AI and the Future of Business Ops

There’s been a lot of chatter about artificial intelligence (AI) over the past several months. Specifically, generative AI; and even more specifically, ChatGPT. In fact, if you’re even a little bit curious, there’s a reasonable chance you grabbed an OpenAI account and have been taking ChatGPT for a test drive. And if you haven’t, I’d encourage you to kick the tires.

AI has captured our imaginations for decades. From HAL 9000 in 2001: A Space Odyssey to the Terminator and Matrix franchises, AI is as scary as it is fascinating. And while there are things to be concerned about (we’re not going to discuss those here), one thing is certain: a carefully thought-out technology strategy that includes the various flavors of AI can have a monumental impact on virtually every industry, and the medical device industry is no exception. In fact, we’ve seen it in use on the clinical side for a handful of years now. But business ops stand just as much to gain from a well-defined plan and thoughtfully designed tools.

But first things first: what is AI, really?

This is an important point, even though it may seem obvious. To over-simplify, AI is a general term for software that can mimic human cognition to perform complex tasks and then learn from those outputs.

Great; but what does that mean?

It means that given the right set of information (data), and the right set of instructions, the software (AI) can provide analysis, answers, predictions, etc. that would otherwise require significant human intervention. And the more it’s used, the more feedback it gets about the accuracy or validity of its outputs, the ‘smarter’ it [can] get, and the more accurate its future outputs [are likely] to be.

Why am I telling you all of this?

It’s important to have some background before diving into whether/how AI in any form can help your business ops.

There are two critical aspects to understanding AI in this context:

1. It absolutely requires data; lots and lots of data. And that data must be clean, meaningful, and understandable. If it’s not; well, garbage in, garbage out.

2. It’s basically dumb (no offense, AI). While AI can parse, decipher, collate, and assimilate massive amounts of information in nanoseconds, it still needs someone—or many someones—to tell it how to interpret that data, what rules to follow, and what actions to take, if any. It also needs to be told when it’s wrong, so it doesn’t build an entire dataset of answers on top of a false premise.

Make no mistake: AI is not consciousness; it’s an incredibly powerful set of tools designed to augment and accelerate human capabilities. Over-simplified? Sure. But not any less true.

Now that we have that out of the way, let’s talk about why AI will play an increasingly important role in medical device business ops and what operators will need to do to enable and take advantage of it.

Automate. Repeat.

Many businesses are rife with highly manual processes that take time, crush efficiencies, and drain energy from teams. Most of those manual processes are legacy leftovers from a time when things simply could not be automated. In general, that is no longer true.

Do we need to write things down on paper? No. Do we need to type the same thing into a CRM every time we get a repeat sale? Nope. Do we need to count every item three times before recording it on paper and passing it along to an admin for spreadsheets? Hell no.

Automation, especially automation that is predicated on past behaviors or behaviors that meet specific criteria, is a sweet spot for AI and its relatives—machine learning and deep learning. And automation is where business ops can reap massive benefits.

Does automation require AI? No. But when you throw artificial intelligence into the mix, you magnify the value of that automation. AI can help decide the path of automation itself, providing much more dynamic, context-specific responses.

This can come in the form of chatbots that answer basic customer support questions (freeing up your team to focus on complex needs), auto-generated inventory replenishments that reduce manual oversight, or systems that predict payment timelines and automate communications. Each of these use cases translates to tangible gains in business ops efficiency.

Analyze and Predict

This is where things get exciting. With enough historical data, AI can begin to analyze the past and predict future behaviors. Think weather forecasts, future traffic conditions, or Amazon shopping cart suggestions.

For business ops in medical devices, the potential is revolutionary.

Imagine a world where digital tools can predict caseloads, pre-emptively allocate resources, and alert you of potential gaps. A world where software predicts what assets are needed, where they sit, which are slow-turning, and how to move them based on proximity, cost, and ROA. A world where patterns—positive and negative—are automatically flagged, categorized, and explained.

That world isn’t hypothetical; it’s already emerging. The question is how fast business ops leaders will adopt it.

Forecast and Plan

Forecasting is a cornerstone of manufacturing and business ops, but it’s often highly manual and, let’s face it, a bit of an art form. AI has the power to make forecasting more precise and less time-consuming.

For example, AI can build multiple models using historical sales and case data, highlight where assets are short, and even calculate when to order based on lead times. Seasonal demand? AI can help you plan staffing and distribution around those cycles.

The greatest value of AI in forecasting for business ops is its ability to process mountains of data and distill it into actionable insights—giving human teams more time to focus on strategy instead of number-crunching.

Ok, so now what?

Some of this may sound futuristic. But we’re not talking about magic beans; these tools are here now and are being implemented in real-world business ops. Startups and smaller manufacturers, in particular, have an edge—they can move quickly, experiment, and pivot faster than large bureaucratic organizations.

Does that mean you should jump in headfirst? Not necessarily. But you can start by asking:

1. What parts of my business ops could benefit from AI?

2. Do I have the right team to brainstorm use cases?

3. Are there risks to define and mitigate?

4. Do I have enough data to make AI useful?

5. Does my team have the bandwidth to test a proof of concept?

6. If it works, can we scale it—or will we need outside help?

If enough of those answers are “yes,” then experiment! The power and potential are clear. Those companies that hesitate on AI in business ops will eventually lose ground.

How to De-Risk the Exploration

To make the journey safer and more valuable, consider:

1. Investigating existing tools that integrate AI into business ops.

2. Using open-source or free AI tools in your daily practice to learn firsthand.

3. Watching educational videos and sharing them with your team.

4. Brainstorming with your team about use cases and risks.

5. Reviewing current or planned systems to ensure AI can be integrated later.

6. Starting small, running fast, and breaking as little as possible.