I joined Miro a year ago this week, back in November 2024.
In my first few weeks I wrote down and shared with the team a few assumptions / goals / thoughts / biases / priors as a kind of pseudo-manifesto for how I thought we might proceed with AI in Miro, and I thought I’d dust them off.
About a month ago we released a bunch of AI features that the team did some amazing work on, and will continue to improve and iterate upon.
If I squint I can maybe see some of this in there, but of course a) it takes a village and b) a lot changed in both the world of AI and Miro in the course of 2025.
My contribution to what made it out there was ultimately pretty minimal, and all kudos for the stellar work that got launched go to Tilo Krueger, Roana Bilia, Mauricio Wolff, Sophia Omarji, Jai McKenzie, Shreya Bhardwaj, Ard Blok Ahmed Genaidy, Ben Shih, Robert Kortenoeven, Andy Cullen, Feena O’Sullivan, Anna Gadomska, George Radu, Rune Schou, Kelly Dorsey, Maiko Senda and many many more brilliant design, product and engineering colleagues at Miro.
Anyway – FWIW I thought it would still be fun to post what I thought I year ago, as there might be something useful still there, accompanied by some slightly-odd Sub-Gondry stylings from Veo3…
When we are building AI for Miro always bear in mind the human-centred team nature of innovation and making complex project work. Multiplayer scenarios are always the start of how we consider AI processes, and the special sauce of how we are different to other AI tools.
The canvas is a distinct advantage for creating an innovation workspace – the visibility and context than can be given to human team members should extend to the AI processes that can be brought to bear on it. They should use all the information created by human team members on the canvas in their work.
Work moves fluidly from unstructured and structured modes, asynchronous and synchronous, solo and team work – and there are aspects of preparation and performance to all of these. AI processes should work fluidly across all of them.
All AI processes aim to preserve and prioritise work done by human teams.
Anything created by an AI process (initially) has a distinct visual/experiential identity so that human team members can identify it quickly.
Don’t teleport users to a conclusion.
Where possible, expose the ‘chain of thought’ that the AI process so that users can understand how it arrived at the output, and edit/iterate on it.
Where possible, expose the AI processes’ ‘chain of thought’ on the board so that users can understand how it arrived at the output, and edit/iterate on it. Give hooks into this for integrations, and make context is well logged in versions/histories.
Humans always steer and control – but AI processes can accelerate and compress the distances travelled. They are mostly ‘pedal-assistance’ rather than self-driving.
What are the AI processes that can accelerate or automate the work around the work e.g. taking notes, scheduling, follows ups, organising, coordinating: so that the human team mates can get on with the things they do best.
Eventually, AI processes in Miro extend in competence to instigate and initiate work in teams in Miro. This could have its roots in composable workflows and intelligent templates, but extend to assembling/convening/facilitating significant amounts of multiplayer/multispecies work on an indvidual’s behalf.
What memory / context can I count on to bring to my work, that my agents or my team can use. How can I count on my agents not to start from scratch each time? Can I have projects I am working on with my agents over time? Are my agents ‘mine’? Can I bring my own AI, visualise and control other AI tools in Miro or export the work of Miro agents to other tools, or take it with me when I move teams/jobs (within reason). Do my agents have resumes?
Petafloptimism 