If you achieve the improbable often enough, even the impossible stops feeling quite so out of reach.
Over the last several decades, artificial intelligence has permeated almost every American business sector. Its proponents position AI as the tech-savvy executive leader’s magic wand — a tool that can wave away inefficiency and spark new solutions in a pinch. Its apparent success has winched up our suspension of disbelief to ever-loftier heights; now, even if AI tools aren’t a perfect fix to a given challenge, we expect them to provide some significant benefit to our problem-solving efforts.
This false vision of AI’s capability as a one-size-fits-all tool is deeply problematic, but it’s not hard to see where the misunderstanding started. AI tools have accomplished a great deal across a shockingly wide variety of industries.
In pharma, AI helps researchers home in on new drugs solutions; in sustainable agriculture, it can be used to optimize water and waste management; and in marketing, AI chatbots have revolutionized the norms of customer service interactions and made it easier than ever for customers to find straightforward answers to their questions quickly.
Market research provides similar backing to AI’s versatility and value. In 2018, PwC released a report which noted that the value derived from the impact of AI on consumer behavior (i.e., through product personalization or greater efficiency) could top $9.1 trillion by 2030.
McKinsey researchers similarly note that 63 percent of executives whose companies have adopted AI say that the change has “provided an uptick in revenue in the business areas where it is used,” with respondents from high performers nearly three times likelier than those from other companies to report revenue gains of more than 10 percent. Forty-four percent say that the use of AI has reduced costs.
Findings like these paint a vision of AI as having an almost universal, plug-and-play ability to improve business outcomes. We’ve become so used to AI being a “fix” that our tendency to be strategic about how we deploy such tools has waned.
Earlier this year, a joint study conducted by the Boston Consulting Group and MIT Sloan Management Review found that only 11 percent of the firms that have deployed artificial intelligence sees a “sizable” return on their investments.
This is alarming, given the sheer volume that investors are putting into AI. Take the healthcare industry as an example; in 2019, surveyed healthcare executives estimated that their organizations would invest an average of $39.7 million over the following five years. To not receive a substantial return on that money would be disappointing, to say the very least.
As reported by Wired, the MIT/BCG report “is one of the first to explore whether companies are benefiting from AI. Its sobering finding offers a dose of realism amid recent AI hype. The report also offers some clues as to why some companies are profiting from AI and others appear to be pouring money down the drain.”
What, then, is the main culprit? According to researchers, it seems to be a lack of strategic direction during the implementation process.
“The people that are really getting value are stepping back and letting the machine tell them what they can do differently,” Sam Ransbotham, a professor at Boston College who co-authored the report, commented. “The gist is not blindly applying AI.”
The study’s researchers found that the most successful companies used their early experiences with AI tools — good or ill — to improve their business practices and better-orient artificial intelligence within their operations. Of those who took this approach, 73 percent said that they saw returns on their investments. Companies who paired their learning mindset with efforts to improve their algorithms also tended to see better returns than those who took a plug-and-play approach.
“The idea that either humans or machines are going to be superior, that’s the same sort of fallacious thinking,” Ransbotham told reporters.
Scientific American writers Griffin McCutcheon, John Malloy, Caitlyn Hall, and Nivedita Mahesh put Ransbotham’s point another way in an article titled — tellingly — “AI Isn’t the Solution to All of Our Problems.” They write:
“The belief that AI is a cure-all tool that will magically deliver solutions if only you can collect enough data is misleading and ultimately dangerous as it prevents other effective solutions from being implemented earlier or even explored. Instead, we need to both build AI responsibly and understand where it can be reasonably applied.”
In other words: We need to stop viewing AI as a fix-it tool and more as a consultant to collaborate with over months or years. While there’s little doubt that artificial intelligence can help business leaders cultivate profit and improve their business, their deployment of the technology must be done strategically — and within the understanding that the business probably won’t see the gains it hopes for on its first attempt to integrate AI.
If business leaders genuinely intend to make the most of the opportunity that artificial intelligence presents, they should be prepared to workshop. Adopt a flexible, experimental, and strategic mindset. Be ready to adjust your business operations to address any inefficiencies or opportunities the technology may spotlight — and, by that same token, take the initiative to continually hone your algorithms for greater accuracy. AI can provide guidance and inspiration, but it won’t offer outright answers.
Businesses are investing millions — often tens of millions — in AI technology. Why not take the time to learn how to use it properly?