{"id":13177,"date":"2026-08-26T10:02:51","date_gmt":"2026-08-26T09:02:51","guid":{"rendered":"https:\/\/rewirenow.com\/?p=13177"},"modified":"2026-08-26T10:06:50","modified_gmt":"2026-08-26T09:06:50","slug":"ai-capability-can-be-bought-reliability-has-to-be-built","status":"publish","type":"post","link":"https:\/\/rewirenow.com\/en\/resources\/blog\/ai-capability-can-be-bought-reliability-has-to-be-built\/","title":{"rendered":"AI capability can be bought; reliability has to be built"},"content":{"rendered":"\n\n<div class=\"at-spacer at-spacer--6a8ef9de2c59e\" style=\"background-color: transparent\">\n<style>\n\/* Ultrawide *\/\n.at-spacer--6a8ef9de2c59e {\n    height: 80px;\n}\n\n\/* Desktop *\/\n@media (max-width: 1920px) {\n    .at-spacer--6a8ef9de2c59e {\n        height: 80px;\n    }\n}\n\n\/* Laptop *\/\n@media (max-width: 1024px) {\n    .at-spacer--6a8ef9de2c59e {\n        height: 60px;\n    }\n}\n\n\/* Mobile *\/\n@media (max-width: 767px) {\n    .at-spacer--6a8ef9de2c59e {\n        height: 40px;\n    }\n}\n<\/style>\n<\/div>\n\n\n<section class=\"container-block   \"  style=\"\" >\r\n        <div class=\"block  container  container--default  width-under--mobile width-under--tablet width-under--laptop  wysiwyg\">\r\n        \n\n<p><em>Enterprise AI deployments often follow a recognisable pattern. A pilot performs well but the move from pilot to production stalls. A common explanation is that the model is not yet capable enough, and that the remedy is to wait for the next generation. Recent research suggests this explanation is incomplete. In a recent publication from Princeton University, \u201cTowards a Science of AI Agent Reliability\u201d (Link.) Sayash Kapoor, Arvind Narayanan and colleagues draw a distinction that is easy to overlook in procurement decisions: the difference between capability and reliability.<\/em><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n<div class=\"at-spacer at-spacer--6a8ef9de2c7a9\" style=\"background-color: transparent\">\n<style>\n\/* Ultrawide *\/\n.at-spacer--6a8ef9de2c7a9 {\n    height: 10px;\n}\n\n\/* Desktop *\/\n@media (max-width: 1920px) {\n    .at-spacer--6a8ef9de2c7a9 {\n        height: 10px;\n    }\n}\n\n\/* Laptop *\/\n@media (max-width: 1024px) {\n    .at-spacer--6a8ef9de2c7a9 {\n        height: 10px;\n    }\n}\n\n\/* Mobile *\/\n@media (max-width: 767px) {\n    .at-spacer--6a8ef9de2c7a9 {\n        height: 10px;\n    }\n}\n<\/style>\n<\/div>\n\n\n\n<h4 class=\"wp-block-heading\">Capability and reliability are not the same metric<\/h4>\n\n\n\n<p>Capability describes how often a system produces the correct result. Reliability describes the nature of its failures rather than their frequency. A system may be highly capable, correct in the majority of cases, and still be difficult to depend on, because its errors are inconsistent, hard to anticipate, or hard to reverse.<\/p>\n\n\n\n<p>To make reliability measurable, the paper borrows four properties from fields with a history of managing failure, notably aviation and nuclear engineering.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Consistency<\/strong> refers to producing the same output for the same input.<\/li>\n\n\n\n<li><strong>Robustness<\/strong> refers to performance under imperfect conditions and tool failures.<\/li>\n\n\n\n<li><strong>Calibration<\/strong> refers to a system signalling uncertainty rather than answering with unwarranted confidence.<\/li>\n\n\n\n<li><strong>Safety<\/strong> refers to failures being recoverable rather than catastrophic. Aviation, for reference, targets on the order of one catastrophic error per billion flight hours.<\/li>\n<\/ul>\n\n\n\n<p>The empirical results are the most relevant part for practitioners of enterprise AI. Across fourteen models over eighteen months, the authors report that <strong>capability (also referred to as \u2018accuracy\u2019 in the paper) improved rapidly while reliability improved only modestly<\/strong>. In their data, each additional point of accuracy was associated with a gain of roughly a fifth to a third of a point of reliability. Consistency was among the weakest dimensions, with the same request producing a correct outcome in an estimated thirty to seventy five percent of runs depending on the model. To put that in perspective: at 75% consistency, a process fed identical inputs five times running will return the same answer every time in just 32% of cases, assuming independence. Stretch it to ten runs and the figure falls to 7.5%.<\/p>\n\n\n\n<p>Depending on the Enterprise AI use case is, such variation may well be unacceptable. Yet it is rarely visible in a single demonstration.<\/p>\n\n\n<div class=\"at-spacer at-spacer--6a8ef9de2ca3d\" style=\"background-color: transparent\">\n<style>\n\/* Ultrawide *\/\n.at-spacer--6a8ef9de2ca3d {\n    height: 30px;\n}\n\n\/* Desktop *\/\n@media (max-width: 1920px) {\n    .at-spacer--6a8ef9de2ca3d {\n        height: 30px;\n    }\n}\n\n\/* Laptop *\/\n@media (max-width: 1024px) {\n    .at-spacer--6a8ef9de2ca3d {\n        height: 30px;\n    }\n}\n\n\/* Mobile *\/\n@media (max-width: 767px) {\n    .at-spacer--6a8ef9de2ca3d {\n        height: 30px;\n    }\n}\n<\/style>\n<\/div>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"875\" height=\"683\" data-src=\"https:\/\/rewirenow.com\/app\/uploads\/2026\/08\/image.png\" alt=\"\" class=\"wp-image-13178 lazyload\" data-srcset=\"https:\/\/rewirenow.com\/app\/uploads\/2026\/08\/image.png 875w, https:\/\/rewirenow.com\/app\/uploads\/2026\/08\/image-300x234.png 300w, https:\/\/rewirenow.com\/app\/uploads\/2026\/08\/image-768x599.png 768w, https:\/\/rewirenow.com\/app\/uploads\/2026\/08\/image-151x118.png 151w, https:\/\/rewirenow.com\/app\/uploads\/2026\/08\/image-77x60.png 77w\" data-sizes=\"auto, (max-width: 875px) 100vw, 875px\" src=\"data:image\/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\" style=\"--smush-placeholder-width: 875px; --smush-placeholder-aspect-ratio: 875\/683;\" \/><noscript><img loading=\"lazy\" decoding=\"async\" width=\"875\" height=\"683\" src=\"https:\/\/rewirenow.com\/app\/uploads\/2026\/08\/image.png\" alt=\"\" class=\"wp-image-13178\" srcset=\"https:\/\/rewirenow.com\/app\/uploads\/2026\/08\/image.png 875w, https:\/\/rewirenow.com\/app\/uploads\/2026\/08\/image-300x234.png 300w, https:\/\/rewirenow.com\/app\/uploads\/2026\/08\/image-768x599.png 768w, https:\/\/rewirenow.com\/app\/uploads\/2026\/08\/image-151x118.png 151w, https:\/\/rewirenow.com\/app\/uploads\/2026\/08\/image-77x60.png 77w\" sizes=\"auto, (max-width: 875px) 100vw, 875px\" \/><\/noscript><\/figure>\n\n\n\n<p>Figure 1. Figure from Kapoor, Narayanan et al., \u201cTowards a Science of AI Agent Reliability\u201d (2026, arXiv:2602.16666), licensed under CC BY 4.0. The shallow slopes are the key point: reliability rises only about 0.18 to 0.33 points \u2013 depending on the benchmark &#8211; per point of accuracy. <a class=\"\" href=\"https:\/\/arxiv.org\/pdf\/2503.14499\"><em>https:\/\/arxiv.org\/abs\/2602.16666<\/em><\/a><\/p>\n\n\n<div class=\"at-spacer at-spacer--6a8ef9de2d7f0\" style=\"background-color: transparent\">\n<style>\n\/* Ultrawide *\/\n.at-spacer--6a8ef9de2d7f0 {\n    height: 30px;\n}\n\n\/* Desktop *\/\n@media (max-width: 1920px) {\n    .at-spacer--6a8ef9de2d7f0 {\n        height: 30px;\n    }\n}\n\n\/* Laptop *\/\n@media (max-width: 1024px) {\n    .at-spacer--6a8ef9de2d7f0 {\n        height: 30px;\n    }\n}\n\n\/* Mobile *\/\n@media (max-width: 767px) {\n    .at-spacer--6a8ef9de2d7f0 {\n        height: 30px;\n    }\n}\n<\/style>\n<\/div>\n\n\n\n<h4 class=\"wp-block-heading\">Why reliability, rather than capability, may govern the business case<\/h4>\n\n\n\n<p>The value of an agent depends heavily on how much supervision it requires. Larger gains appear to lie in work that can run with limited oversight, and a system is usually granted that degree of autonomy only once its behaviour is consistent enough to be trusted over time. <\/p>\n\n\n\n<p>On this reading, an agent that completes a task correctly nine times out of ten but fails unpredictably in the tenth case may still require review of all ten. This gapis one of the reasons why many pilots perform well in demonstration yet comparatively few reach production at scale.<\/p>\n\n\n\n<p>It also helps explain why waiting for a more capable model often disappoints. Capability and reliability do not appear to advance at the same rate. A new model is likely to be more capable, but the available evidence gives little reason to expect a proportional gain in reliability.<\/p>\n\n\n<div class=\"at-spacer at-spacer--6a8ef9de2d931\" style=\"background-color: transparent\">\n<style>\n\/* Ultrawide *\/\n.at-spacer--6a8ef9de2d931 {\n    height: 30px;\n}\n\n\/* Desktop *\/\n@media (max-width: 1920px) {\n    .at-spacer--6a8ef9de2d931 {\n        height: 30px;\n    }\n}\n\n\/* Laptop *\/\n@media (max-width: 1024px) {\n    .at-spacer--6a8ef9de2d931 {\n        height: 30px;\n    }\n}\n\n\/* Mobile *\/\n@media (max-width: 767px) {\n    .at-spacer--6a8ef9de2d931 {\n        height: 30px;\n    }\n}\n<\/style>\n<\/div>\n\n\n\n<h4 class=\"wp-block-heading\">How reliability might be engineered<\/h4>\n\n\n\n<p>The paper&#8217;s framing points toward reliability as something built around a model rather than obtained from it. This reflects our own experience on client projects..<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Measure reliability as much as accuracy<\/strong>. A single successful demonstration provides limited evidence. Running the same task repeatedly and examining behaviour across consistency, robustness, calibration and safety gives a more representative picture than an average score. The dimensions hidden by a one-off run are often the ones that later surface in production.<\/li>\n\n\n\n<li><strong>Constrain the scope<\/strong>. Reliability is generally easier to achieve on a narrow task with clearly defined success criteria than on an open-ended one. Broad scope tends to be where failures become unpredictable.<\/li>\n\n\n\n<li><strong>Decompose long tasks<\/strong>. Agentic systems appear to degrade abruptly rather than gradually as tasks grow longer and larger. Breaking a long run into shorter, verifiable steps helps keep the system within the range where it remains dependable.<\/li>\n\n\n\n<li><strong>Add verification and fallbacks<\/strong>. Independent checks on outputs, guardrails and graceful degradation can convert an unnoticed failure into a detected one. Human review can then be concentrated where an error would be costly, rather than applied uniformly.<\/li>\n\n\n\n<li><strong>Evaluate procurement on reliability<\/strong>. Vendor comparisons that reward benchmark capability alone may select for the wrong property. Consistency and calibration deserve explicit weight.<\/li>\n<\/ul>\n\n\n\n<p>This approach is not new. It reflects established practice in safety critical industries, where the objective is not a flawless component but a system that stays dependable when a component is not. Organisations that run regulated production environments, trading systems or industrial plants will recognise the logic, since it is broadly how those environments are already managed.<\/p>\n\n\n\n<p>Public attention will continue to track capability, and capability is likely to keep improving. The harder question for industrial adoption may be reliability. On current evidence, the advantage lies less with the organisation that holds the most capable model, and more with the one that can turn a capable model into a system dependable enough to run with limited supervision. Capability can be bought; reliability has to be built.<\/p>\n\n\n<div class=\"at-spacer at-spacer--6a8ef9de2db2e\" style=\"background-color: transparent\">\n<style>\n\/* Ultrawide *\/\n.at-spacer--6a8ef9de2db2e {\n    height: 30px;\n}\n\n\/* Desktop *\/\n@media (max-width: 1920px) {\n    .at-spacer--6a8ef9de2db2e {\n        height: 30px;\n    }\n}\n\n\/* Laptop *\/\n@media (max-width: 1024px) {\n    .at-spacer--6a8ef9de2db2e {\n        height: 30px;\n    }\n}\n\n\/* Mobile *\/\n@media (max-width: 767px) {\n    .at-spacer--6a8ef9de2db2e {\n        height: 30px;\n    }\n}\n<\/style>\n<\/div>\n\n\n\n<h5 class=\"wp-block-heading\">About the author<\/h5>\n\n\n\n<p><strong>Dr. Philipp Diesinger\u00a0<\/strong>is a data science executive with over 15 years of global experience driving AI-powered transformation across industries. He has led high-impact initiatives at Boehringer Ingelheim, BCG, and Rewire, delivering measurable value through advanced analytics, GenAI, and data strategy at scale.<\/p>\n\n\n<div class=\"at-spacer at-spacer--6a8ef9de2dc0b\" style=\"background-color: transparent\">\n<style>\n\/* Ultrawide *\/\n.at-spacer--6a8ef9de2dc0b {\n    height: 30px;\n}\n\n\/* Desktop *\/\n@media (max-width: 1920px) {\n    .at-spacer--6a8ef9de2dc0b {\n        height: 30px;\n    }\n}\n\n\/* Laptop *\/\n@media (max-width: 1024px) {\n    .at-spacer--6a8ef9de2dc0b {\n        height: 30px;\n    }\n}\n\n\/* Mobile *\/\n@media (max-width: 767px) {\n    .at-spacer--6a8ef9de2dc0b {\n        height: 30px;\n    }\n}\n<\/style>\n<\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n<div class=\"at-spacer at-spacer--6a8ef9de2dcb2\" style=\"background-color: transparent\">\n<style>\n\/* Ultrawide *\/\n.at-spacer--6a8ef9de2dcb2 {\n    height: 30px;\n}\n\n\/* Desktop *\/\n@media (max-width: 1920px) {\n    .at-spacer--6a8ef9de2dcb2 {\n        height: 30px;\n    }\n}\n\n\/* Laptop *\/\n@media (max-width: 1024px) {\n    .at-spacer--6a8ef9de2dcb2 {\n        height: 30px;\n    }\n}\n\n\/* Mobile *\/\n@media (max-width: 767px) {\n    .at-spacer--6a8ef9de2dcb2 {\n        height: 30px;\n    }\n}\n<\/style>\n<\/div>\n\n\n\n<section class=\"cta-box-block  js-cta-box  block\" >\n    <div class=\"container\">\n        <div class=\"cta-box\" style=\"background: linear-gradient(25deg, rgba(255,244,0, 0.13), rgba(37,206,206, 0.13));\">\n                            <figure class=\"cta-box__image\">\n                        <span class=\"cta-box__img\" data-lazy style=\"background-color: #f1f1f1;\">\r\n        \r\n                    <img data-lazy-full=\"https:\/\/rewirenow.com\/app\/uploads\/2024\/02\/data-foundations-bg-980x600.jpg\" alt=\"\" data-is-lazy>\r\n            <\/span>\r\n\r\n                <\/figure>\n                        <div class=\"cta-box__content\">\n                                    <h4 class=\"cta-box__title\">Agents reward organisations that ask harder questions first.<\/h4>\n                                                    <p class=\"cta-box__description\">The organisations getting compounding value from agentic AI share one trait: they invested in getting the foundations right before scaling. <br \/>\r\n<br \/>\r\nWe&#8217;ve built agent systems across financial services, energy, telecom, and public sector, from early proofs of concept to production systems running at scale. We&#8217;ve seen what separates the deployments that deliver from the ones that don&#8217;t.<\/p>\n                                                    <a class=\"cta-box__link btn btn--next\" href=\"https:\/\/rewirenow.com\/en\/contact\/\" target=\"_blank\" aria-label=\"Let's find the answers for your situation\">Let&#8217;s find the answers for your situation<\/a>\n                            <\/div>\n        <\/div>\n    <\/div>\n<\/section>\n\r\n    <\/div>\r\n<\/section>","protected":false},"excerpt":{"rendered":"<p>AI capability is growing rapidly but reliability is not. The latter is critical for industrialized systems to run at scale. Here&#8217;s how to build it. <\/p>\n","protected":false},"author":4,"featured_media":13182,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[178,149,174],"tags":[146,152,147,138],"class_list":["post-13177","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-agentic-ai-genai","category-data-and-ai-strategy","category-engineering","tag-ai","tag-ai-agent","tag-artificial-intelligence","tag-data-ai-strategy"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v22.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>AI capability can be bought; reliability has to be built | Rewire | Data &amp; AI Consultancy<\/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:\/\/rewirenow.com\/en\/resources\/blog\/ai-capability-can-be-bought-reliability-has-to-be-built\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI capability can be bought; reliability has to be built | Rewire | Data &amp; AI Consultancy\" \/>\n<meta property=\"og:description\" content=\"AI capability is growing rapidly but reliability is not. 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