This whole AI thing is simpler than you think

The invitations keep coming, but the questions have changed. Where I was once asked to speculate on a jobless future or the mechanics of prompt engineering, executives now pose a more plaintive query: why isn't AI working for us? Where are the promised productivity gains? The honest answer is disarmingly simple, and it has little to do with the technology itself.It’s because most organizations have no coherent vision for what they want it to do. This isn't merely a people problem; it's a fundamental architectural failure.Companies are attempting to graft 21st-century AI onto 20th-century industrial frameworks, a mismatch of values and goals that guarantees weak returns. Microsoft’s own data confirms the struggle, showing only a quarter of AI initiatives met expectations over three years.The issue, as critics like Gary Marcus have long argued about LLM hype, isn't capability but application. The core failure is a lack of what my colleague Chris Perry terms a 'Human OS'—a workplace architecture designed first for human flourishing, with technology integrated to augment it.Consider the grocery store or the bank: each was a revolutionary operating system for human interaction within a new economic reality. They worked because their designers considered the human experience.In contrast, our legacy organizational systems, born from industrial-age values of efficiency and repeatability, treat people as cogs to be optimized or replaced. This mindset is not just obsolete; it's actively counterproductive in the AI age.If workers perceive AI solely as a tool for their displacement, any potential for collaboration evaporates. The real opportunity lies in moving from 'more' to 'better'—using AI not to churn out greater volume of the same old work, but to empower human creativity to imagine what does not yet exist.This requires a profound shift: retrieving core competencies, reclaiming company culture, and building institutional trust that values human nervous systems and sensibilities as they explore uncharted territory. In a successful synthesis, machines process data, but humans metabolize it, turning information into wisdom. The path forward isn't buying more software licenses; it's engaging in the harder, more human work of redesigning the organization itself.

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