3 Common Pitfalls You Need To Avoid in Your AI Implementation Strategy

Knowing the most common pitfalls you’ll face in your AI strategy can help you avoid them while ensuring you make the most of your investments.
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Artificial intelligence is a hot topic—and for good reason. AI can help your business reduce costs, improve customer service, and increase revenue. No wonder why so many companies are investing so much in AI initiatives! Yet, implementing AI in your workflow doesn’t automatically translate into instant benefits. In fact, as many companies out there can attest, a lot of things can fail when adopting AI.

The main problem is that not all companies know how to properly integrate AI into their operations. And when you fail in its adoption, AI can quickly lose its value and become an expensive nuisance. So, how can you prevent that from happening to you? By knowing the most common pitfalls you need to avoid in your AI strategy so you can make sure you’re getting the most out of your investment.

1. Vague or Nonexistent Business Goals

If you’ve ever seen AI in action (and I’m fairly certain you have), then you surely know how exciting this technology can be. Perfect product recommendations, precise search suggestions, quick and complete assistance for numerous tasks—AI can do a lot of things right. As impressive as that might be, though, this can lead you to implement it just because of its raw potential rather than adopting it for a specific business purpose. 

That’s a huge mistake right there. If you invest in AI just because everyone else is doing it or because you’re impressed by it, you’re doing it wrong. For AI to deliver real value (and do it in the quickest way possible), you need to have a specific motivation for implementing it. Otherwise, you’ll just be throwing money at something that will be closer to a costly toy than to revolutionary technology. 

How to avoid the pitfall: You need to have a very detailed reason to implement AI. Vague answers, like “I want to automate processes” or “I need to boost my big data efforts,” aren’t enough. You need to take those broader objectives and break them down into precise and achievable goals. Thus, you might think of implementing AI to automate your email marketing processes and maximize your reach and effectiveness. 

What’s even better is tying your AI to a specific and measurable business outcome. For instance, following the example above, you might say that you will adopt AI to double the number of clicks in your emails while reducing the number of people who unsubscribe. You can then define that AI can aid you with that by helping you segment your audiences more strategically while also providing you with assistance when crafting and testing your emails. As you can see, that’s a detailed plan with results you can actually measure. 

2. Lack of AI Skills

O’Reilly’s 2021 “AI Adoption in the Enterprise” report found out that the biggest AI-related challenges for businesses are the lack of AI engineers and, subsequently, the difficulty when trying to hire those roles. Given how you need AI talent to properly leverage the technology’s benefits, I’d say that constitutes a highly visible pitfall that you’ll have to face when adopting AI.

That’s a huge threat to your AI implementation plans. Without the proper talent, your AI initiatives can get derailed really quickly. In the best-case scenario, you might implement a somewhat basic version of an off-the-shelf AI solution that provides you with basic AI functionality. In the worst-case scenario, you won’t be able to do anything with AI, mainly because it isn’t a plug-and-play technology.

How to avoid the pitfall: When you’re out looking for AI engineers, you’ll have to compete with big enterprises like Amazon, Google, and Facebook, all of which may be more appealing to most engineers. That’s why one of the best options for finding AI talent is to train others in-house. You can look for suitable candidates within your company to form an AI team. Naturally, you won’t have AI developers, but you can identify the staffers who are good in related fields like math and computer science. When you identify them, you can train them to fill your open roles.

Will that be enough? Sadly, no. Training an AI engineer takes time, and you might not have it. So, you can always supplement your training efforts with staff augmentation services or delivery teams. Partnering with a development company can make it easier for you to get the talent you need to properly implement AI and even help you with the development of your AI training program.

3. Focus on Proprietary Solutions

At BairesDev, as a tech solutions provider, we always advocate for custom applications. We know the many benefits tailor-made solutions can have and always recommend companies that consider personalizing their applications. Yet, we also know that such a mindset doesn’t always go well with AI.

Building AI solutions is challenging, time-consuming, and costly, which is why not all companies are in the best position to develop them. In other words, focusing on creating a proprietary AI solution for your business might derail your AI implementation or, worse, lead your company to ruin.

How to avoid the pitfall: Unless your core business is closely related to AI, you don’t need to build an AI solution from scratch. There are many big players in the AI industry, all of which offer sophisticated solutions you can integrate into your workflow. What’s more, many of those solutions can be customized (to an extent). That’s why you should always research your readily available options first.

That’s not to say that custom or proprietary solutions are a bad thing. If you have the resources and the bandwidth to build your own AI solution, then do so. Developing an AI of your own will result in a solution that will better integrate with the rest of your digital ecosystem as well as with your workflow. 

Accelerate Your AI Adoption

Even though implementing AI into your company is easier than it used to be, there are several challenges you’ll have to face, especially during the early stages of adoption. Of the many possible pitfalls you might fall into, the 3 outlined above are the most common, so they are a good starting point for developing your AI implementation strategy. 

Dealing with those issues head-on will not only make it easier for you to adopt AI but will also accelerate your AI implementation and bring you closer to the benefits you’re looking for: revenue growth, lower operating costs, and improved customer experience.

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