Artificial Intelligence (AI) is rapidly changing how we work, make decisions, and define success. But when AI or any new technology suggests something unexpected, how do you react? The answer is shaped more by your experiences than the technology itself and more to do with your Data Biography — the sum of your experiences, reactions, and assumptions about data that shape how you engage with new innovations. By understanding your data biography, you can improve your adaptability, enhance decision-making, and ensure you control new technologies — rather than letting them control you.
Read MoreAI, Marriage, and the Systems We Build: Why We Shouldn’t Be Surprised By High Failure Rates
What do AI investments and marriage have in common? A lot more than you'd think.
Recently, while walking past Las Vegas wedding chapels, I was struck by how we’re encouraged to leap into marriage—despite a 76% chance of it leading to dissatisfaction or divorce. If that were your odds of getting hit by a bowling ball, you’d wear a helmet, right?
Yet, we treat AI investments the same way: chasing transformation, pouring money into the latest technology, and ignoring the evidence that most implementations fail. Some studies suggest that over 80% of AI projects never reach deployment or meaningful ROI—but companies keep making the same mistakes.
The real issue? The systems we build produce the results we deserve. Just as societal expectations drive people toward marriage, hype, pressure, and poor planning drive businesses into AI investments that are doomed from the start.
So what if we designed AI strategies with the same scrutiny we should apply to marriage? The results might surprise you.
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#AI #DigitalTransformation #Leadership #Strategy #SystemsThinking #DataDriven
Read MoreWhat If AI’s Mistakes Aren’t Bugs, But Features?
We often say AI’s mistakes are "by design," but they’re really not. AI wasn’t built to fail in these specific ways—its errors emerge as a byproduct of how it learns.
But what if we actively use them as a tool instead of just tolerating AI’s weird mistakes or trying to eliminate them?
Here are some unexpected but potentially valuable use cases where treating AI mistakes as a form of bias—rather than just failure—could lead to new insights and innovations.
Read MoreBreaking the AI Loop: From Static Thinking to Living Intelligence in Governance and Business
A student recently asked: “How can AI transform the relationships between the U.S. and African countries?” The premise is compelling—AI has the potential to drive transparency, trade, and governance reform, resetting relationships on healthier grounds.
But we’ve seen this movie before.
Read MoreThe Serviceberry Mindset: How Nature’s Gift Economy Can Reshape Data Governance
For years, we’ve heard that breaking down data silos is the holy grail of business transformation. We’ve been told that better pipelines, integrated analytics, and AI-driven decision-making will finally unlock the full potential of enterprise data. But here’s the question no one seems to ask: What if we’re still thinking too small?
The real challenge isn’t just technological—it’s conceptual. We don’t just need better data governance or cleaner metadata. We need a way of thinking that moves beyond technical optimization and into deeper creative problem-solving. That’s where multidisciplinary thinking comes in.
Read MoreGenerative AI vs. Predictive AI: Why the Investment Gap Doesn’t Reflect Real Value
The AI Investment Paradox Generative AI is dominating headlines, while predictive AI quietly powers businesses behind the scenes. Despite delivering far greater returns in efficiency and cost savings, predictive AI receives nearly the same level of investment as its flashier counterpart. Why? The answer lies in perception: generative AI dazzles with its creative outputs, while predictive AI quietly drives results. Yet for businesses seeking measurable ROI, predictive AI remains the unsung hero.
Read MoreAI’s Newest Employee: Who Bears the Burden of Your Digital Coworkers?
Digital coworkers are no longer hypothetical. AI-driven agents—agentics—are creeping into every function, every decision process, and every interaction within organizations. In some ways, they are the executive dream—they don’t need coffee breaks, demand raises, or call in sick. And yet, they’re reshaping work in ways few leaders are prepared to handle.
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2024 Annual Letter
As we reflect on 2024 and look ahead to 2025, one truth stands out: in an era of unprecedented AI advancement, human capabilities become more crucial, not less. This paradox defines our moment—as technology grows more sophisticated, the distinctly human elements of transformation become rate-limiting factors for success.
Read MoreThe Merkel Mirror: Leadership Lessons for the Digital Age
When Good Management Becomes an Obstacle to Necessary Change
Angela Merkel’s recently published memoir Freedom arrives at a pivotal moment for organizational leadership. As Yascha Mounk notes in his recent Financial Times analysis, Merkel’s legacy reveals how competent management can coexist with systemic failure. This paradox resonates deeply in today’s digital transformation landscape.
Read MoreThe Great Responsibility Shift: From Corporate Stewardship to Individual Burden
Just as beverage companies once managed their packaging waste, tech companies were once more straightforward about their data practices. But now, we’re seeing a troubling pattern of companies quietly expanding their data collection through obscure settings and opt-out mechanisms buried in lengthy terms of service.
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