Over the years of our research, we’ve continued to refine our understanding of the specific practices that leading companies are doing well and the capabilities they have in place to capture value from AI. Recently, a new set of “frontier” practices has emerged as organizations shift from experimenting with AI to industrializing it. These include machine learning operations practices such as assetization, or turning elements like code into reusable assets that can be applied over and over in different business applications.
But over the years, we’ve also consistently seen a set of foundational practices that these organizations are getting right. Through our work, we’ve learned not to describe these as “basic” practices, because they are some of the most difficult to implement. Many of these involve the people elements that need to be in place for companies to adopt AI successfully, such as having a clear understanding of what specific tech talent roles are needed and successfully integrating AI into business processes and decision making. As proven in many cases, AI engines and people together can create much more value than either can individually.
As the AI frontier advances, we continue to be inspired by some truly innovative applications of AI, such as the use of AI to identify new drugs, create hyperpersonalized recommendations for consumers, and power AI simulations in digital twins to optimize performance across a variety of settings. As individual AI capabilities, such as natural-language processing and generation, continue to improve and democratize, we’re excited to see a wave of new applications emerge and more companies capture value from AI at scale.
Bryce Hall
McKinsey commentary
Associate partner
