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 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 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 MorePart 3: The Purpose Revolution: Redefining Success in the 21st Century
As we entered the 21st century, the landscape of corporate America began to shift dramatically. The relentless pursuit of efficiency and shareholder value that characterized the late 20th century gave way to a new paradigm that placed purpose at the center of business strategy. This transformation, called the “Purpose Revolution,” was driven by a complex interplay of social, economic, and generational factors. Human Resources (HR) played a pivotal role behind the scenes. In this article, we'll explore how this shift is redefining success in the corporate world and the challenges it presents for leadership, measurement, and accountability.
Read MoreLow and High Quality AI: What's the Difference?
When we talk about "low-quality AI," we're referring to AI systems that are less sophisticated, less accurate, or more limited in their capabilities. These systems, interestingly, can sometimes lead to more critical and independent thinking from users.
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Taxonomy v Folksonomy
The concepts of taxonomy and folksonomy hold significant implications, especially in the context of emerging technologies like OpenAI. While traditional taxonomies offer structured hierarchies of knowledge, allowing for a systematic approach to information organization, folksonomies represent a more fluid and emergent way of categorizing information based on user-generated tags and metadata.
However, the challenge arises when technological advancements fail to incorporate divergent thinking and promote groupthink through convergent taxonomies. This phenomenon is particularly evident in language models, where developers' linguistic and cultural biases can influence the interpretation and representation of (the dominant) language.
Read More4 Perspectives to drive effective data translation
When driving data projects, you will encounter business stakeholder challenges that often go unspoken. This is not always because people hold back but because they don't fully know how to vocalize their constraints.
If they can't directly address their requirement, chances are we can't either. To hear others' speech, we start by asking questions from different perspectives.
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Countdown: Book Excerpt Chapter 2
Book Excerpt: While a fully funded budget that supports data as a service is an integral part of a data transformation’s financial picture, few are fully staffed or funded. Three-quarters of executives confirm their organization now has some form of data strategy (however rudimentary), but a paltry 16% say they have the skills and capabilities necessary to deliver it.[1] Even though the average staffing budget is growing yearly, finding the skills and capabilities to execute data projects is becoming harder and harder.
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