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U.S.-based artificial intelligence research organization OpenAI have rolled out a robot hand that can take and solve a Rubik’s Cube.

MATT EDGE /The New York Times News Service

Federico M. Berruti is a partner at McKinsey & Company.

Joshua Gans is a professor of Strategic Management at the Rotman School of Management and the chief economist at the Creative Destruction Lab.

Tiff Macklem is dean of Rotman School of Management at the University of Toronto.

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Last week, the U.S.-based artificial intelligence research organization, OpenAI, rolled out a robot hand that can take and solve a Rubik’s Cube. Creating a robot with visual sense and complex touch and dexterity is an impressive achievement in AI. But it does not bespeak human doom. Instead, this is one more step along the long road to developing machines that can, step by step, master tasks that were previously the exclusive domain of people.

Nonetheless, in just the past few years, we have moved from the lab to a world where most people are touched by AI. Indeed, individual AI-use cases have proliferated to the point we barely notice them. We speak freely to AI bots, have our phones unlock easily and expect AI to correct our spelling and grammar. We rely on AI to tell us what we want to watch, read, listen to and buy. And in our workplaces, we are seeing uses in work triage, fraud detection, codification of rules and data, and the elimination of manual and repetitive work. Boards and management teams in companies have started to learn what AI is and what it can do – everyone is paying attention.

But is that it? These individual use cases solve small and specific problems and are sometimes classified as “narrow AI.” So far, AI is useful but not yet broadly disruptive.

As with other general-purpose technologies, however, disruption is likely around the corner. AI has the potential to create a change in who has economic power. For instance, until recently, knowledge of the street system and traffic patterns gave taxi cab drivers economic power. Their knowledge hasn’t changed, but their power is diminished. Why? Because now, every other driver has this knowledge and experience in the palm of their hands in a navigation app.

The disruptive potential of AI will likely hit upon individual jobs first – taking away the power of those whose skills can be replaced with AI. At the same time, the very same AI is enhancing the value of other skills and providing the opportunity to focus on other parts of jobs. We have seen this before. Your human bank teller didn’t lose their job with the creation of ATMs (indeed, there are more tellers, over all, today). Instead, they expanded their portfolio to better serve bank customers in a variety of other ways and create more value.

As AI improves, companies and markets are next. New technologies have undercut the value of companies in the past. Blockbuster may have been great at running video rental stores and the internet didn’t change that. It just meant that consumers didn’t need a video-rental store any more. AI – and its sidekick, full automation – can do the same. But it is actually quite difficult to predict where disruption might take hold. Sometimes, you don’t know what gave you power until it is gone.

Companies that can reimagine work around new AI capabilities can gain economic power. For example, several companies are competing to transform the operations of warehouses and fulfillment centres by deploying AI-powered robots that communicate with inventory and space management systems. However, the leaders in this space realized that they should not just put the AI robots into the existing warehouses, but needed to fundamentally change the building design, workflow and human jobs around the capabilities (and the current physical limitations) of the AI robots.

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So how do companies prepare for the disruptive power of AI? Unfortunately, there is no quick fix.

It starts with a deep understanding of the key prediction problems and sources of value that if solved or unlocked could open new markets or fundamentally change the business. It requires moving from specific use cases to being AI-enabled across the company. This means AI tools, data and capability to create a platform for innovation integrated across functions. This requires large investments in data sourcing, governance and quality. It also requires standardized AI tools, and governance of the ethical use of AI.

Success will hinge critically on talent that understands both the business and the technology, and can connect across functions to design and implement change. Executives must also adapt their workforce models, redesigning jobs and retraining workers to work with AI.

Engaging with the AI startup community can also accelerate change by helping you see beyond immediate use cases. New ventures bring new imagination, building awareness of how this platform technology can shift economic power. New ventures also bring talent and a disruptor’s culture.

Finally, corporations, academic institutions and the broader community need to start to come together in their mindsets and actions to benefit from the digital age – which means finding ways to embrace disruption, control their own destiny and use technology to the benefit of society. The ability to do this will ultimately define job creation, growth and inclusion. Boards and executives have a fiduciary responsibility to keep track of the rapidly evolving technology landscape and ensure that technology is being used to the benefit of customers, workers and the community, as well as shareholders.

Companies thought the hard part was getting started on AI, but the more difficult jump is moving from piloting specific use cases to managing the disruptive power of AI.

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On Oct. 24, the Creative Destruction Lab will host its fifth annual conference on the business of AI – Machine Learning and the Market for Intelligence. The theme this year is Power and Prediction. McKinsey & Company is the Official Knowledge Partner of the conference.

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