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With the annual Consumer Electronics Show in Las Vegas attracting worldwide media coverage last week, let me be the one to posit that technological innovation is only as effective as our mental models are able to keep up.

To exemplify my point I will invoke a top global management event where the application of the latest network theory techniques to organizational change efforts was discussed last year. Stefan Thurner, a leading complexity scientist, showcased at the 2013 Global Drucker Forum (http://www.druckerforum.org) a novel method intended to map communication flows in organizations as a conduit to superior insight into the effectiveness of organizational transformation initiatives (presentation at http://www.druckerforum.org/2013/the-event/presentations-speeches/.). 

The argument goes something like this: visualizing the structural traits of these communications flows provides an objective reference against the intended outcome as per the organizational transformation blueprint. By tracking who communicates with whom the informal organizational structure becomes explicit, revealing the true “neural pathways” of the enterprise.

The approach promises to at last replace the subjectivity that has traditionally plagued the organizational development field with a rigorous scientific method; or as Roger Martin would put it, reduce the messy heuristic of organizing humans to a neat algorithm. Reinforcing the algorithmic flavor of the approach, the said scientist appropriately framed the end vision for these efforts as “quantifying complexity”.

While the approach is enticing, it may contain non-obvious pitfalls that point to an archetype flaw in tool innovation: old thinking negates the benefits of new tools.

Employing design thinking’s paramount distinction between analysis and synthesis is instrumental in surfacing such pitfalls.

By focusing on quantifying complexity via analytical means, i.e. measuring the quantity of messages being sent between various employees, this particular approach runs the risk of ignoring qualitative aspects of organizational effectiveness which are arguably more important when it comes to human systems.

Communications flows constructed from analysis of message traffic give no insight as to the value of the content being exchanged. Without a message value scale there is no way to validate that high volume communication pathways necessarily coincide with important “neural pathways” of the organization. And what about tacit knowledge that isn’t captured by the voice and electronic communication channels but may be vital to organizational culture development?

Roger Martin has long proposed that a fixation with analytic thinking in business renders innovation and transformation efforts less effective. The examples of large scale transformation efforts approached from an exclusively analytic mindset resulting in ineffective outcomes are many. When thinking models don’t keep pace with tool innovation, the real risk of force fitting new tools into old mindsets thereby nullifying their benefits is very real. Complexity science and its novel technologies are not immune to this risk and indeed no new technology, no matter how avant-garde, is.

But complexity is indeed special. That is because it presents a real opportunity for design thinking’s validity perspective to be firmly reinstated into the science of management. Complexity’s counter-intuitive or even blurred cause and effect relationships do not conform well to analytics, which requires distinct causal relationships. What works instead is abductive reasoning, or inferring causality when clear correlation is lacking. Julian Birkinshaw’s concept of “experienced complexity” provides a useful perspective in this regard. It is reminiscent of Karl Popper’s social reflexivity which proposes the observer cannot objectively separate himself from the experiment he is observing when it comes to human systems. And so, making sense in such “socio-technical” environments cannot be achieved by cold analysis, but rather through constant interaction and hands-on experimentation.

When it comes to complexity there are many questions we should consider very carefully before proceeding on the path of tool development and application, such as: should we pursue a fully rational approach to a complex world? Is it wise to practice scientific precision in human-dominated contexts? Assuming such pursuits are indeed viable, are they also cost effective?

The author’s suggestion is that organizational complexity continues to be approached by first examining our mindsets and only then proceeding with tool technologization.