What it really takes to scale artificial intelligence
It’s an exciting time for leaders. Artificial intelligence (AI) capabilities are on the precipice of revolutionizing the way we work, reshaping businesses, industries, economies, the labor force, and our everyday lives. We estimate AI-powered applications will add $13 trillion in value to the global economy in the coming decade, and leaders are energizing their agendas and investing handsomely in AI to capitalize on the opportunity—to the tune of $26 billion to $39 billion in 2016 alone.
Meanwhile, AI enablers such as data generation, storage capacity, computer processing power, and modeling techniques are all on exponential upswings and becoming increasingly affordable and accessible via the cloud.
Conditions seem ripe for companies to succeed with AI. Yet, the reality is that many organizations’ efforts are falling short, with a majority of companies only piloting AI or using it in a single business process—and thus gaining only incremental benefits.
Many organizations aren’t spending the necessary (and significant) time and resources on the cultural and organizational changes required to bring AI to a level of scale capable of delivering meaningful value—where every pilot enjoys widespread end-user adoption and pilots across the organization are produced in a consistent, fast, and repeatable manner. Without addressing these changes up front, efforts to scale AI can quickly derail.
To scale up AI, companies must make three shifts. First, they must transition from siloed work to interdisciplinary collaboration, where business, operational, and analytics experts work side by side, bringing a diversity of perspectives to ensure initiatives address broad organizational priorities and to surface user needs and necessary operational changes early on.
Second, they must switch from experience-based, leader-driven decision making to data-driven decision making, where employees augment their judgment and intuition with algorithms’ recommendations to arrive at better answers than either humans or machines could reach on their own.
Finally, they must move from rigid and risk averse to agile, experimental, and adaptable, embracing the test-and-learn mentality that’s critical for creating a minimum viable product in weeks rather than months.
Such fundamental shifts don’t come easily. In our recent article, “Building the AI-powered organization,” published in Harvard Business Review, we discuss in depth how leaders can prepare, motivate, and equip their workforce to make a change. Here we summarize the four key areas in which leaders should focus their efforts.
