The AI journey starts with a single step, but too many companies take the wrong first step. The natural tendency is to begin with PoC projects at the departmental level. Start small and see what happens, right? Actually no.

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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, organization-wide commitment, from the C-suite through line-of-business to IT. Less than that results in project failure, bad ROI and futility.

That was the overriding message from Michael Gale, a noted AI thinker and bestselling 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 percent of companies merely experimenting with AI and the 50 percent of AI programs that have no real measurable ROI.

But what about the less than 20 percent of companies classified as “AI thrivers,” which consume 60 percent 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 that can be broken out 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 multi-functional, cross-functional capability within it.”

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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 execs, departmental managers and data scientists collaboratively resolve uncertainties and dislocations brought on by AI.

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