Four Mistakes That Leaders Should Avoid With AI

There needs to be a balance between technology and humanity.

Artificial intelligence (AI) is one of the hottest trends in business today – with good reason. AI is set to transform the way we live and work. According to Bloomberg Intelligence, generative AI alone is set to become a $1.3 trillion market by 2032.

Businesses around the world are rushing to experiment with AI in the hope of gaining strategic advantage. But they risk losing large sums of money if they don’t adopt AI tools that can deliver the right results. Research by intelligent data infrastructure company NetApp revealed only half of U.K. businesses actually understand how AI can benefit their operations, while just 20% have a strong understanding of how they can harness AI technology.

So, what are the key mistakes that leaders need to avoid when implementing AI systems?

AI mistake #1: Sacrificing insight for automation

“AI can help to inform and facilitate decisions, but as a leader, you need to take ownership of every decision,” says Steve Oriola, CEO of Unbounce , a software company that creates AI-powered landing pages. “Automation without insight leaves performance up to chance, driving results that you can’t articulate or replicate. Even if the business is supported by AI, leaders are still accountable for making informed and measured decisions so they can identify why targets are hit or missed.”

Oriola recommends that when using AI to improve performance, leaders should look for a tool that feels like an insightful advisor, helping you to make informed decisions more efficiently. He explains: “The ideal tool is transparent, so you can ensure it works with your existing processes, clearly articulates how it improves performance, and uses its insights and data elsewhere.”

You’ll know you’ve found the right tool when it feels like an extension of your team, Oriola argues. “You wouldn’t trust an underqualified employee with critical aspects of your business, so you should hold AI tools to the same standards.”

AI mistake #2: Using AI to replace, not enhance

Leaders who see AI as a way to replace human labor and cut costs are being short-sighted in their approach. “It’s imperative that we harness AI as a tool to augment, not replace, human ingenuity,” says Christie Horsman, vice president of marketing at online course platform Thinkific. “We’re very deliberate in using AI within the products we create to augment the expression of a creator’s unique genius, not to replace it.”

Horsman says that having a framework helps to minimize the risk that human creativity is sidelined. “By setting clear guidelines around the ethics and principles that matter most to your company, your people and your customers, you can more clearly navigate the complexities of AI integration,” she says, “ensuring that technology complements and enhances human ingenuity rather than competes with it.”

In Horsman’s experience, encouraging this type of symbiotic relationship between teams and AI “acts to amplify creativity and this leads to more innovation and better products for our customers”.

AI mistake #3: Overlooking the balance between technology and humanity

Technological enhancements should not come at the expense of humanity since that would defeat the purpose of those enhancements. Taking the healthcare sector as an example, leaders shouldn’t allow AI to overshadow the “irreplaceable decision-making and compassion of healthcare professionals”, says Dr Daan Dohmen, professor of digital transformation in healthcare at the Open University and CEO of home care platform Luscii.

He adds: “Gradually integrating AI, while fostering trust and identifying synergies between AI and human intuition, is crucial.”

Dohmen believes that prioritizing data privacy is non-negotiable when it comes to the deployment of AI systems in health. He says: “Our focus should be on using AI to enhance care quality and accessibility, and ensuring decisions and treatments are both informed and personalized, without neglecting the vital role of human empathy.”

AI mistake #4: Not establishing a data collection strategy

“AI presents a remarkable opportunity for companies to efficiently analyze vast datasets that were previously overwhelming to comprehend,” says Davin Pinn, CEO of robotics company Brain Corp. “But the first issue that must be considered, which is too often overlooked, is what is the quality of your data? For AI to provide value, it must be fed with data that is accurate and timely.”

Brain Corp helps retailers to automate their inventory management and merchandising execution through robotic and AI solutions that collect and analyze shelf data. Today, however, the majority of retailers rely on manual processes to collect shelf data such as product availability and price. “These manual processes are slow and mistake-prone,” says Pinn, “and provide data which, if relied on to inform AI, can misinform the models. Data quality is fundamental to implementing AI successfully.”

By Sally Percy, Contributor