Every time I encounter the monkey pedestal model I wonder how well this really applies in organizations. When you’re solving or trying to create a technical breakthrough, it’s often straightforward to identify the bottleneck. For example, we have a pretty good idea what the fundamental technical “monkey” is in nuclear fusion—and so multiple scientific team around the world are tackling those issues.
But in organizations it’s harder to identify where the bottleneck is. I think of some of my clients—high-growth tech startups that are usually under-resourced. So, solving the biggest problems usually all fall short of real solutions. They are half measures that aim to mitigate disaster while also husbanding resources and trying to invest in forward progress toward strategic (often technical) goals..
If there were unlimited resources it would be easier to distill the hardest problems into a cohesive target. Why? Because you could solve the basic resource problem and then have a clearer view to structural, systemic or technical juggernauts. It’s a bit like trying to detect a plumbing blockage when the water pressure is low. Until you push enough water through the system to get a back-up, everything just moves slowly—but there’s no detectable difference in drainage speed between blocked or open pipes.
So happy you made this comment. It is true that when there is more uncertainty, it is harder to identify the monkey. I also love you low water pressure example. Sometimes it is only when there is stress on the system that you find out where it will break. That being said, this is a repeating issue with decision making under uncertainty where we must expect that, even though we can't know for sure, that doesn't mean that there isn't value in explicitly taking our best guess.
As an example, in forecasting, I hear all the time something to the effect of, "I can't possibly make a forecast of the probability of X, Y, or Z occurring because it is too much of an unknown." The issue here is that the forecast is already part of the decision. Whether you make it explicit or not, the forecast is an implicit part of the decision you make because you can't choose an option without making a forecast of the probability of different outcomes unfolding. Therefore, it is worthwhile to make the forecast explicit along with the rationale for the forecast because then it can be examined, updated, and you can learn from your errors as you discover how close your forecast was to the reality that unfolded.
It is the same for monkeys and pedestals. The monkey may be hard to identify. That is a given. But in any project plan, the monkey is implicit since the attack plan, presumably, is taking into account the hard part of the problem that needs to be solved for. By making your best guess of the monkey explicit, especially getting independent views of the monkey(s) along with rationales, the team will to be more likely to identify the bottlenecks and come up with a higher quality project plan. Of course, once the water pressure is higher, you may find out that there is a monkey you did not foresee. But that doesn't mean there isn't value in doing the exercise.
Additionally, the monkeys and pedestals mindset focuses the team in on the work that will really matter. There is too much low hanging fruit being tackled in the name of feeling like there is progress when that low hanging fruit is not solving for anything. Without the model, folks will build before doing a single customer interview, as an example. The mindset of trying to identify the bottlenecks, even if it will be noisy and difficult to do so, keeps the team trying to tackle what matters most.
And, of course, the exercise ought to be on a regular cadence so the team can update the monkeys given new information.
In the end, whether it is making forecasts, estimates, or identifying monkeys, under conditions of uncertainty it is unlikely we will get it right if getting it right means hitting the answer exactly. But that is not so much the point of these tools. Rather, all of these things are implicitly included in the decision making process and, for that reason alone, making these things an explicit is imperative for improving decision quality and closing feedback loops.
This is a fantastic post, Annie! I'm reminded of a thread by Ryan Petersen of Flexport who had said this back in 2021, on the supply-chain crisis in LA/Long Beach:
"When you're designing an operation you must choose your bottleneck. If the bottleneck appears somewhere that you didn't choose it, you aren't running an operation. It's running you." https://twitter.com/typesfast/status/1451543795045183490
He went on to tweet: "You should always choose the most capital intensive part of the line to be your bottleneck. In a port that's the ship to shore cranes. The cranes should never be unable to run because they're waiting for another part of the operation to catch up." That I guess is a rule of thumb for spotting the monkey among the many pedestals waiting to be built.
One way to frame low-hanging fruits is by the thinking of the ROI. Because ROI is a ratio, you can strip down the denominator (quick tasks) to show a healthy ROI. But a quick win, as you show, is simply another pedestal. The counter to quick-win thinking is opportunity-cost thinking. What's the cost of the monkey you're giving up by using up resources on pedestals?
Love every word of this. So well-said! yes...some low hanging fruit is high ROI and you should tackle that. That is the subtle difference between low-hanging fruit and pedestals. Pedestals are always low ROI. Easy wins can be high ROI. Such a great way to parse out the difference.
It seems to me that building pedestals is a quick and almost certain win. But spotting a monkey takes time and is only a potential win. It is the uncertainty that drives people away from monkeys.
Every time I encounter the monkey pedestal model I wonder how well this really applies in organizations. When you’re solving or trying to create a technical breakthrough, it’s often straightforward to identify the bottleneck. For example, we have a pretty good idea what the fundamental technical “monkey” is in nuclear fusion—and so multiple scientific team around the world are tackling those issues.
But in organizations it’s harder to identify where the bottleneck is. I think of some of my clients—high-growth tech startups that are usually under-resourced. So, solving the biggest problems usually all fall short of real solutions. They are half measures that aim to mitigate disaster while also husbanding resources and trying to invest in forward progress toward strategic (often technical) goals..
If there were unlimited resources it would be easier to distill the hardest problems into a cohesive target. Why? Because you could solve the basic resource problem and then have a clearer view to structural, systemic or technical juggernauts. It’s a bit like trying to detect a plumbing blockage when the water pressure is low. Until you push enough water through the system to get a back-up, everything just moves slowly—but there’s no detectable difference in drainage speed between blocked or open pipes.
So happy you made this comment. It is true that when there is more uncertainty, it is harder to identify the monkey. I also love you low water pressure example. Sometimes it is only when there is stress on the system that you find out where it will break. That being said, this is a repeating issue with decision making under uncertainty where we must expect that, even though we can't know for sure, that doesn't mean that there isn't value in explicitly taking our best guess.
As an example, in forecasting, I hear all the time something to the effect of, "I can't possibly make a forecast of the probability of X, Y, or Z occurring because it is too much of an unknown." The issue here is that the forecast is already part of the decision. Whether you make it explicit or not, the forecast is an implicit part of the decision you make because you can't choose an option without making a forecast of the probability of different outcomes unfolding. Therefore, it is worthwhile to make the forecast explicit along with the rationale for the forecast because then it can be examined, updated, and you can learn from your errors as you discover how close your forecast was to the reality that unfolded.
It is the same for monkeys and pedestals. The monkey may be hard to identify. That is a given. But in any project plan, the monkey is implicit since the attack plan, presumably, is taking into account the hard part of the problem that needs to be solved for. By making your best guess of the monkey explicit, especially getting independent views of the monkey(s) along with rationales, the team will to be more likely to identify the bottlenecks and come up with a higher quality project plan. Of course, once the water pressure is higher, you may find out that there is a monkey you did not foresee. But that doesn't mean there isn't value in doing the exercise.
Additionally, the monkeys and pedestals mindset focuses the team in on the work that will really matter. There is too much low hanging fruit being tackled in the name of feeling like there is progress when that low hanging fruit is not solving for anything. Without the model, folks will build before doing a single customer interview, as an example. The mindset of trying to identify the bottlenecks, even if it will be noisy and difficult to do so, keeps the team trying to tackle what matters most.
And, of course, the exercise ought to be on a regular cadence so the team can update the monkeys given new information.
In the end, whether it is making forecasts, estimates, or identifying monkeys, under conditions of uncertainty it is unlikely we will get it right if getting it right means hitting the answer exactly. But that is not so much the point of these tools. Rather, all of these things are implicitly included in the decision making process and, for that reason alone, making these things an explicit is imperative for improving decision quality and closing feedback loops.
This is a fantastic post, Annie! I'm reminded of a thread by Ryan Petersen of Flexport who had said this back in 2021, on the supply-chain crisis in LA/Long Beach:
"When you're designing an operation you must choose your bottleneck. If the bottleneck appears somewhere that you didn't choose it, you aren't running an operation. It's running you." https://twitter.com/typesfast/status/1451543795045183490
He went on to tweet: "You should always choose the most capital intensive part of the line to be your bottleneck. In a port that's the ship to shore cranes. The cranes should never be unable to run because they're waiting for another part of the operation to catch up." That I guess is a rule of thumb for spotting the monkey among the many pedestals waiting to be built.
One way to frame low-hanging fruits is by the thinking of the ROI. Because ROI is a ratio, you can strip down the denominator (quick tasks) to show a healthy ROI. But a quick win, as you show, is simply another pedestal. The counter to quick-win thinking is opportunity-cost thinking. What's the cost of the monkey you're giving up by using up resources on pedestals?
Love every word of this. So well-said! yes...some low hanging fruit is high ROI and you should tackle that. That is the subtle difference between low-hanging fruit and pedestals. Pedestals are always low ROI. Easy wins can be high ROI. Such a great way to parse out the difference.
It seems to me that building pedestals is a quick and almost certain win. But spotting a monkey takes time and is only a potential win. It is the uncertainty that drives people away from monkeys.
You made my evening, Annie! See you at the AMA in the morning :)