Artificial Intelligence Parts

Why Do Artificial Intelligence Projects Fail?

Last year, a Gartner report on the artificial intelligence (AI) industry found that a shocking 85 percent of AI projects will not deliver the expected result. Despite all the buzz around AI, it would appear that very few businesses are actually using it successfully. Perhaps it’s no surprise, then, that another industry research report found that among businesses using AI to some degree, only 25 percent have “an enterprise-wide strategy.”

One potential explanation for these trends is that businesses are in a testing phase, which makes sense given that we’re still in the early adoption phase of commercial AI. Another explanation, however, is that because AI projects aren’t delivering as expected, businesses aren’t fully investing in them.

Whether your business is still considering how to best deploy AI or it’s testing out different aspects, there are ways to ensure that the results deliver something that’s of value to the business. Here are a few things to businesses can do to mitigate or avoid failure of their AI project.

1. Align project goals with measurable business outcomes

Not all AI projects will deliver business value, at least not immediately. But, to be sure, an AI project that doesn’t contribute to the business will certainly not deliver the sought after results. By aligning your AI project goals (increase efficiency, increase sales conversion rates, increase customer loyalty), then you can develop metrics to measure whether or not the AI contributes to those goals. However, it’s essential to make sure that the outcomes of the AI do indeed contribute to the business value in some way or another.

2. Understand the problem AI is meant to solve

To address #1 above, it’s important to really understand the problem you’re trying to solve with AI. If, for example, you’re trying to use AI to increase your sales conversions, then it’s important to understand the factors that go into closing a sale to begin with. That includes understanding the stumbling blocks and objections that customers might have through that process. If you don’t spend time understanding these, then there’s really no way an AI could reasonably come up with a solution.

3. Get more data

Data is the lifeblood of nearly every AI project, but in some instances, that data might be hard to come by. Some firms might try to cut corners with a limited amount of data, and the result will almost always be an AI that comes to a wrong conclusion. Just like a detective uses clues to solve a case, an AI needs to have enough data to achieve accuracy.

4. Ensure your data is clean

There’s an old computer science adage that says, “Garbage in, garbage out,” meaning that projects using data that lacks integrity or isn’t accurate to begin with will almost always churn out a result that is useless. This is as true as ever in AI. Take the time to ensure your data is clean and accurate before using it in your AI. Otherwise, you’ll be working off inaccurate assumptions to begin with.

5. Get the right data

Ok, this one seems obvious, but bear with us. Sometimes when we’re trying to test or train an AI, we might think a specific data set is relevant. For example, we might be developing the software to manage a self-driving vehicle. While road maps and speed liit zones would be key data sets that would be extremely helpful, what about information related to speed traps set by police? Would knowing the number of people who get tickets in a specific section of road be helpful? Possibly, but probably not, given that we want our AI to obey speed limits at all times.

These are just a few best practices that a company can do to ensure they get the most value out of their AI project. If you want to learn more, we can help. Contact us to discuss how to ensure your AI project contributes to your business, now and in the long-term.

Best regards,
Florian Horn

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