Over time, the healthcare supply chain has also accumulated what can be described as workflow debt – a build-up of disconnected systems, manual processes and fragmented decision-making across trading partners. While organizations have invested in tools and processes, the industry has yet to fully orchestrate supply chain workflows end-to-end.
As a result, teams spend significant time reconciling data and reacting to issues, rather than acting on early signals of disruption.
As complexity increases, traditional resiliency strategies – such as secondary suppliers, inventory buffers, manual analysis – are too costly and unsustainable to respond quickly to change. It is becoming critical for supply chain leaders not only to see patterns earlier, but act on them faster and with greater coordination.
Healthcare supply chains generate large volumes of data across purchasing, inventory, suppliers, contracts and increasingly, clinical care workstreams.
However, that data is often spread across multiple tools and reports, requiring technical expertise to extract and time-intensive analysis to understand what matters most. When information is fragmented, teams spend more time gathering data than acting on it.
Traditional supply chain management often relies on responding to issues after they occur. Backorders, price discrepancies and unexpected demand changes can force teams into reactive decision-making.
The challenge is no longer data availability. It is turning fragmented data into shared intelligence that supports timely, coordinated action across the supply chain.
This shift reflects a broader evolution in how AI is being applied across industries. Early AI tools focused on supporting decisions – helping users analyze data and generate insights. Increasingly, organizations are looking for AI to go further – embedding intelligence directly into workflows to help teams act on information in real time and reduce manual intervention.
Healthcare supply chain leaders have always managed complexity. But today’s environment requires more than reacting to backorders or analyzing reports after the fact.
When intelligence is delivered within the tools teams already use, insight becomes easier to access and apply.
GHX ResiliencyAI supports this approach by applying advanced analytics within the GHX platform to help transform operational and financial data into actionable insights.
Instead of searching for information, users can see patterns, exceptions and trends and take action on them as part of their daily workflows.
GHX ResiliencyAI is not a standalone AI tool. It is intelligence woven throughout the GHX ecosystem.
By embedding predictive analytics and AI-powered assistants into these solutions, GHX helps supply chain leaders:
This approach helps shift supply chain operations from reactive analysis to more proactive execution – where teams can respond to disruption signals earlier and with greater precision.
AI models are most effective when they are built on a broad set of data. When insight is limited to a single organization, it may be harder to detect larger trends or emerging risks.
Through the GHX ecosystem, ResiliencyAI draws on data across a wide network of providers and suppliers, helping organizations see patterns that may not be visible within their own systems.
This shared intelligence supports better visibility, more informed decisions and stronger collaboration across the supply chain
With GHX ResiliencyAI, healthcare supply chain teams can:
This broader perspective reinforces supply chain’s role as a champion of more resilient operations and supporter of better patient outcomes.
In this model, the value of AI is not just in aggregating data, but in helping coordinate action across the network, supporting more aligned, end-to-end supply chain workflows.
Technology alone does not create transformation. Trust does.
As interest in AI grows, organizations are focusing on how to use it in ways that are transparent, reliable and aligned with real-world workflows.
Healthcare supply chain leaders are right to ask:
Successful organizations take a pragmatic approach, beginning with targeted use cases, demonstrating value and expanding over time. When insight is easier to understand and integrated into existing tools, it also becomes easier for users to trust and adopt.
GHX ResiliencyAI is designed to augment human expertise — not replace it. By automating analysis of large, complex data sets and surfacing earlier signals of risk, it reduces the operational burden on teams and supports more consistent, repeatable action.
As AI becomes more embedded in workflows, the goal is not only to support decisions but to help organizations execute routine processes more efficiently, while maintaining transparency and control.
Healthcare supply chains are unlikely to become less complex. Disruptions, cost pressures and operational demands continue to require faster decisions and better coordination.
Traditional resiliency models rely on inventory buffers and redundancy. While necessary in some cases, those approaches tie up working capital and can lead to waste.
AI-powered resiliency shifts the model.
Organizations that can turn data into clear, actionable insight — and act on it within everyday workflows — will be better positioned to respond to change.
By combining automation, shared intelligence and embedded analytics, solutions like GHX ResiliencyAI help providers move toward a more informed, responsive and resilient supply chain.
Within Resiliency Center, GHX ResiliencyAI can detect early signals for backorders and present substitution options in near real time.
Teams can ask questions in natural language — “Where are we most exposed to disruption?” — and receive insights grounded in network-scale data.
This proactive model enables dynamic re-routing, earlier intervention and more informed sourcing strategies.
It’s resiliency designed for today’s complexity.
Predictive models will become more common. The differentiator won’t be whether an organization uses AI — it will be how effectively that intelligence is embedded into daily operations and translated into action.
The greatest value from AI will not come from improving how work is analyzed but from transforming how work gets done – reducing manual effort, improving coordination and supporting faster, more confident decisions across the supply chain.
GHX sits at the center of the healthcare trading network, with visibility into how providers and suppliers transact across the ecosystem. That position enables GHX to build scalable, continually optimized models and deploy them efficiently across thousands of organizations.
For most providers, partnering is more practical and sustainable than building isolated models internally.
GHX ResiliencyAI reflects a simple idea: intelligence should strengthen how healthcare trading partners work — together.
Healthcare supply chains are no longer behind-the-scenes functions. They are foundational to financial resiliency, operational continuity and patient care delivery.
By embedding AI directly into trusted workflows, GHX is helping providers move from reactive management to proactive performance.
The result is not just better forecasting. It’s stronger partnerships, more predictable outcomes and a supply chain built for resilience.
Marlin Doner is VP of product management at GHX where he leads technology roadmaps for an industry leading portfolio of company products and is responsible for market share growth. He is an experienced leader in Product Strategy and Product Development with a demonstrated work history in enterprise software application management across multiple industry verticals.