Persistent problems often seem intractable because of the frame through which we view them. A fixed point of view on an issue might lead us to struggle because we are trying to solve the wrong problem.
Consider the anxiety in the workplace about the growing role of AI. Business leaders see ever wider applications for increasingly powerful technologies but worry that they don’t have the right talent in place to leverage AI; meanwhile, many workers fret about correspondingly narrower options for their own human contributions. Leaders who are focused on building new strategic capabilities often dismiss employees’ worries about new systems as stubbornness or an inability to learn. That narrative of change-resistant workers is reinforced only when AI implementation stalls, as it often does, due to slow adoption by end users.
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The experience of AI developers working with Duke University Hospital shows what can happen when you look at the problem from a different vantage point: end users’ concerns. Katherine C. Kellogg, Mark Sendak, and Suresh Balu investigated AI deployments at Duke and identified commonalities among the project teams that won user acceptance of AI implementations. From project inception, these teams worked to understand users’ workloads, workflows, and need for autonomy, and they looked for ways to ensure that new AI decision-support tools didn’t undermine their experience. They successfully facilitated adoption by simply looking at the issue from the end user’s perspective rather than focusing only on the objectives of a project sponsor far removed from the front lines. Where managers might have seen the problem as one of front-line workers’ skills or adaptability, the developers saw — and solved — a slightly different problem and were able to obtain the result the organization needed.
The expensive problem of C-suite turnover is another case where the real issues, and corresponding solutions, emerge when you look at the challenge from a different angle. While the problem might seem to be that the organization makes bad hires, research by Kimberly A. Whitler, Ed Tazzia, and Stephen Mann suggests that what’s really going on is that the organization designs bad jobs. Their analysis of job specifications for 185 C-level roles, including CIO, CFO, and CMO positions, showed frequent and significant mismatches between expectations and responsibilities, to the extent that the path to success in the position was perilously narrow. They suggest ways to solve that problem — not the “why can’t we get good candidates?” problem.
Finally, Jonas Solbach, Klaus Möller, and Franz Wirnsperger report on a large-scale experiment they conducted on compensation and motivation, an area where management has not shifted from a pervasive pay-for-performance approach despite years of compelling research showing that such extrinsic motivators are of limited value. Their experiment involved a large sales team that you might intuitively expect to be highly incentivized by money — but their results might encourage you to reframe the problem of employee motivation and solve it in a new way.