- Asana AI is designed to address the problem of coordination overhead by operating on the Work Graph, Asana’s real-time model of how work moves through an organisation, which provides it with the necessary context to be useful, unlike standalone AI tools.
- Key features include Smart Summaries to quickly surface project status, AI Risk Reports to flag potential issues like dependencies at risk or overextended workloads, AI Studio for building custom workflow automations, and AI Teammates for handling recurring coordination tasks directly.
- The value of Asana AI is made or lost in the configuration and adoption, requiring teams to understand their workflows first, identify specific coordination pain points, and then configure the AI features and build the habits necessary to make them stick.
The work around the work
How much of your team’s week is spent on the work, versus on the work around the work?
The week fills up with updates to chase, status meetings to prepare for and summaries to write so the right people have visibility. Meanwhile blockers sit invisible until someone asks, and decisions stall because the person who needs to make them doesn’t have a clear picture.
These are the things that slow good teams down. Not a shortage of effort or skill, but a shortage of time to do the actual work because coordination is consuming it. Asana AI is designed to address that specific problem.
Why AI embedded in your work management is different from AI layered on top of it
AI tools are widely available. Whether the AI has anything useful to work with is a different question entirely.
Standalone AI tools operate on what you give them in a single session. They don’t know what your team is working on, who owns what, which tasks are blocked, which projects are at risk, or how any of it connects to your organisation’s goals. The output is only as good as the context you manually provide, and manually providing context is more coordination overhead.
Asana AI works differently because it operates on the Work Graph. The Work Graph is Asana’s underlying model of how work moves through an organisation: tasks, owners, dependencies, timelines, project status, team capacity and goals. When AI is embedded in that structure, it’s reasoning from a real-time picture of what’s happening across your work rather than generating responses in a vacuum.
That’s what gives it practical utility. The AI has context, and context is what makes AI useful.
Features worth exploring
Smart Summaries: instead of reading through comment threads to understand where a project stands, a summary surfaces the current state in seconds. Better-prepared check-ins, faster onboarding for team members switching between projects or returning from leave.
AI Risk Reports: flags issues before they become problems. Where dependencies are at risk, where workloads are overextended, where timelines have shifted without the plan adjusting. The value is visibility while there’s still time to act.
AI Studio: lets teams build automation specific to their workflows without a developer. Routing intake requests, updating stakeholders when a task moves, creating follow-up tasks based on project outcomes. Each step is small; across a week they return meaningful time to the people currently doing them by hand.
AI Teammates: handles recurring coordination tasks directly. Monitoring project health, sending updates, flagging items needing attention, actioning defined tasks without waiting to be told. The coordination layer that currently lives in someone’s head or inbox, made systematic.
Configuration is where the value is made or lost
The teams we see getting the most from Asana AI are the ones who understood their own workflows first, identified where coordination overhead was highest, and configured AI features to address those specific pain points. That means knowing which projects carry the most risk, understanding how work actually flows through the team, and deciding what should be automated and what should stay in human hands.
Adoption matters too. AI features that go unused don’t reduce friction, they add it, because now there’s a tool people are expected to use and aren’t.
This is where our Asana implementation framework makes a real difference. Getting Asana AI working well is a change management and design exercise as much as a technical one. It requires mapping actual workflows, configuring intelligence around them, and building the habits that make it stick. The organisations that get the most from it approached the setup with that level of care from the start.
Less coordination, more work
The goal is a team that spends less time on the machinery of managing work and more time on the work itself.
Asana AI changes how much of your team’s capability gets directed at things that matter. Fewer status updates. Fewer surprises. Less time in your inbox trying to figure out whether a project is on track. More time designing, building, solving, deciding.
The coordination overhead is real, and we see it consistently across the teams we work with. The tools to address it are available and well-developed. The question is whether they’ve been set up in a way that actually reflects how your work gets done.
If you’d like to understand what that could look like for your team, let’s talk.
