What time frames does the organisation in which you work operate on? There’s the year – financial and calendar (sometimes syncrhonized, but that generally makes for a crap Christmas). There’s your “product” cycle – that could be weeks if you are software company, months in fashion, years in automotive or pharma, decades in pensions or major infrastructure or the top end of the drinks industry.

As I pootle around in my peripatetic professional life, I’m observing that conflicts between these kind of business circadian rythmns appears to be an issue for many corporations. It’s easy to think that the weeks, months, years and decades will neatly slot together like some sort of Gantt chart Matryoshka, but the reality is that you need to gear yourself very differently for different time cycles. Decision making for, say, investment into a new motor vehicle being brought to market (5 to 7 years, apparently from drawing board to showroom) are very different to that you would make in the short sprint cycle of an agile software project.

The clashes come when those things need to be in harmony – the software from Apple or Google or Microsoft running on month-scale cycles being placed into a car. Organisations used to long-term decision making cycles struggle with the snappiness necessary for building software in modern methods or working with small businesses and tech start ups (this whole observation came from a conversation with an auto industry executive at the launch of their technology startup accelerator programme).

But what’s even more ironic, thinking about it, is how decision making for longer-term timeframes tends towards more analysis and pontification even though the longer the time frame the less certain anyone can be about what will actually happen. The navel gazing that takes place on decision making for the long term is an exercise in building certainty when none can exist. You’d be better to leap in and start building than to spend months in investment decision making. Or place your bets and cross your fingers.

Allowing such cycle conflicts to co-exist is going to become increasingly important for all industries as the impact of information technologies and data and scaled statistical analysis (aka AI) become more useful. The acolytes will tell you that these very technologies will make longer-term decisions either easier or obsolete. Don’t fall prey to such marketing hype. The future remains as unpredictable as ever, and merely extrapolating the past is only of limited use. However, understanding the nature of different timeframes in businesses, and the interplay between them – well, that feels to me to be useful analysis to improve in the right places.

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