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A 2023 Verta survey found that 66% of businesses plan to either increase or maintain their artificial intelligence/machine learning spending over 2023. Pair this with the oft-cited 2018 Gartner survey that predicted 85% of AI projects would fail to deliver on their initial promises through 2022, and you have a world where the majority of businesses are investing in AI projects that have or are likely to fail.
The question becomes, then, is the problem with the AI landscape or the ways in which businesses approach this technology?
As someone whose job is to help build custom AI applications for startups and other businesses, I can say with some confidence that the answer is the latter. Often, businesses rush headfirst into building AI for themselves because of their need to keep up with the competition, but they fail to truly consider what they actually need that AI for.
The feeling of lagging behind the competition is a strong force. It can lead to a lot of anxiety and fear, driving leaders to take action and try to build something completely new — even if it means pushing the boundaries of their own innovation capabilities. When it comes to AI, however, it’s crucial not to succumb to this technological FOMO. Because if you do, you’ll end up investing a lot of time and money in a solution that doesn’t work for your business.
AI doesn’t need to be revolutionary
Let’s look at a real-world example. Recently, a customer came to us with big, AI-inspired ideas. This client envisioned a whole new world for their startup and had even secured significant money from investors. We spent several days on it, building on strategy session after strategy session. Finally, the company’s leaders asked us why it seemed like we were trying to develop less of an AI solution than they thought their company needed.
The reason was simple: We were thinking practically, not idealistically. We were committed to building a meaningful AI product from concept to version one that would be ready for public consumption in 90 calendar days. Through this approach, we were able to build the organization a successful AI solution quickly and at a low cost.
AI is an exciting technology, but to make use of it, you have to take it step by step, building something you can actually use straight out of the gate and iterating on that.
But how do you know if you’re starting off on the right foot? By making sure you avoid these four mistakes:
1. No clear strategy
In my experience, there are two ways to make use of AI that nearly always lead to success: to help bring your company into the modern age and to add new value just slightly ahead of the competition. These areas are rarely what people talk about when discussing how AI can help small businesses, however.
Instead, people assume AI’s best use case is to help companies create completely unique solutions that are way ahead of their time. Though this isn’t out of the realm of possibility, the probability of success is significantly lower. In the context of AI for startups, your goal shouldn’t be to transform the world but to improve your bottom line.
What do you want your AI to accomplish? How do you measure success? If you can’t answer these questions, any solution you try to build will be rudderless, most likely leading you nowhere.
2. A lack of quality resources
Falling in love with the next big thing is human nature, but once the honeymoon is over, investors only care about the return on their investment. You can’t pivot to find product-market fits if you run out of cash before getting proper feedback. If your pivots are too far off the mark, you’ll burn through cash faster than any financial model you could imagine, much like what happened with AI startup Mythic.
Just because people have tech experience doesn’t mean they can make a productive AI solution. If success matters to your business, don’t cobble together a ragamuffin team with little-to-no experience. Get an expert to help.
Related: How to Avoid Wasting Millions on AI
3. Poor data quality
A report by Gartner found that bad data costs organizations almost $13 million per year on average. So, perform an assessment of your data ahead of time. If you don’t have enough, or it’s in bad shape, you’ll need to either purchase the information you need or hire a professional to help you bring your ideas to life.
Years ago, we worked with an enterprise that was in love with the idea that it had been collecting quality data for 15 years and was ready to deploy unique models. When our team investigated the situation, we realized that the IT group was overwriting the previous day’s data with the current day, erasing the history of the company’s customer base.
It was a crushing blow to the business’s AI ambitions. Luckily, we were able to get creative and still help achieve its goals. But if it hadn’t gotten the help it needed, the company would have been completely stuck.
4. Underestimating complexity
Startups and established companies alike can easily fall into the trap of believing in data magic instead of data science. Data science applies scientific methods, processes, algorithms and systems to pull knowledge and insights from all kinds of data. Data magic is, well, magic. No one really knows how it works.
If something sounds too good to be true, it probably is. Data science might be more complex, but you’ll actually be able to understand how it works. Understanding the complexity of AI will help you better prepare for the challenges you face along the way. It will also help you create something reproducible and consistent — both vital factors for long-term success.
AI really can be the key to your startup’s success. It can provide the competitive edge you need and help you adapt more quickly to whatever comes next. But embracing AI for the sake of AI is not the way to get there. By taking a methodical, planned approach and taking advantage of the help of AI experts, you can make the most out of AI and truly gain the edge your startup needs to succeed.