What Is Your Most Useful Real-World AI Workflow?

The 'Meeting Synthesiser to Jira' pipeline. I use an AI meeting recorder (like Fathom or Otter) for every client call. I have a Zapier workflow that takes the raw transcript, feeds it to Claude 3.5 Sonnet with a strict prompt to extract only actionable technical requirements, formats them as user stories, and pushes them directly into Jira as tickets, assigning them to the relevant developer based on the context (https://community.atlassian.com/t5/Jira-articles/How-to-automate-ticket-creation-wi...

The 'Literature Review Matrix.' When I have 50 PDFs of academic papers to read, I upload them all to NotebookLM (Google's grounded AI) (https://blog.google/technology/ai/notebooklm-research-tips/). I don't ask it to 'summarise' them. I ask it to build a comparative matrix: 'Create a table comparing the methodology, sample size, and primary conclusions of these 50 papers regarding media consumption.' It does in 30 seconds what used to take a grad student three weeks. It doesn't write the paper fo...

Research synthesis is my most valuable workflow. I use Perplexity for initial landscape mapping, then Claude for deep analysis of specific papers, then NotebookLM to synthesise across multiple sources. The output is a structured research brief that used to take a week and now takes a day. The PwC 2026 AI business predictions suggest this kind of workflow acceleration is where most of the AI productivity gains are actually coming from (https://www.pwc.com/us/en/tech-effect/ai-analytics/ai-predict...

@StartupSurvivor We tried this, but the AI kept hallucinating requirements that the client never actually asked for. A developer spent two days building a feature because the AI misinterpreted a joke the client made. You still need a human to review the tickets before they go to the sprint board.

@DataDrivenDan The Perplexity → Claude → NotebookLM pipeline is exactly what I use for lecture preparation (https://www.youtube.com/watch?v=lferqLJXgTA). I'd add one step: I use the NotebookLM audio overview as a 'sanity check', if the AI-generated podcast discussion of my synthesised notes sounds coherent and accurate, I know my synthesis is solid. It's a strange quality control mechanism but it works.