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In my last column, I talked about how the inevitable end of the globalization fad would lead to a lot of confusion and failure as The Obliterati, organizations stuck with the belief that the status quo is “inevitable,” find a new model.
I described how Roger L. Martin, dean of the University of Torontos School of Management described how we had gotten to this juncture, and I promised to pass along his tips for how to make the change.
As I said, hes the best sort of expert, combining the theoretical with the practical and, most critically, doesnt just describe whats wrong but describes approaches to get to “right.”
The element that he believes makes for survival is design, and for a reason Ill get to in a bit, this is a great trend for giving IT people more voice in strategy.
In Martins view, the algorithmic response to competition, the status quos model of incrementally optimizing how we do what we do now, is too constrained, too much like binary code. It uses what I call an engineering mentality.
Martin thinks to be successful, business will have to behave more like designers than engineers, more masters of heuristics—able to decode and apply them—than managers of algoritha.
Hes noted the 21st century presents us with an opportunity to go at what we currently see as mysteries and create new heuristics to decode them.
Both professions are most likely to benefit from the demand for skill sets required for the designer model work in IT. As it happens, truly good analysts and truly good programmers have the best work styles and personal aptitudes to build the organizations that will survive the implosion of the old model.
In his contrast between traditional organizations and design shops it becomes apparent why ITs good systems analysts and good programmers have a vast advantage in adapting to the design model over people who come from other disciplines.
In the status quo model, tasks are on-going and assignments permanent until they fall apart. In Martins design shop configuration, there are projects with defined calendar time, exactly like the way most analysts and programmers engage work.
In the status quo model, constraints and s.o.p.s prescribe how problems get solved. In the design shop, attacking a problem starts with the idea that nothing cant be solved, the core reason for systems analysis and IT as a discipline.
Finally, in the old model, status is acquired through large numbers: budget and staff reports. In the designer model, its who gets the most wicked problems to attack (and who beats them down). This is exactly the natural hierarchy that programmers gravitate towards.
Okay, IT stands to lead the organization into the future. But how does Martin suggest we get to the design model and escape the limits imposed by the failing globalization model and the Obliterati?
“You need to first understand and empathize with the businesses making the decisions,” Martin writes. “They are caving in because he who runs away lives to fight another day. But if all you do is run, you just do it until you drop. You dont die now, but you have signed your death warrant.
“Its a classic fight or flee reaction, a lot of amygdala in the mix, a lot of fleeing. Lots of companies are stripping out everything that has a chance to create value, and thinking somehow that theyll end up with an end game other than a miserable death. This is not deterministic, but it is accurate.”
The first step to respond to this amygdala-driven response is to, as Martin says, “CHILL,” to not do the scared thing, which just about guarantees a non-functioning response.
The next step is to contemplate what a pleasant end game would look like, a classic systems analysis aptitude. He says, “think ahead to the desired possible results to give yourself the courage to decide if its the right end game.”
Martin uses a real-world example—the Soviet Era Poles and Lithuanians. Most kept hope alive and didnt just say Soviet occupation was “desirable” or “inevitable.” “No,” he said, “you tell your children theyre bastards, and its all bull, keep the faith and a situation will arise where something good can happen.”
Next page: Picking the workable parts of globalization
Picking the Workable Parts
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In the same way, people who work in North American businesses have to disengage from their over-investment in the self-strip-mining tactics that are the status quo, and apply the surplus to contemplating what a pleasant outcome would be.
Martins thinking doesnt make the status quo out to be a villain. He believes parts of the failing globalization model have virtue. “If you care about people, you should care about millions of Indians and Brazilians and Chinese having a chance of a good life, and without the job opportunities, theyd be lucky to get on.”
He believes North Americans should focus attention on doing the things we are more capable of doing right now. This doesnt mean we get to move to a new set of work endeavors and rest there, because in due course, competitor nations will be competing there, too.
“Nothing is static. For the next while, thats got to be in figuring out how to make better products and services because we are proximate to those consumers and can understand their systems and needs better.
“We certainly dont deserve a better standard of living than they do if they can do it better than we can.”
The way work should get done in the new model is outlined, and we now have a heuristic to approach the strategy required to get there. Its like a recipe waiting to be cooked.
Just add IT talent.
Addendum:
1) Martin explains the current model is to grow by repetition and scale. Management drives heuristic knowledge into algoritha and then binary code to increase standardization, drive out cost and increase scale.
My view is that model, by aiming to sell more by cutting costs,
2) guarantees price pressure, which in turn strips out margin and pressures income, incrementally making more existing consumers more price sensitive.
3) That amplifies the need to increase the organizations scope or geographical span or both, increasing complexity (need to manage new products or customers and regulations as they expand globally).
4) The complexity/opportunity ratio gets higher and higher and harder and harder to manage. The management goes from algorithmic to automated in order to eliminate human contributors to pare costs. This results in less judgment being exercised per transaction, leading to more mistakes and wastage, leading to condition 2) above where the cycle starts again.
Jeff Angus is a knowledge management and restructuring consultant who has been working with IT since 1974. His newest book is Management by Baseball: The Official Rules for Winning Management in Any Organization (Harper Collins).
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