A look at how Cardinal Health is moving beyond pilots to embed AI into planning, distribution and customer service workflow.
As healthcare supply chains become increasingly complex and technology-driven, leaders across the industry are turning to artificial intelligence to improve efficiency, resilience and customer experience.
In the following Q&A, Repertoire Magazine spoke with Pete Bennett, Senior Vice President of Global Supply Chain for Cardinal Health’s Global Medical Products and Distribution (GMPD) business. Bennett oversees global and U.S. supply chain operations, including logistics, distribution, planning and inventory management.
How have Cardinal Health’s operations leaders approached the idea of implementing AI?
Pete Bennett:Our Global Medical Products and Distribution business stays aligned with our broader organization’s AI Center of Excellence (COE) team in leveraging AI. This team identifies high‑value AI use cases within our organization, evaluates potential solutions and accelerates the deployment of AI across digital and commercial technologies. The COE ensures AI initiatives address a defined business need, rely on high‑quality and relevant data and enhance the customer experience. The goal is to responsibly leverage AI to support customers, strengthen employee capabilities, and safeguard our data.
We have implemented several AI‑driven solutions in our medical product distribution centers across North America, including automation and robotics technology. These tools help frontline workers pick, pack and ship orders with greater quality, speed, accuracy and safety. AI‑enabled planning and inventory systems also provide supply chain leaders with real‑time inventory visibility and insight into customer demand trends. We’re leveraging equipment manufacturers and vendors who can deliver their AI capabilities within their own products to enhance our workflows. Examples of these technologies include AI-enabled warehouse execution systems to streamline process flows and AI vision coupled with cameras for our material handling equipment to help enable safety and accuracy in our facilities.
What are some examples of how AI would be used in medical product distribution?
Bennett: AI is increasingly embedded across medical product distribution, enhancing how we plan, forecast and deliver products. Today, advanced algorithms help predict demand more accurately, account for changing lead times, and optimize supply plans so customers receive a more reliable picture of product availability. AI also automates the collection and updating of key information such as expected delivery dates, reducing manual work and improving data accuracy across systems.
Beyond planning, emerging AI agent‑based tools are supporting teams across the supply chain by summarizing operational notes, surfacing insights from complex datasets, and assisting with tasks like tracking shipments or preparing documentation for import and export regulatory compliance. These solutions enable faster decision‑making, more efficient customer support and smoother coordination with distribution centers by helping to align dock scheduling and labor planning. AI is helping streamline operations, improve service levels, and create a more responsive and resilient distribution network.
What are customer expectations related to how healthcare companies incorporate AI into their operations?
Bennett: Customers expect healthcare companies to evaluate and implement technology solutions that help make the work of healthcare providers easier and improve the patient experience. They also look for AI and other advanced technologies to enhance transparency and resiliency across their supply chains, reduce costs and deliver a more seamless, reliable customer experience. In fact, many customers are increasingly requesting that suppliers formally include AI in their distribution strategy or blueprint.
What are some of the primary benefits of leveraging AI in medical product distribution?
Bennett: Infusing AI into the supply chain delivers several important operational advantages. It enables improved forecast accuracy, ensuring we have the right mix of products in the right place at the right time. AI also continuously identifies vulnerabilities within the supply chain and can automatically initiate mitigation steps to help our teams achieve the best possible outcomes.
AI further strengthens our distribution operations by enhancing safety of our workforce, increasing accuracy of our inventory, and improving our efficiencies through better flow optimization and labor/task allocation. Together, these capabilities help build a more reliable and resilient medical product distribution network.
What considerations must be made when exploring the use of AI in operations?
Bennett: Implementing AI requires strong change management, ensuring the organization is ready and upskilled to work alongside AI. Effective data governance is also essential, such as understanding how clean our data is (e.g., free of inaccuracies) and maintaining a governance process to keep data clean over time. Organizations must also evaluate the stability and longevity of any AI tool or technology to ensure it will be viable for the long term. It’s also critical for organizations to scale technology beyond the pilot and avoid a potential “pilot stalemate.”
AI is not a one-size-fits-all solution. We focus on deploying AI where its advanced capabilities add the most value to solve the most complex problems and avoid using it to fix something that an algorithm or robotic automation can address.
Finally, up‑time and availability are critical considerations, assessing whether a tool will be highly available and resilient or if it may be prone to crashes.
What are the potential benefits of implementing AI into operations?
Bennett: Implementing AI into operations, if done effectively, can help increase day-to-day efficiencies, drive streamlined workflows to reduce manual work and administrative tasks for employees, support safety and improve overall quality for an organization and its customers. We envision AI helping us move from data sets and queries to actionable intelligence. The insights gained through broad systems monitoring and training will enable increased flexibility and agility in our operations, allowing our teams to adapt quickly to evolving requirements.
What are the direct benefits to your customers?
Bennett: Effectively using AI within our distribution network can help improve the overall customer experience with increased order / invoice accuracy and fewer supply disruptions. Customers can gain greater visibility into the status of their product orders and experience a higher level of resiliency throughout the supply chain, resulting in a more reliable and transparent end‑to‑end experience.
What are some challenges with implementing AI?
Bennett: Ensuring data readiness, obtaining and maintaining clean data and selecting the most optimal AI technology in a saturated marketplace continue to be major challenges for many companies and industries. Technology and supply chain leaders within distribution operations should work together to properly assess solutions and vendors for their capabilities, longevity, security governance and much more if they want to drive successful and compliant AI deployment. Additionally, benchmarking employees’ broad understanding of AI at all levels of an organization can be challenging – and may contribute to some organizations’ inabilities to implement AI long-term. It’s important for companies to offer educational opportunities for their employees and encourage ongoing learning as it relates to AI in a work environment.
SIDEBAR
What are the differences between AI, GenAI and Agentic AI?
Pete Bennett, Senior Vice President of Global Supply Chain for Cardinal Health’s Global Medical Products and Distribution (GMPD) business, provided the following explanations.
AI (Artificial Intelligence): This is the broad concept of machines being able to perform tasks that typically require human intelligence. It encompasses a wide range of techniques, including machine learning.
GenAI (Generative AI): A subset of AI focused on creating new content, such as text, images, code and audio. It uses large language models to predict and generate outputs based on input prompts. Examples include ChatGPT, Claude and GitHub Copilot.
Agentic AI: An advanced form of AI that can plan, reason and take actions across multiple steps to achieve a specific goal. Unlike GenAI, which primarily generates content, Agentic AI can make decisions and execute tasks autonomously with minimal human intervention. It often uses a combination of large language models, machine learning and natural language processing to perform these tasks.
- AI is the overall field.
- GenAI is like a creative partner that generates content.
- Agentic AI is like an autonomous assistant that can strategize and execute complex tasks.