Much like the early days of the internet in the late ‘80s/early ‘90s, and a lot like the emergence of cloud computing in the early aughts, artificial intelligence (AI) packs a promise to revolutionize the world. However, also similar to the emergence of those game-changing technologies – it’s still a bit early for organizations to understand exactly how to get started with the technology. Questions arise as to how best to implement, what problems the technology solves, how big of an undertaking it will be, and how quickly organizations need to jump on board before they miss the boat.

These questions and more are the foundation of conversations I’m having daily with customers and partners as they are trying to realize the potential of AI but maybe waffling with exactly how best to adopt this technology that is often being sold as a do-all, solve-all. And while AI does have the potential to do great and amazing things, AI won’t ever solve all your problems. But it can certainly tackle a ton of them and help your business get to the next level, whatever that looks like for you.

Just like with the world wide web and cloud computing, there is a lot of confusion and mistakes companies are making when approaching AI. So, I thought I’d share the top mistakes (or misnomers) I see businesses make when searching for and implementing an AI solution to hopefully save you time and effort and ultimately help you get an AI initiative off the ground fast and effectively.

Believing It’s a Too Expensive and Time-Consuming

So many folks I talk to are daunted by the idea of AI, thinking it’ll be too time-consuming and economically out of reach for their company. But it doesn’t have to be a giant 2-year project that costs millions, or even hundreds of thousands, of dollars. You can easily test the waters quickly and affordably to see if it’s the right solution to fit the bill.

For example, you can quickly spin up a prototype with a simple training dataset and have results in days, if not minutes. If you wanted to throw a more complicated problem at your AI solution of choice, you can also do this same prototyping and likely have results within weeks and can easily scale up from there.

With this bite-size approach, you can have AI up and running in no time within a budget that works for your business.

Not Understanding What AI is and Isn’t Good For

As I mentioned, oftentimes artificial intelligence is portrayed as a solve-all. However, there are certain problems AI is fantastic at solving, and others not so much. For example, AI is really good at forecasting, detecting patterns and anomalies, and identifying objects. But AI is not magic, so things like taking on a net-new project, creating something from scratch, or simply being creative, AI won’t succeed. That’s because, unlike a human, AI doesn’t understand subtleties, context, or nuances, nor does it have independent thought (at least not yet) – it’s only as good as the data you feed it.

AI in medical

In general, AI is only valuable when it can do something a human currently can’t do or do something better than a human can do. So, we aren’t at a point where AI is replacing your doctor because it currently lacks the ability to have a bedside manner or connect the dots on everything at play that impacts your health, but AI is, and will more broadly in the near future, be assisting doctors to help them do things like diagnose an ailment faster and with greater accuracy. Think about the internet example here. Before the internet, humans were using physical dictionaries, encyclopedias, and phonebooks. Now, we are able to access experience and resources from across the globe with the click of a button and arrive at an answer that may have taken a lifetime of research to realize.

Once you have a general understanding of the core capabilities of AI and what problems it’s best at solving, you’ll be able to take a look at your organization and better prioritize where to insert the technology.

Knowing Exactly How They’ll Utilize AI

This might seem counterintuitive to the point I just made but hear me out. There have been several times where I’ll be chatting with a company that has gone through their own research and discovery phases and are resolute that AI will be the solution to help solve a particular problem. Great – they are ready for AI! Well, not so fast.

Again, let’s look at the internet when it was in its infancy. Everyone knew it would change the landscape of business moving forward but didn’t exactly know-how. At that time, we saw a lot of things that didn’t work well and a lot that did. Looking at AI, many companies were initially turning to the technology to help their business with new innovations or leverage AI to help them stand out from the competition. Now, we are seeing the overlay with the pandemic and economic conditions and a lot of those who thought they knew the best use case for AI in their organization are now pivoting and looking for ways AI can do things like help automate, keep their workers safe and create efficiencies and cost savings.

As you can see, the ‘how’ is always an ongoing discussion and moving target given the broad applicability of AI. Just because a business implements AI to tackle one problem, that certainly doesn’t mean they can’t use the same technology to take on a completely different one. I always recommend we find the best use case to start with and oftentimes we see a snowball effect quickly after that.

Not Having Data to Support the Problem

I’m sure you’ve heard how important data is for successful machine learning and artificial intelligence projects. The thing is, organizations don’t necessarily even have to have an enormous amount of data, but it does need to be relevant and accessible. We’ve gone in-depth on our blog that I encourage you to revisit instead of me droning on here about it.

But before you can even narrow in on the problem and project you’re going to start with, you need to take a look at what data you have accessible to train an AI/ML model. Without the right data in place from the beginning, your AI initiative is going to fail. If you’re not sure, I’d always recommend consulting your AI provider of choice to ensure they can assist with this part of the process as it’s the underlying key to success for any AI initiative.

Now You’re Ready

Alright, did you write all that down? No worries if you didn’t, we are happy to walk you through all of this with an initial consulting conversation. We can chat with your team to understand your challenges, talk through how AI might help, and discuss the key elements you’ll want to make sure you have in place so you can hit the ground running and quickly see artificial intelligence revolutionize your business.

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