10 Steps to Implementing AI in Your Gym

Artificial intelligence (AI) is everywhere these days, whether we’re summoning Siri, binging on Netflix recommendations or following the navigational guidance of Waze.

It’s valuable for the fitness sector, too, allowing gyms to accurately predict the future and take informed action to improve sales, boost retention or any one of a long list of other opportunities.

And the starting point – the fuel for the insights, innovations and results that stem from AI – is the data that already exists in your business. It’s why the first five steps to implementing AI in your health club focus on your data practices.

Step No. 1: Do your research. Look at every area of your business to map where data is captured, from proactive collection, such as membership applications, to automatic datasets, such as entry systems.

For offline data, review what subsequently gets digitalized – and what doesn’t, in case it represents untapped value.

Step No. 2: Widen the net. Have you opted for default data capture levels, only capturing what you need right now? If so, can you affordably expand this?

It’s good policy to collect everything, even if you have no current requirement for all of it. You never know what could be of value in the future.

Step No. 3: Fill the gaps. Give each data source a condition value, 0/10 being “currently a mess,” and 10/10 being complete and perfect.

For any sources scoring below 7/10, put a plan in place to rectify historical gaps – freelancers on sites such as Elance can help – and educate on what’s needed moving forward.

Consider connecting your data sources, too, as data from one may plug gaps in another.

Step No. 4: Appoint a champion. Establish responsibility. A chief data officer may be too grand of a title (though 25 percent of Fortune 500 companies have them), but you want someone who can own the business data performance and report on data conditions.

Step No. 5: Engage your team. Implement a data policy that outlines the importance placed on data within your business. Everyone in the organization must understand the value of what’s being collected and their role in ensuring it’s done correctly.

Step No. 6: Ask yourself why. Now you have your data in check, you’re close to being able to press “go” on your AI plans. Before you dive in, though, ask yourself: “Do I have a problem to solve?”

Don’t ask AI to look for a problem. Don’t adopt it just because it’s trending technology.

AI can be applied in so many ways, but be clear why you want it. If you had xyz insight/prediction, would it be valuable?

Step No. 7: Make time. The results from properly applied data + AI projects can be exceptional, but it’s normally 12 months before we see the true justification. You have to be willing to dedicate time: to implement the platform, to engage with it, to act on its insights.

Step No. 8: Prepare for action. AI will provide a whole new level of transparency but note: the value lies not in the insight but in the action it inspires.

You’ll need (potentially new) workflows in place to take advantage of the new insights.

  • If I know I’m going to miss gym sales targets in three months because of poor social media performance, what can I do to remedy that?
  • If I know which of the members who joined in the last three months have the highest probability of leaving, what will I do to change the likely outcome?

Step No. 9: Make it automatic. Once you’ve worked out what actions you want your new insights to fuel, ask yourself this: Can (any of) these be automatically actioned?

AI + automation – removing the need for any team member intervention – is where the real results are.

Step No. 10: Pick your partner. Finally, who will you work with? Before you go rushing off to find yourself a data scientist, take a look at what’s already available for the sector. The best solution will always be your data fed into fitness operator-specific applications.


Ian Mullane is founder and CEO of AI-powered gym sales and retention platform Register here for a free copy of his latest white paper, Everything You Need to Know About Data & AI.

The editorial staff had no role in this post's creation.