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."

Artificial intelligence (AI) and machine learning (ML) might be high in the hype cycle at the moment. But that doesn't mean organizations are not realizing tangible gains from deploying products that leverage the technologies. Here are three examples of how AI and ML are improving internal business processes and paying off for enterprises. Beacon Street Services needed to have a "single source of truth" for all its company's data, to ensure consistency and accuracy across its applications. The company is the services arm of Stansberry Holdings, which produces financial publications exclusively through purchased subscriptions.