How it works.
Four parts and one simple loop: set the goal, find what’s in the way, clear it with a few clear moves, then look at the progress and go again.
- 01
Goals
Where the company is going, written in the team’s own words, with an owner and a date.
- 02
Constraints
What’s in the way right now, ranked, so the team agrees on the one thing holding it back most.
- 03
Moves
The few actions that clear it, each with one owner and a due date the whole team can see.
- 04
Progress
What moved, what didn’t and what’s next, written up by AJ, our AI, from the team’s updates.
The long-term plan sets the destination.
The vision, the three-year picture and the annual priorities you already set still matter, because a team needs to know where it’s going before it can tell what’s in the way.
The loop keeps the plan alive.
Instead of a plan written once and revisited at the next away day, the team works on whatever is holding it back today, learns as it goes and adjusts the route as the market, the people and the numbers change.
How it looks.
The model on the left, the context on the right, and AJ on hand to keep both up to date.
The model
The goal, what’s in its way and the moves under each, in one picture the whole team can read at a glance.
The context
Everything behind the item you’ve picked, from the current reality to why it matters and how you’ll know it’s done.
The model.
One goal, the constraints in its way and the moves that clear them, all in one place.
The goal sits at the top.
Every workspace has one goal with an owner and a due date, so everything underneath it points the same way.
Constraints, in order.
Drag them into the order that matters; the one at the top is what the team clears first, and each card shows its moves and who owns it.
The moves that clear it.
Open a constraint to see the moves under it, grouped into in progress, not started and done, each with one owner and a due date.
AJ spots when a constraint is cleared.
It checks the team’s updates against how you said you’d know, then asks the owner to confirm, so the call stays with people.
The context.
Pick anything in the model and its context opens beside it, so the whole team reads the same story behind each decision.
Current reality, always current.
A short, plain summary of where things stand, rewritten as updates come in, with every previous version kept.
The thinking behind it.
Why it matters, why we believe it and how we’ll know it’s done, written once and kept in view.
AJ suggests, you decide.
When the evidence says a constraint is cleared, AJ says so, and the owner marks it complete or says not yet.
Everything updated with a conversation.
Tell AJ what’s changed in a sentence, and the model updates itself while you watch.
- 1
Say what happened.
Type an update the way you’d say it in a meeting, one line or several.
- 2
AJ updates the page.
It rewrites the constraint’s current reality straight away, and any change can be undone.
- 3
Evidence, logged.
Anything that counts as evidence is saved against the constraint, so the team can see why things moved.
- 4
Then it asks the next question.
Like a good coach, AJ checks whether it’s a real shift or a one-off before anyone calls it cleared.
Human judgement, front and centre.
AJ, our AI, is trained to act more like a coach than a chatbot, so rather than generating generic solutions, it asks the questions that help the team solve its own problems and puts the onus back on the people doing the work.
Asks before it answers.
AJ asks what’s really in the way and why, so the team does the thinking and owns the plan it comes up with.
Challenges, rather than prescribes.
It tests whether a move will actually clear the constraint, instead of handing over a list of best practice.
The call stays with people.
AJ suggests when something looks done, but the owner decides, so your team stays in charge of every call.
Try it with your own goal.
Start for free, or book a call and we’ll walk you through it; we can also train your team and help you set up your first goal.