Real stories about AI in analytics: the expensive failures, the architectural lessons, and why an answer you can't verify isn't an answer you can act on.
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Every week, AI fails somewhere expensive. A chatbot invents a refund policy. A consultant submits a report citing academics who don't exist. A bank reverses a $40m AI cost-cutting plan after the agents could not flag when they were wrong. We write about each one for a reason: the same failure modes are reshaping how analytics tools should be built. These are the lessons we apply to Askalytics, and the lessons we think every marketing leader, founder, and agency should pay attention to before they buy their next AI tool.
Seventeen articles: stories on AI safety, data quality and analytics disasters, each one a case for why verified AI matters, plus how Askalytics checks its own answers.
Page views counted twice, goals tied to pages that no longer exist, ad clicks that never arrive. What each one costs, and how to check your own GA4.
Read article →Ask your website data a question in plain English and get an answer from your own numbers, checked against your data before you see it. Open to everyone from October 1, with a free 14-day trial.
Read article →Built-in checks for the breaks that cost money, AI findings above a 30% threshold you control, and a finding that tells you what changed, how much, how serious, and what to do next.
Read article →Type a question the way you would say it. The answer comes from your own data, states its date window, names its sources, and shows a check of every figure.
Read article →Seven rules the product enforces: answers from your own data, every figure checked, a receipt for every number, a stated date window, a second AI on every insight, measurement checks, and honest limits.
Read article →DeepMind's cyclone model beat every other model on Hurricane Erin's first three days. Forecasters still ran it beside the old tools for a season and measured it against real storms. That is how AI should earn trust.
Read article →Commonwealth Bank cut 45 roles on a claim that its AI voice bot was reducing calls. Staff said the opposite, and the bank reversed. The lesson is the number nobody checked.
Read article →EY withdrew a 44-page report when a small team found most of its citations were AI-invented, including a McKinsey study that never existed. Here is the analytics lesson.
Read article →An AI coding agent guessed at a fix and wiped a startup's production database and its backups in nine seconds. Here is why analytics AI should never run the query itself.
Read article →Air Canada lost in court when its chatbot invented a bereavement-fare policy. The same failure mode haunts AI analytics, which is why every number you read should trace back to a real source.
Read article →Over 600 lawyers have been sanctioned for citing AI-invented case law. The same architectural flaw breaks AI analytics tools, and there's a known fix.
Read article →Deloitte refunded Australia for a $290k report that cited fake academics and invented court quotes. The pattern is a warning to every AI analytics buyer.
Read article →Google lost $100B in market cap when its AI demo confidently stated a fact that wasn't true. A second AI on a different provider, checking the first, would have caught it.
Read article →Zillow's home-buying AI overpaid for 7,000 homes and lost half a billion dollars. The problem wasn't the model. It was trusting a black box with reporting.
Read article →MIT's 2025 NANDA report found 95% of enterprise GenAI pilots delivered no ROI. The 5% that worked had one thing in common, and it isn't model size.
Read article →47% of marketers encounter AI errors weekly. 36% admit hallucinated content has gone live. Here's the architectural difference between drift and accuracy.
Read article →Klarna's CEO publicly admitted the AI cost-cutting went too far. The lesson for analytics: knowing when AI is wrong is more valuable than what it produces.
Read article →The Bloom & Measure demo property is loaded by default on every free trial account.
The Air Canada chatbot, the Deloitte report, the Google Bard demo: these aren't isolated incidents. They share an architecture: a language model generating fluent text with no requirement that each claim be traceable back to a real source. Once you see the pattern, you see it everywhere.
Most AI analytics products run on the same architecture as the chatbots and report-writers that have already failed publicly. We write about the failures because they're the strongest case for building analytics differently: the AI queries your own data, every number traces back to its source query, and a Confidence Check has a second AI verify every insight.
You don't have to take our word for it. The case law, the consulting refunds, the market-cap losses are public record. Understand the failure modes before you sign a contract.
Because the same failure modes show up in AI analytics tools. A chatbot that invents a refund policy and an analytics tool that invents a number share one root cause: a language model generating fluent text with nothing forcing each claim to trace back to a real source. We study the public failures so we can build against them.
Two layers. First, the AI runs real queries against your own data, so every figure traces back to the query that produced it rather than the model's imagination. Then a Confidence Check has a second AI on a different provider audit every insight before you see it. See how that compares to other tools.
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