AI Commoditizes Programmatic Tasks
Gaurav Agarwal, COO of ClickUp, on how AI is redrawing the competitive map in productivity
This is really a claim about where software value moves next. If AI can handle the clean, repeatable work that used to reward better automation, then the harder thing becomes designing shared workflows that humans and agents can actually trust and follow. For ClickUp, that favors a product built around tasks, approvals, docs, chat, and permissions in one system, because those are the rails that turn raw model output into work a company can run on every day.
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Inside ClickUp, the contrast is between single player AI and multiplayer work. A chat model can brainstorm or research, but once work needs owners, statuses, approvals, access controls, and handoffs across teams, the advantage shifts to software with built in work primitives and shared context.
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This extends ClickUp’s older all in one thesis. The company has spent years bundling tasks, docs, chat, whiteboards, time tracking, and automation into one database, with the idea that fewer separate tools means less context loss. AI raises the value of that architecture because models perform better when the underlying work data is already connected.
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The same pattern is showing up across productivity software. AI is rapidly turning narrow point features into table stakes, which pushes differentiation away from isolated tools and toward the system that holds context, orchestrates workflows, and gives teams a repeatable way to review and ship output instead of just generating drafts.
From here, the winners are likely to look less like standalone copilots and more like operating layers for human plus agent work. The more AI commoditizes drafting, tagging, summarizing, and other programmatic tasks, the more durable advantage shifts to products that own the workflow, the memory, and the human review loop around that automation.