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Why I Created This Platform
I built this platform because I believe there’s a gap in conversations around AI adoption—a gap between the hype (and the fear of missing out, or FOMO) and the real lived experiences of business leaders who have tried to implement AI (successfully or otherwise). I want to hear from those who’ve “been there,” to learn what causes AI initiatives to fail, how leaders respond, and what distinguishes the successes.
What I’m Curious About — Key Questions
- What conditions lead to failure?
What are the recurring pitfalls? Is it lack of clear goals? Data problems? Misalignment with business strategy? Poor infrastructure? Over-reliance on untested models? - What actions do companies take afterwards?
Do they pivot to better alternatives? Revert to previous processes? Shut the project down entirely? Repurpose what they built? - When it does work, what works—and in what contexts?
Are there specific business functions, industries, or applications where AI agents succeed more often? What structures, leadership behaviors, or designs support success? - Learning and intervention design
My goal is to use what I learn: to design frameworks, tools, and interventions that improve the odds of success in future AI implementations.
Why These Questions Are Urgent (With Evidence)
To show why this isn’t just academic curiosity, here are findings from recent research:
- A report from RAND finds that 80%+ of AI/ML projects fail, often due to misunderstanding the problem, lacking sufficient or quality data, unfit technical infrastructure, or aiming for “too much too fast.”
- Another piece (“Why 95% of AI pilots fail”) emphasises the failure points of pilot programs: fragmented data, tools chosen simply because they are trendy, breakage when moving from pilot to production.
- Case in point: McDonald’s halted its AI drive-thru ordering experiment (in many U.S. stores) after issues like incorrect orders (e.g. “bacon topped ice cream”) and operational challenges. It’s a vivid example of how lateral factors—user interaction, ambient noise, integration with human staff—can derail well-funded AI initiatives.
- Triggers of failure are well-documented: lack of clear goals, insufficient change management, poor alignment with business processes, legacy systems, skills gaps, and missing infrastructure.
What I’m Doing With It
- In-depth interviews and case studies with business leaders. I want direct stories: what went well, what went wrong, how companies reacted when things didn’t go to plan.
- Patterns & synthesis. From these stories I’ll identify patterns across industries: common themes, recurring failure conditions, strategies that seemed to work.
- Book project. I’m writing a book that aggregates these interviews and case studies, not just as narratives, but as actionable wisdom: frameworks, principles, stories from the front lines.
- Academic contribution. As a PhD student in Information Systems, this research is part of my dissertation. The data gathered will inform both theory (what we know about AI adoption and failure) and practice (what leaders can do differently).
What I Hope From Business Leaders
If I reach out to you as a business leader, here’s what your engagement would mean:
- Acknowledging the request: even a brief conversation (15-30 mins) is gold.
- Honesty in the story: failures are often more instructive than successes. What really happened, what decisions were made, what trade-offs were faced.
- Permission to share (with appropriate anonymity if desired): your insights will become part of something bigger—being useful for other leaders, researchers, and the economy broadly.
Your Participation Matters
This platform is meant to be collaborative: your voice, your story, your experience matter. With enough stories and reflection, we can help shift the narrative from “AI hype and fear” to “AI adoption with wisdom and impact.”