{ "name": "Find references v2", "nodes": [ { "parameters": { "httpMethod": "POST", "path": "4556b597-2b3b-444d-baa9-8dff5ab11413", "responseMode": "lastNode", "responseData": "allEntries", "options": {} }, "type": "n8n-nodes-base.webhook", "typeVersion": 2.1, "position": [ -224, 880 ], "id": "b43900b8-e5f0-421b-8978-988392b6a4d4", "name": "Webhook", "webhookId": "4556b597-2b3b-444d-baa9-8dff5ab11413" }, { "parameters": { "jsCode": "const claim = $input.first().json.claim;\nconst search_result_text = $input.first().json.highlights;\nconst extractAllNumbersWithOptionalCurrency = (claim) => {\n // Regex explanation:\n // - (\\$|€|£|¥|[A-Z]{1,3}?)? => Optional currency symbols ($, €, £, ¥, or 1-3 uppercase letters)\n // - \\s* => Optional whitespace after currency\n // - (\\d{1,3}(?:,\\d{3})*|\\d+(?:\\.\\d+)?|\\.\\d+) => Matches numbers (with commas, decimals, or standalone decimals)\n const regex = /(\\$|€|£|¥|[A-Z]{1,3}?)?\\s*(\\d{1,3}(?:,\\d{3})*|\\d+(?:\\.\\d+)?|\\.\\d+)(\\%)?/g;\n\n const matches = [];\n let match;\n\n // Find all matches in the string\n while ((match = regex.exec(claim)) !== null) {\n // Combine currency and number (e.g., \"$186\" or \"186.50\")\n const currency = match[1] || \"\";\n const number = match[2];\n matches.push(currency + number);\n }\n\n return matches.length > 0 ? matches : [];\n};\n\n// Test case\n\nfunction checkNumbersInText(numbers, search_result_text) {\n if (!numbers || numbers.length === 0) {\n return { valid: true, results: [] };\n }\n\n const normalizeForComparison = (str) => str.replace(/[^\\d.]/g, '');\n const normalizedSearchText = normalizeForComparison(search_result_text);\n\n const results = numbers.map((number) => {\n const normalizedNumber = normalizeForComparison(number);\n const exists = normalizedSearchText.includes(normalizedNumber);\n return {\n originalNumber: number,\n existsInText: exists,\n normalizedNumber,\n };\n });\n\n const valid = results.every((r) => r.existsInText);\n return { valid, results };\n}\n\nconst numbers = extractAllNumbersWithOptionalCurrency(claim);\nconst results = checkNumbersInText(numbers, search_result_text);\nreturn {\"numbers_check\":results};" }, "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ -32, 880 ], "id": "5512a8eb-a1a8-4c3c-9e57-dc6b495cb07c", "name": "Validate Numbers Mentioned" }, { "parameters": { "promptType": "define", "text": "=%CLAIM%\n{{ $('Webhook').item.json.body.claim }}\n\n%TEXT%\n{{ $('Webhook').item.json.body.search_result_texts }}\n\n%TASK%\nIs claim accurate based on the text? Answer only Yes or No. And then give a very concise reason why.\n\n%ANSWER%\n", "batching": {} }, "type": "@n8n/n8n-nodes-langchain.chainLlm", "typeVersion": 1.7, "position": [ 512, 864 ], "id": "92525731-0c55-4165-a9ca-2decbab3739b", "name": "Basic LLM Chain" }, { "parameters": { "model": "google/gemma-4-31b-it", "options": {} }, "type": "@n8n/n8n-nodes-langchain.lmChatOpenRouter", "typeVersion": 1, "position": [ 512, 1024 ], "id": "937ddd71-9c1e-4629-b40c-42ecbdf67f38", "name": "OpenRouter Chat Model", "credentials": { "openRouterApi": { "id": "baaHEuVk7u6Z0Liu", "name": "OpenRouter account" } } }, { "parameters": { "promptType": "define", "text": "=%CLAIM%\n{{ $('Webhook').item.json.body.claim }}\n\n%TEXT%\n{{ $('Webhook').item.json.body.search_result_texts }}\n\n%TASK%\nIs claim accurate based on the text? Answer only Yes or No.\n\n%ANSWER%\n", "batching": {} }, "type": "@n8n/n8n-nodes-langchain.chainLlm", "typeVersion": 1.7, "position": [ 512, 1488 ], "id": "f1ec1771-6923-4a97-b9e6-258d903cea97", "name": "Basic LLM Chain3" }, { "parameters": { "model": "google/gemma-4-31b-it", "options": {} }, "type": "@n8n/n8n-nodes-langchain.lmChatOpenRouter", "typeVersion": 1, "position": [ 512, 1664 ], "id": "c24861b6-5e86-477b-9ed7-d1eff6530bbe", "name": "OpenRouter Chat Model6", "credentials": { "openRouterApi": { "id": "baaHEuVk7u6Z0Liu", "name": "OpenRouter account" } } }, { "parameters": { "promptType": "define", "text": "=%CLAIM%\n{{ $('Webhook').item.json.body.claim }}\n\n%TEXT%\n{{ $('Webhook').item.json.body.search_result_texts }}\n\n%TASK%\nIs claim accurate based on the text? Answer only Yes or No.\n\n%ANSWER%\n", "batching": {} }, "type": "@n8n/n8n-nodes-langchain.chainLlm", "typeVersion": 1.7, "position": [ 512, 1168 ], "id": "9b8ad078-930c-4416-9e98-061033d3b084", "name": "Basic LLM Chain4" }, { "parameters": { "model": "google/gemma-4-31b-it", "options": {} }, "type": "@n8n/n8n-nodes-langchain.lmChatOpenRouter", "typeVersion": 1, "position": [ 512, 1312 ], "id": "6ee1ffbe-c4b1-43f0-bad5-48cf3f1d68e1", "name": "OpenRouter Chat Model7", "credentials": { "openRouterApi": { "id": "baaHEuVk7u6Z0Liu", "name": "OpenRouter account" } } }, { "parameters": { "jsCode": "// Loop over input items and add a new field called 'myNewField' to the JSON of each one\nconst target = 2;\nconst results = [\n $('Basic LLM Chain').first().json.text,\n $('Basic LLM Chain4').first().json.text,\n $('Basic LLM Chain3').first().json.text,\n];\n\nconst yesCount = results.filter(\n (r) => String(r).trim().toLowerCase() === 'yes'\n).length;\n\nconst valid = yesCount >= target;\n\nreturn {\n results,\n yesCount,\n target,\n valid,\n};" }, "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 784, 1488 ], "id": "ea67503d-d9b2-4e6c-8d6a-73c832b63727", "name": "Check accuracy" }, { "parameters": { "content": "## Claim Accuracy check\nAccepts {\"claim\":\"claim text\", \"search_results_text\":\"search results text\"}, returns whether claim is accurately represented", "height": 1104, "width": 1584 }, "type": "n8n-nodes-base.stickyNote", "position": [ -304, 736 ], "typeVersion": 1, "id": "75a5b46f-f784-477c-bfbc-870750e3b829", "name": "Sticky Note" }, { "parameters": { "assignments": { "assignments": [ { "id": "992b6ea0-2a76-4ec4-9e77-b1c009979a64", "name": "valid", "value": "={{ $json.valid }}", "type": "boolean" }, { "id": "a010a244-8d30-4e5f-bfad-b6a8ad034668", "name": "claim", "value": "={{ $('Webhook').item.json.body.claim }}", "type": "string" }, { "id": "8e7124f2-0c1d-4efc-9610-ddf883b206ed", "name": "search_results_texts", "value": "={{ $('Webhook').item.json.body.search_result_texts }}", "type": "string" } ] }, "options": {} }, "type": "n8n-nodes-base.set", "typeVersion": 3.4, "position": [ 944, 1488 ], "id": "34581460-fa8b-42b2-ad83-5f5cfb59e623", "name": "Edit Fields" }, { "parameters": { "query": "={{ $json.claim }}. Studies, research, or papers", "type": "fast", "numResults": 7, "additionalFields": {}, "contentsOptions": {}, "requestOptions": {} }, "type": "n8n-nodes-exa-official.exa", "typeVersion": 1, "position": [ -1008, 1616 ], "id": "80e8a22b-f087-431a-ae6e-cbd34fa0c2f9", "name": "Search the web1", "credentials": { "exaApi": { "id": "zWjZTTTJMfHuX1a8", "name": "Exa account" } } }, { "parameters": { "assignments": { "assignments": [ { "id": "fe6b207f-a99b-4168-803e-523dbf4cccd1", "name": "claim", "value": "={{ $('Edit Fields3').item.json.claim }}", "type": "string" }, { "id": "0c7e9922-54b0-4263-b7ca-c3996bb89b18", "name": "highlights", "value": "={{ $json.results.map(item => item.highlights) }}", "type": "string" }, { "id": "961bafdb-6637-45a4-96d2-b1722699f2c2", "name": "urls", "value": "={{ $json.results.map(item=>item.url) }}", "type": "array" }, { "id": "8878c2bb-bf20-4309-bebc-5886e7f3d625", "name": "published_dates", "value": "={{ $json.results.map(item=>item.publishedDate) }}", "type": "array" }, { "id": "5849890b-5ec7-4e52-a678-9266a943fa50", "name": "results", "value": "={{ $json.results }}", "type": "array" } ] }, "options": {} }, "type": "n8n-nodes-base.set", "typeVersion": 3.4, "position": [ -832, 1616 ], "id": "dff0d05f-0736-41a7-bc19-65698d9b527f", "name": "Claim + Results1" }, { "parameters": { "httpMethod": "POST", "path": "f324f30c-334d-4286-b9ff-14a08b1bcf6a", "responseMode": "lastNode", "responseData": "allEntries", "options": {} }, "type": "n8n-nodes-base.webhook", "typeVersion": 2.1, "position": [ -1328, 1616 ], "id": "5c80cde7-2948-4059-a4c7-73a398cb1a33", "name": "Webhook2", "webhookId": "f324f30c-334d-4286-b9ff-14a08b1bcf6a" }, { "parameters": { "mode": "raw", "jsonOutput": "={\n \"claim\": \"{{ $json.body.claim }}\"\n}\n", "options": {} }, "type": "n8n-nodes-base.set", "typeVersion": 3.4, "position": [ -1184, 1616 ], "id": "17a005f9-905a-42ff-8399-95e9b60c8dba", "name": "Edit Fields3" }, { "parameters": { "content": "## Search for claim\nAccepts {\"claim\":\"my claim\"}, returns search results per claim with highlights, urls, full results\n", "height": 320, "width": 1024 }, "type": "n8n-nodes-base.stickyNote", "position": [ -1360, 1520 ], "typeVersion": 1, "id": "ce8144d0-fcbe-47a0-a4a0-7f44eab58917", "name": "Sticky Note1" }, { "parameters": { "content": "## Validate final source\n\nAccepts {\"source\":\"source text\",\"claim\":\"text\"}, returns ", "height": 480, "width": 1280 }, "type": "n8n-nodes-base.stickyNote", "position": [ 1312, 1360 ], "typeVersion": 1, "id": "6a733152-1660-4b16-b331-89803e586933", "name": "Sticky Note2" }, { "parameters": { "content": "## Extract source\nAccepts {\"claim\":\"claim text\", \"search_results_text\":\"search results\"}, returns name of any identified source", "height": 592, "width": 1280 }, "type": "n8n-nodes-base.stickyNote", "position": [ 1312, 736 ], "typeVersion": 1, "id": "4a697517-4fba-438c-8ea4-2db1359fbffb", "name": "Sticky Note4" }, { "parameters": { "promptType": "define", "text": "=%CLAIM%\n{{ $json.body.claim }}\n\n%TEXTS%\n{{ $json.body.search_result_texts }}\n\nIf there is a primary source or research mentioned? If not, then surveys or brief mention of where the information comes from. Ignore sources that are agencies who are selling the solution to the claim.\n\n\n%TASK%\nReview the CLAIM, and then the TEXTS and tell me the relevant primary source. Be concise and specific. Just the source I can use in a search, no explanation. Single sentence\n\n\n%SOURCE%\n", "batching": {} }, "type": "@n8n/n8n-nodes-langchain.chainLlm", "typeVersion": 1.7, "position": [ 1504, 896 ], "id": "f6fa7569-2d72-4105-bf09-279ab122738c", "name": "Locate source to research" }, { "parameters": { "model": "openai/gpt-5.4-nano", "options": {} }, "type": "@n8n/n8n-nodes-langchain.lmChatOpenRouter", "typeVersion": 1, "position": [ 1504, 1056 ], "id": "a3a55fcb-3732-4085-a8bf-64e7ad930e4f", "name": "OpenRouter Chat Model2", "credentials": { "openRouterApi": { "id": "baaHEuVk7u6Z0Liu", "name": "OpenRouter account" } } }, { "parameters": { "assignments": { "assignments": [ { "id": "ba6152b3-3e24-464e-a4a5-f9742072e75c", "name": "source", "value": "={{ $json.text }}", "type": "string" }, { "id": "eb0c6e99-ff8d-486d-85bf-e57d629e83d2", "name": "claim", "value": "={{ $('Webhook3').item.json.body.claim }}", "type": "string" } ] }, "options": {} }, "type": "n8n-nodes-base.set", "typeVersion": 3.4, "position": [ 2080, 896 ], "id": "b2235343-91c5-4647-a765-7a6e0dd67437", "name": "Edit Fields4" }, { "parameters": { "promptType": "define", "text": "=Extract just the source name as a single sentence.\n\n {{ $json.text }}\n\n", "batching": {} }, "type": "@n8n/n8n-nodes-langchain.chainLlm", "typeVersion": 1.7, "position": [ 1792, 896 ], "id": "3f82a4ce-c290-45be-8475-78ba058030e1", "name": "Basic LLM Chain2" }, { "parameters": { "model": "openai/gpt-5.4-nano", "options": {} }, "type": "@n8n/n8n-nodes-langchain.lmChatOpenRouter", "typeVersion": 1, "position": [ 1792, 1056 ], "id": "6af1fcad-a487-40ab-ad23-8b6cfeac6dcd", "name": "OpenRouter Chat Model4", "credentials": { "openRouterApi": { "id": "baaHEuVk7u6Z0Liu", "name": "OpenRouter account" } } }, { "parameters": { "httpMethod": "POST", "path": "e3fc4b32-2c5b-47ea-b852-f507c7064375", "responseMode": "lastNode", "responseData": "allEntries", "options": {} }, "type": "n8n-nodes-base.webhook", "typeVersion": 2.1, "position": [ 1344, 896 ], "id": "f7ed7d29-cfe1-4107-b7e9-4727e5b7ca7b", "name": "Webhook3", "webhookId": "e3fc4b32-2c5b-47ea-b852-f507c7064375" }, { "parameters": { "content": "## Extract claims\nSend {\"text\":\"my article content\"}, returns a list of claims to validate", "height": 752, "width": 1024 }, "type": "n8n-nodes-base.stickyNote", "position": [ -1360, 736 ], "typeVersion": 1, "id": "4aa11d89-2677-4161-acd6-2f7d7292b6b5", "name": "Sticky Note5" }, { "parameters": { "model": "google/gemma-4-31b-it", "options": {} }, "type": "@n8n/n8n-nodes-langchain.lmChatOpenRouter", "typeVersion": 1, "position": [ -1120, 1024 ], "id": "e2b9e4e2-deb1-4743-8280-52e835b5c19a", "name": "OpenRouter Chat Model1", "credentials": { "openRouterApi": { "id": "baaHEuVk7u6Z0Liu", "name": "OpenRouter account" } } }, { "parameters": { "jsonSchemaExample": "{\n\t\"claims\": [\"Los Angeles\", \"San Francisco\", \"San Diego\"]\n}" }, "type": "@n8n/n8n-nodes-langchain.outputParserStructured", "typeVersion": 1.3, "position": [ -976, 1024 ], "id": "55034583-3d57-4a4f-ba17-fc45b9fb763c", "name": "Structured Output Parser" }, { "parameters": { "promptType": "define", "text": "=This is an article. list the claims that need validating with research or claims that can be made stronger with research. output as an array. Be strict in ensuring the wording reflects the text accurately when extracting claims.\n\n{{ $json.body.text }}\n\n%LIST OF CLAIMS%", "hasOutputParser": true, "batching": {} }, "type": "@n8n/n8n-nodes-langchain.chainLlm", "typeVersion": 1.7, "position": [ -1120, 864 ], "id": "569f7f86-6192-402b-ad4b-4e30dd64d95a", "name": "List claims for search" }, { "parameters": { "fieldToSplitOut": "output.claims", "options": {} }, "type": "n8n-nodes-base.splitOut", "typeVersion": 1, "position": [ -800, 1152 ], "id": "96c2cde3-59bc-4583-851c-9c74a635ddd9", "name": "Split Out1" }, { "parameters": { "model": "google/gemma-4-31b-it", "options": {} }, "type": "@n8n/n8n-nodes-langchain.lmChatOpenRouter", "typeVersion": 1, "position": [ -1088, 1312 ], "id": "01ede04d-b824-4f74-9a05-9e6575e3069e", "name": "OpenRouter Chat Model3", "credentials": { "openRouterApi": { "id": "baaHEuVk7u6Z0Liu", "name": "OpenRouter account" } } }, { "parameters": { "jsonSchemaExample": "{\n\t\"claims\": [\"Los Angeles\", \"San Francisco\", \"San Diego\"]\n}" }, "type": "@n8n/n8n-nodes-langchain.outputParserStructured", "typeVersion": 1.3, "position": [ -944, 1312 ], "id": "9052f2f1-6826-4e51-b267-518b0fba3cd6", "name": "Structured Output Parser1" }, { "parameters": { "promptType": "define", "text": "=%CLAIMS%\n{{ $json.output.claims }}\n\n%SOURCE TEXT%\n{{ $('Webhook4').item.json.body.text }}\n\n%TASK%\nReview the list of claims that were made from TEXT, remove duplicates, ensure accurate representation, keep only those that require verification. Remove claim type and rewrite as a claim to validate, accurate to the source text\n\nReturn as an Array.\n\n%OUTPUT%\n", "hasOutputParser": true, "batching": {} }, "type": "@n8n/n8n-nodes-langchain.chainLlm", "typeVersion": 1.7, "position": [ -1088, 1152 ], "id": "4f241c2e-7401-4a1f-8ace-8db3bc18450b", "name": "Clean up claims" }, { "parameters": { "assignments": { "assignments": [ { "id": "beabeb6d-5eb7-40d3-b44d-823bca84f213", "name": "claim", "value": "={{ $json['output.claims'] }}", "type": "string" } ] }, "options": {} }, "type": "n8n-nodes-base.set", "typeVersion": 3.4, "position": [ -576, 1152 ], "id": "1a6cdfc1-92ef-4186-97f2-4bf77dbe8d7f", "name": "Claims" }, { "parameters": { "httpMethod": "POST", "path": "de2c2fe1-3b0a-44e9-bc67-d8464590649f", "responseMode": "lastNode", "responseData": "allEntries", "options": {} }, "type": "n8n-nodes-base.webhook", "typeVersion": 2.1, "position": [ -1296, 864 ], "id": "b512b7ae-de49-4b68-b66f-67c659a57ae3", "name": "Webhook4", "webhookId": "de2c2fe1-3b0a-44e9-bc67-d8464590649f" }, { "parameters": { "httpMethod": "POST", "path": "659a515b-fcbf-4eb4-8a68-3bdf39864a0d", "responseMode": "lastNode", "responseData": "allEntries", "options": {} }, "type": "n8n-nodes-base.webhook", "typeVersion": 2.1, "position": [ -1344, 448 ], "id": "24838812-b9d8-4f97-8036-a2839383ff35", "name": "Webhook5", "webhookId": "659a515b-fcbf-4eb4-8a68-3bdf39864a0d" }, { "parameters": { "method": "POST", "url": "https://n8n.iqbala.com/webhook/c708fdc8-d2c6-4d7d-9329-7da150a2a426", "sendBody": true, "specifyBody": "json", "jsonBody": "={\"text\": \"{{ $json.body.text }}\"}", "options": {} }, "type": "n8n-nodes-base.httpRequest", "typeVersion": 4.2, "position": [ -1152, 448 ], "id": "202f9257-dc1b-4713-86b4-b07a25df8f93", "name": "Extract claims" }, { "parameters": { "method": "POST", "url": "https://n8n.iqbala.com/webhook/4556b597-2b3b-444d-baa9-8dff5ab11413", "sendBody": true, "specifyBody": "json", "jsonBody": "={\n\"claim\": \"{{ $json.claim }}\",\n\"search_result_texts\":{{ JSON.stringify($json.highlights) }}\n}", "options": {} }, "type": "n8n-nodes-base.httpRequest", "typeVersion": 4.2, "position": [ -160, 432 ], "id": "b671cd0f-96d7-4ec6-933d-9a9dbdb63ce4", "name": "Claim check accuracy" }, { "parameters": { "conditions": { "options": { "caseSensitive": true, "leftValue": "", "typeValidation": "strict", "version": 2 }, "conditions": [ { "id": "29f22ba7-0b3d-4825-a3cb-7d16e8b43b50", "leftValue": "={{ $json.numbers_check.valid }}", "rightValue": "", "operator": { "type": "boolean", "operation": "true", "singleValue": true } } ], "combinator": "and" }, "options": {} }, "type": "n8n-nodes-base.if", "typeVersion": 2.2, "position": [ 160, 880 ], "id": "4c0052f9-7767-417d-b7a8-0c3f35e208c4", "name": "If" }, { "parameters": { "assignments": { "assignments": [ { "id": "d6c4b1b6-e601-45fd-83c1-38630e7b499a", "name": "valid", "value": false, "type": "boolean" }, { "id": "7c832c2b-2a51-4713-a095-c1e6db76e015", "name": "claim", "value": "={{ $('Webhook').item.json.body.claim }}", "type": "string" }, { "id": "a2c6c31c-b376-4f9a-8a39-6b988ee02335", "name": "search_results_texts", "value": "={{ $('Webhook').item.json.body.search_result_texts }}", "type": "string" } ] }, "options": {} }, "type": "n8n-nodes-base.set", "typeVersion": 3.4, "position": [ 320, 896 ], "id": "6ff01f18-c11c-47eb-8169-c953518ba4f6", "name": "Edit Fields1" }, { "parameters": { "method": "POST", "url": "https://n8n.iqbala.com/webhook/e3fc4b32-2c5b-47ea-b852-f507c7064375", "sendBody": true, "specifyBody": "json", "jsonBody": "={\"claim\": \"{{ $json.claim }}\",\"search_result_texts\":{{ JSON.stringify($json.search_results_texts) }}}", "options": {} }, "type": "n8n-nodes-base.httpRequest", "typeVersion": 4.2, "position": [ 224, 400 ], "id": "89dd583e-f367-4e38-82d3-5d8f9abcbc72", "name": "Extract source" }, { "parameters": { "query": "={{ $json.body.source }}. {{ $json.body.claim }}", "numResults": 5, "additionalFields": {}, "contentsOptions": {}, "requestOptions": {} }, "type": "n8n-nodes-exa-official.exa", "typeVersion": 1, "position": [ 1520, 1520 ], "id": "cdb22717-4ac2-4282-bb32-294471ddabd7", "name": "Deep search1", "credentials": { "exaApi": { "id": "zWjZTTTJMfHuX1a8", "name": "Exa account" } } }, { "parameters": { "method": "POST", "url": "https://n8n.iqbala.com/webhook/4556b597-2b3b-444d-baa9-8dff5ab11413", "sendBody": true, "specifyBody": "json", "jsonBody": "={\n\"claim\": \"{{ $('Webhook6').item.json.body.claim }}\",\n\"search_result_texts\":{{ JSON.stringify($json.text) }}\n}", "options": {} }, "type": "n8n-nodes-base.httpRequest", "typeVersion": 4.2, "position": [ 2160, 1520 ], "id": "7e9494ff-a007-44fc-a4db-e325214c1c3c", "name": "Claim check accuracy2" }, { "parameters": { "assignments": { "assignments": [ { "id": "b9bbe1d6-823a-4717-ae49-951b213948cb", "name": "claim", "value": "={{ $json.claim }}", "type": "string" }, { "id": "2afa4604-cc66-44b2-a173-0fec05e2e9c9", "name": "valid", "value": "={{ $json.valid }}", "type": "string" }, { "id": "96b236af-e712-4ea7-9070-d60044091f3f", "name": "url", "value": "={{ $('Reorder based on trusted domains').item.json.results[0].url }}", "type": "string" }, { "id": "4f2290d8-2516-42e0-9e76-27bb7a3c73e2", "name": "publishedDate", "value": "={{ $('Reorder based on trusted domains').item.json.results[0].publishedDate }}", "type": "string" }, { "id": "bbfc2251-5046-4511-ab63-748b8668c132", "name": "source", "value": "={{ $('Webhook6').item.json.body.source }}", "type": "string" } ] }, "options": {} }, "type": "n8n-nodes-base.set", "typeVersion": 3.4, "position": [ 2336, 1520 ], "id": "d7ee715c-0317-44bb-a8e4-97db9e17e3fa", "name": "Edit Fields5" }, { "parameters": { "httpMethod": "POST", "path": "071a243d-4fc9-4845-a358-f8c2113b1ded", "responseMode": "lastNode", "responseData": "allEntries", "options": {} }, "type": "n8n-nodes-base.webhook", "typeVersion": 2.1, "position": [ 1360, 1520 ], "id": "06a7ca7f-57ec-49d3-9bbd-af057925df9a", "name": "Webhook6", "webhookId": "071a243d-4fc9-4845-a358-f8c2113b1ded" }, { "parameters": { "method": "POST", "url": "https://n8n.iqbala.com/webhook/f324f30c-334d-4286-b9ff-14a08b1bcf6a", "sendBody": true, "specifyBody": "json", "jsonBody": "={\"claim\":\"{{ $json.claim }}\"}", "options": {} }, "type": "n8n-nodes-base.httpRequest", "typeVersion": 4.2, "position": [ -544, 464 ], "id": "2fc7206f-8ab5-4f2d-91d3-21a4d0e95dc0", "name": "Search for claim" }, { "parameters": { "batchSize": 10, "options": {} }, "type": "n8n-nodes-base.splitInBatches", "typeVersion": 3, "position": [ -720, 448 ], "id": "9cfa410b-bc28-4165-8042-bbc67f269be8", "name": "Loop Over Items" }, { "parameters": { "amount": 1 }, "type": "n8n-nodes-base.wait", "typeVersion": 1.1, "position": [ -368, 464 ], "id": "05d60cd9-635b-49a6-bddf-36f1e4468ae4", "name": "Wait1", "webhookId": "9ec6256a-b503-4e91-ba59-e6cb989266d7" }, { "parameters": { "options": {} }, "type": "n8n-nodes-base.splitInBatches", "typeVersion": 3, "position": [ 432, 352 ], "id": "54a1ee92-0afb-426c-8a4e-c1e8263657b6", "name": "Loop Over Items1" }, { "parameters": { "amount": 1 }, "type": "n8n-nodes-base.wait", "typeVersion": 1.1, "position": [ 1200, 368 ], "id": "4b059258-421c-46c4-b85f-9fe776ab2840", "name": "Wait", "webhookId": "41a06ae0-1ed9-40b7-8a1a-676fd57cf065" }, { "parameters": { "jsCode": "// Define trusted domains\nconst trusted_domains = new Set([\n \"hbr.org\",\n \"sloanreview.mit.edu\",\n \"onlinelibrary.wiley.com\",\n \"journals.aom.org\",\n \"jmr.sagepub.com\",\n \"www.jstor.org\",\n \"journals.sagepub.com\",\n \"www.palgrave.com\",\n \"arxiv.org\",\n \"www.ssrn.com\",\n \"ideas.repec.org\",\n \"journals.plos.org\",\n \"doaj.org\",\n \"www.mckinsey.com\",\n \"www.bcg.com\",\n \"www2.deloitte.com\",\n \"www.pwc.com\",\n \"home.kpmg.com\",\n \"www.weforum.org\",\n \"www.oecd.org\",\n \"www.worldbank.org\",\n \"www.imf.org\",\n \"hbswk.hbs.edu\",\n \"www.economist.com\",\n \"www.ft.com\",\n \"www.bloomberg.com\",\n \"www.businessweek.com\",\n \"www.forbes.com\",\n \"www.wsj.com\",\n \"www.bbc.com\",\n \"www.bbc.co.uk\",\n \"www.fastcompany.com\",\n \"www.bls.gov\",\n \"www.census.gov\",\n \"ec.europa.eu\",\n \"unctad.org\",\n \"www.wto.org\",\n \"scholar.google.com\",\n \"muse.jhu.edu\",\n \"archive.org\",\n \"fs.blog\",\n \"online.hbs.edu\",\n \"oyc.yale.edu\",\n \"www.kaggle.com\",\n \"datasetsearch.google.com\",\n \"www.statista.com\",\n \"ourworldindata.org\"\n]);\n\n// Get the input data\nconst inputData = $input.first();\n\n// Function to extract domain from URL\nconst getDomain = (url) => {\n try {\n let domain = url.replace(/^(https?:\\/\\/)?(www\\.)?/, '').split('/')[0];\n if (domain.includes('.')) {\n const parts = domain.split('.');\n if (parts.length > 2) {\n domain = parts.slice(-2).join('.');\n }\n }\n return domain.toLowerCase();\n } catch (e) {\n return null;\n }\n};\n\n// Process each result\nconst results = inputData.json.results.map(result => {\n const domain = getDomain(result.url);\n const isTrusted = domain ? trusted_domains.has(domain) : false;\n\n return {\n ...result,\n isTrustedSource: isTrusted,\n sourceDomain: domain\n };\n});\n\n// Separate trusted and untrusted results\nconst trustedResults = results.filter(result => result.isTrustedSource);\nconst untrustedResults = results.filter(result => !result.isTrustedSource);\n\n// Combine results with trusted first\nconst orderedResults = [...trustedResults, ...untrustedResults];\n\n// Update the original data structure\nconst outputData = {\n ...inputData,\n json: {\n ...inputData.json,\n results: orderedResults,\n metadata: {\n trustedSourcesCount: trustedResults.length,\n totalSourcesCount: results.length,\n trustedSources: trustedResults.map(r => r.url)\n }\n }\n};\n\nreturn outputData;" }, "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 1664, 1520 ], "id": "caba92c6-1524-4158-a924-ab1ee90c151e", "name": "Reorder based on trusted domains" }, { "parameters": { "assignments": { "assignments": [ { "id": "b9bbe1d6-823a-4717-ae49-951b213948cb", "name": "claim", "value": "={{ $json.claim }}", "type": "string" }, { "id": "2afa4604-cc66-44b2-a173-0fec05e2e9c9", "name": "valid", "value": "={{ $json.valid }}", "type": "string" }, { "id": "96b236af-e712-4ea7-9070-d60044091f3f", "name": "url", "value": "={{ $json.url }}", "type": "string" }, { "id": "4f2290d8-2516-42e0-9e76-27bb7a3c73e2", "name": "publishedDate", "value": "={{ $json.publishedDate }}", "type": "string" }, { "id": "bbfc2251-5046-4511-ab63-748b8668c132", "name": "source", "value": "={{ $json.source }}", "type": "string" } ] }, "options": {} }, "type": "n8n-nodes-base.set", "typeVersion": 3.4, "position": [ 864, 368 ], "id": "859be95c-2cf1-4380-b18e-048e5f5e5be5", "name": "Edit Fields6" }, { "parameters": { "url": "={{ $json.results[0].url }}", "options": {} }, "type": "n8n-nodes-browserless-api.browserless", "typeVersion": 1, "position": [ 1824, 1520 ], "id": "cb8fcf64-0bdb-4f35-ad5e-c4f85c0f93c7", "name": "Browserless", "credentials": { "browserlessApi": { "id": "DlwfOY9aHnrU75eu", "name": "Browserless account" } } }, { "parameters": { "operation": "append", "documentId": { "__rl": true, "value": "1Q2KhTom5bLshw-QMlxZQfmXf_nulf0c-a3RXAq1YaXo", "mode": "list", "cachedResultName": "sources_list", "cachedResultUrl": "https://docs.google.com/spreadsheets/d/1Q2KhTom5bLshw-QMlxZQfmXf_nulf0c-a3RXAq1YaXo/edit?usp=drivesdk" }, "sheetName": { "__rl": true, "value": 1194817386, "mode": "list", "cachedResultName": "claims", "cachedResultUrl": "https://docs.google.com/spreadsheets/d/1Q2KhTom5bLshw-QMlxZQfmXf_nulf0c-a3RXAq1YaXo/edit#gid=1194817386" }, "columns": { "mappingMode": "defineBelow", "value": { "Claim": "={{ $json.claim }}", "Validated": "={{ $json.valid }}", "Source": "={{ $json.source }}", "Links": "={{ $json.url }}", "PublishedDate": "={{ $json.publishedDate }}" }, "matchingColumns": [], "schema": [ { "id": "Claim", "displayName": "Claim", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true }, { "id": "Source", "displayName": "Source", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true }, { "id": "PublishedDate", "displayName": "PublishedDate", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true }, { "id": "Links", "displayName": "Links", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true }, { "id": "Validated", "displayName": "Validated", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true } ], "attemptToConvertTypes": false, "convertFieldsToString": false }, "options": {} }, "type": "n8n-nodes-base.googleSheets", "typeVersion": 4.7, "position": [ 1040, 368 ], "id": "baaa9b9b-aebf-4978-93f0-c68770416154", "name": "Append row in sheet", "credentials": { "googleSheetsOAuth2Api": { "id": "dsUvNHMNlqw4axJe", "name": "Google Sheets account" } } }, { "parameters": { "conditions": { "options": { "caseSensitive": true, "leftValue": "", "typeValidation": "strict", "version": 2 }, "conditions": [ { "id": "36f5fe74-f292-4e2f-9e98-7482b358523a", "leftValue": "", "rightValue": "", "operator": { "type": "string", "operation": "equals", "name": "filter.operator.equals" } } ], "combinator": "and" }, "options": {} }, "type": "n8n-nodes-base.if", "typeVersion": 2.2, "position": [ 0, 432 ], "id": "495747d9-e35a-463a-917d-6c2f9f3ed8a8", "name": "If1" }, { "parameters": { "assignments": { "assignments": [ { "id": "26e1d44d-aa4e-45ec-8d84-43f613b88d50", "name": "claim", "value": "={{ $json.claim }}", "type": "string" }, { "id": "98793c1e-63c2-4b46-9ab8-db9f25ad0886", "name": "valid", "value": false, "type": "boolean" } ] }, "options": {} }, "type": "n8n-nodes-base.set", "typeVersion": 3.4, "position": [ 224, 560 ], "id": "8d1d36f9-c178-4dd6-9716-28bb68635980", "name": "Edit Fields2" }, { "parameters": { "operation": "append", "documentId": { "__rl": true, "value": "1Q2KhTom5bLshw-QMlxZQfmXf_nulf0c-a3RXAq1YaXo", "mode": "list", "cachedResultName": "sources_list", "cachedResultUrl": "https://docs.google.com/spreadsheets/d/1Q2KhTom5bLshw-QMlxZQfmXf_nulf0c-a3RXAq1YaXo/edit?usp=drivesdk" }, "sheetName": { "__rl": true, "value": 1194817386, "mode": "list", "cachedResultName": "claims", "cachedResultUrl": "https://docs.google.com/spreadsheets/d/1Q2KhTom5bLshw-QMlxZQfmXf_nulf0c-a3RXAq1YaXo/edit#gid=1194817386" }, "columns": { "mappingMode": "defineBelow", "value": { "Claim": "={{ $json.claim }}", "Validated": "false" }, "matchingColumns": [], "schema": [ { "id": "Claim", "displayName": "Claim", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true }, { "id": "Source", "displayName": "Source", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true }, { "id": "PublishedDate", "displayName": "PublishedDate", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true }, { "id": "Links", "displayName": "Links", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true }, { "id": "Validated", "displayName": "Validated", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true } ], "attemptToConvertTypes": false, "convertFieldsToString": false }, "options": {} }, "type": "n8n-nodes-base.googleSheets", "typeVersion": 4.7, "position": [ 400, 560 ], "id": "f7d60061-139a-4081-a233-f9e2dda15739", "name": "Append row in sheet1", "credentials": { "googleSheetsOAuth2Api": { "id": "dsUvNHMNlqw4axJe", "name": "Google Sheets account" } } }, { "parameters": { "jsCode": "const MAX_CHARS = 20_000; // ~5k tokens, adjust as needed\n\nconst browserless_content = JSON.stringify($input.first().json.markdown?.substring(0, MAX_CHARS));\nconst browserless_content_type = $input.first().json.contentType;\nconst exa_content = JSON.stringify(\n $('Reorder based on trusted domains').first().json.results[0].highlights\n);\n\nfunction isGarbled(text) {\n if(text.length > 5000){\n return true;\n }\n}\n\nlet finalContent = '';\n\nif (!isGarbled(browserless_content)) {\n finalContent = browserless_content;\n} else if (!isGarbled(exa_content)) {\n finalContent = exa_content;\n}\nconsole.log('length:', $input.first().json.markdown?.length);\nconsole.log('type:', typeof $input.first().json.markdown);\nreturn { \"text\": finalContent };" }, "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 1984, 1520 ], "id": "58f3a507-8b7a-490c-b41d-19f8eb3f48f4", "name": "Ensure text if garbled" }, { "parameters": { "method": "POST", "url": "https://n8n.iqbala.com/webhook/071a243d-4fc9-4845-a358-f8c2113b1ded", "sendBody": true, "specifyBody": "json", "jsonBody": "={\"claim\": \"{{ $json.claim }}\",\"source\":\"{{ $json.source }}\"}", "options": {} }, "type": "n8n-nodes-base.httpRequest", "typeVersion": 4.2, "position": [ 672, 368 ], "id": "4ee25000-af1a-4776-b337-8b3d866352c3", "name": "Validate final source", "retryOnFail": true } ], "pinData": { "Claim check accuracy": [ { "json": { "valid": false, "claim": "Companies that invested in AI too early are casualties due to products that don't work.", "search_results_texts": "[[\"This article explores the challenges faced by companies in profiting from \\nartificial intelligence (AI). The case of Swedish multinational networking and \\ntelecommunications company Ericsson highlights that while AI holds great promise, \\nrealizing returns on investment in AI is difficult. This article identifies two main\\n[...]\\nstrategies: bottom-line improvements, which focus on internal efficiency gains, and\\n[...]\\ntop-line growth, which involves creating new businesses enabled by AI. The latter\\n[...]\\nstrategy is particularly challenging given the need for co-specialized complementary\\n[...]\\nassets that amplify challenges related to data, capabilities, and value. This study of\\n[...]\\nEricsson’s experience emphasizes the importance of having clear strategic objectives \\nand a deep understanding of complementarities in efforts to implement AI successfully.\\n[...]\\nmaterialize,3 highlighting\\n[...]\\nfor innovators: how to profit\\n[...]\\nfrom investments in\\n[...]\\nrelated innovation.\\n[...]\\nHowever, few experts explain the real challenges business leaders face in trying to\\n[...]\\nprofit from AI.24 As of 2025, there is an increasing number of failed investments\\n[...]\\nin AI.25 Profits will not always follow from investments—our research on Ericsson\\n[...]\\nshows investments in AI technologies must be accompanied by a deep under\\u0002standing and management of complementarities.\\n[...]\\nWhy is it difficult to capture value from AI? Despite significant investments\\n[...]\\nand efforts, Ericsson’s generation of top-line growth by the use of AI solutions was\\n[...]\\nhampered by several problems, including difficulties in articulating the value of its\\n[...]\\nAI solutions, scaling the solutions in a cost-efficient way, and pricing them. Much\\n[...]\\nof the challenge is associated with co-specialized complementary assets. Key to\\n[...]\\nprofiting from AI in this setting is access to and investments in customer-con\\u0002trolled complementary assets.\\n[...]\\nSince almost every implementation of AI required some level of co-special\\u0002ization between Ericsson’s\\n[...]\\nits customers’ assets, it was difficult to scale AIaaS\\n[...]\\nJust as the original PFI framework\\n[...]\\n32 comple\\u0002mentary\\n[...]\\nnecessary to profit from investments\\n[...]\\nbottom-line improvements. In particular, top-line growth strategies rely on co\\u0002specialized complementary assets distributed across firm boundaries, amplifying\\n[...]\\nthree important AI appropriation challenges: a data challenge, a capability chal\\u0002lenge, and a value challenge (see Figure 4).\\n[...]\\nIt is easy to fall into the trap of developing cool, alluring\\n[...]\\nwhile for\\u0002getting how\\n[...]\\nsolves customer problems\\n[...]\\nit might sometimes be difficult to profit, as Ericsson’s case shows.\\n[...]\\nGiven the complementarities among AI, data, and complementary\\n[...]\\nis impossible to value on its own. In other words, the\\n[...]\\nIt is clear that AI can be used to enable both top-line growth and bot\\u0002tom-line improvements. While all types of AI transformations are challeng\\u0002ing, transformations aiming for top-line growth are especially difficult because\\n[...]\\nof distributed complementary assets, which amplify data, capability, and value\\n[...]\\nchallenges. An important differentiator between AI strategies for bottom-line\\n[...]\\nimprovements and top-line growth is indeed the nature and locus of comple\\u0002mentarities. Our research shows that top-line growth often requires external data\\n[...]\\naccess and integration with complementary customer technologies—thus a level\\n[...]\\nof co-specialization between the innovator’s AI and the customers’ assets—lead\\u0002ing to significant value-capture challenges for the innovator. This sheds new light\\n[...]\\nmessage to management practice is clear: to grow the top line with AI, firms\\n[...]\\nmust analyze complementarities carefully and invest wisely in competence, tech\\u0002nology, data, and dynamic capabilities. In some cases, firms may be better off\\n[...]\\nprioritizing\\n[...]\\nuse of AI for their\"],[\"AI has never moved faster inside organizations – yet for most, it has never paid off less. Investment in artificial intelligence is surging, but almost 90 percent of firms report returns lagging spending. In practice, AI reaches production faster than financial breakeven, so costs rise long before value appears. To understand the gap, Roland Berger’s AI Lab surveyed 203 senior executives across industries and geographies. The finding is stark: only about ten percent consistently capture meaningful impact. These “Industrializers” treat AI as an industrial capability to be engineered, not pilots to be launched. The study shows why so many organizations remain stuck in profitless prosperity – and what it takes to close the AI value gap.\\n[...]\\nAs adoption scaled, it followed a buy-heavy path. Almost 40 percent rely primarily on off-the-shelf solutions, while only a small minority build mainly in-house. This approach has helped accelerate deployment, but it has also limited the development of internal capabilities that compound over time. In short, companies have learned to make AI work, but they have not yet learned how to make AI pay.\\n[...]\\nThis imbalance is reflected in uneven outcomes. Although commitment to AI is now high, budgets and deployment speed are no longer reliable predictors of success. Our analysis groups companies into four segments, led by a small elite of Industrializers that consistently convert AI activity into value. Around ten percent of firms fall into this category, while much larger shares are either Stalled, matching ambition and spend but not returns, or Observers, piloting without scaling. These patterns cut across geographies, industries and company sizes. Crucially, the data shows that speed alone does not drive success; Industrializers make AI outcomes predictable, while others struggle to turn operational progress into financial impact.\\n[...]\\nThe second root cause is a hollow operating model and technology architecture. Many organizations have bought the Ferrari of modern AI infrastructure, but are running it on the go-kart engine of shallow integration. Wrappers accelerate early progress, but collapse under operational load.\\n[...]\\nWhy does the AI value gap persist? Not because organizations lack ambition, capital or access to technology, but because most still treat AI as a series of projects to be bought rather than as a capability to be engineered. A small minority has broken out of this pattern. These Industrializers do not spend more or move faster than their peers – they have changed how AI is run. Across the data, a consistent set of operating principles explains how they convert AI activity into durable value.\\n[...]\\nhas shifted – and\\n[...]\\n. What holds many organizations\\n[...]\\nhas adapted to run AI\\n[...]\\na system rather than\\n[...]\\nof projects.\\n[...]\\nWhy are almost ninety percent of firms underperforming despite record levels of AI investment? The answer is uncomfortable. The market has not stalled for lack of ambition, budget or access to technology. It has stalled because many organizations have misunderstood what the task really is. While AI deployment has accelerated, most still approach it as a set of projects to be launched and optimized in isolation.\\n[...]\\nA small minority has taken a different path. These Industrializers treat AI as a standard industrial capability, designed to be engineered, governed and run at scale. What separates them is not the models they buy, but the machine they build around those models. They wire AI into the enterprise, align speed to production with speed to value and focus on operating systems rather than celebrating launches.\\n[...]\\nAs we move into 2026, this distinction will matter more, not less. The AI value gap is widening, and it will harden into a lasting competitive disadvantage for those that fail to adapt. You cannot buy your way out of this transformation– you have to engineer your way through it.\"],[\"After all the hype over artificial intelligence (AI), the value is hard to find. CEOs have authorized investments, hired talent, and launched pilots—but only 22% of companies have advanced beyond the proof-of-concept stage to generate some value, and only 4% are creating substantial value, according to new BCG research.\\n[...]\\nOur new report yields important insights into what AI leaders are doing to drive real value from the technology, where others fall short, where the value is coming from, how individual sectors are performing, and how companies can change their own AI trajectories.\\n[...]\\nWhen it comes to AI, including generative AI (GenAI), companies across all sectors fall into four categories that range from not doing much (25%) to still focusing on proof of concepts (49%) to scaling value (22%) to operating value engines (4%). Companies in the latter two categories—the leaders—are not exclusively digital natives. More than half of the companies in the leader categories are incumbents that have strengthened their capabilities and are using them to drive greater productivity, increased revenue, and even competitive advantage.\\n[...]\\n- They are more ambitious, setting big targets ($1 billion in productivity improvements at a financial institution, for example, or $1 billion in combined revenue increases and cost reductions at a biopharma firm) and investing in AI and workforce enablement.\\n[...]\\ninvest strategically in a few high-priority opportunities to scale and maximize AI’s value.\\n[...]\\nLeaders expect to generate significant value—45% more in cost reduction and 60% more in revenue growth than other firms. They expect their RoI from AI initiatives in 2024 to more than double what other companies expect from theirs.\\n[...]\\nLeaders are generating 62% of the value of AI in core business processes. (See the exhibit .) They are also reaping success across multiple functions. We look at where companies from different sectors are achieving positive results from AI across a wide range of functions, from customer service to R&D to operations and procurement.\\n[...]\\nMost companies need an effective action plan to create value from AI. Leaders point the way by following a seven-step playbook:\\n[...]\\n1. Set a bold strategic commitment from the top.\\n[...]\\n2. Maximize the potential value of AI with initiatives that include streamlining everyday business processes, transforming entire business functions, and developing new offerings.\\n[...]\\n3. Implement one to three high-value, easy-to-implement initiatives to fund the journey.\\n[...]\\n4. Ensure that the minimal viable infrastructure required for these initiatives exists.\\n[...]\\n5. Perform an AI maturity assessment to baseline current critical capability gaps versus peers.\\n[...]\\n6. Ensure that implementation governance focuses on people and processes over technology and algorithms.\\n[...]\\n7. Set up guardrails to deploy AI responsibly.\"],[\"Global corporate AI investment reached $252.3 billion in 2024, yet only 6% of firms report significant\\n[...]\\nearnings impact. This article argues that AI project failure is fundamentally an organizational learning\\n[...]\\nproblem rather than a technology deficit. Drawing on a systematic synthesis of 20 foundational\\n[...]\\nAs illustrated in Figure 1, the returns have not kept pace with spending. Estimates suggest that over 80% of AI projects\\n[...]\\nfail, roughly double the rate of non-AI IT initiatives [6, 7]. S&P Global’s 451 Research reports that the share of\\n[...]\\ncompanies abandoning the majority of their AI initiatives before production rose from 17% to 42% year over year [8].\\n[...]\\nBCG finds that only 26% of companies have moved beyond proof of concept to generate tangible value [9]. McKinsey\\n[...]\\nThe pattern is not one of insufficient investment. Organizations are spending more on AI than at any point in the\\n[...]\\ntechnology’s history, yet according to the reports readiness scores are declining, project abandonment is rising, and\\n[...]\\nnearly half of business leaders report no gains or results below expectations [4, 5]. Israeli and Ascarza describe this in\\n[...]\\nHarvard Business Review as a “technology-first trap”: organizations deploy AI solutions department by department\\n[...]\\nwithout linking them to enterprise goals, producing technically successful implementations that never reach production\\n[...]\\n[10].\\n[...]\\nfor stamped steel, could not accommodate the AI-generated geometry. The technology worked. The\\n[...]\\nRAND’s study of AI project failure identified five root causes through interviews with 65 data scientists and engineers:\\n[...]\\nleadership-driven failures, data quality limitations, bottom-up technology chasing, underinvestment in deployment\\n[...]\\ninfrastructure, and attempts to apply AI\\n[...]\\nproblems beyond the current state of the art [6]. Of these five, only the\\n[...]\\nIf the root causes are organizational, readiness assessments should show improvement as organizations invest. They do\\n[...]\\nnot. Cisco’s AI Readiness Index, surveying 7,985 senior leaders, found that governance readiness fell year over year\\n[...]\\n(from\\n[...]\\n4% to\\n[...]\\ndropped sharply (from\\n[...]\\ngrew [4].\\n[...]\\nAI-experienced firms reported more challenges related to technical fit and regulatory compliance than newcomers, not\\n[...]\\nfewer [11]. As one study observed, distinct technological challenges became “organizationally entangled” as firms\\n[...]\\n]. Among organizations\\n[...]\\nAt the Integrated stage, organizations can run pilots but many stall. Only 26% have moved beyond proof of concept\\n[...]\\n[9], and 42% abandoned the majority of their AI initiatives before production [8]. This is the “IT Silo Bottleneck.” The\\n[...]\\nAt the Orchestrated stage\\n[...]\\ndone across the enterprise. The MIT SMR-BCG\\n[...]\\nReframe the problem before investing in solutions. The data consistently show that AI failure is an organizational\\n[...]\\ncoordination problem that manifests as technology failure. Before launching the next AI initiative, leaders should ask\\n[...]\\nwhether the barriers they face involve cross-functional alignment, misaligned incentives, or capability gaps across roles.\\n[...]\\nIf they do, additional technology investment alone will not resolve them [10].\\n[...]\\npilots. Organizations that\\n[...]\\ncompetitors progress face compounding costs: not just financial, but through organizational cynicism that undermines\\n[...]\\n]. Organizations that have not built the\\n[...]\\nface a compounding challenge: they must simultaneously develop siloed-\\n[...]\\ncapabilities while competitors navigate\\n[...]\\nThe $252 billion paradox is not a technology problem awaiting a technology solution. It is an organizational learning\\n[...]\\nproblem that requires developmental, not transactional, intervention. The question facing leaders is no longer whether\\n[...]\\nto invest in AI. It is whether their organizations can learn fast enough to make those investments count.\"],[\"Boardrooms are full of discussion about the AI race, but beneath the buzz the reality is more complicated. Organisations are pouring in investment, yet returns are slow to materialise and hard to measure.\\n[...]\\nAcross industries, investment in AI is rising fast. According to Deloitte’s 2025 survey, 85 per cent of organisations increased their investment in the past 12 months, and 91 per cent plan to increase it again this year.\\n[...]\\nYet despite this momentum, most respondents reported achieving satisfactory ROI on a typical AI use case within two to four years. This is significantly longer than the typical payback period of seven to 12 months expected for technology investments.1 Only six per cent reported payback in under a year, and even among the most successful projects, just 13 per cent saw returns within 12 months.\\n[...]\\nAccording to the executives interviewed as part of this research, AI rarely delivers value in isolation. It is typically introduced alongside efforts to improve data quality, reconfigure teams or streamline operations, which makes it difficult to isolate its value.\\n[...]\\nThe challenge becomes even more pronounced with agentic AI. These systems promise end-to-end process automation and autonomous decision-making, but they also come with greater complexity and longer implementation timelines. Of the respondents already using agentic AI (representing 57% of the total respondents), just ten per cent of surveyed organisations said they are currently realising significant ROI from agentic AI. Among these agentic AI users, while half expect to see returns within three years, another third anticipate that ROI will take three to five years.\\n[...]\\nROI remains one of the most persistent challenges in AI adoption. It is a long-term endeavour, often taking several years to realise due to complex implementation, user adoption challenges and the need for process integration. The executives that Deloitte interviewed as part of this research give five key reasons:\\n[...]\\nFragmented systems and siloed platforms make it challenging to track before-and-after impact. Many organisations also overestimate their data maturity – investing in AI applications before addressing core data or infrastructure gaps, which delays results. While Proof of Concepts (PoCs) run on unrealistic dummy data are often a source of optimism for organisations, problems begin to emerge when they are populated with real data.\\n[...]\\nNew tools and use cases appear regularly, changing what is possible and shifting expectations mid-project. Leaders also described how hype and pressure can lead to premature investment.\\n[...]\\nAI is typically rolled out alongside broader digital, operational or structural changes – making it difficult to isolate AI’s contribution.\\n[...]\\nDespite unclear ROI, most organisations are not holding back. Deloitte’s 2025 survey confirms that investment continues to grow, driven by strategic necessity and belief in AI’s long-term impact.\\n[...]\\nlong-term\\n[...]\\nAI is forcing organisations to rethink what counts as value. Traditional ROI models are too narrow. Some 65 per cent now say AI is part of corporate strategy, recognising that not all returns are immediate or financial. This signals a shift: executives increasingly accept that returns may take years to materialise, and that not all benefits can be captured in traditional financial terms.\\n[...]\\nLeaders also recognise that AI’s success depends on a mature ecosystem, including integrated data platforms, reskilled workforces, scalable infrastructure and strong governance frameworks. As adoption accelerates, successful organisations will strike a balance between short-term wins and long-term ambition. ROI will be redefined – not only as cost savings, but as an indicator of innovation, resilience and sustainable growth.\"],[\"- 1.Gartner Q1 2025: 42% of companies with active AI initiatives in 2023 completely abandoned AI by early 2025—not just failed projects but strategic retreat from AI as a category, representing $18B in enterprise investment written off in 18 months\\n[...]\\n- 2.Five paths to abandonment: (1) Pilot graveyard (32%)—8-15 pilots launched, zero production, organization concludes 'AI doesn't work'; (2) Compliance wall (18%)—regulatory risk exceeds business benefit; (3) ROI reality check (25%)—AI works but economics don't justify costs; (4) Talent exodus (15%)—data scientists leave, knowledge evaporates; (5) Leadership change (10%)—new executives kill predecessors' initiatives\\n[...]\\nWhen S&P Global Market Intelligence published the findings of its 2025 enterprise survey, the numbers landed with unusual force: 42% of companies with active AI initiatives in 2023 had completely abandoned them by early 2025. Not paused. Not delayed. Abandoned.\\n[...]\\nThe scale of the writedown is staggering. An estimated $18 billion in enterprise AI investment was eliminated in roughly 18 months, as projects were canceled mid-flight, data science teams disbanded, and purpose-built infrastructure mothballed. This was not the familiar story of an 80% pilot failure rate, a figure the industry has long treated as the cost of doing business. This was something more consequential: organizations making a deliberate strategic decision to exit artificial intelligence as a technology category altogether, revealing a depth of institutional disillusionment that the sector has been slow to acknowledge.\\n[...]\\nThe most common path to abandonment, accounting for 32% of cases, follows a disturbingly predictable arc. An organization launches between eight and fifteen AI pilots across multiple business functions, none reaches production, and after 18 months of investment without measurable business impact, leadership concludes that AI simply does not work for their enterprise.\\n[...]\\nA Singapore-based retail bank illustrates the pattern with uncomfortable clarity. In 2023, the institution launched 11 simultaneous AI pilots spanning fraud detection, personalization, credit scoring, and conversational interfaces. By 2024, the bank had deployed $3.2 million with every pilot still mired in testing. In the first quarter of 2025, the CFO terminated the entire program and redirected its budget to proven technologies. The rationale was succinct: two years, eleven pilots, zero business value delivered.\\n[...]\\nA Thai manufacturing company walked this path after implementing a predictive maintenance system that reduced unplanned downtime by 18%, a meaningful operational gain. The 2024 cost audit, however, revealed a different story. Total annual costs for the system, including licensing, infrastructure, data scientists, and operations, reached $420,000. The value of the downtime reduction amounted to $280,000. The company was losing $140,000 per year on a technically successful AI deployment. By early 2025, it had returned to preventive maintenance schedules.\\n[...]\\n,00\\n[...]\\n. And vendor lock-\\n[...]\\nof 15 to 25 percent year over year. As the CFO involved observed\\n[...]\\nThe 42% of organizations that abandoned AI in 2025 made a rational decision. They invested, observed no return, and redirected capital to opportunities with more predictable outcomes. Viewed in isolation, each individual decision to abandon was defensible.\\n[...]\\nt work,\\n[...]\\n25% abandoned because AI worked but economics didn't justify cost. Thai manufacturing example: predictive maintenance AI reduced downtime 18% (technical success) but cost $420k annually while delivering $280k value (economic failure). Lost $140k/year on 'successful' AI. Hidden costs discovered: model retraining ($60-120k/year), data quality monitoring ($80-150k/year), exception handling ($100-200k/year), infrastructure scaling ($40-80k/year). Prevention: Lifecycle cost budgeting from day one, ROI hurdle of 3x operations cost minimum.\"],[\"2 EXECUTIVE SUMMARY \\nDespite $30–40 billion in enterprise investment into GenAI, this report uncovers a surprising \\nresult in that 95% of organizations are getting zero return. The outcomes are so starkly \\ndivided across both buyers (enterprises, mid-market, SMBs) and builders (startups, vendors,\\n[...]\\nconsultancies) that we call it the GenAI Divide. Just 5% of integrated AI pilots are extracting\\n[...]\\nmillions in value, while the vast majority remain stuck with no measurable P&L impact. This \\ndivide does not seem to be driven by model quality or regulation, but seems to be \\ndetermined by approach. \\nTools like ChatGPT and Copilot are widely adopted. Over 80 percent of organizations have \\nexplored or piloted them, and nearly 40 percent report deployment. But these tools \\nprimarily enhance individual productivity, not P&L performance. Meanwhile, enterprise \\ngrade systems, custom or vendor-sold, are being quietly rejected. Sixty percent of \\norganizations evaluated such tools, but only 20 percent reached pilot stage and just 5 \\npercent reached production. Most fail due to brittle workflows, lack of contextual learning, \\nand misalignment with day-to-day operations. \\nFrom our interviews, surveys, and analysis of 300 public implementations, four patterns \\nemerged that define the GenAI Divide: \\n•\\n[...]\\nof 8 major sectors show\\n[...]\\nparadox: Big firms lead in pilot volume but lag in scale-up\\n[...]\\n• Investment bias: Bud\\n[...]\\nthe success rate\\n[...]\\ninternal builds\\n[...]\\n. Others cite\\n[...]\\nclearest manifestation of the\\n[...]\\nGenAI Divide.\\n[...]\\nwrong side continue investing in static tools that\\n[...]\\ncan't adapt to their workflows, while those crossing the divide focus\\n[...]\\nOTS STALL: THE LEARNING GAP\\n[...]\\nHIND THE DIVIDE The primary factor keeping organizations on\\n[...]\\nwrong side of the Gen\\n[...]\\nDivide is the learning gap, tools that don't learn, integrate poorly, or match workflows. Users prefer\\n[...]\\n, but abandon it for mission-\\n[...]\\nwork due to its lack of memory. What's missing is systems\\n[...]\\n, and evolve, capabilities\\n[...]\\nIn the next few quarters, several enterprises will lock in vendor relationships that will be \\nnearly impossible to unwind. This 18-month horizon reflects consensus from seventeen \\nprocurement and IT sourcing leaders we interviewed, supported by analysis of public \\nprocurement disclosures showing enterprise RFP-to-implementation cycles ranging from \\ntwo to eighteen months. Organizations investing in AI systems that learn from their data, \\nworkflows, and feedback are creating switching costs that compound monthly. \\n\\\"We're currently evaluating five different GenAI solutions, but whichever system best learns and \\nadapts to our specific processes will ultimately win our business. Once we've invested time in \\ntraining a system to understand our workflows, the switching costs become prohibitive.\\\" - CIO,\\n[...]\\nIDE ACTUALLY\\n[...]\\nTakeaway: GenAI is already starting to have workforce impact and it is manifesting through \\nselective displacement of previously outsourced functions and constrained hiring patterns, \\nbut not through broad-based layoffs. Organizations that have crossed the GenAI Divide \\ndemonstrate measurable external cost reduction while slightly decreasing internal \\nheadcount. \\n6.4.1 Displacement Patterns and Organizational Strategy \\nOur analysis reveals that GenAI-driven workforce reductions concentrate in functions \\nhistorically treated as non-core business activities: customer support operations, \\nadministrative processing, and standardized development tasks. These roles exhibited \\nvulnerability prior to AI implementation due to their outsourced status and process \\nstandardization. Executives were hesitant to reveal the scope of layoffs due to AI but it was \\nbetween 5-20% of customer support operations and administrative processing work in \\nthese companies.\"]]" } }, { "json": { "valid": true, "claim": "40% of workers received AI-generated work (or AI-generated 'workslop').", "search_results_texts": "[[\"## 40% of people have received workslop in the last month\\n[...]\\nWorkslop refers to \\\"AI-generated work content that masquerades as good work, but lacks the substance to meaningfully advance a given task.\\\"\\n[...]\\nThat's according to new research from BetterUp, where Niederhoffer is vice president of their research labs, and Stanford Social Media Lab, where Hancock is the founding director.\\n[...]\\nSome 40% of people say they've received workslop in the last month, according to a recent BetterUp and Stanford survey of 1,150 full-time U.S. workers. These staffers estimate an average of 15% of the content they receive qualifies as low-effort, unhelpful, AI-generated work; it's happening across industries but is especially prominent in professional services and technology.\\n[...]\\nAI use has doubled at work since 2023 from 21% to 40%, per Gallup, yet 95% of organizations don't see a measurable return on their investment in the tech, according to a recent MIT Media Lab report. Workslop could be a big reason why, BetterUp and Stanford researchers say.\\n[...]\\nPeople who've encountered it say they spend an average of one hour and 56 minutes dealing with the aftermath of it; that adds up to a roughly $186 invisible tax per month, based on their self-reported salaries.\\n[...]\\nFor an organization of 10,000 workers, that's a $9 million hit to productivity in a year, researchers say.\\n[...]\\nBeyond the financial cost, there's an emotional one. Recipients of workslop say it takes time and mental energy to figure out how to diplomatically address the subpar work with their\\n[...]\\n; 53% report being annoyed, 38% confused and 22% offended.\"],[\"Workslop: Examining the prevalence, antecedents and consequences of low-quality AI-generated content at work | Sciety\\n[...]\\nArtificial intelligence (AI) adoption in the workplace has skyrocketed but produced ambiguous effects, raising questions of when AI use may be beneficial or harmful to productivity and collaboration. To understand the conditions under which AI workplace use is harmful, we examined workers’ experiences with AI-generated workslop: low-quality, predominantly AI-generated content that appears to fulfill a given task, but lacks the substance to meaningfully advance the work at hand. We conducted a representative survey of American full-time desk-workers (n = 962) that found that 52.7% reported sending some amount of AI-generated workslop to others. This behavior was more common among people who had high trust in AI, low agency over AI use, organizational encouragement to use AI, and low psychological safety at work. A substantial proportion (38%) also reported receiving work from their colleagues or teammates that they would consider to be workslop, and that the effort involved in processing, discussing, and revising - or even redoing - the work cost them around 3.4 hours per month on average. In addition to consuming workers’ time, receiving workslop negatively affected their mood and harmed their perceptions of their colleagues as trustworthy, reliable collaborators. Together, our findings paint a cautionary tale regarding AI integration into the workplace by highlighting how using AI under certain circumstances can be counterproductive and undermine interpersonal relationships.\\n[...]\\n### Opinion Monitor Artificial Intelligence. Use and Evaluation of AI in the Workplace—A Question of Occupational Class: Brief Report on a Segmentation Study\\n[...]\\n### Opinion Monitor Artificial Intelligence. The use of AI among working people–Differences between occupational groups, usage profiles, and perceptions of the consequences of AI in the workplace\"],[\"## Over 40% of US-based full-time employees reported receiving AI-generated content that 'masquerades as good work but lacks the substance to meaningfully advance a given task.'\\n[...]\\nIf you’ve been on TikTok, YouTube, Facebook, or even LinkedIn recently, you’re probably well aware of “AI slop”—the low-quality, often misleading content now clogging up news feeds everywhere. But now Stanford researchers have coined a new term for when this type of low-quality AI content manifests in the workplace: “workslop.”\\n[...]\\nOver 40% of US-based full-time employees reported receiving “workslop” in the last month, according to a new survey by the Stanford Social Media Lab and BetterUp Labs, first published in Harvard Business Review.\\n[...]\\nThe researchers defined workslop as AI-generated work content that \\\"masquerades as good work but lacks the substance to meaningfully advance a given task.\\\" Employees surveyed said an average of 15.4% of the content they receive now qualifies as workslop.\\n[...]\\nThe phenomenon occurs mostly between peers, where it accounts for 40% of the work handed over. But workslop is also sent to managers by direct reports, where it accounts for 18% of the work. The scale of the issue depended on the industry looked at, with technology and professional services being the industries most impacted.\\n[...]\\nThis isn't the first time research has emerged, which implies that AI is causing just as many problems as it solves in the workplace. Earlier this year, a paper from the University of Chicago and the University of Copenhagen found that, although AI created modest productivity gains per worker—just a few hours in an average working month—these gains were counteracted by the new tasks created by AI. For example, a teacher might shave a few hours off their workweek using AI for lesson planning, only to lose a few more hours checking students' homework for AI usage.\\n[...]\\nMeanwhile, a study specifically examining programmers found that AI coding tools actually slowed them down while working on complex tasks, as the AI-assisted group spent a significant amount of time prompting the AI or verifying its output.\"],[\"A new study from BetterUp Labs and the Stanford Social Media Lab has identified a significant drag on corporate productivity, coining the term “workslop” to describe the low-quality, AI-generated content that is quietly costing companies millions. The research reveals that while generative AI tools promise efficiency, their misuse is creating a hidden “AI tax” on productivity, with employees spending hours correcting nonsensical emails, reports, and presentations. This phenomenon helps explain why many organizations are not seeing the expected return on investment from their AI deployments, as the time saved by creators is simply offloaded onto recipients, eroding both the bottom line and team morale. The findings provide a critical framework for understanding how generative AI is negating productivity gains in measurable ways across organizations.\\n[...]\\n- The research identifies two user types—proactive ‘Pilots’ and work-avoidant ‘Passengers’—as the key factor determining AI’s value or cost.\\n- Receiving this content damages team dynamics, with 42% of recipients reporting they trust the sender less and 53% feeling annoyed.\\n- Employees spend nearly two hours on average correcting or redoing each instance of low-quality, AI-generated “workslop” they receive.\\n- The Stanford-BetterUp study quantifies a hidden ‘AI tax’ costing companies $186 per employee monthly, or over $9 million annually for a 10,000-person firm.\\n[...]\\nResearchers have put a name to a growing problem in the modern workplace: “workslop.” Jeff Hancock, a communication professor and founding director of the Stanford Social Media Lab, defines it as “ AI-generated content masquerading as good work that doesn’t actually advance the work itself.” This content transfers the burden of effort from the creator to the\\n[...]\\n, creating an illusion of progress while actually introducing\\n[...]\\nThis is not an isolated issue. The study, which surveyed 1,150 full-time U.S. workers, found that respondents estimate 15.4% of all work content they receive qualifies as “workslop.” Furthermore, over 40% of desk workers reported receiving such content within the last month. The data shows it’s a systemic problem, with 40% originating from coworkers, 18% from staff to managers, and 16% from managers to staff, indicating it permeates all levels of the corporate hierarchy.\\n[...]\\nThe financial impact of workslop is stark. The research calculates what it calls an “invisible tax” on companies, stemming from the time employees must spend deciphering, correcting, or simply redoing subpar AI-generated work. Employees reported spending nearly two hours on average dealing with each instance of “workslop” they received. This time loss translates to a hidden monthly cost of $186 per employee.\\n[...]\\nFor a company with 10,000 workers, the cumulative loss in productivity is estimated to exceed $9 million per year. Beyond the balance sheet, the unseen toll on morale is equally damaging. According to the original research published in Harvard Business Review, 53% of recipients felt annoyed, 38% felt confused, and 22% felt offended by the lack of effort. This emotional response directly erodes professional relationships, with 42% of recipients reporting they trust the sender less after receiving workslop.\"],[\"In collaboration with Stanford Social Media Lab, our research team at BetterUp Labs has identified one possible reason: Employees are using AI tools to create low-effort, passable looking work that ends up creating more work for their coworkers. On social media, which is increasingly clogged with low-quality AI-generated posts, this content is often referred to as “AI slop.” In the context of work, we refer to this phenomenon as “workslop.” We define workslop as AI generated work content that masquerades as good work, but lacks the substance to meaningfully advance a given task.\\n[...]\\nAccording to our recent, ongoing survey (which you can take), this is a significant problem. Of 1,150 U.S.-based full-time employees across industries, 40% report having received workslop in the last month. Employees who have encountered workslop estimate that an average of 15.4% of the content they receive at work qualifies. The phenomenon occurs mostly between peers (40%), but workslop is also sent to managers by direct reports (18%). Sixteen percent of the time workslop flows down the ladder, from managers to their teams, or even from higher up than that. Workslop occurs across industries, but we found that professional services and technology are disproportionately impacted.\\n[...]\\nEach incidence of workslop carries real costs for companies. Employees reported spending an average of one hour and 56 minutes dealing with each instance of workslop. Based on participants’ estimates of time spent, as well as on their self-reported salary, we find that these workslop incidents carry an invisible tax of $186 per month. For an organization of 10,000 workers, given the estimated prevalence of workslop (41%), this yields over $9 million per year in lost productivity.\\n[...]\\nThe most alarming cost may be interpersonal. Low effort, unhelpful AI generated work is having a significant impact on collaboration at work. Approximately half of the people we surveyed viewed colleagues who sent workslop as less creative, capable, and reliable than they did before receiving the output. Forty-two percent saw them as less trustworthy, and 37% saw that colleague as less intelligent. This may well echo recent research on the competence penalty for AI use at work, where engineers who allegedly used AI to write a code snippet were perceived as less competent than those who didn’t (and female engineers were disproportionately penalized).\\n[...]\\nOur labs have been tracking predictors of gen AI adoption across the workforce since 2023 and we found that workers with a combination of high agency and high optimism are much more likely to adopt gen AI than those with low agency and low optimism. We call these workers “pilots,” as opposed to “passengers.” Pilots use gen AI 75% more often at work than passengers, and 95% more often outside of work.\"],[\"A recent report from the Stanford Social Media Lab found that similar issues are at play in the workplace, with employees often using AI to take shortcuts that ultimately lead to more work down the road due to the poor quality of what’s produced.\\n[...]\\nThe analysis found that people would often use AI to produce work that was passable and that required the least amount of effort possible. In a nod to the “AI slop” that is cluttering up social media feeds, the researchers refer to it as “workslop.”\\n[...]\\nThe study found that 40% of employees had been on the receiving end of workslop in the last month. Indeed, these poor souls went on to say that over 15% of the content they received probably qualified as AI-generated muck. Most of the slop was sent between colleagues at the same level, but people were far from unwilling to send it to their manager, either. While workslop was common across industries, it was especially so in professional services and tech.\\n[...]\\nThis matters, as while the mental costs associated with producing the AI-generated content are minimal, the study found that tidying up what was produced took recipients an average of just under two hours, or approximately $186 per incident. When extrapolated across the typical organization in the study, this worked out to a productivity hit of over $9 million per year.\\n[...]\\nThis carried inevitable social costs to the relationship, as\\n[...]\\nwho sent work\\n[...]\\ncapable and reliable. Indeed, around half of the respondents went as far as to view senders as less trustworthy. This should come as\\n[...]\\nsurprise, as previous research has shown\\n[...]\\nAI as both\\n[...]\\nand creative.\\n[...]\\nGiven the huge investment in AI, it’s inevitable that there will be a\\n[...]\\nof figuring out\\n[...]\\nIt’s not clear that this\\n[...]\\nhas quite reached work\\n[...]\\nyet, but hopefully\"],[\"As organizations adopt AI tools at increasing speed, a growing number of workers say quality standards are quietly slipping, creating a new and often invisible burden at work. Zety’s Rise of Workslop Report finds that low-quality AI-generated output, known as “workslop,” is now a routine part of many employees’ workloads, costing time, increasing stress, and eroding morale.\\n[...]\\nAccording to the study, most workplaces have not fully embraced “workslop,” but tolerance is rising. While 39% of employees say low-quality AI output is completely unacceptable and corrected, a majority report some level of acceptance, especially when deadlines are prioritized over polish—leaving workers to fix problems themselves.\\n[...]\\n- Workslop is widespread. 1 in 5 employees say low-quality AI work is often overlooked if deadlines are met, while nearly one-third say it is noticed but tolerated.\\n- Time loss is significant. 66% of workers spend up to 6 hours or more each week correcting errors caused by “workslop.”\\n- Employee impact is growing. “Workslop” contributes to higher stress (29%), lower morale (25%), reduced productivity (25%), and burnout (21%).\\n[...]\\n- Generational tolerances differ. More than half of workers (53%) believe younger generations are more tolerant of “workslop” than older colleagues.\\n[...]\\nAs AI tools become embedded in daily workflows, attitudes toward quality appear to be changing. In their workplace, employees report that “workslop” is viewed as:\\n[...]\\n- Completely unacceptable and corrected: 39%\\n- Somewhat unacceptable but tolerated: 31%\\n- Somewhat acceptable and overlooked if deadlines are met: 21%\\n- Completely acceptable, with speed prioritized over polish: 9%\\n[...]\\nWhen “workslop” reaches employees’ inboxes, nearly half (49%) say they fix the issues themselves rather than escalate or reject the work. This added labor often goes unrecognized, yet it adds up quickly. Two-thirds of workers report spending up to six or more hours each week correcting AI-related errors.\\n[...]\\nThe findings presented are based on a nationally representative survey conducted by Zety using Pollfish on January 8, 2025. The survey collected responses from 1,000 U.S. employees who have encountered workslop and examined its impact on their daily work, trust in leadership, stress and productivity, and attitudes toward AI training. Respondents answered different types of questions, including yes/no, scale-based questions where they indicated their level of agreement with statements, and multiple-choice where they could select from a list of provided options. The sample consisted of 49% female, 50% male, and 1% nonbinary respondents, with 12% Gen Z, 30% millennials, 32% Gen X, and 26% baby boomers.\"]]" } }, { "json": { "valid": true, "claim": "AI-generated work 'workslop' is costing an estimated $186 per employee per month.", "search_results_texts": "[[\"Workslop: Examining the prevalence, antecedents and consequences of low-quality AI-generated content at work | Sciety\\n[...]\\nArtificial intelligence (AI) adoption in the workplace has skyrocketed but produced ambiguous effects, raising questions of when AI use may be beneficial or harmful to productivity and collaboration. To understand the conditions under which AI workplace use is harmful, we examined workers’ experiences with AI-generated workslop: low-quality, predominantly AI-generated content that appears to fulfill a given task, but lacks the substance to meaningfully advance the work at hand. We conducted a representative survey of American full-time desk-workers (n = 962) that found that 52.7% reported sending some amount of AI-generated workslop to others. This behavior was more common among people who had high trust in AI, low agency over AI use, organizational encouragement to use AI, and low psychological safety at work. A substantial proportion (38%) also reported receiving work from their colleagues or teammates that they would consider to be workslop, and that the effort involved in processing, discussing, and revising - or even redoing - the work cost them around 3.4 hours per month on average. In addition to consuming workers’ time, receiving workslop negatively affected their mood and harmed their perceptions of their colleagues as trustworthy, reliable collaborators. Together, our findings paint a cautionary tale regarding AI integration into the workplace by highlighting how using AI under certain circumstances can be counterproductive and undermine interpersonal relationships.\"],[\"## Answer: Low-quality AI content in the workplace, and yes.\\n[...]\\nNot all AI-generated content is created equal — there are many times when AI results are less than stellar, and it’s becoming more common in the workplace. The Harvard Business Review has even coined a term for it: workslop.Workslop is workplace content that was produced by AI and while it may look good on the surface, once you actually dig into it, it’s very low quality. The publication’s official definition is “AI-generated work content that masquerades as good work, but lacks the substance to meaningfully advance a given task.” Researchers from Harvard Business Review’s BetterUp Labs and the Stanford Social Media Lab have found that workslop is not only frustrating for employees to deal with, but it is actually costing companies money.\\n[...]\\nOf 1,150 full-time employees surveyed in the U.S., 40 percent reported receiving workslop in the past month. They reported spending an average of one hour and 56 minutes dealing with the problem each time they received workslop. Based on reported salaries, the researchers estimate that this equates to an invisible cost of about $186 per month per employee, which could mean millions in lost yearly productivity for companies with thousands of employees.\"],[\"AI Workslop Is a $9 Million Issue: Stanford, BetterUp Study\\n[...]\\n- Videos\\n- A new study from Stanford University and AI coaching platform BetterUp has coined a new term for subpar AI-generated content: workslop.\\n- About 40% of the 1,150 workers surveyed in the study reported receiving workslop from their colleagues.\\n- The study’s authors say businesses should be transparent with employees about when and how to use AI at work.\\n[...]\\nA new research report has coined a new term for inadequate AI-generated content at work: workslop.\\n[...]\\nThe word refers to content that looks polished but lacks substance. It applies to AI-generated slideshows, lengthy reports, summaries, and code. While the content looks good on the surface, it ends up being incomplete, missing context, or unhelpful to the task at hand. And a new study released on Monday found that 40% of workers have reported receiving workslop in just the past month.\\n[...]\\nTo write the study, Stanford Social Media Lab researchers partnered with AI coaching platform BetterUp to conduct an online survey of 1,150 full-time U.S. desk workers this month. Employees who reported encountering workslop said that it caused them to take extra time and mental energy from their day to figure out how to appropriately address the work with the colleagues who had submitted it.\\n[...]\\nOver half (53%) of respondents were “annoyed” to receive AI-generated work, and 22% were “offended.” Close to half said they thought of their co-workers as “less creative and reliable” after they submitted the workslop.\\n[...]\\nIt also took an average of two hours to resolve each incident, making the invisible tax of workslop about $186 per month, based on the salaries the workers reported receiving. That means that the average annual cost of workslop for a 10,000-person organization is about $9 million per year, the study found.\\n[...]\\nThe difference between workslop and sloppy work is that workslop doesn’t require any effort to create, while sloppy work still requires a little bit of effort, Stanford Professor of Communication and one of the authors of the study, Jeff Hancock, told CNBC.\\n[...]\\nHancock recommended that business leaders give guidance to employees about when and how to appropriately use AI at work. Workers should be clear about when they’re using AI, so colleagues aren’t surprised by it, he said.\\n[...]\\nAnother study author and Vice President of BetterUp Labs, Kate Neiderhoffer, told CNBC that managers should give workers specific reasons for why they should use AI to complete certain tasks. They should offer clarity about the policies and training that go along with using AI, she added.\\n[...]\\n- A new study from Stanford University and AI coaching platform BetterUp has coined a new term for subpar AI-generated content: workslop.\\n- About 40% of the 1,150 workers surveyed in the study reported receiving workslop from their colleagues.\\n- The study’s authors say businesses should be transparent with employees about when and how to use AI at work.\\n[...]\\nA new research report has coined a new term for inadequate AI-generated content at work: workslop.\\n[...]\\nThe word refers to content that looks polished but lacks substance. It applies to AI-generated slideshows, lengthy reports, summaries, and code. While the content looks good on the surface, it ends up being incomplete, missing context, or unhelpful to the task at hand. And a new study released on Monday found that 40% of workers have reported receiving workslop in just the past month.\"],[\"Hi, it’s Beatrice Nolan here, filling in for Jeremy Kahn, who is out today. I’ve spent a lot of time recently thinking about the promise of\\n[...]\\nin the workplace, especially after\\n[...]\\nthe majority of\\n[...]\\npilots weren’\\n[...]\\nIn the past year, the number of companies running entire workflows with AI has almost doubled, while overall workplace use has also doubled since 2023. Despite the dramatic uptake of the tech, a recent MIT Media Lab study still found that 95% of the organizations embracing AI weren’t seeing a clear return on those investments.\\n[...]\\nNew research suggests an alternative explanation: that the presence of AI in the workplace may actually be dragging down productivity. According to a recent and ongoing survey from BetterUp Labs in collaboration with Stanford University’s Social Media Lab, some employees are using AI to create low-effort “workslop,” which is time-consuming to clean up.\\n[...]\\nWorkslop, a term coined by the researchers and based on AI-generated “slop” you can find clogging your social media feeds, is defined as “AI-generated work content that masquerades as good work, but lacks the substance to meaningfully advance a given task.”\\n[...]\\nWhat the new research does show is just how pervasive and costly workslop has become within organizations.\\n[...]\\n## The cost of AI-generated work\\n[...]\\nOut of 1,150 full-time U.S. employees surveyed, 40% said they’ve encountered workslop in the past month. Just under half of this low-quality work is exchanged between colleagues at the same level. A further 18% of respondents said they received it from direct reports, while 16% said it came from managers or people higher up the corporate ladder.\\n[...]\\nFar from speeding up workflows, this AI-generated slop created more work, employees said. According to the research, employees spent just shy of two hours dealing with each piece of AI-generated work. Based on time spent and self-reported salaries, the researchers calculated that workslop could cost single employees $186 per month. For an organization of 10,000 workers, this could mean over $9 million a year in lost productivity.\\n[...]\\nThe incidents have morale costs as well, with employees reporting being annoyed, confused, and offended when they receive the low-quality work. According to the research, half of the people surveyed viewed colleagues who produced workslop as less creative, capable, and reliable. They were also seen as less trustworthy and less intelligent.\"],[\"Despite a surge in generative AI use across workplaces, most companies are seeing little measurable ROI. One possible reason is because AI tools are being used to produce “workslop”—content that appearspolished but lacks real substance, offloading cognitive labor onto coworkers. Research from BetterUp Labs and Stanford found that 41% of workers have encountered such AI-generated output, costing nearly two hours of rework per instance and creating downstream productivity, trust, and collaboration issues. Leaders need to consider how they may be encouraging indiscriminate organizational mandates and offering too little guidance on quality standards. To counteract workslop, leaders should model purposeful AI use, establish clear norms, and encourage a “pilot mindset” that combines high agency with optimism—promoting AI as a collaborative tool, not a shortcut.close\\n[...]\\nAccording toour recent, ongoing survey(which you can take), this is a significant problem. Of 1,150 U.S.-based full-time employees across industries, 40% report having received work\\n[...]\\nin the last month. Employees who have encountered workslop estimate that an average of 15.4%\\n[...]\\nthe content they receive at work qualifies. The phenomenon occurs mostly between peers (40%), but workslop is also sent to managers by direct reports (18%). Sixteen percent of the time workslop flows down the ladder, from managers to their teams, or even from higher up than that. Workslop occurs across industries, but we found that professional services and technology are disproportionately impacted.\\n[...]\\nEach incidence of workslop carries real costs for companies. Employees reported spending an average of one hour and 56 minutes dealing with each instance of workslop. Based on participants’ estimates of time spent, as well as on their self-reported salary, we find that these workslop incidents carry an invisible tax of $186 per month. For an organization of 10,000 workers, given the estimated prevalence of workslop (41%), this yields over $9 million per year in lost productivity.\"],[\"This workslop \\\"creates the illusion of progress\\\" but is largely meaningless, which has huge financial and time-related consequences for the workplace, according to a new study from BetterUp and the Stanford Social Media Lab.\\n[...]\\nAccording to the study, about 40% of workers have received workslop in the last month.\\n[...]\\nJeff Hancock, founding director of the Stanford Social Media Lab, told CNBC that there are several telltale signs of work slop, including purple prose, odd word choice, and incomplete\\n[...]\\nAs a result, AI-generated workslop has real consequences for companies. On average, it takes employees two hours to clean up a workslop-created mess, costing valuable time that could be spent on other projects, the study found.\\n[...]\\nThen, there's the financial cost. The study estimates that workslop causes businesses $186 per month, per employee, or $9 million per year for a 10,000-person company.\\n[...]\\nup to companies that elect to use AI in the office to establish guardrails against worksl\"],[\"-generated \\\"\\n[...]\\ncosting companies millions and hurting\\n[...]\\nAI-generated \\\"workslop\\\" is quietly draining millions from companies and damaging team morale, according to a new study from BetterUp Labs and the Stanford Social Media Lab.\\n[...]\\nIn their research, the authors describe a growing problem: employees flooding each other with low-value, meaningless AI-generated content, which they call \\\"workslop.\\\" In a survey of 1,150 full-time US workers, respondents estimated that 15.4 percent of all work content they receive fits this description.\\n[...]\\nprofessional services and tech\\n[...]\\n## A hidden $186-per-month \\\"AI tax\\\"\\n[...]\\nThe researchers say the costs add up quickly. Survey participants reported spending nearly two hours on each workslop incident. Using these responses, the authors calculated what they call an \\\"invisible tax\\\" of $186 per employee every month. For a company with 10,000 workers, the study estimates more than $9 million in lost productivity each year.\\n[...]\\nWhile a Danish study of 25,000 employees found that AI hasn't meaningfully changed wages or hours, other research points to bigger shifts. According to Anthropic's first AI Labor Market Index, 36 percent of all jobs already use AI for at least a quarter of their tasks, with 57 percent of usage focused on support roles and 43 percent on automation. A separate Stanford study found that employment for 22- to 25-year-olds in AI-exposed jobs dropped by 13 percent since late 2022, even as employment for more experienced workers stayed flat.\"]]" } }, { "json": { "valid": true, "claim": "42% of staff lost trust in colleagues who use the AI-generated work.", "search_results_texts": "[[\"40% of US employees reported encountering \\\"workslop\\\" in their work lives within the last month, says research. Nonetheless, employees are being out under pressure to use gnerative AI tools, despite lack of confidence in quality of output.\\n[...]\\nAI companies continually hail their products as revolutionary tools that will usher in a new era of workplace productivity. But a new, ongoing study by Stanford University's Social Media Lab and behavioural research firm BetterUp Labs suggests it may be doing the opposite while also damaging workplace relationships in the process.\\n[...]\\nAccording to the researchers, 40% of US employees reported encountering \\\"workslop\\\" in their work lives within the last month.\\n[...]\\nWorkslop refers to AI-generated content that appears polished and professional but lacks the depth or accuracy needed to genuinely move a task forward. It is the content that someone else must clean up, rewrite, or verify – costing time, trust, and money.\\n[...]\\nThe Stanford/BetterUp study estimates that sorting through AI-generated \\\"slop\\\" costs employers approximately $186 per employee per month in lost productivity.\\n[...]\\nBeyond productivity losses, \\\"workslop\\\" is also damaging professional relationships. Among those who received AI-generated content, over 50% reported feeling annoyed; more than one-third said they were confused and nearly 25% felt offended.\\n[...]\\nEven more striking is the reputational cost to those who send AI-generated messages. Forty-two percent of respondents said they trusted the sender less after receiving AI-written material. More than a third judged the sender to be less intelligent or less creative than they had previously believed.\\n[...]\\nA recent three-month trial of Microsoft's M365 Copilot in a UK government department found no definitive evidence of improved productivity. While the tool was helpful in speeding up some tasks, the\\n[...]\\noutputs varied widely, depending on the activity.\\n[...]\\nAnother study, conducted by work management platform vendor Asana, found that 64% of British employees view AI agents as unreliable. More than half said the tools either produce incorrect results with unwarranted confidence or ignore feedback completely.\\n[...]\\nat work, with many fearing\"],[\"Artificial intelligence (AI) adoption in the workplace has skyrocketed but produced ambiguous effects, raising questions of when AI use may be beneficial or harmful to productivity and collaboration. To understand the conditions under which AI workplace use is harmful, we examined workers’ experiences with AI-generated workslop: low-quality, predominantly AI-generated content that appears to fulfill a given task, but lacks the substance to meaningfully advance the work at hand. We conducted a representative survey of American full-time desk-workers (n = 962) that found that 52.7% reported sending some amount of AI-generated workslop to others. This behavior was more common among people who had high trust in AI, low agency over AI use, organizational encouragement to use AI, and low psychological safety at work. A substantial proportion (38%) also reported receiving work from their colleagues or teammates that they would consider to be workslop, and that the effort involved in processing, discussing, and revising - or even redoing - the work cost them around 3.4 hours per month on average. In addition to consuming workers’ time, receiving workslop negatively affected their mood and harmed their perceptions of their colleagues as trustworthy, reliable collaborators. Together, our findings paint a cautionary tale regarding AI integration into the workplace by highlighting how using AI under certain circumstances can be counterproductive and undermine interpersonal relationships.\"],[\"title: The dark side of employee-generative AI collaboration in the workplace: an investigation on work alienation and employee expediency\\n[...]\\ndescription: Generative AI (GenAI) has emerged as a powerful tool in the modern workplace, delivering significant benefits to both employees and organizations. As its adoption gains momentum, understanding the potential risks associated with employee-GenAI collaboration becomes increasingly important. While much of the existing research emphasizes the challenges GenAI presents to employees as individuals, this study shifts the focus to explore broader organizational risks, particularly unethical workplace behaviors. Drawing on human-AI collaboration research and the job demands-resources model, we develop and empirically test a novel model to explain how and when employee-GenAI collaboration may lead to employees’ unethical behavioral outcomes in daily organizational contexts. Using an experience sampling approach with longitudinal data from 229 service industry employees, encompassing 1050 matched daily observations, our findings reveal that employee-GenAI collaboration increases work alienation—a sense of disconnection from work—which, in turn, drives employee expediency that compromises work standards. Furthermore, we demonstrate that this effect is pronounced under high digital job demands. By highlighting this unintended consequence, our study contributes to theoretical advancements in understanding the darker side of employee-GenAI collaboration and provides practical insights to help organizations harness the benefits of GenAI while mitigating its potential ethical pitfalls.\\n[...]\\nidentifier: Hai, Shenyang, Long, Tianyi, Honora, Andreawan, Japutra, Arnold and Guo, Tengfei (2025) The dark side of employee-generative AI collaboration in the workplace: an investigation on work alienation and employee expediency. International Journal of Information Management, 83, [102905]. (doi:10.1016/j.ijinfomgt.2025.102905 http://dx.doi.org/10.1016/j.ijinfomgt.2025.102905).\"],[\"Artificial intelligence (AI) promises major productivity gains, but it also raises fundamental questions about how technology can reshape people’s relationship to their work. Historical debates over industrialization warned that technological change could undermine people’s connection to work and sense of meaning. Similar concerns now surround AI, where the key issue may not be whether AI is used, but how it is used. Across a pre-registered experiment (N = 269) and a follow-up survey (N = 270), we examine how different modes of AI use affect the confidence individuals have in completing work without AI assistance (self-efficacy), their sense of ownership over task output, and the meaning they perceive in their work. Participants completed occupation-specific writing tasks under one of three conditions: no AI use, passive AI use (copying AI-generated content), or active collaboration (drafting first and then using AI to refin). We find that passive use undermined self-efficacy, psychological ownership, and work meaningfulness, with declines in efficacy and meaningfulness persisting even when participants returned to manual work. In contrast, collaborative AI use preserved psychological connection to the task, producing outcomes comparable to independent work. Although passive use initially boosted enjoyment and satisfaction, these benefits reversed once participants resumed manual work. A complementary real-world survey mirrored these patterns across tasks beyond writing. Together, these findings show that the psychological consequences of AI use hinge on how it is integrated into human workflows, underscoring that strategies promoting active, collaborative use may help capture AI’s productivity benefits while preserving human workers’ agency, competence, and connection to their work.\\n[...]\\nTo complement our experimental investigation and explore how these dynamics manifest in real-world contexts, we conducted a follow-up correlational study. This study surveyed working adults who varied in their use of generative AI for professional tasks, distinguishing between passive reliance on AI (e.g., using AI to fully generate content) and active collaboration with AI (e.g., using AI to refine self-generated content). The results are broadly consistent with our experimental findings (see Table 1): passive reliance on AI was negatively correlated with self-efficacy (r = − 0.45, p < 0.001), psychological ownership (r = − 0.22, p < 0.001), and outcome satisfaction (r = − 0.16, p = 0.008), but was not significantly related to work meaningfulness (r = 0.05, p = 0.392) or task enjoyment (r = 0.09, p = 0.134). In contrast, active AI collaboration was positively correlated with self-efficacy (r = 0.40, p < 0.001), psychological ownership (r = 0.30, p < 0.001), and outcome satisfaction (r = 0.14, p = 0.020), but was not significantly related to work meaningfulness (r = 0.01, p = 0.941) or task enjoyment (r = − 0.09, p = 0.152).\\n[...]\\nIn contrast, participants who used AI as a collaborator (drafting a version first and then using AI to edit it) didn’t experience these psychological costs. In fact, their self-efficacy and work-related attitudes were statistically similar to those who had not used AI at all. This suggests that when AI is used as a tool to refine one’s own work rather than as a substitute for it, individuals still psychologically “own” the work and retain a sense of competence.\\n[...]\\nand performance 6,40,41,\\n[...]\\naltogether may no longer be practical or\\n[...]\\n. Indeed, many\\n[...]\\nnow ask employees to maximize\\n[...]\\n43, 44.\\n[...]\\nthis emphasis may inadvertently encourage\\n[...]\\n, as doing so\\n[...]\\nsense of meaning that is core to intrinsic motivation. Hence\\n[...]\\n. The central question\"],[\"Abstract \\nThe introduction of ChatGPT has fuelled a public debate on the use of generative AI (large language \\nmodels; LLMs), including its use by researchers. In the current work, we test whether delegating parts \\nof the research process to LLMs leads people to distrust and devalue researchers and scientific output. \\nParticipants (N=402) considered a researcher who delegates elements of the research process to a PhD \\nstudent or LLM, and rated (1) moral acceptability, (2) trust in the scientist to oversee future projects,\\n[...]\\nand (3) the accuracy and quality of the output. People judged delegating to an LLM as less acceptable\\n[...]\\nthan delegating to a human (d = -0.78). Delegation to an LLM also decreased trust to oversee future\\n[...]\\nresearch projects (d = -0.80), and people thought the results would be less accurate and of lower quality \\n(d = -0.85). We discuss how this devaluation might transfer into the underreporting of generative AI \\nuse. \\nKeywords: trust in science, metascience, ChatGPT, GPT, large language models, generative AI, \\nexperiment \\nJEL codes: G00, J24, O33, O34 \\nAcknowledgments \\nThis research was\\n[...]\\nby grant 202\\n[...]\\nthe National Science\\n[...]\\nWe anticipated that, overall, people would view delegating aspects of the research process to a large\\n[...]\\nlanguage model as morally worse than delegating to a human, and that doing so would reduce trust in\\n[...]\\nthe delegating scientist.\\n[...]\\nfrom a junior human researcher\\n[...]\\nassistance is minor enough\\n[...]\\nnot warrant co-authorship\\n[...]\\nthe\\n[...]\\nfield for human and\\n[...]\\nas prominent journals have already expressed that large\\n[...]\\ncannot be listed as co-authors (Thorp, 2023), as was already done in some papers (e.g., \\nKung et al., 202\\n[...]\\n). \\nSecond, we examined in which aspects of the research process are the prospective human-AI disparities \\nthe strongest. If – as we hypothesize – delegating to AI is perceived less favourably, then one can assume \\nthat delegating such processes to AI will have the greatest potential to devalue work done by scientists.\\n[...]\\nexpected, moral acceptability\\n[...]\\nwith trust to oversee future projects, r = 0.\\n[...]\\n1,\\n[...]\\n0.001. However, moral acceptability ratings also correlated highly with accuracy ratings, r = 0.81,\\n[...]\\np < 0.001. Similarly, trust ratings correlated highly with accuracy, r = 0.80, p < 0.001.\\n[...]\\n2.2. Pre-registered analysis \\nWe present the results of the pre-registered analysis in Table 2. Consistent with the hypothesis, people \\nrated delegating the research process to a large language model as less morally acceptable and reported \\nlower trust towards this scientist to oversee future research projects. Moreover, people also rated \\ndelegating to an LLM as producing less correct output. The effect of delegating to a large language \\nmodel (relative to delegating the same to a PhD student) was similar for all three items and thus results \\nfrom the combined dataset (“All items and processes”) can serve as a benchmark for future studies. \\nFor readers accustomed to Cohen’s d (Cohen, 1988), the effect sizes (and 95% confidence intervals) of\\n[...]\\ndelegating to a large language model instead of a human were large: d = -0.78 [-0.99, -0.58] for moral \\nacceptability, d = -0.80 [-1.00, -0.60] for trust, and d = -0.85 [-1.06, -0.65] for accuracy. \\n2.3. Exploratory analysis \\nTable 2 and Figure 1 present how ratings varied across the five research processes and conditions. The \\nadverse effect of delegating to a large language model was strongest for the “Testing and interpreting \\nthe theoretical framework” process and weakest for the “Statistical result analysis” process. However, \\nthe patterns were robust for each of the five research elements, in the d = -0.81 (large effect) to -0.51 \\n(medium effect) range. Therefore, despite some variation across research processes, people nonetheless \\njudged delegation of any process to an LLM as worse than to a human.\"],[\"Organizations are increasingly integrating generative artificial intelligence (AI) tools like ChatGPT for task-support purposes into the workplace. While such tools have been shown to boost productivity, it is critical to understand their psychological impact on employees’ job satisfaction if they are to be implemented effectively. Based on social response theory and the work design model, this study examines how the organizational integration of ChatGPT influences job satisfaction among employees, mediated by perceived organizational support, opportunity to perform, and job insecurity. Using a pre- and post-measurement design, we conducted a 2 × 2 between-subjects vignette experiment with a sample of 202 participants, manipulating the type of task support (i.e., ChatGPT vs. human coworker) (T1) and the nature of supervisor feedback (i.e., positive vs. negative) (T2) in the context of a corporate communication task (i.e., writing an article for the organization’s website about a strategic shift). Our results indicate that ChatGPT task support, compared to task support by a human coworker, leads to lower perceptions of both organizational support and opportunity to perform and higher perceptions of job insecurity, ultimately hindering job satisfaction. The findings suggest that, although generative AI can serve as a form of task support, its use can negatively impact employees’ perceptions. Accordingly, task support provided by human coworkers appears to still be important for maintaining job satisfaction among employees, underscoring the need for the thoughtful integration of generative AI into the workplace.\\n[...]\\n(Noy\\n[...]\\n2023; Yin et\\n[...]\\n5). Gener\\n[...]\\nis an umbrella term for any technology based on machine learning models that is designed to simulate human cognitive abilities\\n[...]\\nnovel content (Dwivedi et al. 2023; Feuerriegel et al.\\n[...]\\n4). As part of the ongoing digital transformation, generative AI task support exemplifies the\\n[...]\\ntechnology use in the workplace, with 71% of organizations already employing such tools to some degree (Maslej et al. 2025). This development is transforming traditional ways of working and may impact how employees perform\\n[...]\\nwork tasks (Jia et al\\n[...]\\n4; Ooi et al\\n[...]\\nwell as how they\\n[...]\\nAccording to our findings from Model 2, ChatGPT task support, in comparison to task support provided by a human coworker, led to weaker perceptions of organizational support (β = − 0.16; p = 0.043), supporting Hypothesis 2. This provides initial evidence that, in the context of a corporate communication writing task, employees feel less supported when their task support comes in the form of generative AI rather than a coworker. Hypothesis 3 and Hypothesis 4 are also fully supported by the findings, as employees who received ChatGPT task support perceived less of an opportunity to perform (β = − 0.28; p < 0.001) and greater job insecurity (β = 0.26; p = 0.001) than those who received task support from a human coworker. The proposed influence of the mediators on job satisfaction was also validated; perceived organizational support (β = 0.31; p = 0.008) and perceived opportunity to perform (β = 0.20; p = 0.043) positively impacted job satisfaction, while perceived job insecurity negatively influenced job satisfaction (β = − 0.14; p = 0.030), thereby supporting Hypotheses 5a, 5b, and 5c.\\n[...]\\nsettings is rapidly gathering pace (Yin et al.\\n[...]\\nexperimental context of corporate communication\\n[...]\\ntask support (i\\n[...]\\nthat provided by a human\\n[...]\\nmust be mindful\\n[...]\\nas this has direct\\n[...]\\nperformance, retention, and overall workplace outcomes (Tett and Mayer 1\\n[...]\\n93; Judge et al\\n[...]\\n01; Brough\\n[...]\\nAccordingly, organizations\\n[...]\\nthat deliberately address\"],[\"AI is reshaping how work gets done, but not always for the better. More than half of U.S. employees say they’ve received “workslop” from a manager, raising new concerns about leadership standards and accountability.\\n[...]\\nWhat is workslop? In Zety’s latest survey of 1,000 U.S. workers, workslop is defined as AI-generated work that looks polished but lacks accuracy, substance, or proper review. For many employees, it becomes a bigger issue when it comes from leadership. In fact, 85% say receiving this kind of work from a manager reduces their trust.\\n[...]\\n- Trust at risk: 55% have gotten workslop from a manager or supervisor, and 85% say receiving it damages their trust in leadership.\\n- AI skepticism rising: 45% say workslop has made them more cautious about AI in the workplace.\\n- Lack of AI knowledge: 1 in 4 have received no AI training or guidance from their employer.\\n- Workslop solutions: The majority of employees say clearer quality standards (57%) and better AI training (51%) would reduce workslop.\\n[...]\\n- 74% say receiving workslop lowers their trust in the sender’s work quality.\\n- 85% say receiving workslop from a manager would reduce their trust in leadership.\\n- 55% say they’ve already received workslop from a manager or supervisor at their company.\\n[...]\\nAs workslop becomes more visible, employees report growing skepticism of AI tools. Nearly half (45%) say it has made them more cautious about AI’s role in the workplace.\\n[...]\\n- 45% say they have received limited AI training or guidance.\\n- 24% say they have received none at all.\\n- Only 31% report receiving detailed training and ongoing support.\\n[...]\\nWhile workslop might seem minor, employees believe it has lasting consequences for teams and organizations. The top risks identified include lower trust in AI (57%), reduced productivity (51%), and damage to company reputation (46%).\\n[...]\\nThe findings presented are based on a nationally representative survey conducted by Zety using Pollfish on January 8, 2026. The survey collected responses from 1,000 U.S. employees who have encountered “workslop” and examined its impact on their daily work, trust in leadership, stress and productivity, and attitudes toward AI training. Respondents answered different types of questions, including yes/no; scale-based questions, where they indicated their level of agreement with statements; and multiple-choice questions, where they could select from a list of provided options. The sample consisted of 49% female, 50% male, and 1% nonbinary respondents, with 12% Gen Z, 30% millennials, 32% Gen X, and 26% baby boomers.\"]]" } } ] }, "connections": { "Webhook": { "main": [ [ { "node": "Validate Numbers Mentioned", "type": "main", "index": 0 } ] ] }, "Validate Numbers Mentioned": { "main": [ [ { "node": "If", "type": "main", "index": 0 } ] ] }, "Basic LLM Chain": { "main": [ [ { "node": "Basic LLM Chain4", "type": "main", "index": 0 } ] ] }, "OpenRouter Chat Model": { "ai_languageModel": [ [ { "node": "Basic LLM Chain", "type": "ai_languageModel", "index": 0 } ] ] }, "OpenRouter Chat Model6": { "ai_languageModel": [ [ { "node": "Basic LLM Chain3", "type": "ai_languageModel", "index": 0 } ] ] }, "OpenRouter Chat Model7": { "ai_languageModel": [ [ { "node": "Basic LLM Chain4", "type": "ai_languageModel", "index": 0 } ] ] }, "Basic LLM Chain4": { "main": [ [ { "node": "Basic LLM Chain3", "type": "main", "index": 0 } ] ] }, "Basic LLM Chain3": { "main": [ [ { "node": "Check accuracy", "type": "main", "index": 0 } ] ] }, "Check accuracy": { "main": [ [ { "node": "Edit Fields", "type": "main", "index": 0 } ] ] }, "Search the web1": { "main": [ [ { "node": "Claim + Results1", "type": "main", "index": 0 } ] ] }, "Webhook2": { "main": [ [ { "node": "Edit Fields3", "type": "main", "index": 0 } ] ] }, "Edit Fields3": { "main": [ [ { "node": "Search the web1", "type": "main", "index": 0 } ] ] }, "Locate source to research": { "main": [ [ { "node": "Basic LLM Chain2", "type": "main", "index": 0 } ] ] }, "OpenRouter Chat Model2": { "ai_languageModel": [ [ { "node": "Locate source to research", "type": "ai_languageModel", "index": 0 } ] ] }, "Basic LLM Chain2": { "main": [ [ { "node": "Edit Fields4", "type": "main", "index": 0 } ] ] }, "OpenRouter Chat Model4": { "ai_languageModel": [ [ { "node": "Basic LLM Chain2", "type": "ai_languageModel", "index": 0 } ] ] }, "Webhook3": { "main": [ [ { "node": "Locate source to research", "type": "main", "index": 0 } ] ] }, "OpenRouter Chat Model1": { "ai_languageModel": [ [ { "node": "List claims for search", "type": "ai_languageModel", "index": 0 } ] ] }, "Structured Output Parser": { "ai_outputParser": [ [ { "node": "List claims for search", "type": "ai_outputParser", "index": 0 } ] ] }, "List claims for search": { "main": [ [ { "node": "Clean up claims", "type": "main", "index": 0 } ] ] }, "Split Out1": { "main": [ [ { "node": "Claims", "type": "main", "index": 0 } ] ] }, "OpenRouter Chat Model3": { "ai_languageModel": [ [ { "node": "Clean up claims", "type": "ai_languageModel", "index": 0 } ] ] }, "Structured Output Parser1": { "ai_outputParser": [ [ { "node": "Clean up claims", "type": "ai_outputParser", "index": 0 } ] ] }, "Clean up claims": { "main": [ [ { "node": "Split Out1", "type": "main", "index": 0 } ] ] }, "Webhook4": { "main": [ [ { "node": "List claims for search", "type": "main", "index": 0 } ] ] }, "Webhook5": { "main": [ [ { "node": "Extract claims", "type": "main", "index": 0 } ] ] }, "Extract claims": { "main": [ [ { "node": "Loop Over Items", "type": "main", "index": 0 } ] ] }, "Claim check accuracy": { "main": [ [ { "node": "If1", "type": "main", "index": 0 } ] ] }, "If": { "main": [ [ { "node": "Basic LLM Chain", "type": "main", "index": 0 } ], [ { "node": "Edit Fields1", "type": "main", "index": 0 } ] ] }, "Extract source": { "main": [ [ { "node": "Loop Over Items1", "type": "main", "index": 0 } ] ] }, "Deep search1": { "main": [ [ { "node": "Reorder based on trusted domains", "type": "main", "index": 0 } ] ] }, "Claim check accuracy2": { "main": [ [ { "node": "Edit Fields5", "type": "main", "index": 0 } ] ] }, "Webhook6": { "main": [ [ { "node": "Deep search1", "type": "main", "index": 0 } ] ] }, "Search for claim": { "main": [ [ { "node": "Wait1", "type": "main", "index": 0 } ] ] }, "Loop Over Items": { "main": [ [ { "node": "Claim check accuracy", "type": "main", "index": 0 } ], [ { "node": "Search for claim", "type": "main", "index": 0 } ] ] }, "Wait1": { "main": [ [ { "node": "Loop Over Items", "type": "main", "index": 0 } ] ] }, "Loop Over Items1": { "main": [ [], [ { "node": "Validate final source", "type": "main", "index": 0 } ] ] }, "Wait": { "main": [ [ { "node": "Loop Over Items1", "type": "main", "index": 0 } ] ] }, "Reorder based on trusted domains": { "main": [ [ { "node": "Browserless", "type": "main", "index": 0 } ] ] }, "Edit Fields6": { "main": [ [ { "node": "Append row in sheet", "type": "main", "index": 0 } ] ] }, "Edit Fields5": { "main": [ [] ] }, "Browserless": { "main": [ [ { "node": "Ensure text if garbled", "type": "main", "index": 0 } ] ] }, "If1": { "main": [ [ { "node": "Extract source", "type": "main", "index": 0 } ], [ { "node": "Edit Fields2", "type": "main", "index": 0 } ] ] }, "Edit Fields2": { "main": [ [ { "node": "Append row in sheet1", "type": "main", "index": 0 } ] ] }, "Ensure text if garbled": { "main": [ [ { "node": "Claim check accuracy2", "type": "main", "index": 0 } ] ] }, "Validate final source": { "main": [ [ { "node": "Edit Fields6", "type": "main", "index": 0 } ] ] }, "Append row in sheet": { "main": [ [ { "node": "Wait", "type": "main", "index": 0 } ] ] } }, "active": true, "settings": { "executionOrder": "v1" }, "versionId": "91bc1003-34be-494e-b4ff-00c67fe3cf77", "meta": { "templateCredsSetupCompleted": true, "instanceId": "a7d3c8fee44ed6dfa5d3bf2eca37e2240651cc218f5d2ad518615bd97f803884" }, "id": "aHZyUTym1JtQKOWC", "tags": [] }