Artificial Intelligence (AI) is increasingly seen as a must-have technology that enables businesses to become agile and innovate at scale. IDC predicts global spending on artificial intelligence (AI) systems will increase from US $50 billion in 2020 to US $110 billion in 2024. But Gartner research estimates that 50 percent of AI implementations are struggling to get past the proof-of-concept stage and be implemented at scale. The reasons vary from overhyped expectations and lack of vision to inadequate data infrastructure and lack of skilled resources. Another important factor is the team that's working on the AI programs.

The adoption of artificial intelligence (AI) in the enterprise continues: More than half (58 percent) of respondents to McKinsey & Company's recent global AI survey say their organizations have embedded at least one AI capability into a process or product in at least one function or business unit, up from 47 percent in 2018. Those increases were reported across all industries. What's more, nearly a third (30 percent) are using AI in products or processes across multiple business units and functions, McKinsey's data says. But, as the McKinsey research and others point out, some organizations are much further along in scaling their AI initiatives. What are teams succeeding with AI doing that others can emulate to propel their efforts?