- Companies are now directing billions of dollars globally each year toward AI development, yet more often than not, they’re frustrated by the lack of progress.
- In fact, only 1 in 10 managers who responded to a recent global survey conducted by MIT SMR and BCG could point to tangible returns.
- With AI investment expected to more than double to $110 billion by 2024, it’s not surprising that some leaders are asking whether that money would be better spent elsewhere in their organizations.
- It’s easy to do AI wrong, and it’s very hard to do it right.
- The organizations that are making the most significant progress on the path toward the intelligent enterprise are the ones that treat the project as a complex systems engineering problem — one that focuses more on adapting the corporate culture than it does on the technology itself.
- Avoiding the Path to Frustration
- Creating an intelligent enterprise is a fundamentally different undertaking than, say, moving a business to the cloud.
- UPS Navigates a Long-Term AI Strategy
- UPS’s ORION (on-road integrated optimization and navigation) algorithm system analyzes 18 million U.S. deliveries each day and informs 66,000 drivers how best to get those packages where they need to be at the appointed times.
- Lessons for the Industry
- The UPS experience is instructive because it tells us that there are no shortcuts to building the intelligent enterprise. It’s tempting to see an AI system beat a Go or Jeopardy! champion and think of it as a milestone that brings the intelligent enterprise closer. But it doesn’t.
- Businesses operate in the real world, facing unbounded problems within a universe of effectively infinite possibilities.
- To give one example, no business had COVID-19 and lockdowns in their 2020 business plans. That’s the kind of surprise that a chess-playing program doesn’t need to anticipate.
- Progress toward the intelligent enterprise requires leaders to concentrate efforts on improving their organization’s decision-making capabilities.
- It requires collaboration across a broad set of disciplines, with experts in different parts of the organization working closely together.
Category 2: The Big Picture of the Digital Age
[genioux fact produced, deduced or extracted from MIT SMR]
Type of essential knowledge of this “genioux fact”: Essential Deduced and Extracted Knowledge (EDEK).
Type of validity of the "genioux fact".
- Inherited from sources + Supported by the knowledge of one or more experts + Supported by research.
Authors of the genioux fact