AI is triggering shifts in the value pools of entire industries. And it is redefining what it takes for companies to achieve competitive advantage. Yet, even as many companies have begun applying AI solutions with impressive results, few have developed full-scale AI capabilities that are systemic and companywide.
BCG’s research, conducted in conjunction with MIT, has identified clear patterns. And it has revealed that unleashing the true power of AI requires scaling it across the entire business. Yet at this decisive stage, companies hit what BCG has termed the AI paradox:
That’s because AI systems change constantly as they ingest data and learn from it. Seemingly isolated uses cases interact and become entangled.
To overcome the AI paradox, you must transform your company’s operating model along three dimensions:
For leveraging machine intelligence at scale, you must optimize machine architecture. Because the structure you use for AI creates your competitive advantage, this detail can’t be left to a small set of technical staff.
End-to-end platforms for managing workflow, such as those used at AI pioneers like Google, Uber, and Facebook, introduce discipline across all the entangled AI systems in your company.
In a world where humans and machines work in concert, your organization structure must be optimized for both.
Despite all the advances in AI technologies, people continue to play an essential role in its successful application. Companies need to hire data scientists and data engineers. And they will need to deploy strategic workforce planning, reskilling, and change management to ensure that AI helps drive a companywide digital transformation.
An AI@scale transformation should occur through a series of top-down and bottom-up actions to create alignment, buy-in, and follow-through. This ensures the successful industrialization of AI across companies and their value chains:
By systematically moving through these steps, the implementation of digital transformation will proceed with much greater speed and certainty. Still, companies must be aware that the transformation can take one to three years, depending on the complexity of the overall organization.
Retailers have big hopes for artificial intelligence since it can personalize and enhance the shopping experience in new ways, but challenges arise in building the data and analytics platform needed to support AI.
As intelligent machines proliferate, their much faster decisions and actions hit the physical limits of synchronization throughout space and time. In nature, the distributed intelligence of an Octopus might provide inspiration for these future business systems.
There is an artificial intelligence bubble in the making—since financial market expectations seem to be overtaking fundamentals as drivers for valuations of companies with AI-based business models.
A BCG roundtable discussion for chief information officers and other digital leaders explored trends in data and AI. They explored the three-pressure points that need consideration, and it largely comes down to people.
Managing Director & Senior Partner