What Innovation Can Learn From Science

In a recent conversation, I remarked that I used to be a scientist.  Thinking about it afterwards, I realized that was wrong.  A more accurate description was that I used to practice as a scientist, first with basic science, then the discovery and development of science-based products.  But I am still a scientist, in other words I take a scientific approach to many aspects of my work; I think like a scientist.

Innovation has a lot to do with the scientific method and can be practiced scientifically.  This statement may not chime well with people who believe it’s more of an art than a science; an inspirational “Eureka” moment where creativity is unconstrained and the driven genius launches the breakthrough market disruptor.

Scientific breakthroughs are often reported as being innovation.  I’m a bit of a purist when it comes to definitions, so if science produces inventions, they only become innovation if they are implemented and add value.

Here’s the sequence:

Most innovation starts with an observation, a recognition that consumers or users see a need for a new benefit; or even discovering a habit or practice that was previously unknown.  What do your users think, do or feel about your product?  Have you observed them to find out whether they actually do what they say they do?  What are their “pain points”?  What do they like and dislike about their experience of your service?  Where is your offering superior and inferior to those of your competitors?

Innovators Compile Data and Information

Innovators compile data and information that serve to increase and consolidate their understanding of the current situation, which includes asking key questions, usually starting with “why?”.  The observation and questioning lead to key insights, the deep and profound understanding that inspires ideas for innovation with competitive advantage.

Further iterations of observation and questioning and refinement of insights lead to the formulation of a hypothesis, a theory of how the future can be different.  Creating a hypothesis involves a lot of creative thinking; it’s where new ideas are developed.  Whilst based thoroughly on data and insights, the creation of something new and different is more than just a logical extension of the status quo.  It’s where the key elements of desirability, feasibility and viability come into play.

The new hypothesis must be testable, and consequently a prediction of the likely results of testing can be made.

Time for the Experiment

It’s then time for the experiment.  It’s rare that the first experiment works, so it’s back to the hypothesis to adjust it in light of the new data, a new prediction and another experiment.  Eventually the hypothesis is proved by experiment.  Good scientists don’t rely on one result, the experiment is repeated and (hopefully) the same results obtained.

The phrase “the experiment has worked” is usually associated with a match between data and hypothesis.  However, an experiment works when useful learning is obtained – witness Edison (“I’ve not failed; I’ve just found 10,000 ways that won’t work”) and Eric Ries’ Lean Startup principles.  Innovators must be prepared to experiment, experiment and experiment again.

Once the experiments have proved the hypothesis, basic scientists make their conclusions and prepare to communicate their results to their peers in the external world.  Innovation should work the same way, except the end result is not a scientific publication; it’s introducing something new, like a product, service or business model that adds value to the organisation.  That’s why the scientific method of innovation has a different final step – exploit.

The Final Step

This final step is where innovation departs from the pure scientific method.  In many – perhaps most – cases, the innovator does not have all the information, hasn’t done all the experiments they could, and needs to go to market with a “good enough” product rather than a perfect one.  That’s because innovation is just as much about launching at the right time and the right price as it is with launching the right product.  A judgment needs to be made with the help of data.  In other words, pragmatism comes before purity.

Science and innovation have many other things in common.  For example, they are both subject to bias, particularly confirmation bias.  There’s a healthy skepticism that looks for proof.  They are both highly competitive.  They both require imagination coupled with discipline.  Ultimately, they are both about discovering the new.

Innovation can learn a lot from science.  And I’m still proud to call myself a scientist.

image credit: Bro. Jeffrey Pioquinto, SJ

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Kevin McFarthingKevin McFarthing runs the Innovation Fixer consultancy, helping companies to improve the output and efficiency of their innovation, and to implement Open Innovation. He spent 17 years with Reckitt Benckiser in innovation leadership positions, and also has experience in life sciences.

Kevin McFarthing




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  1. Paul Hobcraft on September 13, 2015 at 8:53 am

    Nice post – you should be proud Kevin to call yourself an innovating scientist that explore and exploits in equal measure

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