{"id":19695,"date":"2024-11-13T11:37:10","date_gmt":"2024-11-13T11:37:10","guid":{"rendered":"https:\/\/www.equalexperts.com\/?p=19695"},"modified":"2025-03-27T12:54:21","modified_gmt":"2025-03-27T12:54:21","slug":"how-accurate-is-accurate-enough-a-case-study-in-holding-ai-to-an-unrealistic-standard","status":"publish","type":"post","link":"https:\/\/www.equalexperts.com\/blog\/data-ai\/how-accurate-is-accurate-enough-a-case-study-in-holding-ai-to-an-unrealistic-standard\/","title":{"rendered":"How accurate is accurate enough? A case study in holding AI to an unrealistic standard"},"content":{"rendered":"<p>How accurate is accurate enough? If AI is able to process thousands of requests in seconds, plus cite the query and data used for human-verification, isn\u2019t that a momentous step forward?<\/p>\n<p>90% accuracy.<\/p>\n<p>It\u2019s a number that seems to hold almost magical significance in the human psyche.<\/p>\n<p>We as humans are very error-prone; our judgement, emotions, and vulnerabilities inject error into our judgement \u2013 and it\u2019s what makes us human. But are we holding machines to higher standards because they don\u2019t have emotions, or because they can do calculations much faster?<\/p>\n<p>You might argue that, if time and space weren\u2019t an issue and we were in control of our emotions, we\u2019d be as good as machines.<\/p>\n<p>This isn\u2019t just about numbers. It\u2019s about our deep-seated biases and the seemingly arbitrary standards we set. Why do we fixate on 90%? Is it our decimal-based thinking, or our need for near-perfection before we trust machines over humans?<\/p>\n<p>It\u2019s time to confront this paradox and rethink what \u201cgood enough\u201d really means in the age of AI.<\/p>\n<h2>Scenario<\/h2>\n<p>At the 2024 Enterprise Tech Leadership Summit I witnessed a moment that exposes a double standard in tech adoption.<\/p>\n<p>At the conference, one company showed an AI-powered audit tool designed to process thousands of due diligence documents and create a comprehensive audit trail, with the ability to ask deeper questions on the content. The tool\u2019s performance was impressive, boasting a 90% accuracy rate. However, despite this high level of precision, the business stakeholders were reluctant to sign off on its implementation.<\/p>\n<p>This prompted me to ask a crucial question: \u201cWhat percent accuracy are your human auditors achieving today?\u201d<\/p>\n<p>The response was telling. The presenters glanced at each other with a short pause \u2014 they didn\u2019t have a clear answer. This moment of realization highlighted a significant disconnect in how we evaluate AI versus human performance.<\/p>\n<h2>The double standard<\/h2>\n<p>This scenario unveils a potential double standard in how we approach AI adoption, in particular the KPIs for go\/no-go decisions:<\/p>\n<ul>\n<li aria-level=\"1\"><strong>Quantified AI performance:<\/strong> We demand precise metrics from AI systems, expecting near-perfect accuracy before we\u2019re willing to trust them.<\/li>\n<li aria-level=\"1\"><strong>Unquantified Human Performance:<\/strong> Paradoxically, we often lack concrete data on human performance in the same tasks, yet we implicitly trust human judgement.<\/li>\n<li aria-level=\"1\"><strong>Unrealistic Expectations:<\/strong> We may be setting the bar unrealistically high for AI, expecting a level of perfection we don\u2019t demand \u2014 or even measure \u2014 in human performance.<\/li>\n<\/ul>\n<h2>The implications: Unintended consequences of our AI skepticism<\/h2>\n<p>In a previous role, I ran an AI Lab where I was tasked to add scientific rigor around developing an innovative AI-powered text-to-SQL tool. This tool, in part, was to convert natural language questions into SQL queries, extract information from databases, and then use RAG to formulate human-readable answers.<\/p>\n<p>Our AI model achieved a repeatable accuracy of 82% leveraging the latest advancements in language models, but it was frowned upon as it didn\u2019t hit the expected 90\u201395% accuracy.<\/p>\n<p>When I presented the tool to potential clients, the response was often lukewarm. \u201cWhat about the 20% of queries the tool gets wrong?\u201d one CIO asked. \u201cWe can\u2019t risk making business decisions based on potentially incorrect data,\u201d another executive chimed in.<\/p>\n<p>The irony? When we dug deeper into how these companies currently handled database queries, we found that their processes were far from perfect. Many relied on a small team of data analysts who manually converted requests into SQL queries. These human experts, while skilled, were not infallible. They made errors in query construction and also produced incorrect results. Moreover, the process was slow, creating bottlenecks in data-driven decision making.<\/p>\n<p>Despite these advantages, the company was hesitant to adopt the AI solution, unable to get past \u201conly\u201d 80% accuracy.<\/p>\n<p>This scenario highlights several critical implications of our AI double standard:<\/p>\n<ul>\n<li aria-level=\"1\"><strong>Delayed innovation:<\/strong> The reluctance to adopt this AI system means that a potentially transformative tool for data democratization is sitting unused. How many insights are being missed, and how many decisions are being delayed while waiting for a level of perfection that even human experts can\u2019t achieve?<\/li>\n<li aria-level=\"1\"><strong>Missed opportunities for synergy:<\/strong> Instead of viewing AI as a replacement for human expertise, imagine the possibilities if it were used as a complementary tool. Data analysts equipped with this AI assistant could potentially achieve accuracy rates higher than either human or machine could alone, while dramatically increasing their productivity.<\/li>\n<li aria-level=\"1\"><strong>Erosion of trust in AI:<\/strong> Each time we reject an AI system that outperforms humans, we reinforce the narrative that AI isn\u2019t trustworthy. This creates a cycle of skepticism that can be hard to break, even as the technology continues to improve.<\/li>\n<li aria-level=\"1\"><strong>Overlooked human errors:<\/strong> Our laser focus on the AI\u2019s 20% error rate obscures the fact that humans are making errors at least as often, if not more. This oversight could lead to complacency about improving human performance and processes.<\/li>\n<li aria-level=\"1\"><strong>Ethical dilemmas:<\/strong> If we know that an AI system can provide faster, more consistent, and potentially more accurate results in data analysis, is there an ethical obligation to use it, especially when decisions based on this data could have significant impacts?<\/li>\n<\/ul>\n<p>So the question becomes a multitude of questions:<\/p>\n<p>How accurate is accurate enough? How do we hold AI to a higher standard? If it\u2019s able to process thousands of requests in seconds, plus cite the query and data used for human-verification, isn\u2019t that a momentous step forward?<\/p>\n<h2>Moving forward: A balanced approach<\/h2>\n<p>The path forward isn\u2019t about lowering our standards for AI. Rather, it\u2019s about applying the same rigorous, balanced evaluation to all solutions \u2014 human or artificial. Only then can we ensure that we\u2019re making decisions that truly serve our goals, whether in data analysis, business intelligence, or any other field touched by the promise of AI.<\/p>\n<p>To address this issue, I propose the following strategies:<\/p>\n<ul>\n<li aria-level=\"1\"><strong>Establish human baselines:<\/strong> Before implementing AI systems, organizations should invest in measuring and understanding the accuracy of their current human-driven processes.<\/li>\n<li aria-level=\"1\"><strong>Contextual evaluation:<\/strong> Evaluate AI performance not in isolation, but in comparison to current human performance in the same tasks.<\/li>\n<li aria-level=\"1\"><strong>Incremental adoption:<\/strong> Consider implementing AI systems alongside human workers, allowing for a gradual transition and ongoing comparison of performance.<\/li>\n<li aria-level=\"1\"><strong>Continuous Improvement:<\/strong> Focus on the potential for improvement over time rather than expecting perfection from the outset.<\/li>\n<li aria-level=\"1\"><strong>Holistic assessment:<\/strong> Look beyond just accuracy as the single go\/no-go metric for success. Consider factors like consistency, speed, scalability, and the ability to handle large volumes of data.<\/li>\n<\/ul>\n<h2>Final thoughts<\/h2>\n<p>As we stand at the crossroads of human expertise and AI, our journey reveals a crucial truth: the way we evaluate and adopt AI technologies is fundamentally shaping our future.<\/p>\n<p>The stories we\u2019ve explored are likely not isolated incidents. They are symptomatic of a broader challenge we face in the AI era: our struggle to reconcile the promise of AI with our deeply ingrained trust in human judgment.<\/p>\n<p>The next time you\u2019re evaluating an AI solution, I urge you to ask these questions:<\/p>\n<ul>\n<li aria-level=\"1\">Are we holding this technology to a fair and realistic standard?<\/li>\n<li aria-level=\"1\">Have we accurately assessed our current human-driven processes?<\/li>\n<li aria-level=\"1\">Could this AI, despite its imperfections, significantly improve our outcomes?<\/li>\n<li aria-level=\"1\">Are we missing opportunities for innovative human-AI collaboration?<\/li>\n<\/ul>\n<p>In the end, the true measure of our success in the AI era will not be in how well we\u2019ve preserved the status quo, but in how boldly and wisely we\u2019ve embraced the potential of human and AI working in concert.<\/p>\n<p>The future is not about choosing between human or AI \u2014 it\u2019s about reimagining what\u2019s possible when we leverage the strengths<span style=\"font-weight: 400;\"> of both.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>How accurate is accurate enough? If AI is able to process thousands of requests in seconds, plus cite the query and data used for human-verification, isn\u2019t that a momentous step forward? 90% accuracy. It\u2019s a number that seems to hold almost magical significance in the human psyche. We as humans are very error-prone; our judgement, [&hellip;]<\/p>\n","protected":false},"author":268,"featured_media":19696,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"categories":[806],"tags":[99,579,414],"location":[],"class_list":["post-19695","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-ai","tag-ai","tag-data-and-ai","tag-gen-ai"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.9 (Yoast SEO v25.9) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>A case study in holding AI to an unrealistic standard | Equal Experts<\/title>\n<meta name=\"description\" content=\"If AI can process thousands of requests in seconds, plus cite the query and data used for human-verification, shouldn&#039;t that be momentous step forward?\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.equalexperts.com\/blog\/data-ai\/how-accurate-is-accurate-enough-a-case-study-in-holding-ai-to-an-unrealistic-standard\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How accurate is accurate enough? A case study in holding AI to an unrealistic standard\" \/>\n<meta property=\"og:description\" content=\"If AI can process thousands of requests in seconds, plus cite the query and data used for human-verification, shouldn&#039;t that be momentous step forward?\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.equalexperts.com\/blog\/data-ai\/how-accurate-is-accurate-enough-a-case-study-in-holding-ai-to-an-unrealistic-standard\/\" \/>\n<meta property=\"og:site_name\" content=\"Equal Experts\" \/>\n<meta property=\"article:published_time\" content=\"2024-11-13T11:37:10+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-03-27T12:54:21+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.equalexperts.com\/wp-content\/uploads\/2024\/12\/How-accurate-is-accurate-enough.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"514\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Ryan Elmore\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@EqualExperts\" \/>\n<meta name=\"twitter:site\" content=\"@EqualExperts\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Ryan Elmore\" \/>\n\t<meta name=\"twitter:label2\" content=\"Estimated reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"6 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/www.equalexperts.com\/blog\/data-ai\/how-accurate-is-accurate-enough-a-case-study-in-holding-ai-to-an-unrealistic-standard\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.equalexperts.com\/blog\/data-ai\/how-accurate-is-accurate-enough-a-case-study-in-holding-ai-to-an-unrealistic-standard\/\"},\"author\":{\"name\":\"Ryan Elmore\",\"@id\":\"https:\/\/www.equalexperts.com\/#\/schema\/person\/1864268478732c19638877057f4789dc\"},\"headline\":\"How accurate is accurate enough? 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