The Uneasy Alliance Between Business Leaders And Artificial Intelligence

• Updated on: 2021-06-30
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What does it take to be an executive these days? In my youth, there was a prime-time television show called Arnie, about a loading-dock worker who comes up with a money-saving idea for his company, and gets vaulted to the executive suite. Along with ample time to practice golf putts in the office, Arnie needs to learn the art of assembling and filing reports for his higher-ups. Such was corporate life in the 1970s.

Of course, C-level executives these days don’t need people bird-dogging and assembling reports for them, they have systems that automatically deliver reports, on-demand, with relevant data right to their laptops or mobile devices. While not widely reported, entire levels of middle management were rendered unnecessary and wiped out. Until now, upper-level executives still made the decisions at the end of this data chain, but artificial intelligence may render some of them unnecessary as well.

So, what does it take to be an executive these days? An ability to bring together and communicate with multiple constituencies is needed, as well as collaboration skills. That all needs to be bolstered by technology savvy, the ability to apply the right technology to not only seamlessly deliver information, but also to render products and services to the customers who need them, when they want them.

AI is seen by many inside and outside the corporate world as a mysterious, dark art that only data scientists and algorithm developers can love. And, tellingly, according to some reports, six in 10 executives feel “threatened” by AI, and a like amount simply don’t trust it yet. But this is only skirting the top of a deep well of mixed emotions toward AI among business leaders. That is, fear of AI, fear of rushing into AI, and then fear of not having enough AI. Let’s dig deeper.

AI stirs up a range of emotions, both in executives and line employees. Overall, among business leaders, the “sentiment isn’t entirely positive,” says Ellen Campana and Swami Chandrasekaran, both with KPMG, in a recent analysis. Close to half of executives responding to the KPMG survey, 44%, think their industry is “moving faster on AI than it should.” In addition, 74% say AI is being overhyped, and this level of skepticism has risen sharply since KPMG’s last survey in 2019. “In both the financial services and retail sectors, for example, 75% of executives now feel AI is overhyped, up from 42% and 64%, respectively,” the authors illustrate.

But AI is probably needed now more than ever, and even more essential in the future, because there is simply too much data flowing in, which is overwhelming human decision-making. Eight in 10 business leaders responding to a survey of 1,000 C-suite leaders released by AI Signal. Four-fifths of these executives already use AI at some point in their decision-making, the survey shows.

The KPMG survey mirrors these results, noting that 79% of executives report AI is at least moderately functional at their organization. Still, “only 43% say it is fully functional at scale,” Campana and Chandrasekaran state. “It is still common to find people who think of AI as something to be purchased—like a new piece of machinery—to deliver immediate results.”

For all their reservations about AI, business leaders have high hopes for the technology. More than 90% responding to the AI Signal survey believe they should leverage AI to augment aspects of work. A third of business leaders believe that by using technology to make decision making easier they would increase revenue for their company by at least 60%. That’s a tall order for a technology that is still functioning in a limited capacity, or within pilot projects in enterprises.

“While they may have experienced some success with AI—often small proofs of concept—many organizations have learned that scaling them to enterprise level can be more challenging,” the KPMG authors observe. “It requires access to clean and well-organized data; a robust data storage infrastructure; subject matter experts to help create labeled training data; sophisticated computer science skills; and buy-in from the business.”

KPMG’s Campana and Chandrasekaran provide some advice for advancing AI within organizations while maintaining the comfort levels of both executives and employees:

A strategic investment in data. “Organizations need clean, machine-digestible data labeled to train AI models, with the help of subject matter experts.”

The right talent. “Organizations unable to build a full team of scientists internally will need external partners who can fill in the gaps and help them sort through the ever-expanding array of AI vendors and offerings.”

A long-term AI strategy guided by the business. “Let the business, not the IT department, drive the agenda. When AI investments tied to a business-led strategy go wrong, they become opportunities to fail fast and learn, not fast and burn.”

Culture and employee upskilling. “Winning the commitment of employees requires providing them with at least a rudimentary understanding of the technology and data, and an even deeper understanding of how it will benefit them and the enterprise.”

A commitment to ethical and unbiased use of AI. “Every organization should develop an AI ethics policy with clear guidelines on how the technology will be deployed. Organizations should continuously monitor the models for bias and drift, and ensure explainability of model decisions are in place.”