Building AI products and businesses requires making tough choices about what to build and how to go about it. I’ve heard of two styles:
- Ready, Aim, Fire: Plan carefully and carry out due diligence. 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, but I think 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 your team 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 product 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.