See how NotebookLM, Gemini, and Canva turn an entire partnership-evaluation cycle into a single afternoon, all inside the Google Workspace your organization already pays for.
Thanks for sharing this workflow! It's super nice for people to get into automating part of their business with AI, not to mention it saves a ton of mental space.
The next step is to build it all under one Skill file in Claude Code / Hermes so the whole chain can tun unattended. But yeah, walk before you run, build it manual first like Raghav and Ashwin laid out.
whoah! I'd call this Wonderful Workflow, the heavy weight here isn't the speed, it's the shift in where the work happens. The bottleneck used to be creating summaries, decks, and memos. Now it's asking the right questions, validating assumptions, and making the final decision. AI is getting very good at compressing analysis, but judgment is still the scarce resource. That's a much more interesting change than "AI saves time."
Hey Petar! Thank you for reading and your thoughts on it. I actually ran this on an existing workflow, maybe the Subtsack post doesn't capture the full essence but it does work (albeit you would have to tinker around a bit into getting the context right which happens anyway with non-deterministic LLMs) :)
Share with colleagues. Thanks for sharing
Glad to hear!
Two hours to evaluate a partnership that used to take two weeks that's the real unlock.
Working smarter ;)
Hello John! Glad you found this useful, how has your experience been using AI in enterprise workflows?
Thanks for sharing this workflow! It's super nice for people to get into automating part of their business with AI, not to mention it saves a ton of mental space.
The next step is to build it all under one Skill file in Claude Code / Hermes so the whole chain can tun unattended. But yeah, walk before you run, build it manual first like Raghav and Ashwin laid out.
Great point, Dan!
Thanks Dan! Yes the next step would be to bring all under a skill file or project!
whoah! I'd call this Wonderful Workflow, the heavy weight here isn't the speed, it's the shift in where the work happens. The bottleneck used to be creating summaries, decks, and memos. Now it's asking the right questions, validating assumptions, and making the final decision. AI is getting very good at compressing analysis, but judgment is still the scarce resource. That's a much more interesting change than "AI saves time."
Yes! Had you read @Raghav Mehra ’s work before?
Strong example of end-to-end AI workflow, but it still reads more like a demo of tools than a real-world case with proven constraints
Hey Petar! Thank you for reading and your thoughts on it. I actually ran this on an existing workflow, maybe the Subtsack post doesn't capture the full essence but it does work (albeit you would have to tinker around a bit into getting the context right which happens anyway with non-deterministic LLMs) :)