#missing-information

#missing-information

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Organizations are drowning in dashboards, KPIs, performance metrics, behavioral traces, biometric indicators, predictive scores, engagement rates, and AI-generated forecasts. We have more data than we know what to do with. We pretend that the mere presence of data guarantees clarity. It does not. That's data hubris—the arrogant belief that because something can be measured, it can be mastered.

My role was straightforward: write queries (prompts and tasks) that would train AI agents to engage meaningfully with users. But as a UXer, one question immediately stood out - who are these users? Without a clear understanding of who the agent is interacting with, it's nearly impossible to create realistic queries that reflect how people engage with an agent. That's when I discovered a glitch in the task flow. There were no defined user archetypes guiding the query creation process. Team members were essentially reverse-engineering the work: you think of a task, write a query to help the agent execute it, and cross your fingers that it aligns with the needs of a hypothetical "ideal" user - one who might not even exist.

Ever since our ancestors first stood upright and squinted at the horizon, we've been wired to notice patterns. A rustle in the grass might have meant a stalking predator. Dark clouds often meant rain. Those who made these connections and guessed that one thing caused another tended to survive. Over time, this ability to link events became one of our most significant evolutionary advantages. It's how we built tools, tamed fire, and eventually invented Wi-Fi.

AI was everywhere, but I wasn't focused on product launches. I was looking at how companies think about data itself: how it's shared, governed and ultimately turned into decisions. And across conversations with executives and sessions on security and compliance, a pattern emerged: the technical limitations that once justified locking data down have largely been solved. What remains difficult is human. Alignment, trust and confidence inside organizations are now the true barriers.

Consistent with the general trend of incorporating artificial intelligence into nearly every field, researchers and politicians are increasingly using AI models trained on scientific data to infer answers to scientific questions. But can AI ultimately replace scientists? The Trump administration signed an executive order on Nov. 24, 2025, that announced the Genesis Mission, an initiative to build and train a series of AI agents on federal scientific datasets "to test new hypotheses, automate research workflows, and accelerate scientific breakthroughs."

Time pressure, limited information, confusion, fatigue, and mortality salience combine to set the stage for decision-making errors, sometimes with grave consequences. An example is the downing of Iran Air Flight 655 by a missile launched by the USS Vincennes in 1988, resulting in the death of 290 passengers and crew. In a time of heightened tension between the U.S. and Iran, the captain of the Vincennes misidentified the airliner as an incoming hostile aircraft and ordered his crew to shoot it down.