How to use the Automation candidates template

Start a board from it
Use this template opens a new Freero board with the Automation candidates template on it (sign up free first). On a board you already have, open Templates in the toolbar and search for “Automation candidates”.
Follow the instruction on the board
“Ask each team for their three most boring weekly tasks. Start with tasks that are frequent, rule-based and cheap to get wrong.”
Fill in each part
- Table Columns: Task · Hours / week · Rule-based? · Data ready? · Cost of a mistake · Verdict
Bring your team and your agents
Invite people to the board or share a link, and work on it together live. The timer is in the top bar, private mode and voting in the board menu, and Claude or ChatGPT can join through Freero’s MCP server.

Work on it with an AI agent

Connect an assistant once and it works on the board with you, with your permissions: by default it reads and proposes, and people decide.

You ask: “Fill in the Automation candidates board from these notes.”
What happens It reads the board, then proposes sticky notes for it. Nothing changes until someone on the board accepts them.
You ask: “Group what we wrote into themes and list the actions.”
What happens It proposes clusters with a title each and task cards for the actions, labelled with its name and yours.

Human + agent workshop
Sort a team’s tasks by how much judgement and how much repetition they involve, to decide what agents do and what people keep.
Agent workflow design
Design a workflow an AI agent runs: what starts it, what it gathers, what it does, how its work is checked and how it hands back.
Agent handoff map
Who hands what to whom when people and agents share a piece of work, and the check that has to pass at each handoff.
Agent permissions review
Decide what an AI agent may do on its own, what needs a person’s approval and what it must never do, action by action.
Human review queue
A board for people to review what agents produced: waiting, in review, sent back with a reason, and approved.
Agent incident review
Look into a time an AI agent got something wrong: what happened, why it happened, why checks missed it, and what changes.