A lot of AI automation fails not because the tech is bad, but because the wrong process gets automated. If a process is unclear or constantly changing, automation doesn’t fix it, it just speeds up the mess. I now ask myself one thing before automating: would this still be a problem if it ran instantly? If yes, it’s probably a process issue, not an automation one. How do you decide what’s worth automating?

We were doing research wrong. Every Monday morning, 4 of us would independently search ArXiv, Google Scholar, and PubMed for papers on AI automation and document processing. 3 hours each. 12 hours total team time. Then we'd compare notes and realize we'd all found the same 7-8 papers. Complete duplication of effort. THE BREAKING POINT: One Monday, our lead researcher was sick. We had a client call Tuesday needing current research on AI extraction methods. Nobody had done the Monday search. We looked unprepared. "We can't keep doing research this way. There has to be a better system." WHAT I BUILT: n8n workflow that runs daily at 6 AM: SCHEDULE TRIGGER - Fires every morning before team arrives ACADEMIC SEARCH NODE - Searches ArXiv, Google Scholar, Semantic Scholar simultaneously for "AI automation document processing" papers published in last 24 hours DATE FILTER - Code node that validates papers are actually from last 24 hours (some databases have lag) VALIDATION FILTER - Ensures paper has abstract, PDF link, and author information (removes incomplete entries) LIMIT TO TOP 5 - Selects highest-cited papers only NOTION INTEGRATION - Creates database page with: Title, Authors, Abstract, PDF link, Citation count, Source, Publication date SLACK NOTIFICATION - Sends formatted morning digest to #research channel The whole workflow took 30 minutes to set up. THE RESULTS: Team arrives each morning to curated research digest. Zero manual search time. Everyone sees same papers. Complete metadata automatically captured. From 12 hours weekly search → 45 minutes weekly review. Found 3 papers in last 4 months that directly influenced client projects. Would've missed them with old manual approach because they were published Thursday afternoon (we only searched Mondays). CURRENT STATUS: Been running 4 months. Zero maintenance required. Notion database now has 480+ papers with full metadata. Searchable knowledge base that grows automatically. Team actually reads MORE papers now. Discovery friction removed.