Proven concepts for scaling AI

Poor, misunderstood artificial intelligence (AI). It’s alternately overhyped as a digital nirvana or vilified as a dystopian menace. Yet in the pragmatic here and now, it is neither.

Primarily, AI is a way to augment human capabilities and performance, creating better outcomes for people—customers, employees, partners, or other stakeholders—and better financial returns for businesses. Think human aid, not humanoid.

Nearly one third of executives surveyed said they plan to boost their investment in AI as a result of the pandemic.

The growing adoption of AI is reflected in metrics the IBM Institute for Business Value (IBV) has tracked biannually since 2016. Data from thousands of C-level business executives across regions, industries, and functions points to a trend we expect to accelerate modestly as a result of the pandemic:

• Companies with AI in active use have increased from 26 percent four years ago to 44 percent in 2020.
• In the midst of the pandemic, 84 percent expect a similar or higher level of organizational focus on AI.
• Nearly one third plan to boost their investment in AI as a result of the pandemic.

These trends are consistent with other recent estimates, with IDC forecasting that worldwide spending on AI will increase in 2020—in contrast to a decline in overall IT spending— and double in the next four years.

AI as a strategic capability

But successful scaling— shepherding AI projects in some cases from IT sandboxes and minimum viable products (MVPs) all the way to industrial-strength commercialization—has bedeviled many companies. As the IBV observed in mid-2018, “organizations are knee-deep in AI pilots and proofs-of-concept…and foraying piecemeal into exciting but isolated use cases”—a reality later acknowledged by many other market observers.   

Yet even now, 90 percent of companies have difficulty scaling AI across their enterprises. So, it’s not surprising that about half of AI projects fail.

To be sure, AI is a complex, multi-faceted innovation with layers of interconnected and moving parts. No one aspect can single-handedly ensure success in moving AI projects into commercial use. There is no silver bullet, no panacea.

Without discipline, companies risk becoming mired in endless cycles of experimentation.

What’s needed is a step-function change in the role of AI: a shift from being viewed at arm’s length as the latest incarnation of technological wizardry to a strategic capability embedded throughout the business. From proof-of-concept to proof point.

To advance, organizations must treat AI as an engineering discipline. They need robust engineering and ethical principles, rigorous operations and governance, and an adaptable approach that emphasizes pragmatism over theory. And they also must apply greater focus on scientific innovation— with R&D-like capabilities that continuously explore the bleeding edge in order to differentiate. Otherwise, companies risk becoming mired in endless cycles of experimentation, ever dabbling but never doing.

Learn how taking a more strategic, holistic approach can help your organization capture the true potential of scaled AI—and deliver more robust business value.