The Artificial Intelligence Journey Has Only Begun

We’re still very much in the early days of artificial intelligence (AI). However, money is pouring into AI initiatives at astounding rates, and enterprises need to move at a deliberate speed to adopt and leverage AI across their systems, applications, and data.

That’s the word from a recently published study conducted by the McKinsey Global Institute, which presses the urgency of AI adoption and explored benefits being seen. The research report concludes that AI investment is growing fast and estimates that tech giants spent $20 billion to $30 billion on AI in 2016, with 90% of this spent on R&D and deployment and 10% on AI acquisitions (“Artificial Intelligence: The Next Digital Frontier?”).

Both inside and outside the tech world, AI adoption is still in its early, pioneering stages. Few firms—only 20%—have deployed it at scale or as a core part of their businesses, McKinsey’s survey of 3,073 C-level executives finds. “Many firms say they are uncertain of the business case or return on investment,” state the survey report’s authors, who found more than 160 use cases of AI being deployed commercially only 12% of the time. The use case review finds “commercial considerations can explain why some companies may be reluctant to act.” In the survey, firms reported “poor or uncertain returns” as the primary reason for not adopting. Regulatory concerns have also become much more important.

The McKinsey report also looked at technical challenges shaping up on the journey to AI. These challenges vary from industry to industry. “While big tech and academia are pushing advances in the performance of the underlying technology, engineering solutions need to be worked out for specific use cases, requiring both data and talent. Industries such as financial services, and high tech and telecom have generated and stored large volumes of structured data, but others, including construction and travel, lag far behind.”

Most of the firms in the McKinsey survey expect to increase spending on AI in the coming 3 years. However, the survey documented relatively modest growth projections—only one-fifth of responding firms expected to increase expenditures greater than 10%.

McKinsey’s research also finds that AI can deliver real value to serious adopters and can be a “powerful force for disruption.” Early AI adopters in the survey that combined strong digital capability with proactive strategies had higher profit margins and expect the performance gap with other firms to widen in the future. Serious AI adopters with proactive strategies report current profit margins that are 3 to 15 percentage points higher than the industry average in most sectors and expect this advantage to grow in the future. In the next 3 years, these AI leaders expect their margins to increase another 5 percentage points more than the industry average.

The survey also finds evidence of an emerging “digital divide”: Large firms—those with more than 500 employees—have much higher rates of adoption and awareness. They are at least 10% more likely than smaller firms to have adopted at least one AI technology at scale or in a core part of their business.

There are reasons for this, the study’s authors point out. “Larger firms typically have access to more and better-structured data, and are more likely to have employees with the technical skills needed to understand the business case for AI investment and to successfully engage suppliers. Bigger firms also have an advantage because the kind of fixed-cost investment required for AI tends to generate higher returns when applied to a bigger base of costs and revenue.”

At the same time, the McKinsey team adds, AI is just as open to smaller companies, as well. They identified the characteristics of the early adopters:

They are investing in cloud and big data: “Early AI adopters are from sectors already investing at scale in related technologies, such as cloud services and big data.”

They tend to be larger companies: “This again is typical of digital adoption, in which, for instance, small and midsized businesses have typically lagged behind in their decision to invest in new technologies.”

They don’t limit themselves to one type of technology: “They go broader as they adopt multiple AI tools addressing a number of different use cases at the same time.”

They are just as motivated by potential growth as they are in cutting costs: “AI is not only about process automation, but is also used by companies as part of major product and service innovation.”

They have strong executive support: “Respondents from firms that have successfully deployed an AI technology at scale tended to rate C-suite support nearly twice as high as those from companies that had not adopted any AI technology.”                     



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