The Lab

The fruits of riotous experimentation.

A turnip seed never grows into a parsnip

For those of us who travel extensively, one of the most valued Christmas gifts is the opportunity to take time over the holidays to read heavy books. These may not be mentally demanding nor intellectually ponderous but their physical dimensions preclude them as traveling companions; when packing I err toward the gazelle rather than the mule.

On re-reading some essays by George Orwell I was struck once more by the impact the past has on our future direction, and how little overt cognisance is taken of this point in standard strategy generation processes. Snowden summed up it more succinctly than Orwell (a rare feat and a compliment) and though I can’t find the exact quote, it is close to, “the past informs the future, but does not predict it.” Nevertheless, despite Snowden’s pithiness, Orwell’s insight recorded in "England their England," is still worth reporting in full.

Clearly Orwell seems to have concluded that our collective cultural ‘DNA’ may modify itself over a period of time, but some expression of the underlying character remains. The details of De Gaulle’s veto of UK membership into the European Union, could be well been reiterated without modification by his most recent successors.

So how does this tie back to strategy generation and the application of the Cynefin framework?

The starting for generation and implementation of a successful strategy is a clear understanding of the starting point; an internal perspective (the organisation) and an external perspective (the market place) - nature and nurture is a useful analogy. Both impact each other, and both are complex—hence the importance of having a clearly understood starting point from which to see patterns emerging as a result of changes.

The wider the range of strategic options considered (and the greater the departure from the existing corporate ‘expression’) the greater the importance of pursing the options through a safe-to-fail methodology.
This natural science approach to strategy generation is the antithesis of the more popular mechanistic method and yet, given the growing evidence of the limits of the mechanistic method, it is quite remarkable that businesses and business schools still retain such a focus, and faith, on the ‘A->B’ approach to strategy generation. I am not decrying the employment of the standard tools such USP, SWOT analysis; these concepts and tools have their place and are powerful when applied appropriately and with caveats, but they lack the deeper insight by the business of the organisation and the market. I have also recorded that some of my clients have historically spent a disproportionate amount of time focussing on building a highly comforting, but ultimately mis-leading, detailed and measurable end-points placed some 5-10 years hence. Time spent capturing narrative research on the current state would yield (and has proven to yield) far greater value.

Here is how I apply this learning to strategy generation.

1. I provide the background to complex adaptive thinking using the Cynefin framework as the primary communication tool.
2. The leadership team then discuss the strategic challenges facing the business, and place these challenges (usually in question format) upon the Cynefin framework. Typically, organisation (people) and the market (strategy) challenges are more likely to be complex and, relative to these, most operational challenges are more simple or complicated; this is after all, a framework, not a categorisation tool.
3. Focusing on the more Complex issues, we then enter the organisation and the market to undertake some research combining quantitative research with narrative research. For most clients the narrative research is novel and, without exception (to date), provides the output which is the most insightful and actionable.
4. We seek dots and patterns in the data, and generate a variety of possible routes forward from the results.
5. The options which divert furtherest from the current starting point (established in step 3), or the options which are likely to demand large amounts in capital (especially where the spending decision is large and binary) are the prime candidates for safe-to-fail experimentation. (Note: these can be internal or external experiments). Thus you now have a list of potential safe-to-fail experiments which should be cohesive around the implementation of strategy and to which the guidelines can apply.

Examples of where this approach has been applied include:

- A former Government owned agency that needed to become more entrepreneurial but wasn’t sure which of a number of routes to achieve this (at lowest cost and stress), and ran a number of experiments
- A steel company wishing to move toward pricing by value rather than ‘cost-plus’, and set up a number of external pricing experiments, and a number of internal experiments to test how to build a culture that welcomes and exploits tolerated failure

In my next post, I will detail one method used to give a clearer picture on the starting conditions of the organisation.