MIT Sloan Management Review and Boston Consulting Group surveyed more than 2,500 executives and conducted 17 interviews with leading experts to provide a data-driven view of what organizations that succeed with AI are doing and what real success with AI looks like.
Companies that capture value from their AI activities exhibit a distinct set of organizational behaviors. They:
- Integrate their AI strategies with their overall business strategy.
- Take on large, often risky, AI efforts that prioritize revenue growth over cost reduction.
- Align the production of AI with the consumption of AI, through thoughtful alignment of business owners, process owners, and AI expertise to ensure that they adopt AI solutions effectively and pervasively.
- Unify their AI initiatives with their larger business transformation efforts.
- Invest in AI talent, data, and process change in addition to (and often more so than) AI technology. They recognize AI is not all about technology.
Shivaji Dasgupta, managing director of Deutsche Bank, for example, notes a growing concern that there will no longer be “a level playing field” thanks to AI, especially in a highly regulated industry like banking. New competitors from industries not bound by rules imposed on incumbents are already creating competitive risks. Take Apple with its Apple Pay and recently launched Apple Card and Amazon with its Amazon Cash. With massive amounts of data, the ability to apply AI and other technologies to capitalize on that data, and their loyal customer bases, the tech giants’ respective moves into financial services pose formidable threats to traditional banking and financial services companies.
For one financial services organization, building an AI capability was tightly integrated into its overall strategic transformation effort. One executive noted, “Digital transformation was really the end goal and never really separate from our adoption of AI.”
Another executive said that prior to digital transformation, he observed AI use cases that would involve data from siloed sources — housed in a particular business unit — while now, the company has data that spans multiple lines of business, allowing for models that have a much richer lens than the models it uses to solve use cases in a single line of business.
In a relationship business, in particular, many benefits derive from these models because they take what was a fragmented view of a customer and meld the pieces together into a comprehensive understanding. The company gains the ability “to see different events all now in a sort of cohesive thread, being able to look at the time-serious nature of those signals, and build models and capabilities powered by machine learning that allow us to have insights that we didn’t have before.” With these new insights, the company works effectively as a single organization rather than as a collection of organizational units. Instead of myopically optimizing on a locally beneficial metric, managers can connect their decisions to consequences throughout the organization.