When I was in my 20s I worked on a project involving Evolutionary Algorithms – I used them to try to find better mathematical solutions to a given problem than by using other approximation or problem solving strategies. The idea is to have a population where each individual represents a solution to the equation, no matter how bad it is, a set of operators which manipulate the population or cross genes from one individual to the other in a new generation and an evaluation function which is the target problem to solve. Every generation the operators perform mutation and crossover of the given individuals of the generation to create a number of offspring. Those individuals get evaluated for their quality of solving the problem and a selection function mixes a number of new individuals and some of the existing ones into the population on the new generation. And then the mechanism starts over until an abortion criteria is met, e.g. no changes in quality of the current evaluation function over a certain amount of generations, a total number of generations or any other criteria which can be thought of.

This is a very limited view of Evolution, similar to the methodology behind Digital Transformation, as the environment – in this case the evaluation function is not dynamic – the population/gene pool under the given set of operators and selection criteria optimises itself to solve a static problem. The problem with that is obvious – in digital worlds the environment is not static – no environment ever really is. Assuming a fixed target to work towards is understandable from a simplification standpoint – it’s like setting a milestone but then you have to adjust your target to the changed environment. Agile and short iterations is the digital counterpart of evolutionary generations but all they do is create new a generation for a static problem. In order to truly evolve, targets/goals/milestones have to be adjusted. Evolution is not a straight line from A to B; thats a transformation at best (even maybe a just a little one).

Natural evolution (similar to personal development) works best if you have may external influences that evaluate your behaviour and performance in the most possible ways and with a high degree of variety – only then can it’s next generation and overall optimisation perform best. Isolated human cultures may have become experts in certain areas – which is good for dedicated missions – but really the skill of head hunting and cannibalism doesn’t really compete with self guided rockets and virtual reality. Point taken. We are talking about integration and interaction – APIs and Data Exchange.

When you open up your IT landscape and systems you will find that some systems have weaknesses that can’t be overcome. Some need to be retired, some might be able to adapt. Some you can allow to live for some time, knowing that they can’t cope with the new digital world requirements but since you can’t operate on a green field, some of those weaknesses can be tolerated for some time. Evolution doesn’t change everything in a instant – it’s a process and that’s ok. You will also find individuals that protect their tribes. There will be roadblocks, nay-sayers on all corporate levels; but there are people, companies and technologies out there to help. Let’s talk about #DigitalEvolution.