A Blog by Jonathan Low


Jun 24, 2013

The Learning Organization in the Algorithmic Age

That machines are taking over is less of a concern than what that means for the organizations that utilize them and the people who remain to manage them.

The most successful adaptations of technology during the dotcom and social media eras were accomplished by enterprises that realized plopping new devices or systems into their midst would not have the desired impact unless they reorganized to reflect and capture the benefits of the technology in question.

One of the challenges to making this transference effective was that said benefits were not always immediately evident. The pace of the learning organization had to be accelerated. The time for contemplation, review and reflection had been dramatically shortened by the introduction of processes which sped up the ability to make decisions, however incomplete the data on which they were based may have been.

We have now, as the following article explains, embraced and internalized those gains. But bigger data sets and more effective ways of capturing what they offer have dramatically increased the pace of learning and acting. Again. The danger for many institutions is that they are resistant to making the changes necessary to incorporate the advantages this new information provides, hindering its transformation into knowledge and then, for the lucky few, wisdom.

The despair reflected in the notion that 'the Luddites were right,' is overblown. Which is not to gainsay the pain of the generation(s) who may bear the brunt of the adaption cycle via underemployment and reduction in living standards while society figures out how to make it all work. There is an important place for people in this process, but only if roles and responsibilities are carefully thought through and then implemented. Machines work best at the enterprise's behest if they are properly programmed, if the data they produce is intelligently interpreted and if the resultant knowledge is then effectively communicated to everyone in the value chain, from suppliers through employees, investors and then customers. People are required to make that happen.

These changes require transformation, which is neither easy, conflict-free or cheap. Tasks, organizational designs, physical, mental and emotional space must be re-evaluated. This is, in the truest sense of the word, disruptive. It is not a phrase on an inspirational powerpoint, but a reality with which real people will have to live and, in some cases, suffer or enjoy. The lesson is that learning, like the power of technology, must accelerate for organizations and their people to optimally benefit from the changes this portends. JL

Greg Satell comments in Digital Tonto:

We are entering a new industrial revolution and machines are starting to take over cognitive tasks as well.  Therefore, much like in the first industrial revolution, the role of humans is again being rapidly redefined. Organizations will have to change the way that they learn and managers’ primary task will be to design the curricula.

Before the industrial revolution, people were valued for knowing a trade.   However, when machines took over physical labor, those skills became devalued and most people either performed simple, repetitive tasks or managed those who did.
By the late 20th century, a knowledge economy began to take hold.  Now, workers’ value lay not so much in their labor , but in specialized knowledge, much of which was inscrutable to their superiors.  In order to thrive, enterprises had to become learning organizations.

First Principles vs. Experience

The true nature of knowledge has been a source of fierce debate for over two thousand years, beginning with a disagreement between Plato and his most famous student, Aristotle.
Plato believed in ideal forms.  To him, true knowledge consisted of familiarity with the forms and virtue (which, in modern terms would be closer to ability than to morality) was a matter of actualizing the forms in everyday life.  Plato would have felt comfortable as a factory manager whose workers carried out instructions to the tee.
Aristotle, on the other hand, believed in empirical knowledge, that which you gain from experience.  In contrast to Plato, we can imagine Aristotle as a Six Sigma black belt, constantly analyzing data in order to come up with a better way of doing things.
Both methods, the indoctrination of principles and the collection of data have played a role in learning organizations.  The difference now is that much of the learning is being taken over by machines.

How Machines Are Learning To Take Over

Not so long ago, we depended on human knowledge for many things, such as setting up travel itineraries, trading financial instruments and buying media that are highly automated today.  As we progress, new areas such as making medical diagnoses, legal discovery and even creative output are becoming mediated by computers.
Perhaps not surprisingly, the algorithms blend Platonic and Aristotelian approaches just like humans do.  Initially, their thinking is driven by time honored principles supplied by human experts (sometimes called “God parameters”).  Then, as more information comes in, the computer begins to learn from its own mistakes, getting better and better at its task.
This process continues at accelerating speeds.  Much like the rise of the knowledge economy empowered knowledge workers, because they had expertise that their bosses didn’t, computers are now coming up with answers that knowledge workers themselves can’t understand.  That will prove incredibly disruptive in the years to come.
It also presents a particularly thorny problem: How can organizations empower employees whose skills are being outsourced to the cloud?

Consequences of An Algorithmic Age

Just as the first industrial revolution transformed business and society, this new algorithmic age will bring not just efficiency, but significant, cultural changes.  While the future is uncertain, some of the shifts are already becoming clear:
Bayesian Strategy:  The knowledge economy coincided with the rising influence of business strategists.  Highly trained executives would analyze business conditions and devise intricate plans for the future.  Managerial performance, therefore, was widely evaluated as a function of their ability to “execute the plan.”
However, good strategy is becoming less visionary and more Bayesian.  Strategic plans will play a similar role to “God parameters” that will be honed through an evolutionary process of simulation and feedback.  Strategists, to a great extent, will become hackers rather than planners.
Brands as Open API’s:  One little noted consequence of the knowledge economy is the rise of intangible value, which often far exceeds tangible assets in corporations.  Brands, therefore, became tightly controlled assets that were nurtured and protected.
That’s changing as brands are becoming platforms for collaboration rather than assets to be leveraged.  Marketers who used to jealously guard their brands are now aggressively courting outside developers with Application Programming Interfaces (API’s) and Software Development Kits (SDK’s).  Our economy is increasingly becoming a semantic economy.
Firms ranging from Microsoft to Nike to The New York Times have also created accelerator programs, where young companies get financial, managerial and technical support to come up with new innovations (and potentially, enhance the business of their benefactors).
The Human Touch:  While much of the discussion about the rising tide of technology focuses on cognitive skills, Richard Florida argues that social skills will be just as important.  Many of the fastest growing professions are those which emphasize personal contact.
As computers take over more of the work, the role of humans will increasingly focus on caring for other humans.

Flying By Wire

Pilots don’t fly planes anymore, not really.  Whereas they used to have direct control over the aircraft, now they fly by wire.  Today, their instruments connect not to the airplane’s mechanism, but to computers which carry out their commands, modulated by the collective intelligence gained from millions of similar flights.
In essence, pilots perform three roles: they direct intent (where to go, how fast, when to change course), manage knowledge and (rarely) take over during emergencies.  Professionals in other industries will have to learn to perform their jobs in a similar way.
The function of organizations in the industrial age was to direct work.  The function of organizations in the algorithmic age will be to focus passion and purpose.
Managers, rather than focusing on building skills to recognize patterns and take action, will need to focus on designing the curricula, to direct which patterns computers should focus on learning and to what ends their actions should serve.


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