On not going bust
...or why staying in the game matters
I want to drop a little reality on you all here. Every transformation ends prematurely. They may end because we’ve reached “good enough” (ideally…); they may end because the sponsor got promoted, or transferred, or fired; they may end because the budget was drained.
They usually end because the change work is being crowded out by work that’s perceived as immediately meeting more pressing goals.
A moment to define ‘change work.’ The change agents in your organization are doing ‘change work.’ The people they interact with to create change – who are being trained, or coached, or doing double work (my personal favorite – doing Agile meetings PLUS waterfall meetings to keep the old PM reporting model intact) are doing change work.
It’s work that subtracts from capacity by burning hours or dollars and is aimed primarily at change, and not at all or indirectly at direct value creation. Essentially, take all the time your external agile coaches spend, plus the time your internal agile change agents spend PLUS all the time of everyone they interact with – and that’s your budget for change work.
Change work is an investment – a necessary one, I’ll argue – but one that indirectly creates value rather than creating value by itself. It’s like refactoring or retiring technical debt (in fact, I often refer to it as retiring organizational debt).
And, like refactoring, and retiring technical debt, it’s always a struggle to set organizational capacity aside for change work. Because the endless pressure of the day to day drives decisionmaking toward targeting their limited capacity on things that traceably generate immediate wins.
What I want to talk about here is a little abstract thinking about how to better shift that decision, which leads a bit into better ways of what I call ‘anchoring change’ in organizations.
Change is anchored when the wins from change are visible and offset or exceed the cost of change. Those wins may be financial; they may be in terms of organizational performance or cultural health. But they need to be visible and measurable - concrete.
The mental model I use is a game – specifically, poker or blackjack. Why those? Because they are typically composed of many ‘subgames’ (hands) and because there are two games going on at once – success in each hand, and resource allocation (betting) across the hands.
And, because resources are finite, the game ends when either you choose to quit (you’re tired, want to do something else, etc.) or – more likely – when you’re bust. And over time, everyone goes bust.
(A personal note; my father was a very skilled mathematician which he applied to breaking Japanese military codes in WW2 and to having a parallel career as a gambler for virtually his entire adult life. There were years when he won as much gambling as he earned as an executive at a construction company. He was good. And even he tapped out from time to time.)
Nassim Taleb writes about this in The Logic of Risk Taking – forgive the long quote, but there’s a point to it:
Consider the following thought experiment.
First case, one hundred persons go to a Casino, to gamble a certain set amount each and have complimentary gin and tonic –as shown in the cartoon in Figure x. Some may lose, some may win, and we can infer at the end of the day what the “edge” is, that is, calculate the returns simply by counting the money left with the people who return. We can thus figure out if the casino is properly pricing the odds. Now assume that gambler number 28 goes bust. Will gambler number 29 be affected? No.
You can safely calculate, from your sample, that about 1% of the gamblers will go bust. And if you keep playing and playing, you will be expected have about the same ratio, 1% of gamblers over that time window.
Now compare to the second case in the thought experiment. One person, your cousin Theodorus Ibn Warqa, goes to the Casino a hundred days in a row, starting with a set amount. On day 28 cousin Theodorus Ibn Warqa is bust. Will there be day 29? No. He has hit an uncle point; there is no game no more.
No matter how good he is or how alert your cousin Theodorus Ibn Warqa can be, you can safely calculate that he has a 100% probability of eventually going bust.
The probabilities of success from the collection of people does not apply to cousin Theodorus Ibn Warqa. Let us call the first set ensemble probability, and the second one time probability (since one is concerned with a collection of people and the other with a single person through time).
…
We saw with the earlier comment by Warren Buffett that, literally, anyone who survived in the risk taking business has a version of “in order to succeed, you must first survive.” My own version has been: “never cross a river if it is on average four feet deep.” I effectively organized all my life around the point that sequence matters and the presence of ruin does not allow cost-benefit analyses; but it never hit me that the flaw in decision theory was so deep. Until came out of nowhere a paper by the physicist Ole Peters, working with the great Murray Gell-Mann. They presented a version of the difference between the ensemble and the time probabilities with a similar thought experiment as mine above, and showed that about everything in social science about probability is flawed.
What he’s saying (as I take it, Taleb would doubtless explain why I was missing his point) is that we need to focus first and foremost on not going bust. And, that over time, everyone does.
Why do I as an agile change agent care (unless I’m heading to the casino for a holiday)?
Because there is likely to be, in every transformation, a point where there’s no game no more. And we – as change agents – want to do two things: first, push that out as far as we reasonably can; and second make sure that the client (and our reputation) walks away intact.
So we’ve defined out vision and our strategic goal.
How do we do it? How do we stay in the game as long as possible and make sure than when we drop out, we’re OK (and our client is better off?)
Well, I have some ideas…