I recently shared some observations about trying to use generative AI tools at work and shared the experience on LinkedIn.
As an experiment, the Mosey team paid for a bunch of generative AI tools (Slack AI, Notion AI, OpenAI, GitHub Copilot) and tried to incorporate them into our everyday work.
Here’s what we found.
Summaries, while boring, are extremely useful. Slack recaps help everyone maintain “ambient awareness” of other projects without getting overwhelmed. It helps that our communication norms default to in-public and in-channel (not DMs).
The barriers to trying new things with AI are so low you can just “play with it” really quickly. Small prototypes that previously felt like a lot of work can be spun up in hours—a signup spam detector, generating configuration data, analyzing a report, a classifier for mail—all started with a one-off prompt to see what happens.
AI tools excel at making sense of unstructured data like text but don’t yet work well for getting answers from more structured data (multiple API calls, translating into SQL queries, etc.). This exposed the need for more internal tools like full-text search where you more or less know what you want and have knowledge of how to find it. You can’t always sprinkle AI on it and make it work.
Data silos blunt AI. Notion has all of our briefs, processes, and notes but there’s no way to have Slack bring in that context when using it’s AI features. (Update: this is now in beta!). The same applies to other hubs of communication like email and HubSpot. This will become the operating system’s job (see Apple Intelligence).
The out-of-the-box stye of most LLMs is really bad for business writing. You have to prompt very carefully to not end up sounding like a vapid, jargon filled mess. It’s such a caricature of corporate speak that would be really funny if it wasn’t so sad.
Using AI tools in the team’s day-to-day sparked more ideas about where to apply AI to more processes. This is a self-reinforcing loop where the team tries more things, gets promising results that encourage trying yet more things.
I personally believe that the ability to get good results from okay generative AI tools will become a career-building skill that will eventually be required. I’ve received positive feedback from the team about being able to learn this new skill together and on the job.
Links to this note
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Dify mashes together LLMs, tools, and an end-user facing UI together to make an LLM workshop. The builder is a visual programming interface (similar to iOS Shortcuts) where each step is pre-defined units of functionality like an LLM call, RAG, and running arbitrary code.
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Several startups are touting AI employees that you can hire to perform a specific function. Itercom announce Fin, an AI customer service agent and so did Maven AGI. Piper is an AI sales development representative and so is Artisan. Devin is a software engineer.