Businesses that adopt Agile often miss out.

Don’t misunderstand. I’m all in favor of Agile development, although I’m less than sanguine about its ongoing evolution from simplicity and charm to complexity and excessive proceduralization.

But the missing out comes from a failure to recognize what Agile isn’t, namely, it isn’t limited to application development. Agile is a way of thinking, not a series of steps. And its way of thinking applies to any situation where an organization needs to address some set of problems and opportunities with a design and a plan, but the problems and opportunities are deeply fluid.

And oh, by the way, those whose opinions about the problems and solutions govern decisions are in flux as well.

Start by imagining, just hypothetically you understand, your boss calls on you to charter and launch a new department. It will be called the Math Department and its purpose is to solve math problems for the rest of the enterprise.

Any and all math problems.

Tell her when you’ve finished the assignment.

Hoo Hah! Say what?

Further imagine your professional career began in IT, where you were schooled in Waterfall methodologies.

And … you’re doomed.

To design the Math Department you need to understand Math. Which you start out to do, reading everything you can get your hands on about Math.

But the more you learn about Math, the more branches of mathematics you learn about. The subject is, as Einstein … using math, by the way … pointed out about the universe, finite but unbounded. So you go back to your manager, explain the impossibility of carrying out your assignment, polish your resume, and don’t look back.

Or, you could apply Agile methods.

You’d start with a few Epics … say, basic arithmetic, algebra, and trigonometry. These would comprise your initial Backlog.

You’d then take the simplest and, happily, most needed of the Epics from the Backlog … arithmetic … write user stories (yes, you could turn story problems into user stories), and write a position description to hire someone who can solve all the user stories. Which is how you come to hire Michael Vincent Peterson. You nickname him MVP (did I really need to spell this out?) and the Math Department is off and running.

Well, walking anyway.

Once Arithmetic Services is stable (are stable? No, it’s a thing — “is stable”) you follow the same pattern for algebra, and follow that pattern for trig.

It’s right about here you discover that just having experts isn’t enough. The Math Department has become popular enough that it needs some level of management — enough to decide how to process requests and set priorities. How should you handle this?

The same way: You add an Epic to the Backlog, this one for designing and implementing Mathmanagement (catchy, eh?), just as you’d do if one of your executives came along to tell you he needs the Math Department to handle differential calculus.

If you boil Agile down to its essentials, you’ll find principles you can apply to a whole lot more than application development, for example, the principle that there’s little point spending time designing solutions you won’t be in a position to implement before they become irrelevant.

So the moral of this story is that more often than not, businesses can achieve important large-scale change one small change increment at a time. And they can do so with far less disruption and risk than trying to design a comprehensive solution.

Which gets us to two consequential and immutable universal laws. The first, articulated by my college roommate Jack Buckmiller states, “If a meal takes longer to cook than it takes to eat, you’ve done something terribly wrong.”

Add Lewis’s corollary: “Buckmiller’s Law only counts the time I spend cooking a meal, not the time someone else spends making one for me.”

I’m pretty sure these are relevant to the subject at hand. You’re welcome to disagree.

In any event, a second, contemporaneous but somewhat better known rule is Gall’s Law: “A complex system that works is invariably found to have evolved from a simple system that worked. A complex system designed from scratch never works and cannot be patched up to make it work. You have to start over with a working simple system.” (John Gall, Systemantics: How Systems Really Work and How They Fail (1975))

To which I suppose we should add one more non-business-derived business principle. It’s something I learned in Driver’s Ed: Don’t over-drive your lights.

You heard it here first, but, he said it better, “he” being Professor Ravi Bapna of the University of Minnesota’s Carlson School of Management; “it” being a discussion of “Two Things Companies Should Do Now to Set Up for a Post-COVID-19 Future.”

Well, okay, he actually said it first too — beat me by a week.

Professor Bapna’s recommended two things are: (1) upskilling your workforce, because “as organizations shift to an AI-first world, they need a workforce which understands the world of data, analytics, and AI”; and (2) re-thinking operations and strategy toward an “AI-first strategy.”

So let me up the ante with KJR’s Thing One and Thing Two: AI-based business modeling and anticipatory customer re-identification.

AI-based business modeling

While our pre-COVID-19 fascination with Digital transformation frequently led to little more than Digital superficialities, it did lead to one salutary change in executive thinking — recognition that increasing revenue is just as legitimate a strategic outcome as cutting costs. It didn’t, sadly, overcome the metrics obsession that’s the root cause of management’s over-reliance on cost-cutting, but it was a start.

Briefly, the issue is that connecting a cost-cutting effort to an actual cost reduction is, for the most part, pretty simple, while connecting revenue-enhancement efforts to actual increased sales is frustratingly multivariate.

What’s needed to manage effectively isn’t more and better metrics. It’s the ability to model complex cause-and-effect relationships.

Start here: For many companies, strategic change isn’t really strategic in nature. Planning is based on the unstated assumption that the business details might shift from year to year, but the basic shape of the business doesn’t change. The buttons and levers management can push and pull to make profit happen are constant.

To the extent this unstated assumption is true, it should be possible to direct the attention of machine-learning technology to a business’s inputs, outputs, and operating parameters so that, after some time has passed, the AI will be able to determine the optimal mix for achieving profitable revenue growth.

And in case you’re curious … no, I’m not remotely qualified to delve very far into the specifics of how to go about this. That would call for deep expertise, and I’m a broad-and-shallow kind of guy. I do know someone who built this sort of model the hard way, and she verified that yes, it can be done and yes, machine learning would be a promising alternative to doing it the hard way.

Anyway, while I’m a broad-and-shallow kind of guy, I’m not so shallow that I can’t suggest Thing #2, which is:

Anticipatory customer re-identification

Right now, as pointed out here a couple of weeks ago, most businesses are just trying to survive until the future gets here. And please don’t misunderstand. Succeeding at this will, for most businesses, be nothing to sneeze at (insert your own COVID-19 snark here).

But smart business leaders will take their planning to another level, and it has everything to do with their expectations regarding what the economy will be like once the crisis has passed.

My own, everything-I-know-about-economics-I-learned-on-a-street-corner expectation is that as we’re reaching Great Depression levels of unemployment we shouldn’t expect the post-COVID-19 consumer population to look just like it did before we started self-isolating.

As with the Great Depression most working-age adults will be employed, so there will be consumers to sell to. If we use the Great Depression as the benchmark of our worst-case-not-including-total-societal-collapse analysis we’ll figure about 20% unemployment as the basis for customer re-identification — my just-invented term for Figuring Out Who You Want to Sell To.

The KJR point of view: There will still be consumers and they will still be spending. Fewer and less, for sure, but still well above the zero mark. The affluent and wealthy won’t go away either, and it wouldn’t surprise me if many do quite well in the aftermath and decide this is an excellent time to buy stuff.

I’m not going to try to identify specific consumer segments here. That’s for you and your fellow strategic planners in the business to do. What I’m recommending is that business leaders shouldn’t wait to find out who will be spending what, and shouldn’t undertake their survival efforts based on an expected return to status quo ante.

Make your adjustments based on positioning the business for the consumer marketplace to come, and which segments within it you want to cater to.

And yes, that includes those businesses that don’t sell to consumers, because in the end, no matter how long the business-to-business-to-business value chain, it’s always consumer spending that pays for the steps in between.