Ready, Fire, Aim

 Ready, Fire, Aim

Working on projects requires making tough choices about what to build and how to go

about it. Here are two distinct styles:

✓ Ready, Aim, Fire: Plan carefully and carry out careful validation. Commit and

execute only when you have a high degree of confidence in a direction.

✓ Ready, Fire, Aim: Jump into development and start executing. This allows you to

discover problems quickly and pivot along the way if necessary.

Say you’ve built a customer-service chatbot for retailers, and you think it could help restaurants,

too. Should you take time to study the restaurant market before starting development, moving

slowly but cutting the risk of wasting time and resources? Or jump in right away, moving

quickly and accepting a higher risk of pivoting or failing?

Both approaches have their advocates, and the best choice depends on the situation.

Ready, Aim, Fire tends to be superior when the cost of execution is high and a study can shed

light on how useful or valuable a project could be. For example, if you can brainstorm a few

other use cases (restaurants, airlines, telcos, and so on) and evaluate these cases to identify

the most promising one, it may be worth taking the extra time before committing to a direction.

Ready, Fire, Aim tends to be better if you can execute at low cost and, in doing so, determine

whether the direction is feasible and discover tweaks that will make it work. For example, if

you can build a prototype quickly to figure out if users want the product, and if canceling or

pivoting after a small amount of work is acceptable, then it makes sense to consider jumping

in quickly. When taking a shot is inexpensive, it also makes sense to take many shots. In this

case, the process is actually Ready, Fire, Aim, Fire, Aim, Fire, Aim, Fire.

After agreeing upon a project direction, when it comes to building a machine learning model

that’s part of the product, I have a bias toward Ready, Fire, Aim. Building models is an iterative

process. For many applications, the cost of training and conducting error analysis is not

prohibitive. Furthermore, it is very difficult to carry out a study that will shed light on the

appropriate model, data, and hyperparameters. So it makes sense to build an end-to-end

system quickly and revise it until it works well.

But when committing to a direction means making a costly investment or entering a one-

way door (meaning a decision that’s hard to reverse), it’s often worth spending more time in

advance to make sure it really is a good idea.