An orthodox Software as a Service (SaaS) business is, in part, math that an organization tries its best to manage.
Think about all the math that goes into the construction of a typical SaaS firm. At the core there’s some customer with a job: a goal against which the customer wants to make progress. They can have a mathematical representation in a database somewhere. A bunch of technologists write some code, which is all math, and a bunch of creatives take a few photographs, which expresses itself a mathematical representation, and some data is Created Read Updated and Destroyed in a database somewhere, which is all just more math. And it’s all abstracted by yet more math at the processor level.
Most decisions about the math are kind of set and forgotten about.
Some decisions about math are pretty clever.
Once upon a time, an insurance company programmed its quote generator to discourage risky drivers from buying insurance from them, quoting a deliberately terrible price to just the bad drivers, but quoting good prices for good drivers. In so doing, that insurance company improved the quality of its risk pool while contaminating its competitors pools. Those decisions were automated into the quote algorithm and tool. It was some clever math.
Writing instructions once and having a processor repeat them is a form of decision automation. There’s a decision to move electrons through atoms in a very specific way for a very specific reason. (At least, we like to think all the time that it’s deliberate…)
Writing instructions and having a machine intelligence generate judgements is another form of decision automation. It just happens to be more specialized math.
A massive number of decisions are accumulated and automated so that a human doesn’t have to spend time making them.
In digital at least, a whole bunch of decisions are automated.
The math enables that.
Strategy is choice among alternatives. Good strategy is deliberate choice among weighted alternatives. Great strategy is formed with an end in mind, with clear linkages between the outcome and the choices.
How are good decisions about strategy made?
Some people would argue that an awful lot of data has accumulated in that skull of ours. Our experiences have left residue in there and formed new connections among ideas. That’s all data. Whether or not we can choose to remember some things accurately is another story. How our mind tricks us about our experiences, how we attribute success, failure, and existence, is another.
Some people would argue that there is an objective reality that exists outside the 1 billion corners of that skull. That nature is knowable if you ask it the right way. And that it should be an input into how strategy formed.
And I would argue that discussions among peers about both the experiences, the reality, and the preferences are themselves important both for the enumeration of alternatives, their weighting, their selection, and the resultant commitment to execution.
Planning is preparation of the mind and long run alignment engenders very short run flexibility.
It’s not just about looking back to the way things were before, but ahead to the way things could be, and then stating the way things should be.
The Generation of New Decision Pathways
Even a great strategy is math.
A strategy doesn’t contain an enumeration of every single decision that the humans in the organization have to make because it’s not comprehensive enough. It has to be specific enough to be an executable that the social system of a firm can run, but it has to be short enough for it to be read and understood.
The generation of a new strategy, or modifications to an existing locked-in strategy, will enable the generation of new decision pathways. The keyword is enable. To the extent that those in the organization respond to the strategy with action, and real modification of decision pathways, can be extremely variable.
Some of these pathways will be surprising, in that they are novel and on strategy. Others are unpleasant surprises, in that they may be novel, but won’t be on strategy. Some may make it all the way to decision automation. But not all.
The existence of decision automation does not mean that new decision pathways can’t be discovered and added. They create what public policy analysts call lock-in, and what technologists will frequently call technical debt. You probably experience lock-in through your fingertips every day.
The QWERTY keyboard is locked-in. It could be modified and changed. It’s just that we’ve all trained ourselves on how to use it the way it is, so, any move to modify it will be greeted with resistance. It’s the way it’s always been. It isn’t broken. It works. And we’ve all invested so much in learning how to type.
And you can hear that in organizations where years of muscle memory has been built up.
The existence of decision automation doesn’t mean that new pathways can’t get lit up, and even replace old ones. It’s just a bit tougher. And, more importantly, the strategy may include massive disruptions to people, workflow, technology, customers, and yes, even cash flow (!).
If there is a deliberate choice.
If it’s a real choice.
If the strategy is to just optimize existing decisions, if the firm is in the state of profit maximization on a market it has captured (earned or regulatory capture), then there should be no surprise that there isn’t an explosion of new decision pathways.
If the strategy is to grow a rapidly expanding product set, if the firm is in the state of market share maximization on a market it has not yet earned (or captured via bribery), there should be no surprise when one division starts fighting with another over the nature of shared services. This is to be entirely expected.
There are just so many things that aren’t even real choices. A dollar is a dollar and it can only be invested against one initiative or another. Initiatives may be organized in such a way to minimize investment and maximize shared dependency. That, itself, is a either an obvious feature of a choice, or, it might even surprise somebody to learn.
When a new decision pathway is spun up, the incumbent pathway remains. If the new pathway is rejected by just one organ of the firm, and there is no executive present or willing to execute it, then it will persist. The firm may realize a few gains from the new pathway, but has massively complicated its decision stack. A quarterly gain may be realized, but it really starts to bite a year out.
A lot of the pop business literature talks about the different kinds of models to manage lock-in. A key feature of Moore (1991) was that a tech startup would have to burn its innovators to have any chance of crossing the chasm. In effect, can the firm modify its decision pathways to get to the other side? Much of this older language has been replaced by a Silicon Valley term called a pivot. The spirit remains fundamentally the same.
Does the organization have a culture that enables it to rewrite the math at the core?
Highly evolvable cultures have the ability to compete and thrive because they can rewrite their math and automate new decision pathways.
It may be a trait worth pursuing.
The math can be changed. It doesn’t have to cause lock-in. It can be evolved.
It just isn’t instantly flexible because the people that manage, as best as they can’t, aren’t instantly flexible.