NOTE: This post is an excerpt of our ebook Leading with trust: A people-first guide to AI integration in healthcare. Click here to access the full publication.

In our experience, there are three primary areas that – if not addressed – can get in the way of successful AI adoption in healthcare and life sciences. Fortunately, these areas are also relatively easy to address with the right foresight and planning

If you recognize your organization in any of these examples, you’re not alone. And, most importantly, there are clear steps you can take to get back on track. More on that later.

Challenge No. 1: A lack of resources or prioritization

AI transformation isn’t purely a financial investment. There’s a time investment that typically spans multiple teams – and if those teams don’t have bandwidth, or don’t prioritize the initial required work, implementation can quickly derail and projects can stall.

While executive leaders may be eager to move fast, the reality is that implementation often requires collaboration from teams already operating at capacity (IT, operations, compliance, etc.), and without the necessary resources, AI initiatives risk becoming deprioritized in favor of more immediate or better-understood work.

Challenge No. 2: No buy-in from leadership

If leadership isn’t aligned or doesn’t thoroughly understand the value of implementing AI, they won’t drive usage or encourage proper implementation. Or, as in the challenge above, they won’t commit the necessary resources. 

Organizational leaders must be champions of the technology, advocating for its use and making sure it’s applied appropriately. After all, without executive support, even the best technology won’t be used correctly (if it’s even used at all).

Challenge No. 3: User-level distrust or skepticism

This is a big one. When a leadership team makes the decision to bring on an AI solution, it likely means the day-to-day work of some staff will change. Generally, that change will be positive – Infinitus AI agents were created to help healthcare workers spend less time doing frustrating, monotonous tasks, and to take on work that people can’t or don’t want to do (for example, answering phone calls at 1 a.m.). But change can still be anxiety provoking, and people may fear being replaced, or doubt the AI’s effectiveness. 

    At Infinitus, we’ve heard firsthand stories of operations teams that didn’t trust an AI agent’s outputs, and therefore re-did much of its work, negating the time savings and ROI that the AI agent was brought on to create. 

    None of these challenges are unsolvable. The key is a proactive, strategic approach – which we dive into in-depth in our guide to change management in healthcare AI. You can download a full copy here.

    Ready to skip the book and talk to us directly? No sweat: You can reach out to schedule a demo or have a conversation to learn more right here.