{"id":24902,"date":"2025-09-23T16:56:12","date_gmt":"2025-09-23T14:56:12","guid":{"rendered":"https:\/\/blinqx.ai\/uncategorized\/ai-should-not-gamble-the-lesson-for-b2b-saas"},"modified":"2025-11-11T10:44:40","modified_gmt":"2025-11-11T09:44:40","slug":"ai-should-not-gamble-the-lesson-for-b2b-saas","status":"publish","type":"post","link":"https:\/\/blinqx.ai\/en\/blog-en\/ai-should-not-gamble-the-lesson-for-b2b-saas","title":{"rendered":"AI should not gamble: the lesson for B2B SaaS"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><strong>During my college days, I had a set strategy on exams. Some of the questions I knew for sure. The rest? I guessed. With a 25% chance, you could get a point.    <\/strong><\/p>\n\n<p class=\"wp-block-paragraph\">That worked fine in a lecture hall. But in the world of enterprise AI and B2B SaaS, gaming is disastrous. Yet Large Language Models (LLMs) do just that. When they don&#8217;t know the answer, they give an answer anyway. In science, this is called a <em>hallucination<\/em>: a seemingly convincing but factually wrong result.    <\/p>\n\n<p class=\"wp-block-paragraph\">For an exam, maybe smart. For an AI system making decisions in legal, financial or operational processes, it is unacceptable. <\/p>\n\n<h2 class=\"wp-block-heading\">Why LLMs hallucinate<\/h2>\n\n<p class=\"wp-block-paragraph\">I recently read research by OpenAI in which they explain this phenomenon. The comparison to an exam is apt: LLMs function like a student who does not receive punishment for wrong answers. <\/p>\n\n<p class=\"wp-block-paragraph\">The training process rewards certainty, not honesty. &#8220;I don&#8217;t know&#8221; is punished. Result: models learn to prefer to always say something rather than honestly show their uncertainty.  <\/p>\n\n<p class=\"wp-block-paragraph\">This explains why benchmark scores are often impressive. Models score high on multiple-choice tests like SWE-bench, but do so partly by guessing. Just like I did on exams back in the day. Interesting for scientists; risky for companies.   <\/p>\n\n<h2 class=\"wp-block-heading\">The price of AI hallucinations in B2B SaaS<\/h2>\n\n<p class=\"wp-block-paragraph\">For a consumer asking for a nice recipe, a wrong answer is harmless. For an insurer, lawyer or accountant, it is different. A misanalyzed claim can lead to erroneous payouts. A legal AI agent who incorrectly summarizes a document can harm a client&#8217;s litigation position. A miscalculation in accounting can cause tax risks.In B2B SaaS, it&#8217;s all about reliable AI. An AI that gambles undermines client trust. And in B2B SaaS, without trust there is no adoption.      <\/p>\n\n<h2 class=\"wp-block-heading\">Our approach at Blinqx: reliable AI from the start<\/h2>\n\n<p class=\"wp-block-paragraph\">At Blinqx, we have taken this problem seriously from the beginning. Within our Qore\/AI platform, we build reliability into our models. That means:  <\/p>\n\n<ul class=\"wp-block-list\">\n<li><strong>Honestly stating when something is not certain<\/strong>. Our models can explicitly report back that they do not have enough certainty. <\/li>\n\n\n\n<li><strong>Fallback mechanisms<\/strong>. If the knowledge is lacking, an additional check can be requested, such as via retrieval or human validation. <\/li>\n\n\n\n<li><strong>Central guard rails.<\/strong>  Security mechanisms are built into Qore\/AI by default, keeping agents we build predictable and controllable.<\/li>\n<\/ul>\n\n<p class=\"wp-block-paragraph\">These principles make AI usable and scalable in our industries, where erroneous outputs can have a direct impact on users.<\/p>\n\n<h2 class=\"wp-block-heading\">OpenAI and the need for reliable AI<\/h2>\n\n<p class=\"wp-block-paragraph\">OpenAI&#8217;s recent publication on reducing AI hallucinations shows that this is not a nice-to-have, but a necessary step in the evolution of agentic AI.<\/p>\n\n<p class=\"wp-block-paragraph\">By rewarding models differently &#8211; not only on correctness, but also on honesty &#8211; the frequency of hallucinations can be drastically reduced. This confirms the approach we already take at Blinqx: rather an AI that says &#8220;I don&#8217;t know,&#8221; than one that returns something wrong with conviction. Because it likes to give an answer.  <\/p>\n\n<h2 class=\"wp-block-heading\">From slot machine to digital colleague<\/h2>\n\n<p class=\"wp-block-paragraph\">The transition from generative AI to agentic AI makes this issue even more urgent. Agents don&#8217;t just work reactively; they make decisions and execute actions independently. If such an agent guesses, the impact can be much greater than one wrong answer: entire processes can be derailed.  <\/p>\n\n<p class=\"wp-block-paragraph\">That&#8217;s why I see it as a fundamental responsibility of any CAIO or CTO: don&#8217;t build AI that allows gambling. Build reliable AI that knows what it knows, and honestly identifies what it doesn&#8217;t know. Only then can your agent live up to the role of trusted digital colleague.  <\/p>\n\n<p class=\"wp-block-paragraph\">My own gambling strategy worked fine in college classrooms. But for B2B SaaS, one thing is clear: gambling is not an option in our customers&#8217; practices. <\/p>\n\n<p class=\"wp-block-paragraph\"><strong><em>Check out the latest Blinqx developments around AI <a href=\"https:\/\/blinqx.ai\/ai\">here<\/a><\/em><\/strong>.<\/p>\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-wide\"\/>\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n<h2 class=\"wp-block-heading\">Frequently asked questions about hallucination by LLMs<\/h2>\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n<h3 class=\"wp-block-heading\">What do we mean by &#8220;AI guessing&#8221;?<\/h3>\n\n<p class=\"wp-block-paragraph\">Many Large Language Models (LLMs) give an answer anyway, even if they are not sure. This is called a <g id=\"gid_0\">hallucination<\/g>: a seemingly convincing but factually wrong answer. For consumer applications, this can be harmless, but in enterprise SaaS it is risky.  <\/p>\n\n<h3 class=\"wp-block-heading\">Why do LLMs hallucinate in the first place?<\/h3>\n\n<p class=\"wp-block-paragraph\">LLMs&#8217; training process rewards certainty, not honesty. &#8220;I don&#8217;t know&#8221; is punished. As a result, models always learn to say something, even when in doubt. Benchmark scores seem impressive, but often include &#8220;guesswork.&#8221;   <\/p>\n\n<h3 class=\"wp-block-heading\">How does Blinqx ensure reliable AI solutions?<\/h3>\n\n<p class=\"wp-block-paragraph\">Our <strong>Qore\/AI platform<\/strong> prevents gaming by:<br\/><strong>Honesty<\/strong>: AI indicates when it does not know something<br\/><strong>Checks &amp; fallback:<\/strong> additional validation via retrieval or experts<br\/><strong>Guardrails<\/strong>: built-in safety mechanisms for predictable output<\/p>\n","protected":false},"excerpt":{"rendered":"<p>During my college days, I had a set strategy on exams. Some of the questions I knew for sure. The rest? I guessed. With a 25% chance, you could get a point. That worked fine in a lecture hall. But in the world of enterprise AI and B2B SaaS, gaming is disastrous. Yet Large Language [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":24016,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","content-type":"","hr_contact_first_name":"","hr_contact_image":"","hr_whatsapp_url":"","hr_phone_number":"","current_post_id":"24902","footnotes":"","_members_access_role":[],"_members_access_error":"","_links_to":"","_links_to_target":""},"categories":[126,118],"tags":[],"post_branche":[232],"class_list":["post-24902","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog-en","category-industrie-inzicht","post_branche-tech-ai-en"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>AI should not gamble: the lesson for B2B SaaS - Blinqx<\/title>\n<meta name=\"description\" content=\"Why AI hallucinations are disastrous for B2B SaaS. 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