AI That Fits Existing Workflows
How AI is transforming productivity apps
The real edge in AI productivity is not adding AI everywhere, it is finding a painful job where AI removes concrete work people already hate doing. In this panel, the founders keep coming back to the same pattern. Drafting, summarizing, organizing, and tagging get better fast with LLMs, but adoption only sticks when the product fits an existing workflow, like handling delegation in Double or turning messy notes into usable project structure in Taskade.
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This is why AI features inside productivity apps spread so quickly. A company like Notion can add writing, summarization, and draft generation directly inside a tool people already use, instead of asking them to adopt a separate AI product first.
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It also explains why standalone AI tools are fragile when they only offer a neat demo. Gamma works when it removes the blank page problem and creates a presentable deck structure fast, but that value is still close enough to core office software that incumbents can absorb it.
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The stronger businesses are usually the ones that pair general models with specific context and workflow. Glean moved from enterprise search into an assistant connected to company data, and Hebbia positions itself as the layer that actually completes finance and legal knowledge work after retrieval.
Going forward, the winners in AI productivity will look less like generic AI wrappers and more like workflow software that quietly uses AI underneath. As models improve, the differentiator shifts to context, permissions, interfaces, and trust, meaning the biggest products will be the ones that make real work feel shorter, not the ones that advertise AI the loudest.