 | by Kent Aitken |       |
In a recent report on the next frontier of digital technology, Accenture created a model of the long history of challenges that have faced management.
http://www.accenture.com/us-en/Pages/insight-looking-digital-being-digital-impact-technology-future-work.aspx
In short: the industrial era was characterized by a transition from individual craftsman and artisans to large-scale processes, and this transition was enabled by repeatability. A worker didn’t need to know how the factory ran to screw part A into part B, and if he left, a replacement could be trained incredibly quickly. This was the age of Taylorism, of precision and measurability permitted by process and structure.
Throughout the last century we’ve transitioned into an economy far more based on knowledge work (see Deloitte’s assessment, below), which meant the industrial management style ran into a crisis of rigidity, the solution to which was adaptive processes. Judgment, discretion, if-then statements, case management.
http://dupress.com/articles/the-future-of-the-federal-workforce/
However, for senior executives ultimately managing a variety of adaptive processes, the problem then became one of complexity. There’s too much going on, it’s too hard to understand, and the performance reports that are so useful for widgets-per-second are far less revealing.
Accenture suggests that the solution to complexity is in digital. Specifically, “smart digital processes”, which would feed decision makers key information exactly when they need it. My response is: maybe? In some cases? It seems the more plausible answer is a return to process - which is happening all around us, albeit which a crucial difference from the Taylorism of old.
Process in the Knowledge Economy
There's a common thread among the emerging approaches to governance. In his equation for today’s public policy, Nick highlighted several, including design thinking, behavioural economics, and public sentiment. We could add the field of facilitation, the practice of public participation, and innovation labs to them mix. All of which are hugely reliant on defined processes.
The key difference is that the interim goal of the process of old was to remove the need for learning, whereas the process of today is designed to maximize the speed of learning. At the end of this post there are some links to example process kits: if-then guides to, essentially, helping humans understand other humans and the systems they live in.
The end goal is the same: scalability and repeatability. In this case, it’s repeatably, reliably solving unpredictable, emerging, or complex problems. We’re on the same arc as the first graph, but for a completely different organizational paradigm.
So the challenge for management becomes a new, grander problem of complexity. Where executives have been struggling to manage adaptive processes via industrial-inspired organizational designs, they’re going to be overwhelmed by managing a variety of learning processes without significant changes in management style. In some cases the if-then flow will be impossibly complicated, and in others it’ll need to be thrown out the window. A single node in a hierarchy will never be able to understand each process, only the principles behind them.
What's in it for Us?
We need to do it. It’s where the performance gains in a complex environment will come from. I’ll exapt an HBR article about how our personal learning curves regularly plateau. Here’s the graph, with learning on the Y axis and time on the X axis:
http://blogs.hbr.org/2012/09/throw-your-life-a-curve/
Success comes from knowing when to jump to the next learning curve, which is incredibly hard at the outset but maximizes the speed of progress.
Embracing this learning curve will be cost-effective in two ways. First, there’s evidence that consensus-building through learning processes costs less in the long term than making and defending decisions (which will apply to both internal management and policy/program governance). Second, in the latter part of that learning curve we’ll reach a level of sophistication that allows economies of scale:
- We’ll be able to reliably pull from a menu of processes and adjust to new situations, rather than starting near scratch every time
- We’ll be able to recognize when we can leave these learning processes to citizens, businesses, and NGOS, and govern accordingly
- We’ll be able to share and teach approaches broadly
Returning to Accenture’s claim, organizations have run into a problem of complexity. Particularly for governments, however, I don’t buy their claim that the answer is in smart digital. Instead, I think we have to recognize that in many ways we’re back at the beginning, worried about about scale and repeatable processes. Just very different processes.
Example process kits:
http://www.involve.org.uk/blog/2005/12/12/people-and-participation/
http://labcraft.co/
http://stepupbc.ca/explore-your-career-increase-collaboration/idea-navigators
http://www.mindtools.com
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