Helping Good Ideas Take Root and Grow

Helping Good Ideas Take Root and Grow

Whilst the concept of innovation is innately connected to new ideas, it would be a mistake to believe that having a new idea is the hardest, or even most important part of innovating.  In our work we say that the idea is about 10% of the innovation – the rest is what you do to bring the idea to successful fruition.  Like most of the innovation life cycle, developing an idea towards implementation is rarely a solo activity but one which requires collaboration and which in turn requires transparency and co-ordination We have written extensively about how to address the challenges of innovating in a post-Covid world where remote working will be more like the norm rather than the exception.  This article continues this theme by looking at what we do with ideas once we have them.

 

The Magic

Steve Jobs articulated the role of the development process in the 1990 video “The Lost Interview”.

“You know, one of the things that really hurt Apple was that after I left, John Sculley got a very serious disease. It’s the disease of thinking that a really great idea is 90% of the work. And if you just tell all these other people “here’s this great idea,” then of course they can go off and make it happen.

And the problem with that is that there’s just a tremendous amount of craftsmanship in between a great idea and a great product. And as you evolve that great idea, it changes and grows. It never comes out like it starts because you learn a lot more as you get into the subtleties of it. And you also find there are tremendous trade-offs that you must make. There are just certain things you can’t make electrons do. There are certain things you can’t make plastic do. Or glass do. Or factories do. Or robots do.  Designing a product is keeping five thousand things in your brain and fitting them all together in new and different ways to get what you want. And every day you discover something new that is a new problem or a new opportunity to fit these things together a little differently.

And it’s that process that is the magic.”

Now, we don’t all work for Apple, nor are most of us working on the next iPhone, but the same principle applies.  There is an awful lot that we don’t know at the point at which we have an idea newly hatched and what comes next is about crafting what is often no more than a notion into a finished article.

The idea we’re working on may not leave us with 5,000 things in our brain that we need to process, but sometimes even the simplest sounding idea has a lot of moving parts from the outset and this presents a real challenge for project teams who not only need to identify these moving parts but keep them constantly in view for the whole team.  This requirement for transparency is one area where  technology-based innovation platforms are extremely useful – especially  for distributed teams.  An effective innovation platform should enable teams to identify and store the assumptions, the unknowns, the uncertainties, and the decisions that they make as they get stuck into “the magic”.  The platform ensures these are kept in the “front of brain” and enables the team stay on the same page in terms of the status of work done to progress the idea.  Without  an innovation platform, it can prove all but impossible for distributed, remote teams.

 

Accelerated Learning

With the new idea before us, it can all feel like we have been thrown against a smooth, sheer cliff face with nothing to hold onto.  There are so many options, alternatives, unknowns, and uncertainties that it’s as though there is very little solidity to the project at all.  That is normal.  The bald fact is that we can do anything at this stage – but we can’t do everything.  The initial task we face is to find the quickest route to establish a toehold on the cliff.  A toehold in this context is a growing confidence, based on facts, data, and evidence, that we can justifiably recommend that either the project should move into full-scale development or indeed that it should be shelved.  It must be emphasised that each of these is a good result, as long as the conclusion is reached quickly and without spending a lot of money in the process.

The most effective way to approach this stage in the innovation process is to treat it as a rapid learning exercise.  It’s about identifying and executing those actions that we can take to learn more and reduce our uncertainty about the most critical aspects of our idea – and to do this as fast and as cheaply as possible.  For new products, this learning can be related to the required product specification, the technology, the manufacturing, the market, the pricing, and the distribution.  This learning will provide the platform for the eventual launch.

 This activity should be divided into two distinct stages.  The first of these is often called “discovery” or “exploration”.  Discovery is basically a rough-cut feasibility stage where the objective is to grow our confidence in the project to the point that we can make a recommendation to invest in its development – or to shelve the project if that is the right thing to do.   This discovery stage is about systematically setting out to learn more to reduce our uncertainty.  To do this we must identify the biggest uncertainties – those items that are critical to the success of the idea but where there are big gaps in our knowledge.

If an idea survives the discovery stage (and bear in mind that most do not) then it can progress on the basis that the company/team is committed to making it happen.  This is when the idea enters the development stage.  At this stage we have a firmer blueprint for the idea, and we are prepared to authorise spend and allocate scarce resources to bring it to fruition.  No idea is guaranteed to succeed, but by the time it reaches the  development stage we should have a reasonable degree of confidence that it will – based on the facts, evidence, data, and decisions that we’ve accumulated during discovery.

 

Chasing the RAT - The Riskiest Assumption Test

A systematic approach to this stage is to list the riskiest assumptions we would have to make if we were to recommend investment in the idea.  To help narrow this down, it’s useful to consider these riskiest assumptions under the following headings:

Desirability – the extent to which the customer will want this idea and provide customer pull

Feasibility – can we produce this?; can it be made?; is it doable?; can it be scaled?

Viability – does this make commercial sense?; does the numbers model stack up?

Identifying and addressing the riskiest assumptions systematically will help greatly increase the speed at which ideas can be driven towards a conclusion.  To illustrate this, the following is an excerpt from Managing the Design Factory by Donald G. Reinertsen.

 “Psychologists have found that the absence of factual information actually increases people’s attachment to their point of view, rather than decreasing it.  [...with good data] we get better buy-in to the team’s decisions. When everybody operates with the same set of facts, rather than with some hidden beliefs that have not been discussed, we get a stronger consensus.  When a decision is made, it stays made, rather than having the same issue resurface over and over in the team for re-evaluation.”

Your innovation system should make it much easier for everyone in the team to be dealing with the same set of facts and for surfacing hidden beliefs that, if not addressed, can be life-threatening for the project.  This is probably more critical for distributed teams.  Groups of people who work in closer proximity and have more regular informal discussions on the idea are much more likely to stumble across these hidden beliefs and, therefore, have more opportunity to address them.   It is important that your innovation system performs the same function very effectively for remote-working teams.  By ensuring that all dimensions of an idea and its workings are articulated clearly then these hidden beliefs are much less likely to slip through the net and cause damage later.

 

Learning Cycles

A practical way to achieve accelerated learning is through what is referred to as the ‘scientific method’ or alternatively the Deming Cycle of Plan Do Study Act (PDSA).  Through this cycle our riskiest assumptions during the exploration stage are put to the test through appropriately designed small scale experiments, tests, surveys, research, trials, modelling, and very rough-cut prototypes.  These actions  should be designed to be conducted fast and cheap and  should serve to grow confidence and momentum behind the idea.

Ideally, a PDSA cycle should be conducted in the space of a week.  Basically, the work done each week should lead to some learnings and conclusions which are then used to create the next PDSA iteration.  So, when the project team meets at the end of a cycle, they should have some learnings from the agreed tasks and then agree whether they need to make any changes to the idea/concept or to the uncertainties list. 

Then, based on this assessment, they should agree what the next PDSA action will be.  This next iteration will, in turn, have new tasks agreed for the following week’s work.  And so on, and so on…until the team starts to nail down some elements of the project – design and spec, market, target price, distribution options, etc. 

In effect to start to establish a toehold.  This will only emerge as the team learns more through each week’s tasks.  At this stage, for each iteration the team needs to have a real focus on the idea as described (we describe how  communicate ideas in another piece in this series “The importance of clear communication of ideas”) and also to have some tolerance for rough and ready estimates and calculations – as long as the assumptions behind these estimates and calculations are made completely visible.  The team should just be trying to grow their confidence to either kill the project or take it forward to development (investment).  Also, they need to be ready and able to pivot quickly to another angle as and when they prove to themselves that the current angle/opportunity isn’t likely to work the way they need it to.

The nature of this activity requires a high level of co-ordination – managing who is doing what, why, by when, and with what desired outcome.   These activities do require that your innovation system provides effective task allocation and tracking capabilities.   By creating transparency   around the idea exploration work that is being progressed, team members can be much more easily focused on delivering their tasks on time and to the required standard.  This helps to drive progress on ideas and avoid the confusion and conflicting agendas that plague many innovation efforts. 

 

Avoiding the Fear of Falling

For teams that can embrace the myriad uncertainties associated with bringing new ideas to fruition, they often find that the constant and relentless focus on learning is a very enjoyable and rewarding way of working.  To do so, however, requires that they can have confidence that they won’t fall off the cliff. 

In the new, hopefully enhanced, world of innovation, giving your teams the confidence they will need starts with  the desire to keep everyone involved and up to date at all times, so that we can create more toe-holds - and in doing so make changes to our innovation systems that naturally scale the pace and output of our innovation programmes.

 

 

 

 

 

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