A Survey of 3,000 Executives Reveals How Businesses Succeed
with AI
Highly important issue that will change our world
The buzz over artificial intelligence (AI) has grown loud enough to penetrate the C-suites of organizations around the world, and for good reason. Investment in AI is growing and is increasingly coming from organizations outside the tech space. And AI success stories are becoming more numerous and diverse, from Amazon reaping operational efficiencies using its AI-powered Kiva warehouse robots, to GE keeping its industrial equipment running by leveraging AI for predictive maintenance.
Key learning's are:
Don’t believe the hype: Not every business is using AI… yet. While investment in AI is heating up, corporate adoption of AI technologies is still lagging
Believe the hype that AI can potentially boost your top and bottom line. Thirty percent of early AI adopters in our survey — those using AI at scale or in core processes — say they’ve achieved revenue increases, leveraging AI in efforts to gain market share or expand their products and services. Furthermore, early AI adopters are 3.5 times more likely than others to say they expect to grow their profit margin by up to five points more than industry peers
Without support from leadership, your AI transformation might not succeed. Successful AI adopters have strong executive leadership support for the new technology.
You don’t have to go it alone on AI — partner for capability and capacity. With the AI field recently picking up its pace of innovation after the decades-long “AI winter,” technical expertise and capabilities are in short supply. Even large digital natives such as Amazon and Google have turned to companies and talent outside their confines to beef up their AI skills
Resist the temptation to put technology teams solely in charge of AI initiatives. Compartmentalizing accountability for AI with functional leaders in IT, digital, or innovation can result in a hammer-in-search-of-a-nail outcome: technologies being launched without compelling use cases.
Take a portfolio approach to accelerate your AI journey. AI tools today vary along a spectrum ranging from tools that have been proven to solve business problems (for example, pattern detection for predictive maintenance) to those with low awareness and currently-limited-but-high-potential utility (for example, application of AI to developing competitive strategy)
Digital capabilities come before AI. We found that industries leading in AI adoption — such as high-tech, telecom, and automotive — are also the ones that are the most digitized. Likewise, within any industry the companies that are early adopters of AI have already invested in digital capabilities, including cloud infrastructure and big data.
The biggest challenges are people and processes. In many cases, the change-management challenges of incorporating AI into employee processes and decision making far outweigh technical AI implementation challenges. As leaders determine the tasks machines should handle, versus those that humans perform, both new and traditional, it will be critical to implement programs that allow for constant reskilling of the workforce.
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