Make learning moves: Overcoming decision paralysis
When the path ahead isn't clear, optimize for learning instead of trying to leap ahead by guessing. This post covers some strategies for navigating when you seem to have run out of options.
Your team has been working for 2 weeks now to plan the next milestone of a critical project. You knew this was a challenging increment, but they seem stuck. You poke your head into their planning session and what you hear concerns you - they haven’t landed on a plan, they’re still debating options. The whiteboard is stained with hastily erased plans and incoherent scribbles. You’ll need to act.
Countless times in my career I’ve found myself in conversations where the team isn’t sure what path to take. This usually happens when there are multiple options and not enough information to choose the right path. The key signal? Not enough information. How could you get more?
A powerful question is: What is the least amount of work we could do to learn more?
This approach works best in environments that support experimentation, and works exceptionally well in environments where lean and agile principles are applied.
Think in small increments
Lean and Agile principles remind us that we learn as we build. Every step we take, every new piece we construct, puts us in a more educated position than we were before we built that piece. When debate drags on focusing on opinion alone, pause the conversation. Ask the question above.
The goal with this is to shift the team to action - is there some action that’s either small enough that we don’t mind trying it, or that we’re confident enough in that we all agree we should do it? Can we find a small step to take that will give us a little more info?
Some increments open huge doors
Have you ever played minesweeper? If you have, then you’ve had that experience of feeling like you’re out of moves - you have to make a bet, you have to guess. You click a block and boom - game over. But sometimes a click opens an entire area - suddenly there are 20 different moves available where previously there were none.
Same deal: pick a square, watch the board change.
Often, after a move or two, the right path becomes much more obvious allowing you to execute rapidly.
But what about the boom?
Let’s address that concern. If you’re diffusing a bomb or performing surgery, experimentation is not advised. However, if you are designing a UI, tuning a model, refactoring a backend or doing anything where a wrong move at worst sets you back a bit - then I’d argue the upside is worth the risk.
The focus here should be on what move will help you learn. This isn’t just random guessing, this is about talking through what happens next. You can imagine what making that move might result in - what would happen? What could you possibly learn? If you thought of this move purely as an experiment, is there a different move that would teach you more? The point is to think about how to maximize learning instead of guessing at what might accidentally overcome an obstacle.
If you made 3 moves, and 2 of them set you back, but 1 of them propelled you forward and opened up 10 options you didn’t have before - is that a setback? I think it’s a win.
Psychological Safety becomes critical to pull this off
To do this your team will need to get comfortable with small setbacks, they are going to happen from time to time. The purpose of this exercise is to make little setbacks safe, so bigger bets are more informed. In fact, what often leads teams down a path toward indecision is pressure to be right, so make sure you’re making space for people to learn by running experiments that don’t always work.
When we ask this question we need to be clear that learning involves discovering we’re right as well as discovering we’re wrong. There are no failed experiments in this scenario, but there are invalidated assumptions. If someone in power is going to get upset or resist having their assumptions invalidated, this isn’t going to work.
A few small move examples
Let’s go through a few examples to help crystalize the idea.
Team has raised concerns about the design of an upcoming UI element.
The work to build this UI element is material and they are trying to make sure the design is right, but they’ve been circling the plan for a week without a concrete plan. When we ask “What is the smallest amount of work we could to do learn more?” the team considers a small UI mockup they could provide to a few customers as well as some review of logging data they could do that may help inform the correct answer. The first round doesn’t yield 100% clarity, but it opens up 1-2 more experiments the team can do. It takes a few days, but by the end of the following week the team has much higher confidence about a new UI design.
Impact: We moved to action a few weeks faster than we would have otherwise, and the team’s confidence was significantly higher.
Team is stuck during an incident and isn’t sure how to debug further
The team has tried all the things they knew to do and the issue is still not being resolved. Most of the prior attempts were swings at a solution, so we step back and look at small increments that could teach us more. This turns up a few experiments that would help clarify how the system is working today and validate some assumptions we are making. As we run these experiments, one of them exposes a bad assumption, which explains why one of our previous fixes should have fixed it but didn’t. With a few tweaks to that approach, the problem gets resolved.
Impact: We restored service for customers hours faster than we would have by continuing to make guesses.
You want to introduce a new process, but your managers are pushing back
You have a strong sense that this process would improve outcomes, but your managers are resistant and believe it will have a negative impact. Through this question, you agree that the smallest thing you could do is to run a small pilot with a team to assess one part of the process and understand if your managers assumptions about what might happen are validated or challenged. You run this experiment and find that your managers are absolutely correct, you tweak the experiment again and run a pilot in another team, and once again your manager’s concerns are validated.
Impact: We avoided a costly change management mistake that would have hurt our teams and the relationship with our team.
Bigger moves to reduce risk and uncertainty
Two tools for making bigger moves to reduce uncertainty are the Pre-mortem and a Future Backwards. I’ll tackle those in more detail in another post but for now, if you want to read ahead of the class, have fun! There are a ton of facilitation tools for exploring risk and uncertainty, exploring these should provide some new ideas about what you can do in different situations.
Try this
Next time your team is debating opinion, pause the conversation.
Ask aloud: What’s the smallest step we can take to learn more about the right option?
Time box the step - < 1-2 days.
Bring results back for discussion, what’s next?
Repeat until the signal changes
The team should signal when you are done - they’ll want to move in bigger leaps in a direction, they’ll have confidence they know what to do.
Outcome: You’ll trade opinion for data and regain momentum. Bonus, the team will probably ask themselves this same question for a while.
(Reach out and tell me what you tried - I’ll feature one or two in this post)