
AI adoption is accelerating across industries, even in historically risk-averse segments, like healthcare. With a growing body of evidence demonstrating that AI can enhance efficiency and spur innovation, medical device manufacturers and distributors are looking for ways to integrate AI-driven tools into their daily operations. The [obvious] goal: save time, improve performance, and reduce human error.
So, should you do it too?
Definitely … maybe.
That’s not a very satisfying answer. But just because something is easy (or relatively easy) to do, and just because it seems like everyone else is doing it, doesn’t mean it is the right (or even best) option. As with any novel solution, if you are considering implementing AI, your first step should be thoughtful reflection on the why, the when, and the how.
- Why are we doing this?
There are great reasons to implement AI, in its many flavors. But that doesn’t mean they’re great reasons for you and your specific environment. Before making that determination, you should ask yourself and your team, “Why are we considering doing this? What outcomes do we expect to see? What do we hope to see? Why is this better than the other options? (What are the other options?) What will this do to our overall operations? How will it impact our position in the market? What are the risks? And is our “why” big enough to outweigh those risks?”
This is the first hurdle. If you can’t clear this bar with your team and leadership, the timing isn’t right, even if the technology is. Sometimes, this is as much about creating the right narrative as it is about having the right solution. Actively engaging these questions with your team will help you define the story that shows the alignment between the goals you have, the opportunities you see, and the technology you’re proposing. And if it doesn’t, you need to go back to the drawing board.
- When should we do this?
Assuming your “why” passes muster, the next critical question is, “When should we do this? Can this be done with our current resources, or do we need outside help? Are there times of the year when our implementation risk is lower (or higher) than others? If we wait, how will that impact our position in the market? If competitors do this before us, does that provide them an unfair advantage? If we do it first, how will we benefit? If there is no discernible competitive advantage by being a first-mover, do we gain anything by waiting to see how it works out for the competition?”
Even if you determine AI (or any new technology) is a meaningful tool for your operations, that doesn’t mean it needs to be implemented immediately, as soon as possible, or (as many like to say) “yesterday.” Timing can be more important than tuning. Even if you have the right solution and your implementation is flawless, poor timing can be disastrous, and that type of failure can be the most difficult to bounce back from.
- How do we make this successful?
Once you get through the “why” and the “when,” your team needs clarity on how you will actually make this work. More to the point, you need to know, “How do we define success? How do we know that we’ve been successful? What are the metrics that will prove it? Do we have KPIs that we all agree on, and do those KPIs align to the “why” that is driving this initiative? Who needs to be a part of this and how do we get them on board? What are the impacts if we can’t get them on board? Is that reason enough to kill the effort?”
While clearing “why” and “when” are fundamental, if you can’t nail the “how,” you should pause your efforts until you get those questions figured out. That doesn’t mean abandoning the project; it means you have implementation gaps that should be addressed. Can those be figured out in flight? Sure, some of them can. But even more important than the timing of answering these questions is understanding the actual questions you need to ask and acknowledging when some of them still need answers. After all, “how” isn’t just about nuts-and-bolts execution; it’s about ensuring that execution maximizes why you’re doing this in the first place.
In the end, AI (and other novel technologies) hold significant promise for every industry, including Medical Devices (not to mention healthcare more generally). But the process you use to determine if you implement something is as important as the tech you ultimately decide to use. If you don’t have a process that has been designed to support your success, there is a high likelihood you’ll end up throwing time, money, and effort out the window. And that’s not good for any business.
Recent Comments