Innovation explained

Recently I had various opportunities to talk to corporates and startups about innovation.

This is the summary.

So let’s start with this question, why is innovation so hard?

Well, innovation is hard because unless you are a visionary, you don’t really see that well into the future. In fact, this should be the basis of what innovation should be understood from. If it can be derived from our current understanding, it is really just an ‘improvement’ (though it can still be huge and somewhat game changing), but by definition, it is not innovation.

Innovation is something into the future that most (all?) of us can’t see. 

Yet, this is how most companies and startups approach innovation:

Screen Shot 2018-05-06 at 14.59.11

It usually starts with a problem, then it is digested through a set of ‘system’ (knowledge, experience, know how, rules, etc), and finally it is solved through layers of systematic top down analysis.

Don’t get me wrong, this is perfectly fine to address ‘improvement’ opportunities but if we go by our definition of innovation, this is wrong in many perspectives.


First, the problem addressed this way is usually ‘generic’. And as a rule of thumb, generic problem generally attracts, and deserves, generic solution. The problem with generic solution is that while it may be appealing to senior management and co-founding teams, they are not very implementable.

Second, these problems are often ‘framed’ by the subjective experience of the person tackling with the problem (a third party, i.e. often ‘management’). Usually very little attention is paid to the ‘person’ who is actually experiencing the problem.


Innovation do not generally (though it can) fit into existing set of systems. We can’t use what we know to find out what we don’t know!

The second problem is that established understanding can really hinder our ability to differentiate assumption, hypothesis and possibilities. Seniority and authority dictate the ability to challenge each of the assumption, hypothesis and possibilities.


Analysing is great but it is only useful for something that is established, something that is well understood. For these cases, it makes sense to ask ‘why would this work’?

However, in view of innovation, when we are not sure about the potential outcome (even expectations), it makes more sense to ask the question, ‘how could this work’?

Therefore, I am proposing we should approach problem solving differently:

Screen Shot 2018-05-06 at 14.59.15


Understand the problem or pain points through the eyes of the actual person dealing with the problem. For example, when there is a sales issue, don’t just assume the sales representatives are ‘bad’. Sure, there are often bad staff but the key to come up with innovation, rather than general improvement, is that we need to take on an empowerment mindset. If people are given the right tools, purpose, and environment, they generally do much better than otherwise. This is how innovation happens!


Our cognitive function tells us we need to know everything. Our education system teaches us how to respond as if we know everything. Google and Wiki help create this curtain that we all can hide behind to pretend we know everything (usually don’t go past the second page of the search result too).

We need to have the patience and humility to ask ‘why’ and admit we really don’t know everything. Even for things that we think we know it all already. Otherwise, where would innovation come from?


Taking little steps is always much more meaningful than winning big arguments. Especially in the realm of innovation where everything is assumed to be unknown, doing ‘some’ things is much more impactful than thinking ‘some’ things – even if this means we will need to iterate much more often. And the reason why we can afford to iterate much more often is not just about our ability to confront ‘failure’ but because modern technologies allow us to do so – at a much different pace than before. So this creates the learning path to innovation.


While innovation is the latest hype, it does not, and should not, be expected to solve ‘everything’. Whilst the impact of innovation can be huge, the implementation of innovation should always be small, or agile, so that learning can happen at a rate that matches the externalities.

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