[{"data":1,"prerenderedAt":1240},["ShallowReactive",2],{"site-nav-content":3,"hiring-banner-content":177,"blog:\u002Fblog\u002Fai-washing-and-what-regulators-already-ask":189,"blog-index-copy":727,"blog:\u002Fblog\u002Fai-washing-and-what-regulators-already-ask:surround":748,"site-cta-content":1221},{"header":4,"productNav":9,"nav":42,"footer":61,"askAI":132,"id":161,"title":162,"archived":163,"authors":164,"badge":164,"body":165,"date":164,"definedTerm":164,"department":164,"description":169,"extension":172,"eyebrow":164,"faqHeader":164,"faqs":164,"footerBand":164,"headline":164,"image":164,"industry":164,"jobType":164,"listed":131,"location":164,"navigation":131,"openRoles":164,"pageLayout":164,"path":173,"relatedHeading":164,"seo":174,"series":164,"sitemap":163,"status":164,"stem":175,"subhead":164,"tags":164,"video":164,"whyJoin":164,"workplaceType":164,"__hash__":176},{"productLabel":5,"loginLabel":6,"contactLabel":7,"contactSalesLabel":8},"Product","Log in","Contact","Get started for free",[10,14,18,22,26,30,34,38],{"label":11,"to":12,"description":13},"Overview","\u002Foverview","Seven layers. One closed loop.",{"label":15,"to":16,"description":17},"Conflux","\u002Fproduct\u002Fconflux","Where your team, workstreams, and agents meet.",{"label":19,"to":20,"description":21},"Agent Teams","\u002Fproduct\u002Fagent-teams","Specialist teams - governed from day one.",{"label":23,"to":24,"description":25},"Lifecycle Graph","\u002Fproduct\u002Flifecycle-graph","Intelligence that compounds across every interaction.",{"label":27,"to":28,"description":29},"Company Wiki","\u002Fproduct\u002Fwiki","Playbooks and policies where expertise stays.",{"label":31,"to":32,"description":33},"Workstreams","\u002Fproduct\u002Fworkstreams","From brief to signed-off deliverable on one canvas.",{"label":35,"to":36,"description":37},"Perception Console","\u002Fproduct\u002Fperception","Ask your whole business in plain English.",{"label":39,"to":40,"description":41},"Governance","\u002Fproduct\u002Fgovernance","Frontier AI you can actually sign off on.",[43,46,49,52,55,58],{"label":44,"to":45},"Models","\u002Fmodels",{"label":47,"to":48},"Pricing","\u002Fpricing",{"label":50,"to":51},"Integrations","\u002Fintegrations",{"label":53,"to":54},"Security","\u002Fsecurity",{"label":56,"to":57},"Partners","\u002Fpartners",{"label":59,"to":60},"Insights","\u002Fblog",{"productHeading":5,"companyHeading":62,"resourcesHeading":63,"legalHeading":64,"docsLabel":65,"docsUrl":66,"statementLines":67,"copyright":70,"companyLinks":71,"resourcesLinks":86,"legalLinks":102,"socialLinks":109,"bottomLinks":119},"Company","Resources","Legal","Docs","https:\u002F\u002Fdocs.gonimbus.ai",[68,69],"Stop training someone else's model.","Control your AI.","© 2026 Nimbus Intelligence, Inc. All rights reserved.",[72,73,74,75,76,78,81,84],{"label":47,"to":48},{"label":50,"to":51},{"label":53,"to":54},{"label":59,"to":60},{"label":77,"to":57},"Partner Program",{"label":79,"to":80},"Careers","\u002Fcareers",{"label":82,"to":83},"System status","\u002Fstatus",{"label":7,"to":85},"\u002Fcontact",[87,90,93,96,99],{"label":88,"to":89},"Glossary","\u002Fglossary",{"label":91,"to":92},"Compare","\u002Fcompare",{"label":94,"to":95},"Evaluate","\u002Fevaluate",{"label":97,"to":98},"Problems","\u002Fproblems",{"label":100,"to":101},"Use cases","\u002Fuse-cases",[103,106],{"label":104,"to":105},"Terms of Service","\u002Fterms",{"label":107,"to":108},"Privacy Policy","\u002Fprivacy",[110,113,116],{"label":111,"href":112},"LinkedIn","https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Fgonimbusai\u002F",{"label":114,"href":115},"X","https:\u002F\u002Fx.com\u002Fgonimbusai",{"label":117,"href":118},"Instagram","https:\u002F\u002Fwww.instagram.com\u002Fgonimbus_ai\u002F",[120,122,124,127,128],{"label":121,"to":105},"Terms",{"label":123,"to":108},"Privacy",{"label":125,"to":126},"Compliance","\u002Fcompliance",{"label":82,"to":83},{"label":129,"to":130,"external":131},"LLMs.txt","\u002Fllms.txt",true,{"text":133,"prompt":134,"platforms":135},"Ask AI about Nimbus","I'm researching enterprise intelligence platforms and want to know how Nimbus combines perception, collaboration, and autonomous agents to drive strategic decision-making. Summarize the highlights from Nimbus's website: https:\u002F\u002Fgonimbus.ai",[136,141,146,151,156],{"name":137,"label":138,"icon":139,"hrefPrefix":140},"chatgpt","ChatGPT","simple-icons:openai","https:\u002F\u002Fchatgpt.com\u002F?prompt=",{"name":142,"label":143,"icon":144,"hrefPrefix":145},"perplexity","Perplexity","mdi:magnify","https:\u002F\u002Fwww.perplexity.ai\u002Fsearch\u002Fnew?q=",{"name":147,"label":148,"icon":149,"hrefPrefix":150},"grok","Grok","simple-icons:x","https:\u002F\u002Fx.com\u002Fi\u002Fgrok?text=",{"name":152,"label":153,"icon":154,"hrefPrefix":155},"claude","Claude","simple-icons:anthropic","https:\u002F\u002Fclaude.ai\u002Fnew?q=",{"name":157,"label":158,"icon":159,"hrefPrefix":160},"google-ai","Google AI","simple-icons:google","https:\u002F\u002Fwww.google.com\u002Fsearch?udm=50&aep=11&q=","content\u002Fshared\u002Fnav.md","Site navigation",false,null,{"type":166,"value":167,"toc":168},"minimark",[],{"title":169,"searchDepth":170,"depth":170,"links":171},"",2,[],"md","\u002Fshared\u002Fnav",{"title":162,"description":169},"shared\u002Fnav","Q7sDe7TuGEwhwUMamyeOxjvGlsUaF3Iay9VTW0KaE_U",{"enabled":163,"message":178,"linkLabel":79,"linkHref":80,"id":179,"title":180,"archived":163,"authors":164,"badge":164,"body":181,"date":164,"definedTerm":164,"department":164,"description":169,"extension":172,"eyebrow":164,"faqHeader":164,"faqs":164,"footerBand":164,"headline":164,"image":164,"industry":164,"jobType":164,"listed":131,"location":164,"navigation":131,"openRoles":164,"pageLayout":164,"path":185,"relatedHeading":164,"seo":186,"series":164,"sitemap":163,"status":164,"stem":187,"subhead":164,"tags":164,"video":164,"whyJoin":164,"workplaceType":164,"__hash__":188},"We're hiring! Join the team building the Sentient Enterprise.","content\u002Fshared\u002Fhiring.md","Hiring banner",{"type":166,"value":182,"toc":183},[],{"title":169,"searchDepth":170,"depth":170,"links":184},[],"\u002Fshared\u002Fhiring",{"title":180,"description":169},"shared\u002Fhiring","1zs3boivKda1e-b-hAyuNcmZSKjZUAXmecnwHVgcHzk",{"id":190,"title":191,"archived":163,"authors":192,"badge":196,"body":198,"date":702,"definedTerm":164,"department":164,"description":703,"extension":172,"eyebrow":164,"faqHeader":704,"faqs":707,"footerBand":164,"headline":164,"image":164,"industry":164,"jobType":164,"listed":163,"location":164,"navigation":131,"openRoles":164,"pageLayout":164,"path":717,"relatedHeading":164,"seo":718,"series":719,"sitemap":131,"status":164,"stem":720,"subhead":164,"tags":721,"video":164,"whyJoin":164,"workplaceType":164,"__hash__":726},"content\u002Fblog\u002Fai-washing-and-what-regulators-already-ask.md","AI Washing Enforcement: Why Unbacked Claims Are Now a Liability",[193],{"name":194,"to":195},"Nimbus Research","https:\u002F\u002Fgonimbus.ai",{"label":197},"Thought Leadership",{"type":166,"value":199,"toc":691},[200,204,213,216,219,273,276,281,289,292,354,358,361,364,371,377,388,450,454,462,475,478,482,485,491,502,508,512,515,523,526,530,533,536,539,542,545,548,620,624,631,635,638,641,644,648],[201,202,203],"p",{},"The first enforcement cases were not about rogue models. They were about companies describing AI they did not have. Boards should assume their own claims will be read the same way.",[201,205,206,207,212],{},"On 18 March 2024 the U.S. Securities and Exchange Commission announced settled charges against two investment advisers for false and misleading statements about artificial intelligence. Delphia (USA) Inc. and Global Predictions Inc. agreed to pay $400,000 in total civil penalties. The Commission’s ",[208,209,211],"a",{"href":210},"https:\u002F\u002Fwww.sec.gov\u002Fnewsroom\u002Fpress-releases\u002F2024-36","press release"," is short. The facts are the whole lesson.",[201,214,215],{},"Delphia, the order found, had claimed from 2019 into 2023 — in filings, a press release, and on its website — that it used AI and machine learning on client data to predict which companies and trends would “make it big”. It did not have those capabilities. It had even agreed, after an examination, to correct the statements, and further misleading claims continued. Global Predictions had called itself the “first regulated AI financial advisor” and advertised “expert AI-driven forecasts”. Those claims were false. Both firms were also charged under the marketing rule, which prohibits advertisements that include an untrue statement of material fact.",[201,217,218],{},"This is not a science-fiction enforcement theory. It is advertising law applied to a fashionable noun. Any company that tells investors, customers, or its own board that AI is doing a job should be able to point at the job.",[220,221,222,241],"table",{},[223,224,225],"thead",{},[226,227,228,232,235,238],"tr",{},[229,230,231],"th",{},"Firm",[229,233,234],{},"What it told the market",[229,236,237],{},"What the order found",[229,239,240],{},"Civil penalty",[242,243,244,259],"tbody",{},[226,245,246,250,253,256],{},[247,248,249],"td",{},"Delphia (USA) Inc.",[247,251,252],{},"AI and machine learning on client data would predict which companies and trends “make it big”, in filings, a press release, and on the website, from 2019 into 2023",[247,254,255],{},"It did not have those capabilities. Misleading statements continued after it agreed, following an examination, to correct them",[247,257,258],{},"$225,000",[226,260,261,264,267,270],{},[247,262,263],{},"Global Predictions Inc.",[247,265,266],{},"“First regulated AI financial advisor”; “expert AI-driven forecasts”",[247,268,269],{},"The claims were false",[247,271,272],{},"$175,000",[201,274,275],{},"Both were also charged under the marketing rule: an advertisement may not include an untrue statement of material fact. The exhibit was the description, not a model card.",[277,278,280],"h2",{"id":279},"the-claim-is-now-part-of-the-control-environment","The claim is now part of the control environment",[201,282,283,284,288],{},"For a decade, “we use AI” was a flourish. It sat in a keynote and a recruiting page. Enforcement has moved it into the same family as performance claims. The ",[208,285,287],{"href":286},"https:\u002F\u002Fwww.ftc.gov\u002Fbusiness-guidance\u002Fblog\u002F2023\u002F02\u002Fkeep-your-ai-claims-check","Federal Trade Commission warned in February 2023"," that it would look at exaggerated AI marketing: claims of a product being AI-powered when the automation is banal, claims that AI is more accurate or fair than a person without evidence, and claims that quietly overstate what the system can do. The vocabulary varies. The test does not. If you would not put the sentence in a footnote with a method attached, do not put it in the headline.",[201,290,291],{},"Operating companies sometimes assume this is an asset-manager problem. It is not. A retailer that tells the market its service operation is “AI-run” while humans still handle the exceptions that generate the complaints is making a claim about the business, not about a model. A manufacturer that tells customers its quality system is “AI-assured” while the model only drafts a shift report is doing the same. The SEC cases happened to arise under the Advisers Act. The underlying mismatch — words ahead of the system — is available to every industry that publishes.",[220,293,294,310],{},[223,295,296],{},[226,297,298,301,304,307],{},[229,299,300],{},"Where the sentence appears",[229,302,303],{},"What a reader is entitled to assume",[229,305,306],{},"What often turns out to be true",[229,308,309],{},"What to write instead",[242,311,312,326,340],{},[226,313,314,317,320,323],{},[247,315,316],{},"Keynote or recruiting page",[247,318,319],{},"A capability is already in production",[247,321,322],{},"A pilot, a vendor demo, or a plan",[247,324,325],{},"The tense you can defend this week",[226,327,328,331,334,337],{},[247,329,330],{},"Customer proposal",[247,332,333],{},"The control described is the control they will get",[247,335,336],{},"“Human in the loop” means a digest the next morning",[247,338,339],{},"The actual checkpoint: who sees which words, and when",[226,341,342,345,348,351],{},[247,343,344],{},"Risk factor or annual report",[247,346,347],{},"The exposure is specific enough to govern",[247,349,350],{},"A peer’s sentence about hallucinations “may” occur",[247,352,353],{},"Which customer process is exposed, and what the human check is",[277,355,357],{"id":356},"what-a-board-should-inventory-before-the-next-letter-to-shareholders","What a board should inventory before the next letter to shareholders",[201,359,360],{},"Read the last annual report, the last customer proposal, and the last all-hands as if you were an examiner. Highlight every sentence in which AI is the subject of a verb that implies a capability: decides, predicts, detects, prevents, personalises, assures, autonomously resolves. For each sentence, name the system, the data it actually sees, the human who can override it, and the metric that would falsify the sentence.",[201,362,363],{},"You will find three kinds of claim.",[201,365,366,370],{},[367,368,369],"strong",{},"Aspirational, labelled as current."," The roadmap has been written in the present tense. This is the Delphia pattern: a future model described as a present process, including after someone inside the firm knew the description was wrong. The corrective is dull and effective. Change the tense. “We intend” is not a confession. “We do” without a system is.",[201,372,373,376],{},[367,374,375],{},"Real, but narrower than the adjective."," A model ranks tickets. The website says it “resolves” them. A model drafts a forecast commentary. The earnings script says the outlook is “AI-generated”. Narrow claims survive. Inflated ones become exhibits.",[201,378,379,382,383,387],{},[367,380,381],{},"Unreconstructable."," Nobody can show an example. The team that built the demo has left. The log was not retained. This is the dangerous category, because the company cannot even retreat honestly. ",[208,384,386],{"href":385},"https:\u002F\u002Fwww.nist.gov\u002Fitl\u002Fai-risk-management-framework","NIST’s framework"," assumes an organisation can map the AI systems it runs. If you cannot map them, you cannot describe them, and you should stop describing them until you can.",[220,389,390,406],{},[223,391,392],{},[226,393,394,397,400,403],{},[229,395,396],{},"Kind of claim",[229,398,399],{},"How you recognise it",[229,401,402],{},"What an examiner will do with it",[229,404,405],{},"The corrective this week",[242,407,408,422,436],{},[226,409,410,413,416,419],{},[247,411,412],{},"Aspirational, written as current",[247,414,415],{},"Present tense for a system that is still a roadmap",[247,417,418],{},"Treat the future as a statement of fact",[247,420,421],{},"Change the tense. “We intend” is allowed. “We do” requires a system",[226,423,424,427,430,433],{},[247,425,426],{},"Real, but narrower than the adjective",[247,428,429],{},"The verb is bigger than the workflow (“resolves” when the model ranks)",[247,431,432],{},"Compare the verb with one real transcript",[247,434,435],{},"Shrink the verb to the step the system actually performs",[226,437,438,441,444,447],{},[247,439,440],{},"Unreconstructable",[247,442,443],{},"No example, no owner, no retained log",[247,445,446],{},"You cannot retreat honestly, because you cannot show what was true",[247,448,449],{},"Stop describing it until you can map it",[277,451,453],{"id":452},"disclosure-is-not-the-same-as-a-press-release","Disclosure is not the same as a press release",[201,455,456,457,461],{},"Public companies already face a more ordinary version of this test in risk factors and in the management discussion. The question from a sophisticated reader is no longer “do you use AI?” ",[208,458,460],{"href":459},"https:\u002F\u002Fwww.mckinsey.com\u002Fcapabilities\u002Fquantumblack\u002Four-insights\u002Fthe-state-of-ai","McKinsey’s 2025 survey"," suggests the answer is yes for nearly nine in ten organisations, at least in one function. The question is whether that use is material to results, to risk, or to the story you are telling about growth. A risk factor that says AI “may” hallucinate, copied from a peer, is less informative than a sentence that says which customer process is exposed and what the human check is.",[201,463,464,465,469,470,474],{},"Regulators outside the United States are building the same expectation into product law. The ",[208,466,468],{"href":467},"https:\u002F\u002Feur-lex.europa.eu\u002Flegal-content\u002FEN\u002FTXT\u002F?uri=OJ:L_202401689","EU AI Act"," imposes transparency and, for higher-risk uses, documentation and oversight duties. The ",[208,471,473],{"href":472},"https:\u002F\u002Foecd.ai\u002Fen\u002Fai-principles","OECD AI Principles"," have said for years that actors should be transparent and accountable. Neither document requires a particular adjective in a Super Bowl spot. Both make it harder to pretend that capability claims are mere puffery once a system affects people.",[201,476,477],{},"There is also the internal version, which auditors will care about before any agency does. If the bonus scheme or the investor deck depends on an AI productivity number, the audit committee should ask who measured it and what was held constant. A vendor case study is not a measurement. The SEC’s marketing-rule theory in the adviser cases — you must be able to substantiate — is a sound house rule even when the statute does not reach you.",[277,479,481],{"id":480},"how-claims-go-wrong-in-ordinary-companies","How claims go wrong in ordinary companies",[201,483,484],{},"Three patterns recur.",[201,486,487,490],{},[367,488,489],{},"Inherited adjectives across subsidiaries."," A subsidiary buys a tool and the parent’s communications team inherits the adjective. Nobody in the parent has seen the workflow. The sentence is written by someone rewarded for clarity, not for fidelity.",[201,492,493,496,497,501],{},[367,494,495],{},"Generalising pilot results."," Twenty users liked a drafting aid. The company announces that a function has been “transformed”. The quarterly numbers do not move. The next announcement is quieter, but the first one is still on the website, where a plaintiff’s lawyer or a journalist can find it. ",[208,498,500],{"href":499},"https:\u002F\u002Fwww.reuters.com\u002Ftechnology\u002Fgoogle-ai-chatbot-bard-offers-inaccurate-information-company-ad-2023-02-08\u002F","Reuters’ report on Alphabet’s February 2023 Bard demo"," — a factual error in a promotional video, and a market value swing measured in tens of billions of dollars in a single session — is a reminder that public AI claims are priced in real time. Most companies will not move a market. They can still move a customer’s trust or a regulator’s interest.",[201,503,504,507],{},[367,505,506],{},"Implying automatic controls."," “Human in the loop” appears in the proposal. On inspection, the human sees a batch the next morning, or sees only the cases the model was unsure about. That is a design. It may even be a good design. It is not the design the words implied. Write the design.",[277,509,511],{"id":510},"what-to-do-before-someone-else-reads-the-website-for-you","What to do before someone else reads the website for you",[201,513,514],{},"Appoint a single owner for outward AI claims. Not a committee that reviews tone. A person who can demand the artefact: a log, a policy, a named process owner. Give that person the right to strike a sentence.",[201,516,517,518,522],{},"Align the customer-facing bot with the claim. If you say the assistant follows policy, load the policy that is in force and keep the version. The ",[208,519,521],{"href":520},"https:\u002F\u002Fwww.canlii.org\u002Fen\u002Fbc\u002Fbccrt\u002Fdoc\u002F2024\u002F2024bccrt149\u002F2024bccrt149.html","Air Canada tribunal"," did not fine an adjective. It enforced an answer. Claims and answers are the same risk seen from the board and from the contact centre.",[201,524,525],{},"Retire the slide that says “AI-powered” with no noun after it. Powered to do what, for whom, with what stop? If the answer is “summarise internal documents for employees who already have access,” say that. Specificity is not modesty. It is how you stay out of a file named after your own marketing.",[277,527,529],{"id":528},"a-sentence-by-sentence-reading-of-your-own-site","A sentence-by-sentence reading of your own site",[201,531,532],{},"Take the public pages that mention AI and read them as an examiner with no interest in your roadmap. Highlight every verb: “uses artificial intelligence to,” “powered by,” “predicts,” “ensures,” “the first,” “fully automated,” “bank-grade,” “human-level”. For each verb, write in the margin the artefact that makes it true this week: a log, a policy version, a model you actually call, a person who approves, a metric with a definition. If the margin is empty, the verb comes out. This is not a branding exercise. It is the exercise the SEC applied, in March 2024, to two investment advisers. The amounts are modest next to a franchise. The exhibit is the website.",[201,534,535],{},"Do the same for the product, not only the adjective. If the site says the assistant follows your policy, produce a current answer and the policy passage it used. If you cannot, you are one screenshot from a different kind of case, the kind where the words are enforced rather than fined as advertising. Air Canada’s chatbot told a traveller they could claim a bereavement fare after travelling; the tribunal held the airline to that answer. Marketing and the contact centre are the same risk team on the day the sentence is wrong.",[201,537,538],{},"The FTC’s instruction to keep AI claims in check is practical. Do not claim a capability you cannot demonstrate. Do not claim a control the product does not apply. Do not imply that a general-purpose model is a regulated fiduciary, a safety system, or a guarantee. Specificity is the defence. “Drafts a reply from the current return policy, which a person sends” is a claim you can live with. “AI-powered customer care” is a claim that will be filled in by the worst transcript.",[201,540,541],{},"Extend the reading to recruiting, lending, pricing, and fraud pages, and to the sales deck the field actually uses. Decks drift ahead of the product because they are edited locally. A local deck is still the company’s speech when a customer relies on it. Put the same owner on the deck and the site, with the right to strike a sentence without a steering committee. Committees protect tone. Owners protect verbs.",[201,543,544],{},"Then decide what you will not say while the programme is still a pilot. Most organisations are using the technology somewhere and have not scaled it. “We are piloting drafting on a defined class of tickets” is an accurate sentence and a dull one. Dull sentences do not attract the file. They also leave you room to promote the pilot later without having to walk back a superlative. The companies that get this right will sound, for a while, less ambitious than the companies that do not. That is the point.",[201,546,547],{},"Use the margin test before the next page ships. An empty cell is a verb that comes out, including on the field deck.",[220,549,550,563],{},[223,551,552],{},[226,553,554,557,560],{},[229,555,556],{},"Verb on the page",[229,558,559],{},"Artefact that makes it true this week",[229,561,562],{},"If the cell is empty",[242,564,565,576,587,598,609],{},[226,566,567,570,573],{},[247,568,569],{},"Predicts, detects, assures",[247,571,572],{},"The system, the data it sees, and the metric that would falsify the sentence",[247,574,575],{},"Delete or change to “we intend”",[226,577,578,581,584],{},[247,579,580],{},"Ensures, fully automated, bank-grade, human-level",[247,582,583],{},"The control as it actually runs, not as the proposal described it",[247,585,586],{},"Strike the adjective. Write the design",[226,588,589,592,595],{},[247,590,591],{},"AI-powered, the first",[247,593,594],{},"The noun: powered to do what, for whom, with what stop",[247,596,597],{},"Replace with the specific job",[226,599,600,603,606],{},[247,601,602],{},"Follows our policy",[247,604,605],{},"A current answer and the policy passage it used, with the version kept",[247,607,608],{},"You are one screenshot from a case about the words, not the adjective",[226,610,611,614,617],{},[247,612,613],{},"Human in the loop",[247,615,616],{},"Who sees the outbound sentence, and whether they see it before it is sent",[247,618,619],{},"If they see a digest the next morning, say that",[277,621,623],{"id":622},"strike-the-verb-you-cannot-open","Strike the verb you cannot open",[201,625,626,627,630],{},"Read the public pages as an examiner. Every “predicts,” “ensures,” “fully automated,” and “AI-powered” needs an artefact in the margin this week: a log, a policy version, a person who approves. An empty margin means the verb comes out, including on the field deck. ",[208,628,629],{"href":210},"The settlements"," were about descriptions. The description you can live with is specific and dull. Dull is the defence.",[277,632,634],{"id":633},"a-call-to-chief-executives-and-general-counsel","A call to chief executives and general counsel",[201,636,637],{},"The first AI cases that stuck were about honesty. That should be a relief. You do not have to settle a philosophical argument about machine agency to comply. You have to describe what you run.",[201,639,640],{},"Go through the claims. Keep the ones you can demonstrate this week. Rewrite the ones that describe next year. Delete the ones nobody can explain. The companies that treat “AI” as a fact to be evidenced, rather than a mood to be signalled, will find the regulatory conversation boring. Boring is the point.",[642,643],"hr",{},[277,645,647],{"id":646},"references","References",[649,650,651,657,662,667,672,677,681,686],"ul",{},[652,653,654],"li",{},[208,655,656],{"href":210},"U.S. SEC, false and misleading AI statements, 18 March 2024",[652,658,659],{},[208,660,661],{"href":286},"U.S. FTC, Keep your AI claims in check, 27 February 2023",[652,663,664],{},[208,665,666],{"href":459},"McKinsey, The State of AI: Global Survey 2025",[652,668,669],{},[208,670,671],{"href":385},"NIST AI Risk Management Framework",[652,673,674],{},[208,675,676],{"href":467},"Regulation (EU) 2024\u002F1689",[652,678,679],{},[208,680,473],{"href":472},[652,682,683],{},[208,684,685],{"href":499},"Reuters, Alphabet shares dive after Bard error in a public demo",[652,687,688],{},[208,689,690],{"href":520},"Moffatt v. Air Canada, 2024 BCCRT 149",{"title":169,"searchDepth":170,"depth":170,"links":692},[693,694,695,696,697,698,699,700,701],{"id":279,"depth":170,"text":280},{"id":356,"depth":170,"text":357},{"id":452,"depth":170,"text":453},{"id":480,"depth":170,"text":481},{"id":510,"depth":170,"text":511},{"id":528,"depth":170,"text":529},{"id":622,"depth":170,"text":623},{"id":633,"depth":170,"text":634},{"id":646,"depth":170,"text":647},"2026-09-22","How regulatory bodies are auditing misleading AI claims, and what legal teams must document to prove real model deployment.",{"eyebrow":705,"title":706},"Short answers","Claims, not model failures",[708,711,714],{"question":709,"answer":710},"What is AI washing?","Describing AI the company does not actually have. The first enforcement cases were about the claim, not about a rogue model.",{"question":712,"answer":713},"What will a regulator ask?","What the system does, what data it uses, who is accountable, and whether the public description matches the product. Boards should assume their own claims will be read the same way.",{"question":715,"answer":716},"Is a slide that says AI-powered enough to worry about?","Yes, if the feature is a rules engine, a human, or a vendor you do not control. The mismatch between the sentence and the system is the exposure.","\u002Fblog\u002Fai-washing-and-what-regulators-already-ask",{"title":191,"description":703},"insight","blog\u002Fai-washing-and-what-regulators-already-ask",[722,723,724,725],"thought-leadership","regulation","governance","disclosure","C9mKSQDniI4Qpdb-jmFJnoqG-pURwkKow7O0IqtvDYk",{"hero":728,"id":730,"title":731,"archived":163,"authors":164,"badge":164,"body":732,"date":164,"definedTerm":164,"department":164,"description":736,"extension":172,"eyebrow":737,"faqHeader":164,"faqs":164,"footerBand":738,"headline":164,"image":164,"industry":164,"jobType":164,"listed":131,"location":164,"navigation":131,"openRoles":164,"pageLayout":164,"path":60,"relatedHeading":744,"seo":745,"series":164,"sitemap":131,"status":164,"stem":746,"subhead":164,"tags":164,"video":164,"whyJoin":164,"workplaceType":164,"__hash__":747},{"filename":729},"u2221455217_Flat_design_of_a_futuristic_minimalist_landscape__5d589295-cdea-4ea9-a262-be766881accf_1.png","content\u002Fblog\u002Findex.md","Exploring the future of intelligence.",{"type":166,"value":733,"toc":734},[],{"title":169,"searchDepth":170,"depth":170,"links":735},[],"Deep dives into pre-cognitive intelligence, sentient enterprises, and the evolving landscape of AI-driven business transformation.","Latest Research",{"headline":739,"description":740,"primaryLabel":741,"primaryTo":742,"secondaryLabel":743,"secondaryTo":12},"Stay at the frontier.","Subscribe for product updates and new insights.","Subscribe","\u002Fnewsletter","Explore the platform","More research",{"title":731,"description":736},"blog\u002Findex","BFSWGYO9bcTlaulivKYWyg08_DJHsdGg3OC6g_CG1Hw",[749,164],{"id":750,"title":751,"archived":163,"authors":752,"badge":754,"body":755,"date":702,"definedTerm":164,"department":164,"description":1201,"extension":172,"eyebrow":164,"faqHeader":1202,"faqs":1204,"footerBand":164,"headline":164,"image":164,"industry":164,"jobType":164,"listed":163,"location":164,"navigation":131,"openRoles":164,"pageLayout":164,"path":1214,"relatedHeading":164,"seo":1215,"series":719,"sitemap":131,"status":164,"stem":1216,"subhead":164,"tags":1217,"video":164,"whyJoin":164,"workplaceType":164,"__hash__":1220},"content\u002Fblog\u002Fcustomer-trust-after-the-first-public-ai-mistake.md","Customer Trust and the Cost of Your First Public AI Mistake",[753],{"name":194,"to":195},{"label":197},{"type":166,"value":756,"toc":1192},[757,760,767,836,840,843,846,907,911,914,920,923,994,998,1011,1018,1022,1025,1028,1031,1034,1037,1087,1091,1094,1097,1143,1147,1150,1153,1155,1157],[201,758,759],{},"Customers do not experience your architecture. They experience a sentence with your name on it. When that sentence is wrong, the argument that “the AI said it” lands the way “the intern said it” would have landed, except the intern did not speak at the scale of a website, and the intern could be asked what they meant. The model cannot. Trust, after the first public mistake, is rebuilt by showing a change in how sentences are allowed to leave, not by a post that says you take the issue seriously.",[201,761,762,763,766],{},"The case law and the market have already supplied the examples. Air Canada told a grieving passenger, through its website chatbot, that a bereavement fare could be claimed after travel. The policy page said otherwise. The tribunal refused the idea that the bot was a separate entity. In February 2023 Google’s Bard demo included a factual error about a satellite; ",[208,764,765],{"href":499},"Reuters reported"," that Alphabet shed on the order of $100 billion in market value in the session. Different stakes, same mechanism: a public sentence, an identifiable company, an audience that does not grant a hallucination discount.",[220,768,769,781],{},[223,770,771],{},[226,772,773,775,778],{},[229,774],{},[229,776,777],{},"Air Canada, 2024",[229,779,780],{},"Alphabet, February 2023",[242,782,783,794,805,825],{},[226,784,785,788,791],{},[247,786,787],{},"The sentence",[247,789,790],{},"A bereavement fare could be claimed after travel",[247,792,793],{},"A factual error about a satellite, in a promotional demo",[226,795,796,799,802],{},[247,797,798],{},"What the company wished were true",[247,800,801],{},"The policy page, which said otherwise",[247,803,804],{},"The model was not yet the product customers should rely on",[226,806,807,810,822],{},[247,808,809],{},"Who decided",[247,811,812,813,816,817,821],{},"A tribunal. The bot was not a separate entity. ",[208,814,815],{"href":520},"The decision"," held the airline to the answer. ",[208,818,820],{"href":819},"https:\u002F\u002Fwww.cbc.ca\u002Fnews\u002Fcanada\u002Fbritish-columbia\u002Fair-canada-chatbot-lawsuit-1.7116416","CBC’s account"," is the operating version",[247,823,824],{},"The market, in one session, on the order of $100 billion in value",[226,826,827,830,833],{},[247,828,829],{},"What a customer or investor could check",[247,831,832],{},"A screenshot against the policy",[247,834,835],{},"The claim against a known fact",[277,837,839],{"id":838},"why-the-apology-fails","Why the apology fails",[201,841,842],{},"The standard crisis statement promises a review, a model update, and a commitment to accuracy. Customers have learned what that sequence means. The review will be internal. The model update will not be visible. The next mistake will sound just as confident. Trust is not a tone. It is a prediction that the next interaction will be governed. If nothing in the customer’s experience changes — no clearer policy, no easier path to a person, no visible correction of the specific claim — the prediction does not change.",[201,844,845],{},"There is also a fairness problem the apology skips. The customer who relied on the wrong sentence has often already paid, travelled, or declined another offer. Air Canada’s passenger had flown. A correction that arrives as a lesson for the company, without a remedy for the person, teaches the market that reliance is the customer’s mistake. The tribunal did the opposite. It treated reliance as reasonable. Your recovery design should assume a future decision-maker will do the same.",[220,847,848,861],{},[223,849,850],{},[226,851,852,855,858],{},[229,853,854],{},"Line in the statement",[229,856,857],{},"What customers have learned it means",[229,859,860],{},"What would change the prediction",[242,862,863,874,885,896],{},[226,864,865,868,871],{},[247,866,867],{},"“We are reviewing”",[247,869,870],{},"The review will be internal",[247,872,873],{},"The sentence, preserved, and the name of who can now stop the next one",[226,875,876,879,882],{},[247,877,878],{},"“We are updating the model”",[247,880,881],{},"The update will not be visible",[247,883,884],{},"The rule, published where the assistant lives, identical to the assistant",[226,886,887,890,893],{},[247,888,889],{},"“We take accuracy seriously”",[247,891,892],{},"The next mistake will sound just as confident",[247,894,895],{},"A person sees the class of promise that costs money, before it is sent",[226,897,898,901,904],{},[247,899,900],{},"A lesson for the company, and no remedy",[247,902,903],{},"Reliance was the customer’s mistake",[247,905,906],{},"The remedy the correct policy would have given, quickly",[277,908,910],{"id":909},"what-to-do-in-the-first-week-and-how-to-prevent-a-second","What to do in the first week, and how to prevent a second",[201,912,913],{},"In the first week, find the sentence. Not the topic. The sentence. Preserve it. Identify who could have stopped it and why they did not. If the answer is that nobody was assigned, say that internally without euphemism. Offer the customer the remedy the correct policy would have given, or a better one, quickly. Speed of remedy is the only part of the apology people can verify.",[201,915,916,917,919],{},"Then change the gate. Customer-facing assistants should be bound to the current policy, not to a pile of pages in which the current policy is merely the most popular. When the assistant and the policy disagree, the assistant loses, and the disagreement is ticketed. A human sees the exact outbound words for the classes of promise that cost money: fares, credits, coverage, delivery, exceptions. The ",[208,918,468],{"href":467},"’s oversight language — people able to interpret outputs and intervene — is a good design test even for uses the Act does not classify as high-risk. A footer that says “AI-generated” is not intervention.",[201,921,922],{},"Publish, where you can, the rule you corrected. Customers trust companies that show the rule more than companies that show the model. You do not need to publish weights. You need to publish the bereavement rule, the return window, the coverage trigger, in the same place the assistant lives, and keep them identical.",[220,924,925,938],{},[223,926,927],{},[226,928,929,932,935],{},[229,930,931],{},"By when",[229,933,934],{},"Action",[229,936,937],{},"Done when",[242,939,940,951,962,973,983],{},[226,941,942,945,948],{},[247,943,944],{},"Day 1",[247,946,947],{},"Preserve the exact sentence. Name who could have stopped it",[247,949,950],{},"The sentence is in the incident file, not paraphrased",[226,952,953,956,959],{},[247,954,955],{},"Day 1 to 3",[247,957,958],{},"Remedy the customer who relied",[247,960,961],{},"They have what the correct policy owed them, or better, and they can see that they do",[226,963,964,967,970],{},[247,965,966],{},"Day 7",[247,968,969],{},"Bind the assistant to the current rule. Retire the page that contradicted it",[247,971,972],{},"A disagreement between bot and policy opens a ticket, and the assistant loses",[226,974,975,977,980],{},[247,976,966],{},[247,978,979],{},"A person sees outbound words for fares, credits, coverage, delivery, and exceptions",[247,981,982],{},"The approver sees the sentence, not a summary of the model’s intention",[226,984,985,988,991],{},[247,986,987],{},"Same week",[247,989,990],{},"Publish the corrected rule where the assistant lives",[247,992,993],{},"A customer can compare the next answer with the rule without asking which page is real",[277,995,997],{"id":996},"the-internal-audience-is-also-the-public","The internal audience is also the public",[201,999,1000,1001,1005,1006,1010],{},"Staff read the incident. If leadership’s lesson is “don’t get caught,” staff will route around the sanctioned bot and draft answers in personal tools. ",[208,1002,1004],{"href":1003},"https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002Fworklab\u002Fwork-trend-index\u002Fai-at-work-is-here-now-comes-the-hard-part","Seventy-eight percent of AI users"," already bring their own. A public mistake followed by a ban, with no usable alternative, increases that share. The next error will be harder to find because it will not be in your log. ",[208,1007,1009],{"href":1008},"https:\u002F\u002Fwww.cnbc.com\u002F2023\u002F05\u002F02\u002Fsamsung-bans-use-of-ai-like-chatgpt-for-staff-after-misuse-of-chatbot.html","Samsung’s restriction"," after a leak made sense as a pause. As a permanent strategy it hides the work. Recovery includes a path staff prefer to their phones, because the public brand is whatever path they actually use.",[201,1012,1013,1014,1017],{},"Marketing will want to move on. Let them move on only after the gate exists. The ",[208,1015,1016],{"href":286},"FTC’s warning on AI claims"," is a constraint on the comeback campaign. Do not announce that the assistant is now “trusted” or “accurate” because you retrained something. Announce the control a customer can understand: a person checks promises over a threshold; the policy page and the bot cannot diverge without an alert; here is how to reach a human. Claims you can demonstrate are the only claims that repair anything.",[277,1019,1021],{"id":1020},"the-second-mistake-ends-the-argument","The second mistake ends the argument",[201,1023,1024],{},"Customers will forgive a first error if the second interaction proves the system changed. They will not forgive a second error of the same kind. The second error says the apology was a holding statement. It is also the moment journalists, regulators, and plaintiff firms stop treating the incident as a glitch and start treating it as a practice. A public demo error moved a market in a day. A repeated customer-facing error moves a reputation more slowly and more permanently, because each screenshot confirms the last.",[201,1026,1027],{},"Design the fortnight after the incident as a control sprint, not a communications sprint. Freeze the class of promise that failed. Route it to humans. Sample every answer in adjacent classes. Publish the corrected rule where the assistant lives. Tell staff, in writing, what they may not paste into personal tools while the freeze holds, and give them the sanctioned draft that already contains the corrected rule. If you only freeze the website bot, the phone channel will recreate the sentence by lunchtime.",[201,1029,1030],{},"Then look for siblings. The bereavement rule was wrong because a stale answer and a current page were both allowed to speak. Search for other pairs: a return window in the bot and a different window in the terms; a coverage phrase in sales macros and a different phrase in the policy. Each pair is a future screenshot. Retire one side this fortnight. The tribunal’s point was that customers are not obliged to know which of your pages is the real one. Neither are your new employees.",[201,1032,1033],{},"Report to the board in sentences, not themes. What was said. Who relied. What remedy was given. What gate now stops a repeat. What remains unfixed. A board that receives “we have reinforced our commitment to accuracy” has not been briefed. A board that receives the sentence and the control can govern. Claims you cannot demonstrate should stay out of the comeback, including the claim that the problem is solved.",[201,1035,1036],{},"Trust is a lagging indicator. The leading indicators are contradictions found, promises routed to a person, and time-to-remedy for the customer who already relied. Put those three in the weekly customer review until they are boring. Boring means the operating change took. A campaign means it did not.",[220,1038,1039,1052],{},[223,1040,1041],{},[226,1042,1043,1046,1049],{},[229,1044,1045],{},"Indicator",[229,1047,1048],{},"What you count",[229,1050,1051],{},"What “boring” looks like",[242,1053,1054,1065,1076],{},[226,1055,1056,1059,1062],{},[247,1057,1058],{},"Contradictions found",[247,1060,1061],{},"Pairs of answers for the same noun: bot versus terms, macro versus policy",[247,1063,1064],{},"The count is non-zero while you are looking, then falls because one side was retired",[226,1066,1067,1070,1073],{},[247,1068,1069],{},"Promises routed to a person",[247,1071,1072],{},"Fares, credits, coverage, delivery, exceptions that waited for a human",[247,1074,1075],{},"The class that failed no longer leaves unattended",[226,1077,1078,1081,1084],{},[247,1079,1080],{},"Time-to-remedy",[247,1082,1083],{},"Hours from the screenshot to the customer receiving what the correct rule owed",[247,1085,1086],{},"The customer can verify the speed. The model update is not a substitute",[277,1088,1090],{"id":1089},"what-sorry-must-contain","What “sorry” must contain",[201,1092,1093],{},"Name the sentence that was wrong. Name the remedy for people who relied on it. Name the rule that replaces it, and the hour the old source was withdrawn. Name the person who now sees that class of promise before it is sent. Leave out the sentence about your commitment to innovation. Customers cannot test a commitment. They can test the next answer. Your repair will be judged the same way the Bard error was: by the next transcript, not by the statement.",[201,1095,1096],{},"Search the siblings in the same week. Two numbers for one noun, anywhere a customer or a seller can see them. Each pair gets an owner and a retirement date. Report the open pairs to the board until there are none. Do not claim the channel is now safe in the abstract. Claim the pairs you closed. Trust is the customer’s ability to predict you. Prediction is one version of the rule, in every tool your staff actually use.",[220,1098,1099,1109],{},[223,1100,1101],{},[226,1102,1103,1106],{},[229,1104,1105],{},"Put this in the note",[229,1107,1108],{},"Leave this out",[242,1110,1111,1119,1127,1135],{},[226,1112,1113,1116],{},[247,1114,1115],{},"The sentence that was wrong",[247,1117,1118],{},"“We take this seriously”",[226,1120,1121,1124],{},[247,1122,1123],{},"The remedy for people who relied",[247,1125,1126],{},"A commitment to innovation",[226,1128,1129,1132],{},[247,1130,1131],{},"The rule that replaces it, and the hour the old source was withdrawn",[247,1133,1134],{},"“The model has been updated” with nothing a customer can compare",[226,1136,1137,1140],{},[247,1138,1139],{},"The person who now sees that class of promise before it is sent",[247,1141,1142],{},"“The channel is now safe”",[277,1144,1146],{"id":1145},"a-call-to-chief-marketing-and-customer-officers","A call to chief marketing and customer officers",[201,1148,1149],{},"Assume the screenshot. Design the assistant as if the worst answer will be attached to a complaint. That is not cynicism. It is how Air Canada’s case was proved. Then rehearse the recovery before you need it: who freezes the bot, who authorises the remedy, who changes the rule, who tells the board the sentence rather than the vibe.",[201,1151,1152],{},"Trust after a public AI mistake is not a communications project. It is an operating change the customer can feel on the next visit. If they cannot feel it, they are right not to come back.",[642,1154],{},[277,1156,647],{"id":646},[649,1158,1159,1164,1168,1173,1177,1182,1187],{},[652,1160,1161],{},[208,1162,1163],{"href":819},"CBC News, Air Canada found liable for chatbot’s bad advice",[652,1165,1166],{},[208,1167,690],{"href":520},[652,1169,1170],{},[208,1171,1172],{"href":499},"Reuters, Alphabet shares dive after Bard error",[652,1174,1175],{},[208,1176,676],{"href":467},[652,1178,1179],{},[208,1180,1181],{"href":1003},"Microsoft and LinkedIn, 2024 Work Trend Index",[652,1183,1184],{},[208,1185,1186],{"href":1008},"CNBC, Samsung restricts generative AI",[652,1188,1189],{},[208,1190,1191],{"href":286},"U.S. FTC, Keep your AI claims in check",{"title":169,"searchDepth":170,"depth":170,"links":1193},[1194,1195,1196,1197,1198,1199,1200],{"id":838,"depth":170,"text":839},{"id":909,"depth":170,"text":910},{"id":996,"depth":170,"text":997},{"id":1020,"depth":170,"text":1021},{"id":1089,"depth":170,"text":1090},{"id":1145,"depth":170,"text":1146},{"id":646,"depth":170,"text":647},"Why customers equate chatbot promises with core brand failure, and how to build incident response plans for public AI errors.",{"eyebrow":705,"title":1203},"After the wrong sentence is public",[1205,1208,1211],{"question":1206,"answer":1207},"Why is a wrong public answer a trust event?","Customers will not separate your chatbot from your brand. The sentence is yours the moment it is on your site or in your name.",{"question":1209,"answer":1210},"Is a better apology the recovery?","No. The recovery is operational: what was said, who could have stopped it, what changed so the same sentence cannot go out again.",{"question":1212,"answer":1213},"What should we keep before the incident?","The exact output, the source it used, and the person or rule that was supposed to approve it. Without that, the apology is all you have.","\u002Fblog\u002Fcustomer-trust-after-the-first-public-ai-mistake",{"title":751,"description":1201},"blog\u002Fcustomer-trust-after-the-first-public-ai-mistake",[722,1218,1219,724],"brand","customer-trust","Gek2Aees_jj5IPsCJWAYmB31PsWpBdSW_EFv2GG7G44",{"fold":1222,"id":1226,"title":1227,"archived":163,"authors":164,"badge":164,"body":1228,"date":164,"definedTerm":164,"department":164,"description":169,"extension":172,"eyebrow":164,"faqHeader":164,"faqs":164,"footerBand":1232,"headline":164,"image":164,"industry":164,"jobType":164,"listed":131,"location":164,"navigation":131,"openRoles":164,"pageLayout":164,"path":1236,"relatedHeading":164,"seo":1237,"series":164,"sitemap":163,"status":164,"stem":1238,"subhead":164,"tags":164,"video":164,"whyJoin":164,"workplaceType":164,"__hash__":1239},{"headline":1223,"description":1224,"primaryLabel":8,"primaryTo":1225,"secondaryLabel":743,"secondaryTo":12},"Run frontier AI your business actually owns.","Governed workstreams, 3,000+ integrations, and a proprietary knowledge graph. Start on Free.","\u002Fsignup?plan=free","content\u002Fshared\u002Fcta.md","Site CTAs",{"type":166,"value":1229,"toc":1230},[],{"title":169,"searchDepth":170,"depth":170,"links":1231},[],{"headline":1233,"description":1234,"primaryLabel":8,"primaryTo":1225,"secondaryLabel":1235,"secondaryTo":85},"See what governed AI looks like on your stack.","Connect your tools, run a workstream, and keep every decision on your ledger. Start on Free.","Talk to our team","\u002Fshared\u002Fcta",{"title":1227,"description":169},"shared\u002Fcta","eYqahyaPnbp8GKrWpoORbZdtmkWmgHr5F61ZHOnb8sY",1791647054712]