Executive Insights

In my quest to learn more about AI, I have treasured information, concepts, and real-life applications discussed by Digital Health Foundry’s Tarun Kapoor, MD, MBA (TK as he is known to many). One point shared by TK was to consider an AI Mindset. This inspired me because it stretches beyond the technology and challenges us to view how AI can improve how we get work done.

An AI mindset is an approach that views artificial intelligence as a collaborative partner to amplify human capabilities rather than a replacement or a simple search tool.

Artificial intelligence has rapidly moved from an emerging technology to an everyday business tool. Across healthcare, we’re using AI to improve clinical care, financial performance, workforce productivity, patient experiences, and operational decision-making. Healthcare supply chain is particularly well positioned to benefit from this tool. Supply chain sits at the intersection of data, technology, purchasing, clinical operations, finance, logistics, and supplier relationships—areas where AI can potentially create significant value.

But there is an important distinction between having an AI strategy and creating an AI mindset.

An AI strategy answers questions such as: Where should we invest? What technologies should we deploy? What problems will AI solve? How will we govern it? Those questions matter. However, an AI strategy alone may not prepare an organization to recognize the hundreds of opportunities for AI that emerge every day.

An AI mindset is different. It changes how people think about work itself.

Instead of asking, “Where can we implement AI?” an organization with an AI mindset begins asking, “How could AI help us do this work better?”

That shift may ultimately be more important than the technology. I may not understand how the technology works, but I do know about the challenges we face and the opportunities available to improve our efficiency and effectiveness. Healthcare supply chain leaders have traditionally focused on strategies around cost reduction, standardization, contracting, inventory optimization, clinical engagement, supplier performance, and operational efficiency. These remain critical priorities.

AI should not replace those priorities. It should change how we pursue them.

Consider a supply chain team preparing for a contract negotiation. An AI strategy might identify contract analytics as a priority and lead to the purchase or development of an AI-enabled solution. An AI mindset goes further. Team members begin looking at the entire process and asking:

How much time do we spend gathering information before a negotiation?
Could AI summarize historical purchasing patterns?
Could it identify price variation or unusual purchasing behavior?
Could it compare contract terms across suppliers?
Could it surface opportunities that a human analyst might overlook?
Could it help prepare questions for the supplier?
Could it create a first draft of the business case for leadership?

One of the risks of treating AI exclusively as a strategic initiative is that AI becomes someone else's responsibility. An organization may create an AI committee, identify several priority use cases, approve a technology investment, and establish governance. All of those activities are valuable. But if the rest of the organization continues working exactly as it did before, the transformation will be limited.

An AI mindset distributes ownership.

The buyer asks how AI could improve sourcing. The analyst asks how AI could reduce the time required to prepare reports. The inventory manager asks whether AI can help anticipate demand. The value analysis team asks whether AI can accelerate product comparisons. The contracting team asks whether AI can identify opportunities hidden in thousands of pages of agreements. The supply chain leader asks whether AI can provide better visibility into performance and risk.

The best AI opportunities may not originate in the IT department. They are more likely to originate with the people closest to the work.

Leveraging AI starts with the work, not the technology. Creating an AI mindset does not mean telling everyone to start using ChatGPT or another AI tool. It means teaching people to look at work differently.

The goal is not to automate everything. The goal is to identify where people spend time on work that could potentially be accelerated, simplified, augmented, or eliminated through AI.

As we embrace an AI Mindset, our success will not be defined by how many AI tools we’re using. Healthcare supply chain should measure what changes:

How much time was saved preparing a sourcing analysis?
How quickly can a team identify contract opportunities?
How much manual work was removed from a recurring report?
Did AI help identify a previously overlooked savings opportunity?
Did an inventory-management application improve availability while reducing excess inventory?
Did a value analysis team reduce the time required to evaluate new products?
Did employees spend more time on strategic work because AI reduced administrative tasks?

These are the outcomes that matter.

AI should ultimately be evaluated in the same way supply chain evaluates any other improvement initiative: Does it improve performance, reduce unnecessary work, mitigate risk, improve the experience of our people, or create measurable value?

An AI strategy can establish priorities. An AI mindset creates momentum. And in a rapidly changing healthcare environment, momentum may be the competitive advantage that matters most.

An AI mindset changes how people think about what is possible within that process. Instead of viewing AI as a standalone technology project, it encourages people to continually ask: Could AI help us understand this problem faster? Could it uncover patterns we cannot easily see? Could it generate or evaluate potential solutions? Could it automate part of the process?

The difference is subtle but powerful. The strategy creates a specific initiative. The mindset creates continuous curiosity.