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.
Read more here: [Insert article link]
#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 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 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 More