Why AI Bills Keep Exploding and AI Chatbots Keep Stalling: eGain Says It’s the Same Problem
Two new whitepapers argue that neither failure is about how smart the AI is, and that a bigger model fixes neither
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SUNNYVALE, Calif., Sept. 29, 2026 (GLOBE NEWSWIRE) — As thousands of AI builders and business leaders gather at The AI Conference in San Francisco, eGain Corporation (NASDAQ: EGAN) published two whitepapers on the two problems most likely to stop a corporate AI project, and argues both trace back to the same overlooked cause: companies never organized the information their AI depends on.
The first paper, Ending Runaway Token Costs in Enterprise AI, addresses a complaint moving from software teams into customer service and back-office operations: AI bills that grow faster than expected and are nearly impossible to forecast. Companies pay for AI by the unit of work it does, and eGain’s argument is that most of that work is wasted. Asked a simple question, the system reads through a sprawl of company documents from scratch, every single time, because no one ever organized that material or mapped how it fits together. eGain likens it to sending a new hire into an unlabeled warehouse to find one box, then sending them back in for the next box, and the next. A second source of waste is using AI to handle work that is really just a rule. Calculating a late fee or checking whether someone qualifies for a refund does not require judgment, and asking an AI to reason it out fresh each time is an expensive way to do arithmetic. eGain’s answer is to organize company knowledge into a single trusted, mapped source so the AI goes straight to what it needs, and to route routine decisions to ordinary software. The company is confident enough in the savings that it prices many contracts on results rather than usage, absorbing the risk of the bill itself.
The second paper, The Trust Gap in AI Self-Service, explains why so many customer-facing AI projects look impressive in a demo and stall before going live. When a human service rep uses AI, the rep catches the mistakes. When the customer talks to the AI directly, no one does. That makes the industry’s favorite scorecard misleading. A system that is 95% accurate is not defined by the 95%. It is defined by what the other 5% looks like. An AI that says “I’m not certain, let me get you a person” has failed safely. An AI that states the wrong thing about a customer’s mortgage, confidently, with the bank’s name on it, has not. The paper is also pointed about a popular technical shortcut: letting the AI decide in the moment which company system to reach into and what to do there. eGain argues that approach is not dependable enough for live customer use, because there is no expert standing by to notice that the wrong account was charged.
“A runaway AI bill almost never means the model is too small. It means nobody told it where to look,” said Ashu Roy, CEO of eGain. “And once a customer is talking to the AI directly, there is no one left to catch a mistake before it lands. Getting the underlying information right is not a feature. It is the whole job.”
Availability: Both whitepapers are at eGain.com/resources. Journalists and analysts at The AI Conference can request advance copies and executive briefings at press@egain.com.
About eGain
eGain is a leading provider of AI-powered knowledge management and customer experience automation solutions and was named a Leader in the Gartner inaugural Magic Quadrant for Customer Service Knowledge Management Systems. With over 25 years of experience in knowledge management, eGain helps enterprises unify siloed content, automate trusted knowledge workflows, and deliver measurable AI-ROI through proven frameworks and methods. Global 2000 companies across industries rely on eGain to transform customer service, improve employee productivity, reduce costs, and accelerate AI adoption. Visit www.eGain.com for more info.
Gartner Disclaimer
Gartner, Magic Quadrant for Customer Service Knowledge Management Systems, Pri Rathnayake, Jennifer MacIntosh, Patrick Quinlan, Drew Kraus, 16 July 2026.
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