blog post<\/a>, Being \u201cConfidently Wrong\u201d Is Holding AI Back, Gopal describes what he calls the \u201cverification tax.\u201d<\/p>\n\u201cI don\u2019t know when I might get an incorrect response from my AI. So I have to forensically check every response.\u201d<\/p>\n
This tax explains much of what MIT labeled as the GenAI Divide. Enterprises eagerly launch pilots, but employees end up spending so much time double-checking outputs that the promised efficiencies never materialize.<\/p>\n
It\u2019s not that generative AI lacks raw horsepower \u2014 the models can be dazzling. It\u2019s that their confidence is uncalibrated. In regulated or high-stakes industries, one bad answer can outweigh ten good ones. As Gopal puts it: \u201cFor serious work, one high-confidence miss costs more credibility than ten successes earn.\u201d<\/p>\n
The Learning Gap<\/p>\n
MIT\u2019s researchers framed the same issue differently. They found that most enterprise AI tools don\u2019t retain feedback, adapt to workflows, or improve over time. Without those qualities, they stall.<\/p>\n
Gopal agrees. \u201cWithout high-quality uncertainty information, I don\u2019t know whether a result is wrong because of ambiguity, missing context, stale data, or a model mistake. If I don\u2019t know why it\u2019s wrong, I\u2019m not invested in making it successful.\u201d<\/p>\n
That insight matters because it reframes the entire conversation. If AI isn\u2019t failing due to lack of capability, but because it hasn\u2019t been designed to communicate its limits and learn from corrections, then the fix is less about building bigger models \u2014 and more about building humbler ones.<\/p>\n
How PromptQL Solves It<\/p>\n
PromptQL has built its entire platform around solving this exact problem \u2014 what Gopal calls the difference between being \u201cconfidently wrong\u201d and \u201ctentatively right.\u201d<\/p>\n
Instead of presenting outputs as gospel, PromptQL calibrates confidence at the response level:<\/p>\n
Quantifies uncertainty. Every answer comes with a confidence score. If the system is unsure, it abstains \u2014 effectively saying \u201cI don\u2019t know.
\n Surfaces context gaps. Rather than hiding uncertainty, the system flags why an answer may be unreliable: missing data, ambiguity, or lack of context.
\n Builds an accuracy flywheel. Each abstention or correction becomes training fuel. PromptQL captures those signals, letting the system improve continuously \u2014 closing the \u201clearning gap\u201d MIT identified as the number one cause of pilot failure.
\n Integrates into workflows. Instead of sitting in a chatbox, PromptQL embeds directly into enterprise processes like contracts, engineering, or procurement, so uncertainty flags and corrections appear exactly where the work is happening.<\/p>\n
\u201cThe starting point of this loop is if an AI system could tell the user when it\u2019s not certain about its accuracy in a concrete and native way,\u201d Gopal writes. That loop \u2014 abstain, get corrected, learn \u2014 is what he calls the accuracy flywheel. \u201cWe don\u2019t need perfection; we need a loop that tightens.\u201d<\/p>\n
Tentatively Right Beats Confidently Wrong<\/p>\n
This humility-first approach has led to adoption in some of the most skeptical corners of the enterprise market. While 95% of pilots stall, PromptQL is closing seven- and eight-figure contracts with Fortune 500s, government agencies, and regulated industries \u2014 the exact places MIT says AI has struggled to gain traction.<\/p>\n
The company is living proof that enterprise AI is not failing. The wrong kind of enterprise AI is.<\/p>\n
As Gopal puts it: \u201cNo amount of solving any other problem \u2014 integration, data readiness, organizational readiness \u2014 will change the fact that AI\u2019s tendency to be confidently wrong keeps it out of real-world use cases.\u201d<\/p>\n
A Different Conclusion<\/p>\n
The takeaway, then, is not that AI is doomed to fail. It\u2019s that enterprises must demand a different kind of AI: one that is transparent about its uncertainty, tightly integrated into workflows, and capable of improving with every interaction.<\/p>\n
The MIT report is right to highlight the GenAI Divide. But if we only focus on the 95% that failed, we miss the 5% that are actually scaling \u2014 and why.<\/p>\n
The companies that build and adopt AI that admits when it doesn\u2019t know are quietly rewriting the story. PromptQL is one of them.<\/p>\n
And if their traction holds, the conclusion isn\u2019t that enterprise AI is a bubble. It\u2019s that a small handful of companies have already figured out how to burst it.<\/p>\n","protected":false},"excerpt":{"rendered":"Young Happy Businesswoman Using Laptop Computer in Modern Office with Colleagues. Stylish Beautiful Financial Advisor Working. VFX Hologram…\n","protected":false},"author":2,"featured_media":88861,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[554,733,4308,86,56,54,55],"class_list":["post-88860","post","type-post","status-publish","format-standard","has-post-thumbnail","category-artificial-intelligence","tag-ai","tag-artificial-intelligence","tag-artificialintelligence","tag-technology","tag-uk","tag-united-kingdom","tag-unitedkingdom"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/posts\/88860","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/comments?post=88860"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/posts\/88860\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/media\/88861"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/media?parent=88860"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/categories?post=88860"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/tags?post=88860"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
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