Can ideas developed thousands of years ago help leaders make better decisions about AI, data and digital transformation today? In this episode of Tech Talks Daily, I speak with Alfonso Asensio, author of Digital Wisdom: Leading Transformation With the Sophia Factor and head of data measurement for global clients at Google in Tokyo. Alfonso has spent his career working across data, digital business and global client leadership, but his latest work looks beyond technical capability. He asks what changes when organizations bring sound judgment, ethical reasoning and human purpose into the decisions that shape digital change. We begin with Sophia, the classical Greek idea of wisdom. Alfonso argues that modern business often treats wisdom as another word for knowledge, even though the older idea also included practical intelligence and judgment. Technical expertise can tell a company whether a system can be built. Wisdom asks why it should be built, who benefits and what consequences may follow. That distinction matters when businesses feel pressure to adopt AI because competitors are doing the same. Alfonso shares the story of a large company with the resources, talent and urgency to pursue an ambitious digital program. When he asked what the business was trying to achieve, the answer became a list of fashionable technologies. AI-driven customer activity, blockchain supply chains and data optimization had become substitutes for a clear objective. His conclusion was that the company was attempting to build its future on buzzwords. Sometimes the better decision is to remain analog in a particular process if that choice serves customers and employees better. We also consider Socratic thinking in organizations where boards and investors expect certainty. For Socrates, confusion was a stage in learning rather than a failure of leadership. Alfonso believes leaders can use probing questions to expose assumptions and contradictions before a technology plan becomes expensive. Admitting uncertainty can be difficult, but false certainty can send a business confidently in the wrong direction. Heraclitus offers another useful comparison. Technology resembles a river that never stops moving, while employees need stability and meaning. Alfonso argues that leaders should create stable banks around that flow through a consistent capacity for improvement, adaptation and long-term thinking. The tools will continue to change, but organizations can reduce exhaustion when people understand the purpose behind that change and have a reliable way to respond. One of the most memorable parts of our conversation compares large language models with the Oracle of Delphi. Ancient leaders sought answers from an institution whose workings they could not fully see. Modern users can receive equally confident guidance from AI systems without knowing which data, assumptions or commercial interests influenced the response. Alfonso's point is not that machines are mystical. It is that people need discernment, source awareness and judgment when an answer arrives with authority. We then turn to Epicurus and the idea of ataraxia, or freedom from anxiety. In a business setting, Alfonso connects this with reducing unnecessary friction, decision fatigue and overload. Systems should be reliable, suited to the organization and valuable to employees as well as customers. Governance sometimes requires deliberately adding friction before investment, so teams can pressure test assumptions and ask whether people will be served by a tool or forced to serve it. Alfonso closes with two questions for any leader considering a major AI decision. What is our identity as an organization, and are we acting ethically? He uses Blockbuster as an example of a company that defined itself through videotape rental rather than entertainment. A clear identity can help a business choose technology that supports its purpose. The ethical check then asks whether transparency, consent and accountability are present, or whether data and algorithms are being used to manipulate people. Are organizations giving themselves enough time to ask why an AI system should exist before asking how quickly it can be deployed? Listen to the conversation, then share your thoughts with me.

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