I've been trying to work out for years why so many good decisions arrive late.
Not wrong, exactly. Late.
Because the people making them are usually careful and properly smart. Wanting more data before you commit is a perfectly sensible instinct. Most of the time.
And then the window closes while the team is still polishing the slide that would have opened it.
Beautifully argued. Slightly too late.
I say this as someone who has, more than once, researched a decision so thoroughly that it went and made itself while I wasn't looking.
So this programme puts your managers somewhere the data is never complete and the ground keeps moving, and watches what they do. Then it takes one real decision that's stuck on "we need more data" and gives it a date.
Planetfall is the game at the heart of my learning agility training. It's one of the 7 games.
How the change actually happens: play, then EPPA
Every session runs on one loop. I call it EPPA.
- Experience. The game itself. Ninety minutes to two hours of play: probing, guessing, arguing and occasionally crashing.
- Patterns. The room names what kept repeating. Who waited, who probed, who defended the round-one plan long after round one.
- Principles. The room connects those patterns to how they handle the unknown at work. In their own words, because it lands better when they get there themselves.
- Application. A named behaviour, a real situation and a date. This is where people usually stall, so it's the step I've built the most around.
Application is also the only place AI comes in.
The game at the heart of it: Planetfall
In Planetfall, your managers are trying to land a spaceship on an alien world. The information is incomplete, and the planet gets harsher the deeper they probe. Late in the game a sealed forecast opens a hole in the system they trusted most, and the game is built so they find that out at about the point where it starts getting expensive to fix.
Each crew orbits a planet nobody has mapped and tries to land in one piece. Every round, every player except the Commander takes one cube and spends it on one action: fire a probe to learn something about the atmosphere, decode a reading already on the table, harvest supplies, or install a ship upgrade. The Commander holds no cube and runs the round instead. Test, learn, adapt, repeat. There's deliberately no right answer, which is what turns guesswork into disciplined experimenting.
The game runs 8 rounds, 90 to 120 minutes of play, and the planet is built to get harsher the deeper they probe. Late in the game, a sealed forecast opens a hole in the system they trusted most. Every shift is another chance to notice and adjust. Or not.
Most ambiguity training I've seen hands people a perfectly good model for handling uncertainty, and the models are fine. The room is just usually quite certain. Nothing moves. Planetfall moves the ground, which is the whole idea, and honestly it isn't much of one.
What the game is built to bring out
Every crew starts with a plan. The plan is good. It's well argued, somebody has drawn it on the flipchart, there is broad agreement. Then the planet turns, and the plan is wrong.
That's the moment the whole game is built around. Some people will want to dig in - "we committed to this, we don't have enough data yet." Some will want to change this instant. And a few may go very quiet and wait to see which way the room leans... which is its own kind of answer.
The game is designed so the crews who can't make the meta-decision feel it: when do we stop debating and act? A beautiful position that arrives ten minutes late costs them.
And it is designed to reward the crews who set a decision rule early and reach for it every time the conditions move again.
I won't tell them any of that. The game is built to, which I think lands far better than me saying it.
What you'll see under uncertainty
- The prober and the waiter. Who spends their cube to learn something, and who holds on for a safer moment that is never going to arrive.
- Why the game is built so the manager who announced the plan in round one is the last to let go of it.
- How each person meets not knowing. Some reach for control. Some lean into it. The game is built to make both visible early.
- Whether the team builds one shared picture from the same face-up readings, or splits into five people politely reading them five different ways.
- Whether decision speed holds across rounds, or falls apart as the pressure stacks up.
Where AI comes in, and where it doesn't
There's no AI on the planet. The game is probes, partial data, a lot of argument and the occasional crash landing.
AI only shows up at Application, after the room has named its own patterns. And it works on a real decision from their actual job, not on the game.
- Each manager brings one real decision that's stuck on "we need more data".
- They frame it first, using the Double Diamond, so they know which problem they're actually deciding before anyone opens a chat window.
- AI sorts what they know from what they don't, and drafts a decision rule. "We act when X."
- The manager sets the date to act.
What stays human: when to stop debating and act.
AI is good at sorting a messy pile of facts and guesses into two neat columns. Deciding that the columns are good enough is still a person's job, and I think it should be.
What your managers walk out with
- One stuck decision, unstuck. Framed, sorted and dated, before they leave the room.
- A decision rule they wrote themselves, in plain words. The same trick the game rewards teams for reaching for in round one.
- An honest read on their own lean, toward action or toward analysis. Ninety minutes to two hours of play in a made-up spaceship is designed to show that more clearly than a questionnaire does.
- Imagine the next meeting where someone says "we need more data" and someone else asks, quite kindly, "what would X have to be for us to act?"
- A shared vocabulary for the team. Probers, waiters, and the round-one plan.
Patterns it is built to surface
- Who changes course fast when conditions shift, and who defends the first decision well past its sell-by date
- Who says out loud what a new reading means straight away, and who goes quiet to think it over first
- How the group handles disagreement about direction. A clear decision, a slow drift, or one person deciding while everyone else goes along quietly
- Who acts on partial information, and who keeps holding out for a certainty that is not coming
What I can show you
I'll be straight with you. Planetfall hasn't earned a score of its own yet, so I won't pretend it has.
What I can tell you is how the rest of my work has landed.
- I've been training founders and leaders since 2019, with founders in the US, Canada, Australia, Singapore and India.
- On one programme at a Fortune 100 company, across six session-days, the satisfaction score came to 8.82/10 from 113 respondents.
- Across every session-day I've had scored, it's 8.40/10 from 244 respondents. Which is probably the more useful number of the two, because it includes the days that went less well.
Who this is for
- Managers in fast-changing environments
- Organisations in the middle of a transformation, which is most of them
- Teams walking into a new market
- High-potential groups, where learning agility is the exact thing you're trying to build
- Teams whose post-mortems keep landing on "we knew conditions had changed and stuck with the plan anyway"
- Strategy offsites, where it's worth talking about how the team handles uncertainty before committing to a plan built on certainty
Who would probably get more from something else
- Groups under 8. The splits need enough people to become visible.
- Leadership teams whose real problem is political rather than cognitive. Ripple Effect or Welcome to Zombiepuram will get closer to what's actually going on, and I'll point you there instead.
- When the real issue is who gets to decide, not how well they decide. That's a structure question, and a game won't fix it. A good conversation with your HR partner probably will get further.
A few fair worries
"How is this different from a normal business simulation?"
Most simulations I've seen score the outcome, and that's useful in its own way. Planetfall watches the process. How did your team behave the moment the right call stopped being obvious? In the debrief I will walk back through the decision points instead of tallying results, because the score was never the interesting bit.
"Our people have played simulations before."
Fair, and it should help. They'll be past the mechanics and much quicker to spot their own patterns. It doesn't need novelty to do its job.
"What if the group is very senior?"
In my other games, senior rooms tend to give the richest picture. The habits that made someone brilliant at 28 can be quietly expensive at 48, and nobody at that level gets told. Planetfall doesn't tell them either. It lets them feel it.
"Can AI really help with a decision like this?"
Honestly, it can't make the decision, and I'd be worried if it did. What it does well is the sorting. Separating what you know from what you're assuming takes most of us an afternoon of arguing. AI gets you a first draft of that list quickly, and then the arguing can be about the right things.
Come and see
Tell me who's in the room and what you're trying to shift, and I'll tell you straight whether this is the right programme. If it isn't, I'll say so.
Worst case, we have a conversation and you come out of it with a clearer brief.
Let's find out if it fits your managers
Your managers, the decision that's been waiting for more data, and a straight answer on whether this is the right programme. And if it turns out you just need the data... that's a perfectly good answer too.
Come and have a look at the calendar
With love from Bengaluru, this is Arvindh saying over and out.