The Top Five AI Mistakes Business Leaders Make

As a business leader, you're constantly looking out for the best interests of your organization and employees—and in 2023, that likely means figuring out how artificial intelligence fits into your company to improve processes, create efficiencies, save money, and give you a leg up on the competition.

While your intentions with AI may be good, you're not immune from making mistakes when it comes to integrating this relatively new technology. Read on to learn five of the biggest AI mistakes business leaders are prone to, so you can avoid them in your own strategic planning, implementation, and execution.

Mistake #1: You ignore AI completely.

Whether it's fear of the technology, lack of time to learn it, budget concerns, there are plenty of reasons why some business leaders are in denial about AI. But it's time for a reality check: By failing to adopt AI through machine learning into your models and processes, you're likely losing money, missing out on opportunities for increased productivity, and forfeiting business to competitors who are harnessing the power of AI.

In McKinsey & Company's annual report, "The state of AI in 2023," 55 percent of those surveyed reported that their organizations have adopted AI. And now generative AI—AI that creates new content like text, audio, and visuals—is on the rise, with one-third of respondents saying their organization is already regularly using the technology in at least one function.

Mistake #2: You "set it and forget it."

Just as your vehicle needs maintenance or software requires upgrades, your AI service must continually evolve to achieve optimal performance. It's not enough to make an investment in the original scope and never touch it again. "You implement the solution and then, two years later, you're not getting the results you originally hoped for. Maybe the preferred payment choice changed or something about the environment evolved," says Joe Hinrichs, vice president of IT and product development for Bailiwick. "You need to be aware of that and continue to make enhancements to your baseline so you can maximize the value you're expecting."

Mistake #3: You're not prepared with the right resources and people.

Don't go into a new AI project underestimating implementation hurdles like resource requirements and technical challenges that can lead to delays and budget overruns. Ensure you have the proper infrastructure for machine learning—such as storage and system components—as well as the right people who understand the complexities of AI.

If you choose to outsource your AI implementation to a third party, make sure the partner also understands your industry. "If they don't understand your business and aren't sensitive to what your business challenges are, you're not going to be successful," cautions Jimmy Hinshaw, a retail technology and automation solutions expert for Bailiwick.

Mistake #4: Your data quality is poor.

You know how the saying goes: "You're only as good as your weakest link." And when it comes to your AI systems, your weakest link cannot be poor data quality. AI models must be trained with an adequate amount of accurate data in order to perform the way they're intended to.

Intelligent video surveillance company Realwave, for example, uses AI to analyze data from common area cameras, sensors, and other infrastructure technology to identify trends, problem areas, guest behaviors, and more. In order for that data to be analyzed, 4,000 to 10,000 images of whatever the cameras are being trained to capture must first be fed to the AI system.

"We have to get some sample video, and then we start to dictate throughout the scene what each component is," says John Sullivan, CEO of Realwave, Bailiwick's Integrated Intelligence partner. "You must have a good data set in order to have a good capture of what it is you're looking at. Then you can send that to the AI engine, and the AI engine will start to learn."

Mistake #5: You expect AI to do everything and be everyone.

AI is powerful but not infallible. Business leaders who expect AI to solve all their problems are setting themselves up for disappointment when the technology doesn't meet unrealistic expectations.

In the same vein, you can't rely solely on AI to carry out human tasks. Viewing AI as a replacement for human workers rather than a tool for collaboration can lead to employee dissatisfaction and hinder the potential benefits AI can bring. You need to foster a culture where humans and AI can work together.

Bailiwick® Integrated Intelligence is the world's premier white glove service for implementing connected event monitoring across your entire organization. Powered by Realwave AI/ML, our comprehensive solution uses AI to analyze data from common area cameras, sensors and other infrastructure technology to identify trends, problem areas, guest behaviors and more.