The business is keen to move AI from the lab to the field, where it will presumably usher in a new era of efficiency and profitability. But it turns out that AI behaves quite differently on the testbed than it does in the actual world, so this is not as simple as it seems. Overcoming the barrier between the lab and real-world applications is increasingly becoming the next important goal in the race to deploy AI. Because intelligent technology relies on a consistent stream of trustworthy data to work effectively, a controlled setting isn't always the best place to test software. With AI, the actual test is now an uncontrolled environment, and many models are failing.

As more companies scale AI projects, turning proof-of-concepts into drivers of business transformation, a clearer picture of what it takes to succeed with real-world AI is taking shape. When it comes to AI teams, a broader set of skills are required than previously known, with a particular need for people with experience in operations and in translating AI concepts into business terms and vice versa. Get the latest insights with our CIO Daily newsletter. In fact, enterprises need blended teams to succeed with AI, says Louise Herring, partner at McKinsey & Co. "If you look at the technical side, the emphasis is increasingly on how we can make sure we have production-ready code and we have elements available for reuse throughout the organization," she says. "But the key area of emphasis that we see first of all is about translators: people who can make the connection between the business and the technical side."

A huge confidence gap exists between the number of companies that try digital manufacturing strategies and those that successfully apply them, a new McKinsey & Co. survey found. In the 2018 Manufacturing Global Expert Survey, 92 percent of respondents think they lead or are on par with competitors in Industry 4.0 manufacturing strategies. The survey consisted of 700 companies in seven nations. Each had at least 50 employees and $10 million in annual revenue. It found that two-thirds rank digitizing the production value chain as a top priority.

John Kelleher is a partner at McKinsey & Co. and the co-chair of Next Canada. Laura McGee is an engagement manager at McKinsey & Co. and co-founder of #GoSponsorHer. There's no doubt that Canada could lead the planet in artificial intelligence (AI). Canadian academics such as Geoffrey Hinton and Yoshua Bengio essentially created the field of deep learning and put Canada on the map; today, Edmonton, Toronto and Montreal are globally important centres of AI research. The best AI talent in the world is also increasingly coming to Canada to launch AI businesses such as integrate.ai

John Kelleher is a partner at McKinsey & Co. and the co-chair of Next Canada. Laura McGee is an engagement manager at McKinsey & Co. and co-founder of #GoSponsorHer. There's no doubt that Canada could lead the planet in artificial intelligence (AI). Canadian academics such as Geoffrey Hinton and Yoshua Bengio essentially created the field of deep learning and put Canada on the map; today, Edmonton, Toronto and Montreal are globally important centres of AI research. The best AI talent in the world is also increasingly coming to Canada to launch AI businesses such as integrate.ai

John Kelleher is a partner at McKinsey & Co. and the co-chair of Next Canada. Laura McGee is an engagement manager at McKinsey & Co. and co-founder of #GoSponsorHer. There's no doubt that Canada could lead the planet in artificial intelligence (AI). Canadian academics such as Geoffrey Hinton and Yoshua Bengio essentially created the field of deep learning and put Canada on the map; today, Edmonton, Toronto and Montreal are globally important centres of AI research. The best AI talent in the world is also increasingly coming to Canada to launch AI businesses such as integrate.ai