Business leaders are rushing to harness the quasi-magical powers of artificial intelligence (AI), with a projected annual spend of $60 billion on AI models by 2026. Yet, revenue from AI is only expected to reach about $20 billion per year by that point, flagging a substantial gap between investment and returns. In fact, recent studies show that roughly 75% of AI initiatives don’t succeed.
Since the launch of generative AI, we’ve been conducting extensive research involving CEO interviews and deep dives within leading companies. This work has provided inside knowledge about the success of AI initiatives and has culminated in the book The Humachine. Here are some of these insights.
One AI doesn’t fit all
AI is copyable—and one size doesn’t fit all. What’s not copyable is a unique business model, processes, and integration of humans with that technology.
Our research finds that the massive rush to apply AI technologies to existing business models and old processes will not lead to success.
Spencer Fung, the CEO of global supply chain giant Li & Fung, provides an analogy: “Companies acquiring AI without a new business model is like a company digitizing a horse and carriage—while the competition has created a digital automobile.”
Adding AI to a business model of the past doesn’t lead to competitiveness—it simply solidifies old processes. AI is essential but insufficient in providing a competitive advantage. Before attempting to integrate AI into their businesses, corporate leaders need to first reassess and update their business models.
The data doesn’t hold up amid volatility
AI is based on historical data that may not be reliable in unpredictable and ever-changing global business environments.
“Every math-based model collapsed when the pandemic hit. None of the assumptive parameters could be trusted,” as John Sicard, the CEO of supply chain software leader Kinaxis, told us.
Business decisions aren’t made in a vacuum separate from issues of labor, inflation, and geopolitics. Experienced workers bring domain expertise and deep knowledge of their environment. They step in when digital analyses aren’t enough much like a pilot taking control under unusual circumstances.
This knowledge is essential—and ignoring its value is fraught with peril. Sicard sums it up with this warning: “Blind obedience to the model is dead. It led us off a cliff during the pandemic. It is reckless.”
This echoes our recent discussion with chess grandmaster Garry Kasparov, the first chess player defeated by a computer. Though Kasparov concludes that machines are better than humans in 95% of cases, humans must know when and how to intervene the remaining 5% of the time. That’s critical.
Kasparov notes that advantage comes to the person who knows when to rely on gut instinct and intuition. That’s the difference between a good decision-maker and a great one. “A little tweak here and there has the highest return. We don’t have to challenge machine superiority in 95% of the cases. But we do in the other 5%,” he explained.
It’s also important to know when to be humble enough to allow algorithms to work autonomously. AI tools lack the ability to understand context—but we shouldn’t.
This insight helps leaders grasp the essential human elements that drive successful AI implementations. As Ted English, the former CEO of TJX Companies, a Fortune 100 apparel and home fashion retailer, says, leadership demands “a lot of instinct, experience, and knowledge. You can’t get that from a machine.”
AI requires new human skills
As AI becomes commonplace, companies need to cultivate new human skills among their workforce. In our executive interviews, we repeatedly heard that the new competitive advantage comes down to “human interpersonal skills,” “human creativity,” and “personal relationships.”
Peter Cameron, the CEO of Lenox, told us, “Nothing replaces long-term relationships that are personal—and the longer the relationship is, the better.”
Rod Harl, the CEO of Alene Candles, a company with 80% revenue growth over five years, shared that their best decision was investing in training employees on interpersonal skills and mindfulness techniques. Combining these skills with human creativity, Harl notes, “is the secret sauce.”
As Maria Villablanca, co-founder and CEO of Future Insight Network, put it: “Companies need people who can be creative and innovative in the way they find solutions. Companies need creative problem solvers with interpersonal skills. Machines cannot compete with that.”
As AI takes over more tasks, there’s a risk of skill atrophy and loss of knowledge. In addition to holding onto experienced talent, companies need to consider paths to develop decision-making skills across their human resources.
Today, the human skills deemed most critical by leaders are interpersonal skills: basic conflict resolution, communication, emotional detachment, and mindfulness practices. While digital literacy is expected, effective interpersonal skills are the priority. These uniquely human skills are in short supply—and may require training.
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