The artificial intelligence (AI) journey starts with a single step, but too many companies take the wrong first step. The natural tendency is to begin with proof-of-concept projects at the departmental level. Start small and see what happens, right?
Actually, no. With AI, starting small usually means staying small, with the small, neutral, or negative returns on investment that too many companies have experienced. Half measures run contrary to what we’re learning about the inherently high-powered nature of AI in the enterprise. AI isn’t a tactical tool in search of point solutions; it's a strategic technology that requires silo-free, organizationwide commitment, from the C-suite through line-of-business to the information technology (IT) department. Less than that results in project failure, bad return on iinvestment (ROI), and futility.
That was the overriding message from Michael Gale, a noted AI thinker and best-selling author of The Digital Helix: Transforming Your Organization's DNA to Thrive in the Digital Age, at Nvidia’s recent virtual GTC Conference. The immediate source of his insights was an IBM study that included interviews with 550 business leaders, and Gale observed that, although AI is becoming “table stakes” when it comes to IT transformation, the IBM study shows a correlation between the 55% of companies merely experimenting with AI and the 50% of AI programs that have no real measurable ROI.
But what about the less than 20% of companies classified as "AI thrivers" that consume 60% of the growth in AI’s collective ROI? Who are they, and what traits do they have in common? Gale said they share seven traits, characteristics of which can be broken into two broad characteristics.
Organize for Scale, Not Experimentation
The first characteristic is that AI thrivers don’t count on AI to spontaneously and organically proliferate. Instead, thrivers from the outset instill a vision of weaving AI throughout their businesses by forming a cadre assigned with “proactive planning for scale” even as AI adoption is still at its formative stages.
“For what we would call core principles, for what we call measured success, if you build that core team, it's got to be cross-functional even if the (initial) AI investments may focus on one small area,” Gale said. “If you don't have a lot of cross-functional involvement, it's really difficult to even hypothesize scale, let alone deliver it. You've got to design scale from the very beginning. The proof-of-concept idea is not at all bad, but you've got to move off that idea very fast. And you have to have a team involved that has multifunctional, cross-functional capability within it.”
A key task of this core team: “handling the inevitable ambiguity.” By this Gale refers to the confusion and disruption that result when AI is integrated into business processes, issues that can be handled only if senior executives, departmental managers, and data scientists collaboratively resolve uncertainties and dislocations brought on by AI.
Gale cited GM Financial's efforts to instill that ethos. He quoted Lynn Calvo, assistant vice president of emerging data technology, who said, “Our goal is to leverage machine learning across our entire organization through a center-of-excellence model. One of the biggest things that keeps me up at night is moving from experimentation to production.”
He also cited a large company in the sports industry that, to instill a sense of mission and vision within its AI cadre, told it to produce 100 interesting findings during the first 90 days of its work, findings not just about data and ways to use it but also about collaboration about communication.
“The team placed on a wall a set of Post-It notes, 100 of them,” said Gale, “that people looked at, and they brought people in to start to ‘osmosify’ this level of learning experience through this process. So you should think about unique ways of showing progress and success collaboratively as you start to go through this process.”
It’s all about building “pathways to scale,” Gale said.
“We've learned from thousands of hours with with clients going through this process … if you don't build those pathways to scale really early on, you'll be stuck in a set of islands of experiments,” he said. “They may be very successful experiments, but they're not going to make enough radical difference to the organization.”