{"id":356522,"date":"2026-07-09T12:23:24","date_gmt":"2026-07-09T12:23:24","guid":{"rendered":"https:\/\/peraltafinancing.com\/analytics\/why-adding-a-human-doesnt-automatically-make-ai-better\/"},"modified":"2026-07-09T12:23:24","modified_gmt":"2026-07-09T12:23:24","slug":"why-adding-a-human-doesnt-automatically-make-ai-better","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=356522","title":{"rendered":"Why Adding a Human Doesn’t Automatically Make AI Better"},"content":{"rendered":"


\n<\/p>\n

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The assumption that humans plus AI will always outperform either alone has become a cornerstone of how organizations are deploying AI today. But what if that assumption is wrong \u2014 or at least, far more complicated than we think?<\/span>\u00a0<\/span><\/p>\n

In Episode 4 of AI Horizons, \u202f<\/span>Prasanna \u201cSonny\u201d Tambe<\/span><\/a>, faculty co-director of\u00a0<\/span>Wharton Human AI Research<\/span><\/a>, hosted\u00a0<\/span>G\u00e9rard Cachon<\/span><\/a>, Fred R. Sullivan Professor of Operations, Information, and Decisions at the Wharton School,\u00a0and\u00a0<\/span>Alex Moehring<\/span><\/a>, assistant professor at Purdue University\u2019s Daniels School of Business, to examine the real-world friction points in human-AI collaboration. Drawing on empirical research and economic theory, the conversation challenged some of the most widely held beliefs about how humans and AI work\u00a0together, and\u00a0offered a more grounded way forward. The following are key takeaways from that discussion.<\/span>\u00a0<\/span><\/p>\n

Humans with AI\u00a0don\u2019t\u00a0automatically outperform humans without it, even when the AI is excellent.<\/span><\/b>\u00a0<\/span><\/h3>\n

In a large-scale study involving hundreds of professional radiologists, Moehring and collaborators found that radiologists given access to an AI diagnostic tool performed, on average, about the same as those who didn\u2019t have access to it, despite the AI outperforming roughly three-quarters of human radiologists on its own. The finding was stark: a highly capable tool produced no measurable improvement when paired with human reviewers. For organizations rolling out AI tools and assuming immediate productivity gains, this is a critical reality check. Deployment alone is not enough.<\/span>\u00a0<\/span><\/p>\n

The problem\u00a0isn\u2019t\u00a0that humans ignore AI \u2014\u00a0it\u2019s\u00a0how they use it.<\/span><\/b>\u00a0<\/span><\/h3>\n

Radiologists in Moehring\u2019s study\u00a0weren\u2019t\u00a0tuning out the AI; their assessments did shift in response to its predictions. The issue was more nuanced: humans tended to underweight the AI\u2019s signal relative to their own judgment, and crucially,\u00a0failed to\u00a0account for the fact that they and the AI were often looking at the same underlying information. This \u201cdouble-counting\u201d led to worse performance when the AI was uncertain. As Moehring put it, \u201chumans are not optimally using AI tools,\u201d and that gap between current usage and\u00a0optimal\u00a0usage\u00a0represents\u00a0a significant,\u00a0largely untapped\u00a0opportunity for organizations to improve outcomes.<\/span>\u00a0<\/span><\/p>\n

As AI gets better, the incentive problem gets worse<\/span><\/b>\u00a0<\/span><\/h3>\n

Cachon\u2019s research introduces an economic lens that most organizations\u00a0aren\u2019t\u00a0applying:\u00a0when AI handles tasks correctly most of the time, it becomes increasingly costly to motivate employees to rigorously inspect its output. Workers rationally calculate that careful review is unlikely to catch an error, so effort declines. \u201cThe price you have to pay as a firm owner to motivate that inspection can get quite high, especially as AI gets even better,\u201d\u00a0Cachon\u00a0noted. The implication is counterintuitive but important: improving AI quality\u00a0doesn\u2019t\u00a0resolve the oversight problem, it can\u00a0actually intensify\u00a0it. Organizations need to think about incentive design, not just tool quality.<\/span>\u00a0<\/span><\/p>\n

The most valuable human skill in an AI-augmented workplace may be knowing when not to use AI.<\/span><\/b>\u00a0<\/span><\/h3>\n

Both researchers converged on a skill that\u00a0doesn\u2019t\u00a0get enough attention, which is the ability to judge which tasks AI handles reliably and which it\u00a0doesn\u2019t. Cachon described this as a \u201cthird skill\u201d beyond just reviewing or fixing AI output. A radiologist who knows which pathologies AI reads well, and which it\u00a0doesn\u2019t, can delegate confidently in the first case and step in meaningfully in the second. \u201cHaving humans that have the judgment of knowing when to let AI take the task, and when to step in, that\u2019s an incredibly powerful skill,\u201d\u00a0Cachon\u00a0said. This mirrors traditional management judgment, and organizations should start treating it as a core competency to develop.<\/span>\u00a0<\/span><\/p>\n

Better AI tools are\u00a0not the same as\u00a0better human-AI teams. Design them differently.<\/span><\/b>\u00a0<\/span><\/h3>\n

A key insight from Moehring\u2019s research is that training the best-performing AI model is\u00a0not the same as\u00a0training the best model for human-AI collaboration. In some cases, a slightly weaker AI (one trained on information independent of what the human already sees) can\u00a0actually improve\u00a0team performance by\u00a0eliminating\u00a0the double-counting bias. The dominant paradigm of\u00a0optimizing\u00a0AI on benchmarks and handing it to humans assumes that benchmark performance translates into real-world gains. It often\u00a0doesn\u2019t. \u201cIt\u2019s not obvious that the best performing model is actually going to be the best for humans,\u201d Moehring noted. Firms deploying AI should evaluate human-AI team performance, not just model performance in isolation.<\/span>\u00a0<\/span><\/p>\n

Workflow design matters as much as the technology itself.<\/span><\/b>\u00a0<\/span><\/h3>\n

Organizations focused on which AI tool to use may be missing the more important question: how is the human review process structured around it? Cachon emphasized that anything making the inspection process faster or easier for humans will improve outcomes. That could mean redesigning review interfaces, building AI tools that help humans audit other AI output, or, as Tambe noted, periodically removing AI access to keep human skills sharp. Moehring added that in settings where AI confidence is high, automating those decisions entirely and redirecting human attention to harder, higher-judgment cases may produce better results than keeping a human nominally in the loop on everything.<\/span>\u00a0<\/span><\/p>\n<\/p><\/div>\n\n","protected":false},"excerpt":{"rendered":"

The assumption that humans plus AI will always outperform either alone has become a cornerstone of how organizations are deploying AI today. But what if that assumption is wrong \u2014 or at least, far more complicated than we think?\u00a0 In Episode 4 of AI Horizons, \u202fPrasanna \u201cSonny\u201d Tambe, faculty co-director of\u00a0Wharton Human AI Research, hosted\u00a0G\u00e9rard […]<\/p>\n","protected":false},"author":1,"featured_media":356523,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12033],"tags":[10937,19133,7997,1064],"dealstore":[],"offerexpiration":[],"class_list":["post-356522","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics","tag-adding","tag-automatically","tag-doesnt","tag-human"],"yoast_head":"\nWhy Adding a Human Doesn't Automatically Make AI Better - Som2ny Network<\/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:\/\/fivemor.com\/?p=356522\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Why Adding a Human Doesn't Automatically Make AI Better - Som2ny Network\" \/>\n<meta property=\"og:description\" content=\"The assumption that humans plus AI will always outperform either alone has become a cornerstone of how organizations are deploying AI today. 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