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Shixian Xie, John Zimmerman, Sijia Xiao, Motahhare Eslami
Don’t Let FOMO Be Your Organization’s AI Strategy
Authors identify ways to improve innovation outcomes after interviews with AI Deciders
The Breakdown
- AI innovation projects are failing at an unsustainable rate of up to 95%.
- “AI deciders” often rush to choose a solution without exploring other concepts.
- Authors share 3 takeaways for innovators looking to maximize AI benefits and minimize AI harms for their organization.
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With everyone talking about artificial intelligence (AI) these days, companies are throwing AI at their innovation problems, hoping it will solve everything.
Driven by the fear of falling behind their competitors, organizations are ignoring best practices for selecting new initiatives. A new mindset to hurry up and “AI this problem” has emerged, and it’s not going well.
Research shows that throwing AI (and therefore money) at a business problem is not a panacea for most solutions. In fact, up to 95% of AI projects fail – a number that is neither cost-effective nor sustainable. At a downward trajectory like this, some fear the AI boom could become an AI bubble, bursting in the near future.
So, in order to learn more about why so many AI projects are failing, Carnegie Mellon University (CMU) authors are taking a step back to explore the decision-making that takes place before the investment in AI.
The majority of CEOs currently claim that AI innovation is a top priority, but less is known about the people who choose, approve, and guide companies’ AI initiatives. This empirical study with 25 interviews investigates how those people – called “AI Deciders” in the paper – reason about AI benefits and risks.
The authors found similar, avoidable process gaps across a variety of organizations.
“We found that organizations often commit to a single idea too early in the process,” said Shixian Xie, Ph.D. student at the CMU Human-Computer Interaction Institute and lead author on the paper. “When teams move too quickly into implementation, they may overlook other AI opportunities that could deliver stronger outcomes with fewer risks.”
“We want to share these findings with people in industry who may be struggling to find value with AI. They may not realize that this is happening to everyone,” said John Zimmerman, co-author on the paper and Tang Family Professor of Artificial Intelligence and Human-Computer Interaction. “We want to help them understand that there are better ways to create value with AI.”
The authors offer three takeaways for other AI deciders looking for tangible ways to get more value for their organizations.
- Brainstorming saves time. Take the time to explore several innovation opportunities before going all in on an idea. Starting with a better AI project concept reduces the number of pivots later, getting teams to success faster.
- Don’t decide on a solution alone. Be sure to leverage the collective intelligence within your organization. Diverse perspectives create a productive friction that reveals where AI capability, financial viability, and user needs align.
- Estimate your return on investment (ROI) up front. Before writing any code, do a back-of-the-envelope calculation to show that an AI concept can generate more value than it will cost. A little math can help you to avoid an expensive failure and pivot.
The authors also identified opportunities for design and HCI to support impactful and responsible AI innovation. Their goal is to reduce AI project failure.
“The biggest challenge to achieving an AI boom and avoiding an AI bubble is not technical; it’s organizational,” Zimmerman added.
This paper, "Investigating How Leaders Decide on AI Innovations: Opportunities for HCI," was presented at the recent Association for Computing Machinery Conference on Human Factors in Computing Systems (CHI 2026).
In addition to Xie and Zimmerman, the CMU authoring team included Sijia Xiao, Cindy Peng, and Motahhare Eslami from the HCII, and Ganesh Mani of the Tepper School of Business.
This research received partial funding from the National Science Foundation, National Institute of Standards and Technology, and Carnegie Mellon University AI Measurement Science and Engineering Center (AIMSEC).
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