{"id":5685,"date":"2026-04-28T11:27:15","date_gmt":"2026-04-28T11:27:15","guid":{"rendered":"https:\/\/deniztuncalp.com\/?p=5685"},"modified":"2026-11-06T17:04:11","modified_gmt":"2026-11-06T17:04:11","slug":"ai-lessons-from-nasas-challenger-disaster-how-organizations-lose-what-they-know","status":"publish","type":"post","link":"https:\/\/deniztuncalp.com\/?p=5685","title":{"rendered":"AI Lessons from NASA’s Challenger Disaster: How Organizations Lose What They Know"},"content":{"rendered":"\n

On the morning of January 28, 1986, the Challenger space shuttle was launched. Seventy-three seconds later, it broke apart.<\/p>\n\n\n\n

The event is often recounted as a technical failure. Cold weather affected the O-ring seals in the solid rocket boosters. A seal failed, hot gas escaped, and the shuttle was destroyed. That account is correct, but incomplete.<\/p>\n\n\n\n

Before the Challenger disaster, NASA already possessed information about such risks. Engineers at Morton Thiokol had warned about the possible effects of low temperatures on O-ring performance. Damage records existed from earlier flights. There was clear unease among the engineers. The risk was recognized, yet it did not retain the same weight when the decision was made. That failure contributed to the disaster.<\/p>\n\n\n\n

This is precisely the issue I examine in my latest article<\/a>, published today in the Journal of Management History<\/em>.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

During the teleconference on the night before Challenger’s launch, what the engineers knew had to be conveyed to management. The information was presented in slides, using an apparently straightforward presentation format. But the slides were more than a presentation tool: they became a filter determining the form in which knowledge could be discussed.<\/p>\n\n\n\n

The engineers’ knowledge was fragmented, probabilistic, and troubling. There was no definitive safety margin for O-ring behavior in cold weather. Previous damage, considered alongside the temperatures expected that morning, indicated that the launch should be postponed. Yet the information changed form when translated into slides. It was broken into bullet points and subordinate statements. Uncertainties were weakened and obscured. Some data that could have served as comparisons became invisible. Rather than reflecting the level of concern among engineers, the risk was presented in a form management could process more comfortably.<\/p>\n\n\n\n

That distinction matters because organizations often make decisions not on raw information but on representations of information. A table, slide, summary, scorecard, or model output does not merely transmit knowledge. It also selects what to tell decision-makers and what to leave out.<\/p>\n\n\n\n

That is what happened with Challenger. The risk did not disappear, but it was thinned out as it entered the decision process.<\/p>\n\n\n\n

What reached NASA’s managers was a shortened, organized, meeting-ready version of engineering knowledge. Such versions are often useful. Organizations cannot operate while carrying every detail into every meeting. Yet some decisions turn on precisely those details. The decision to proceed with the launch of Challenger was one of them.<\/p>\n\n\n\n

In the article, this is the point at which I move from the Challenger disaster to today’s generative artificial intelligence (GenAI). Organizations are now performing the same operation with different tools.<\/p>\n\n\n\n

Large language models summarize lengthy legal case files, produce assessments from patient records, condense financial reports into brief notes, and translate technical documents into language for executives. All of this saves time, but it also changes what decision-makers believe they know.<\/p>\n\n\n\n

The slides used in NASA’s decision-making had physical limits. AI systems likewise have context windows, token limits, and attention mechanisms. A slide omitted what would not fit on the page. A model may similarly exclude information that does not fit within its context window, give less weight to sections it treats as less important, or omit details from its response.<\/p>\n\n\n\n

As a decision-maker, you can sometimes sense what is missing from a slide. Missing information is harder to detect in an AI-generated answer. The prose flows smoothly, the sentences are complete, and the tone is confident. Users may almost automatically infer from the polish of the text that its coverage is equally sound.<\/p>\n\n\n\n

This is why asking only whether a model is hallucinating is insufficient. A fabricated source, an invented decision, or an incorrect date can be a visible error. But omitting information that genuinely exists in the summarized source may have more profound consequences. A model can assemble an incomplete whole from accurate fragments. Because it does so so skillfully, the user may never notice what is missing.<\/p>\n\n\n\n

In my article, I call this token-based epistemic filtering<\/em>. A technical constraint becomes a problem of organizational knowing. The system does not see or adequately process certain information, while people read the output as though the complete source file had been considered.<\/p>\n\n\n\n

The Challenger slides carried some of the engineering knowledge. What they failed to carry was decisive for making the right decision. Today’s AI systems can create a similar risk. The part summarized by the model may be correct. The problem is that what is left out may matter more for the decision.<\/p>\n\n\n\n

A second issue is the displacement of expertise.<\/p>\n\n\n\n

Engineers were present on the night before Challenger’s launch. They spoke and warned. Yet in the decision process, engineering judgment was weakened by management’s demand for a particular form of proof. The risk was expected to be demonstrated conclusively. But engineering knowledge does not always arrive in that form. Some warnings arise from experience, previous anomalies, and an intuitive but trained judgment about material behavior.<\/p>\n\n\n\n

A similar shift is occurring with AI. At first, the model is asked only for a draft. Then it is given the first reading. Then it produces the notes that go into meetings. Before long, the expert no longer constructs an understanding of the file from the beginning; the expert merely checks the framework produced by the model. This may look like a small change in the division of labor, but the authority over knowledge has shifted.<\/p>\n\n\n\n

The organization still places responsibility on a human being. A human signs off. But if the model establishes the initial framing of the decision, expertise is pushed to the final checkpoint. Under time pressure, that last check often ceases to be a substantive review.<\/p>\n\n\n\n

In law, this might mean overlooking an exception in a case file. In healthcare, a brief note in a patient’s record may disappear. In finance, a debt covenant on the final pages of a report may be omitted from the summary. In engineering, a technical warning may be diluted under the heading of ‘general risk.’<\/p>\n\n\n\n

These are not identical events, but they raise the same question: what does the person making the decision actually know?<\/p>\n\n\n\n

This is Challenger’s enduring lesson. NASA’s problem was not simply a lack of information. The central problem was that knowledge lost strength as it circulated through the organization. That matters just as much for organizations adopting AI today. How quickly does the model write? How polished are its summaries? These are secondary questions. We should first ask what information the system leaves out, and who is able to recognize the omission.<\/p>\n\n\n\n

In high-stakes work, AI output should not become the decision itself. It should serve as material for preparing the decision. Users should be able to see which parts of the documents a model processed, the limits under which it operated, and the information it did not cover. Expert verification should be built into the process, not added as a precaution afterward.<\/p>\n\n\n\n

At a time when speed\u2014and the expectation of speed\u2014can be dazzling, organizations sometimes need to slow down. Not every polished summary should go straight to the decision table. Someone has to ask, ‘What did this summary leave out?’ That question may reduce efficiency. But in some high-stakes settings, reducing false confidence matters more than increasing efficiency.<\/p>\n\n\n\n

In Challenger, engineering knowledge also lost its power to shape the decision. The knowledge existed, but it did not reach the moment of decision with its original force.<\/p>\n\n\n\n

We face a similar risk with AI: knowledge exists, but it becomes weakened as it is smoothed into a seamless output. If organizations incorporate AI into decision-making without recognizing this, future failures will not arise only because machines produce wrong answers. They will also arise because people place too much confidence, too quickly, in incomplete answers that look convincing.<\/p>\n\n\n\n

At this rate, AI use could lead us toward many more Challenger-like disasters.<\/p>\n","protected":false},"excerpt":{"rendered":"

On the morning of January 28, 1986, the Challenger space shuttle was launched. Seventy-three seconds later, it broke apart. The event is often recounted as a technical failure. Cold weather affected the O-ring seals in the solid rocket boosters. A seal failed, hot gas escaped, and the shuttle was destroyed. That account is correct, but […]<\/p>\n","protected":false},"author":1,"featured_media":5032,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[33,56,43],"tags":[],"class_list":["post-5685","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","category-management","category-tech"],"yoast_head":"\nAI Lessons from NASA's Challenger Disaster: How Organizations Lose What They Know - DenizTuncalp.com<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/deniztuncalp.com\/?p=5685\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI Lessons from NASA's Challenger Disaster: How Organizations Lose What They Know - DenizTuncalp.com\" \/>\n<meta property=\"og:description\" content=\"On the morning of January 28, 1986, the Challenger space shuttle was launched. 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