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As yields from the offshoring and globalization fads fade and fall below the event horizon, business and governments inevitable kiss-off of the decade-long model will be scary to most of those whove been shaping their organizations to play that game.
For those that optimize their success by repetition and not change, theres an assured crisis.
An insightful thinker has written a little about the reasons for the decaying of the status quo. And hes delivering the good news and the better news.
The good news is hes written in detail how organizations can thrive. The better news is the professions most likely to benefit from the skill sets required for the replacement model both 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.
What those skill sets are, and why they are so vital Ill explain in Part II, but its important to first understand how we got to where we are and where were likely headed.
The work of Roger L. Martin, dean of the University of Torontos School of Management, is the most illuminating (and enjoyably readable) short treatise on the subject. A copy of his original article is here (PDF) and I encourage you to read all if it, but Im going to summarize it here so I can explain a few things he doesnt mention, including why the future trend is so promising for good IT people.
He calls out three words to help describe his view: mysteries, algorithms and heuristics.
Mysteries, he suggests, are those events around us that are observable but that we cant explain or predict variations on. Mysteries are the early triggers for human systems. Perceptive medieval observers could see that objects without support always fall. But other objects, like birds or leaves in a strong wind, dont.
Concerned observers hadnt inherited coherent systems to explain gravity, wind resistance and aerodynamics to explain the phenomena.
But even without knowing the precise physics of these effects and without instruments to measure them, people were able to fashion explanations based on a number of observations. Sometimes they got closer to predictive heuristics; sometimes they went down blind alleys like Sir Bedevere in Monty Python & The Holy Grail. But eventually, groups of humans came to understand the heuristics of a phenomenon.
The economy of most of the 20th century, according to Martin, was based on creating value by turning heuristics, intrinsically loose guidelines, into algorithms, to promote mass production and the scope of their organizations.
So, he asserts, most of the last decades initiatives, from supply chain management to cost controls to CRM were executed to further the very Taylorist road map large organizations have favored for more than 80 years.
The basic view: A headquarters-driven, expansive leviathan that creates wealth by getting ever more efficient at doing the same thing.
Martin antes up McDonalds as a poster corporation for the model. Lots of restaurants made hamburgers, many produced them in a low-formality, to-go delivery mechanism, but McDonalds turned every step into a precise factory-floor process.
The product was not as good as most competitors average, but it had fewer complete failures, was more consistent and cheaper to make and gave McDonalds the chance to use price as a competitive advantage.
He cites Procter & Gamble as the algorithm-builder for brand management and EDS for codifying systems integration.
Next page: Growing a business doesnt necessarily make it better.
Growing a Business Doesnt
Necessarily Make it Better”>
For organizations of this ilk, success is not about superior product but superior standardization of process to increase scale. And, Ill add, the ability to use their scale to short-circuit competitors ability to survive.
The result is fewer, bigger organizations producing a higher quantity of lower-quality, but standardized, output.
Roll in our own tools and computers, and algoritha get turned into binary code, which is even less flexible and judgement-driven than standardized humans in the workplace.
The tail end of this movement, past its utility, obliterates human jobs, obliterates product quality and obliterates margin. The clueless managers who insist continuing down this cul-de-sac is inevitable have a name: The Obliterati.
I believe Martin has nailed a clean explanation of where we came from and how we got here. But heres why I think this evolution hes described so well has got a limited life span. Waves of intensification (doing the same thing but increasing harder when returns arent growing) yield lower and lower returns.
Organizations run by The Obliterati think about growth as a function of scale, so they expand when under pressure in existing markets. Expanding adds significant complexity (think for example of opening up a market in Russia in the 1990s and the new cultural, regulatory & logistic hits).
And complexity results in more need to cut costs to recover net, which leads to an intensification cycle (see sidebar), and has a strong tendency to lead to a collapse.
Thats the current model, and its headed for a near certain inability to continue. Whats next, if Martin is right, is the great opportunity for IT people to take a bigger, more influential role in leading organizations to a healthy haven for survival. Ill tell you about it in part II.
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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Sidebar: A little more explanation on ruination by growth
Sidebar
: A Little More Explanation on Ruination by Growth”>
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, guarantees price pressure which in turn strips out margin and pressures income, incrementally making more existing consumers more price sensitive.
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).
The complexity/opportunity ratio gets higher and higher, and harder and harder to manage. The management goes from algorithmic to automated to eliminate human contributors to pare costs.
This results in less judgment being exercised per transaction, leading to more mistakes and wastage, leading to the cost-cycle above, where the cycle starts again.