Thriving by Dis-AutomationBy CIOinsight | Posted 06-08-2005
Thriving by Dis-Automation
One particular segment of the IT population is driven by exuberance and other irrational motivations to automate everything; and they have reaped a lot of bottom-line victories over the last few years. But their ability to continue to deliver is tapped out.
That's not to say the demented utopia in which all value-added processes are conducted by computers interacting only with other computers isn't a sustainable model - for a minority of functions within a minority of organizations.
For the rest of the world, Automatopia is a delusional ideal in the service of a business model that has already crested and is now dissolving as precipitously as did the Soviet Union's business model.
Exuberant automators who never question ifa process should be automated, but only how or when, are able to feed their obsession only through the availability of cheap labor which, in turn, raises the incentive for more countries to create cheap-labor IT shops.
Driving out labor costs and some minor paper costs has delivered some cost benefits, though with some quality degradation.
As long as everyone is playing the same game, quality levels that are tanking don't affect market share, because the buyer comes to assume everyone's quality (or lack of it) is the same.
That system is close to equilibrium, however. Far from being a good thing, equilibrium means there are only a few more cycles of juice left in what we anthropologists call "intensification" (doing the same thing but harder when returns aren't growing).
The red flag signaling this end-stage is, well, a red flag.
Red China, which was building a market as an IT outsourcer by underpricing India, is itself losing some sweatshop contracts to cheaper sweatshops in Saipan, Malaysia and other Asian countries.
When U.S. executives feel free (or forced) to press slave labor providers to show more margin, there's little left to squeeze out in the labor or hard goods areas.
Those who continue with this model are doomed to face-fault on The Tripping Point. Those who have the agility to move on to the Next Big Thing, on the other hand, stand to reap great competitive advantage.
That next big thing is rationalization figuring out what processes and parts of processes are more effectively done by human beings rather than computers, and integrating those human capabilities with automated processes rather than replacing one with the other.
Knowing what to dis-automate and how to work the transfer between carbon- and silicon-based workers will be the vital elements in winning and losing.
A winning example of rationalization is a rising company named Savage Beast.
When you read about it, you'll assume it's a special case. It's not - it's the first application in the inevitable wave of rationalization projects that will anchor the next profitable model for business technology.
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Savage Beast's deliverable is specific to one field, but the basic function is essential to a global economy that requires consumer choice without limits.
The deliverable is music discovery—the ability to find new music consumers will enjoy (and buy) based on what they've liked in the past.
The means of doing this are not the myriad automated-pattern recognition processes other music services use, though some are surprisingly good.
It's not collaborative filtering through affinity networks mapped by computers (If Fred Flintstone loves A and B but hates C, and you love A but hate C, you get a recommendation for B).
According to founder Tim Westergren, Savage Beast uses a team of trained music analysts to describe each song using a set of attributes that make up what Savage Beast calls the Music Genome Project.
Just as the human genome project maps genes and gene combinations against the characteristics that appear when they're expressed, this music genome project attempts to map core musical forms and identify them in specific songs.
Customers define what they like, and software scans the entire genome, identifying songs to suggest by matching the pattern of a customer's likes with the pattern in a set of more than 400 attributes assigned by a human analyst at Savage Beast.
The resulting matches range across styles and genres, turning "categorization" on its head because instead of refining choice down in ever-narrowing hyper-optimizations, it offers a listener an opportunity to grow into other genres with a few songs they have a high probability of liking but would not encounter if they stuck to familiar genres.
There are three things that make Westergren's analysts functional, only the last of which might someday be sensibly automated: formal music theory background ("at least a four-year degree"), corporate training ("so that everyone identifies the same way"), and what Westergren calls a good ear.
Once analysts start tagging each cut, they have to labor in physical proximity with other analysts to build uniformity of decision-making and attribute assignment.
Once identified, the knowledge gets committed to a database, and that's where the technology comes in.
Savage Beast customers can plumb a deep database and get quick, informed suggestions that match their musical proclivities.
How did Savage Beast get to this very uncommon, anthrocentric, approach? Westergren says the company's founding CTO Will Glaser (who is no longer with the firm) made it clear that company founders needed to define what they wanted as an end product first, and then allow the team to build towards it. That's an old model, but a highly effective one.
The Genome Project became the bridge between what exists (hundreds of thousands of musical cuts) and what was sought (a way to use past preferences to explore for currently attractive choices).
The Westergren/Glaser model for high-impact projects demands building to a defined goal, and applying humans to do what technology can't do well while applying technology for what it does well.
How does this affect you? It could give you a much more effective way not only to organize your projects, but to put the technology you already use to a much more effective use than you did in the past.
In the next column, I'll suggest ways you can think about the model in your own line of work.
Jeff Angus is a management consultant and has been working with IT since 1974. He has held IT management positions in user interface design, marketing, operations and testing/analysis. Look for his book, "Management by Baseball: A Pocket Reader." Jeff's columns have appeared in The New York Times, The Washington Post, the St. Louis Post-Dispatch and the Baltimore Sun.
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