A couple of weeks ago I was reading Gillian Tett's column in the Financial Times. She typically writes about the financial industry, and I enjoy the way she is able to make complicated ideas clear and understandable.

As I started reading this particular column I was surprised to see that the first two paragraphs described some work I led with P&G as a client. This was some excellent emerging markets work, and P&G is a great client to have as they are usually willing to explore directions that will shake up their categories. The bulk of the article, however, discussed a more important point which is how companies need to learn to understand new cultures before they try to expand current product offerings into new markets. It then goes on to suggest that the rewards, in addition to success in entering the new market, can include "reverse innovation" opportunities. The term "reverse innovation" is used to describe innovations developed for an emerging market, that can then be used in existing markets.  Two things struck me as I read the article.

First, useful innovation is not achieved by solely understanding a culture, but by translating that understanding into meaningful products and services that will benefit the culture. Tett identified that this type of understanding is important, stating that she draws from her background in social anthropology to understand the financial industry she writes about so well. She is correct in that social anthropology tools and thought processes are important.  I have also seen people make similar analogies to the design industry.  In my opinion, both types of skills are important - yet both alone are insufficient to create meaningful innovation.  From my experience, the particular tools used to make sense of a new culture are less important than the ability to translate what is learned into their implications in a different domain. In the example in the article, understanding what motivated people with little means in Brazil was only the first step. The most important step was to translate what this meant for a specific type of product to be useful in their lives. Because it is difficult to observe and often intuitivey accomplished, this step is often overlooked by those who study innovation and market research processes from the outside.

Second, it's important to note that this project was very similar to many innovation projects, regardless of the specific culture in question. It's important to understand that a company has its own culture which is different from the culture of its market. As such, every innovation project requires a study of the culture of the market, even in a market that's within the company's native geographic region. Sure, the outcomes from studying what's important in a Brazilian culture of little means will be different from the outcomes from a different culture. But the bigger difference is between the culture of the company and the culture of a market in general. Companies are great at understanding their current products. They often confuse this expertise with their ability to understand how anyone would perceive their products. This is the real reason behind the success or failure of any new product, regardless of the location of the market.

I was happy to see that these issues and the results of the work are getting noticed. However, there is still great confusion about what it takes to achieve innovation success. It's important to observe behaviors, point to tools from various fields of study, and glean critical thinking skills from successful innovation efforts. But let's not forget that the magic happens in the translation of what is learned into a relevant new offering. I'm sure Tett possesses excellent intuitive translation skills as she studies and writes about the financial industry. There's not a rule book for how to do that, but I'm working on it.

I teach a multidisciplinary product development course at Boston University. For their semester project, the students have to develop a new product that incorporates what they are learning in Marketing, Operations Management, Finance, and Information Systems. At this point in the class it is beginning to dawn on the students that there are no fixed, prescriptive answers for what they should be doing, or how they should be doing it relative to their specific projects. Up until this point, there were clear guidelines for what was expected of them. In terms of course material, there still are clear guidelines for what they should study and how they will be tested on the material. However, the project is different. Some students who performed well in fact-based classes find themselves at a loss for what to do - the project is terrifying. Others find the project to be a platform in which they can exercise leadership skills that didn't have an outlet previously, and thrive in the ambiguous, autonomous environment - the project is liberating. All of them come away with a much better sense of how work needs to be done when they enter the workforce.

John Hagel recently posted a review of his colleague's new book "A New Culture of Learning". According to Hagel's review, the book discusses the need for new models of learning that will embrace tension and ambiguity, and stress the development of new ideas by encouraging imagination and play.  I couldn't agree more.

What's interesting to me is that what I'm seeing in the classroom is exactly what I see happening with my clients. In the past, their success was clearly defined and measured. However, it is no longer possible to compete by using static metrics that encourage static behaviors in our increasingly dynamic economy. The industrial revolution has had a good run, but it has run its course.

People resist change because it's scary, and one of the things I think is most scary is not knowing whether or not you're on the right track. When I work with both students and clients, I spend most of my time creating appropriate evaluation methods and metrics. For students I help them to think through whether or not they are solving the right problem, and then I help them to figure out how to know that their solutions are working.  It's the same with clients, but to a much more detailed, in-depth degree which often takes the form of innovation strategy and process development.  It's also what I often find missing in much of the writing out there on innovation. There are lots of thoughts on how to come up with new ideas. There are far fewer thoughts on how to identify a successful idea. I look forward to continuing to develop new learning models that do not tell people what to think and do, but help people to think through the implications of what they do.

There is an interesting post on the Innovation Policy Blog.  In this post they echo and comment on many of the statements from the President's State of the Union address about the need to invest in innovation.  I agree with most of these statements, but like many ideas I hear about innovation, they are "correct but insufficient" to truly encourage American innovation.

Why? First, they make some fairly explicit assumptions that innovation is synonymous with explorations in science and technology.  Granted, the group that supports the blog is the Information Technology and Innovation Foundation, but I feel that these assumptions define innovation too narrowly.  This can lead to a misunderstanding of innovation by the people needed to support the right innovation policies.  I would not expect politicians to understand all the details involved to realize the greatest value from innovation.  It is our job to present the issues so that the right decisions will be made.

Second, it talks about funding scientific exploration without much discussion of what the goals of the exploration would be, or how progress would be measured.  While the blog is supposedly non-partisan, framing the issue in this way will probably scare anyone who thinks we need better controls on government spending.  These same people would most likely invest in sound propositions with a likelyhood of a positive return.

If it were up to me, I would reframe this discussion.  The way it is presented, spending on innovation could easily be perceived as throwing money into a deep abyss that would create a scientific playground without much value coming from it.  Instead, there is an opportunity to discuss the fact that there is much more to innovation than scientific skunkworks.  From my experience, there are many areas of innovation that would benefit from increased funding.  Here's a short list:

• When working in innovation, it's important to understand that there are right answers and wrong answers.  Often there is more than one right answer, and that can confuse people into thinking that anything new is good.  The rest of the list is based on this understanding.
• Some people are better at the cognitive skills necessary for innovation. They are inherently better at recognizing right from wrong answers when presented with new ideas.  This skill needs to be better understood and developed.  I see at as having similar to having aptitude toward math, art, science, etc.
• There are ways to make sure we are solving the right problem before we start offering solutions.  No matter how "new", "innovative", or "different" an idea is, if it doesn't solve a necessary problem, it's not a necessary solution.
• There are ways to assess the viability of new ideas in context before investing in development. Even those who are the best will benefit from evaluating and improving ideas before they are developed. This helps to develop financial projections so development dollars are channeled to the right places.
• We can create metrics to evaluate innovation performance. They will be different from measures currently used to evaluate performance on known initiatives.
• There are ways to make the logic behind a new idea more transparent.  This helps those who are not as skilled to make better downstream decisions during development.

These are topics that have evolved from my years of working in the innovation space, and they are the focus of my current research. I would hate to think they wouldn't get funded because they would not fit under the heading "scientific exploration".  It's time to elevate the general comprehension of innovation.  While I absolutely do not believe that every dollar spent needs to have a specific return identified before starting (then it wouldn't be innovation after all), I do not believe that just throwing money at "exploration" will be very useful in the long run either.

I hope I read the blog post too literally.  I hope that everything I've talked about is embedded in the agenda to "invest in innovation".  However, based on what I hear I'm not sure it is.  Any ideas for what we can do to to get more money spent, in the right ways, and on the right things to ensure that investment in innovation is a true opportunity and not an abyss?

Last year I wrote a post about abductive and adductive reasoning, and how they are important skills for innovation.  A couple of months after I wrote it, I was in a discussion about the topic with someone who insisted that abductive reasoning is the only valid form of hypothetical reasoning.  He pointed out that adductive reasoning isn’t listed in the Stanford Encyclopedia of Philosophy as evidence that it was an irrelevant term. 

I had always thought that adductive thinking was not only relevant, but a necessary complement to abductive reasoning when speaking about hypothetical concepts.  I'd like to go through my thought process for why it's relevant, and I'd love input from others about these ideas.  For me it can best be summed up in a matrix to show how the types of reasoning relate to each other as I use them.

The matrix may be best explained with an example of Sir Isaac Newton’s apple tree. 

If we start with the inductive and deductive reasoning boxes, we see that the inputs to both are real, observable, linear, and provide reliable outcomes. So, if we observe that when an apple gets detached from a tree, it falls straight to the ground, we could draw the following conclusions. 

By using inductive reasoning we would create a rule stating that when apples detach from the tree they fall straight to the ground.  We could also easily apply this rule to anything that is unsupported in the air, will fall straight to the ground. 

By using deductive reasoning, we could draw the following conclusion. If an apple became detached from the tree and fell straight to the ground, and I was standing underneath the apple, it would hit me on its way down to the ground. The application of this conclusion is what would make us uneasy if we were to walk under a crane lifting a heavy block.  

The inputs to creating both the rule and the conclusion are observable and real.  We can reproduce these situations and achieve predictable, repeatable results, which we would call a highly reliable outcome. 

This is the type of reasoning and measurement that is highly valued in areas of business that are responsible for delivering reliable results.  But is this the type of reasoning that led Newton to develop the idea that some force from the earth may be pulling the apple toward it?  As with most new ideas, inductive and deductive reasoning are necessary, but not sufficient, to develop radically new theories. 

If inductive reasoning creates a rule for what is happening, ie: the apple falls straight down to the ground, abductive reasoning is seeking to explain why that rule is true, ie: why is the apple falling straight down to the ground?  Different hypotheses could be developed to explain why this is happening.  Further observation could add another rule: the apple is falling faster as it gets closer to the ground.  This additional information could rule out some hypotheses, and support others.  Eventually a theory is developed that seems to be a likely reason why all of these rules are true.  In this case, that there is a force from the earth that pulls objects toward it, later to be known as gravity. 

If this theory is true, then what else could we conclude?  This is where adductive reasoning comes into play.  Adductive reasoning seeks to find plausible new conclusions based on the reason why a rule is true.  In other words, what else could be true if there actually was a force within the earth that pulled objects toward it?  It is this type of thinking that leads to different applications of the laws of gravity, eventually enabling us to determine the effects of different gravitational fields on our planet and others.   

Where inductive and deductive reasoning produce outcomes with a high degree of reliability, abductive and adductive reasoning produce outcomes with a high degree of validity.  That is, rather than seeking to set up an experiment that will produce repeatable results, it is necessary to set up experiments that help us see different aspects of our hypothesis.  It helps us to more fully describe phenomena that cannot be observed, and instead needs to be inferred. Ultimately it helps us to know whether one hypothesis answers our question or solves our problem better than another hypothesis. 

By now two things must be pretty clear as to where I’m going with this.  First, I do believe that adductive reasoning is different from abductive reasoning.  I have observed people who are much better at one than at the other.  I have also observed people who have trouble with hypothetical situations in any capacity.  This tells me that all four types of reasoning require slightly different skills, aptitudes, and/or ways of perceiving the world, that they are equally necessarily, and that none alone are sufficient in gaining a full understanding of new ideas. Second, I hope that this sheds more light on why innovation is so difficult in many companies.  If a company is (as most companies are) responsible for delivering reliable, repeatable products and services, then they will value inputs that are real, observable, and yield reliable outcomes. 

However, even though the conclusions drawn from abductive and adductive reasoning are not linear, they are certainly not without logic.  This is what is difficult for many to grasp.  While the measures may not be the same for new ideas, there should still be measures that ensure the validity of one idea over another.  And these measures will be different from those used to measure day-to-day processes. 

Think of it this way.  What could Newton have concluded if his boss had suggested that he sit under 1000 apple trees to prove “with statistical significance” that his observation was correct? 

A couple of weeks ago I was given an advance copy of a new book called The Other Side of Innovation: Solving the Execution Challenge, by Vijay Govindarajan and Chris Trimble, two faculty members from the Tuck School of Business at Dartmouth.  The publishing date will be September 2, 2010.

I'll start by saying that this is one of the most useful books on innovation I've read in a long time.  I want all of my clients to read it.  A few months ago, I wrote a post cautioning business leaders against expecting innovation to happen within their development processes.  In this book, the authors focus on this point, and provide clear guidance for developing innovative ideas that cannot (or should not) be developed within the existing development process.

The book is broken into four main parts:

In the introduction, the authors provide some good background information to help the reader to discern whether or not a new idea should be developed within the current development process, or whether a new process should be created.  They use clear examples, and illustrate which types of ideas the current processes should be able to handle, and show examples of companies that have executed both within new and existing processes.  In this section they also lay the groundwork for their basic premise that managing innovation is not a wild, maveric process.  It is a controlled, disciplined process that just happens to look very different from managing the day to day business.  The rest of the book discusses how to create and manage the new process once it is determined that the innovation cannot be developed within the existing process.

The next two sections present the heart of their recommendations for executing innovation.  The first section contains three chapters discussing how to build the team.  They call the team charged with developing the new innovation the Dedicated Team, and clearly point out how this is different from the team that is charged with managing day to day development, called the Performance Engine.  These designations are very useful, and the authors do an excellent job of describing how the two teams need to collaborate, how to select people for the Dedicated Team, and how to manage the partnership between the two.  They also make a nice distinction between the responsibilities and challenges the leader of the Dedicated Team will face, and the role of the senior executives who need to support them.  What I found particularly useful is that they identified just about every "pink elephant" that could be in the room when discussing these issues, and this book could be used as a guide to foster objective discussions about potentially sensitive issues.

The second of the two sections discusses the management of the innovation initiative itself, referring to the work of the Dedicated Team as running a disciplined experiment.  These chapters provide an excellent resource for illustrating how the work of executing innovation is very different from the work of the day to day development process.  It discusses performance metrics, and how Performance Engine metrics will be harmful to the execution of innovation initiatives.  They present an enlightening way to talk about planning, acknowledging that plans for innovation initiatives can only be based on assumptions.  The goal of the process is to focus on learning, considering the initial plan as a hypothesis, and adjusting it as more information is learned.  They also discuss the fundamental difference between this approach and the Performance Engine approach, which is to focus on results and adherence to plan.  Again many "pink elephants" are exposed as the authors point to the many ways it is easy to fall into the trap of using Performance Engine metrics when evaluating against assumptions.

The final section is the conclusion, and here the authors share several innovation myths, many of which center around one main idea.  Managing the innovation process is not the result of people breaking rules, creating crazy ideas, and throwing things against the wall to see what sticks.  This is akin to what I would call running a casino inside your company.  Instead, managing innovation requires just as much discipline and rigor as managing the Performance Engine.  In fact there is no room for running on autopilot, as the team needs to be on their toes constantly to evaluate what they are doing to see if they are getting closer to their goal.  The authors do a great job of calling out and dispelling these myths repeatedly throughout the book, and it's nice to see them listed out at the end.

Finally, this book is clearly for people looking to manage the execution of innovative ideas to make them real within an organization.  It is not for people who are looking create new ideas.  In fact there was only one statement I disagreed with, and it came at the end of the book.  As the authors were reiterating their point that innovation cannot happen without disciplined execution, they correctly point to the fact that most organizations focus most of their innovation efforts on the Big Idea Hunt.  They then say that the Idea Hunt may be serendipitous and difficult to manage, but that this random nature applies only to the Idea Hunt.  Here I strongly disagree.  In my work, I apply similar principles and discipline to the Idea Hunt.  What this tells me is that regardless of whether you are trying to create new ideas, or develop the most promising ones, developing innovative ideas is anything but random and Govindarajan and Trimble have presented an excellent guide for how to execute them.

I was recently asked to contribute a short piece to the blog section of a new website - OnInnovation.  It's sponsored by the Henry Ford Foundation, and is intended to collect insights from innovators and thought leaders, and enable people to connect with their ideas.  I thought it was a great idea, and one worth supporting.  Below is an intro and link to my post:

Preserving Dignity to Drive Creativity
Posted August 10, 2010
We’ve all had the nightmares in one form or another.  You find yourself at a podium and you forgot what your speech was about.  Or you are at the office and realize that you aren’t wearing any pants.  These nightmares are powerful reminders of our deep seated fear of exposing ourselves to the judgment of [...]  Read Complete Post

I'd love to know what you think of the site.  Does it have legs?

I was recently reminded of the "hit rate" for investment in innovation (I'm also including entrepreneurial ventures here).  Most companies, VC's and others use estimates as rules of thumb.  An optimistic one is something like 1/3 of their investments will be winners, 1/3 will break even, and 1/3 will fail.  Others are more like 1% will be wild successes, 2 - 3% will do OK, and the rest will fail.

What's interesting to me is that most people accept these rules of thumb.  The reason why this is interesting is because this means that for the most part, people buy into the idea that the likely success of a new venture is largely random.  And yet there is a lot of money being spent on market research, market testing, basic due diligence, etc. before deciding to pursue a new opportunity. 

Clearly, there are forces at work that the current business community and education system do not yet recognize, let alone support and develop.  Steve Jobs' success at identifying and developing winning ideas may be intuitive, but it is not as if he is a magic person with a crystal ball.  He is just an example of someone who naturally has the ability to connect underlying market motivations with new and different offerings to satisfy them.  I'm sure there are others who have this natural ability, but if they do not also have the desire or skill to be the CEO of a large company, then the output of this ability will not be as apparent. 

There is a lot of talk about the "new economy", and how the skills that got us through the industrial age will not work to take us forward.  And yet we are so entrenched in nurturing the skills necessary for success in the industrial age, that we no longer remember the fact that it took a lot to get people to focus less holistically so they could work more efficiently within the required corporate silos. 

Success in the future will require that we recognize, develop, and nurture the ability to think holistically; to be able to see similarities in ideas and objects that appear dissimilar on the surface.  This is what is necessary to make a connection between an underlying motivation, and developing a new solution to satisfy that motivation.  In my opinion, the current "rules of thumb" tolerate a lot of waste in the system.  I think we can do better.  Until then I'll continue my work in helping to build a linear path for organizations to reduce the random nature of their innovation investments.  It seems that it's currently the only way for the people who can think like Steve Jobs, but are not Steve Jobs, to make their ideas heard.

This all makes me wonder about two things.  Will we ever get to the point where most of us can spot the difference between a good creative idea and a bad creative idea before we invest in developing a product to see real market results?  And will we ever get to a point where people will recognize the limitations of linear logic, and use it in good balance with more holistic logic?

There's no question that truly innovative people are creative.  But creativity alone is not enough.  True innovators, those people who can deliver new ideas and offerings that are relevant to the market, possess something in addition to creativity.  I've been doing a lot of work in this area lately, and will be working with PDMA and with a consortium at the Steven's Institute to help identify best practices and build a body of knowledge in this area, especially as it relates to enterpreneurship and new venture creation.

However, before we can build a credible body of knowledge in this area, we must first be able to identify what "it" is that successful innovators have that others - even highly creative people - may not have.  I've written about this briefly where I've metaphorically described this skill as the ability to make synesthesia-like connections between seemingly unrelated ideas or observations.  But ultimately, I think this is just one way to describe the way they perceive the world and make connections within it. 

What's interesting to me is that we currently don't have a good way to talk about the way people perceive relative to how it may help or hinder them in doing specific jobs.  Is there anything on your resume that talks about the way you perceive the world?  I would guess not.  And if there was, would it be even remotely helpful to the person reading your resume?  Again, probably not. And yet this is the one area where I see people either succeed or fail in their ability to perform as early stage entrepreneurs, investors, and innovation managers in large companies.

I'm also not sure if it's the actual perceptual skills that are different, or the way our brains process what we are perceiving.  For example, if a person who sees the similar in the dissimilar looks at a block of ice, a puddle of water, and a cloud of steam, they would describe them all as similar - they are all composed of H2O.  Others would look at the surface details and describe them as three different structures.  Is that perceiving, or processing?  I hope to collect different points of view on this question as part of the body of knowledge. 

What I do know now is that when you are assessing someone's ability to successfully innovate, it might be useful to stop and think about how they perceive the world.  Are they able to see similarities in dissimilar things?  Do the similarities make sense?  Is the person assessing them able to tell the difference?  As John Hagel states in many different ways in his blog, we are about to enter a Great Shift in how our world works.  It might be time to figure out how to define perceptual skills that could go easily unrecognized in the old economy, yet will be absolutely necessary for success in the new economy. 

I've been talking to some people in the software industry about innovation, and we were comparing my innovation and development processes to agile and waterfall development processes used in the software industry.  At first glance, I thought it was a straightforward comparison; that the innovation process I describe is analogous to the agile development process, and the development process I describe is analogous to the waterfall development process.  After thinking about it more, I realized that it wasn't so simple.

The similarities are that the innovation process and the agile process are both iterative, while the development process and the waterfall process are both sequential.  In fact the development process I describe the waterfall development process used in the software industry are very similar, and work well when a process is known, needs to be repeated, and it's important to optimize it.  As I described many times, the system falls apart when companies try to innovate beyond incremental improvements when using this process.  The problem in the software industry is that the waterfall process is too often used to develop very new products.  As a result, people spend time trying to estimate very detailed steps required to develop something new.  However, if it's completely new, these steps are often unknowable.

While the development and waterfall processes are similar, I realized that the innovation and agile processes are only similar in that they are iterative.  The main difference is that the innovation process as I describe it is used to figure out what new products and services should be developed.  It requires a deep understanding of the market, and the ability to translate market needs into criteria for a successful offering.  On the other hand, the agile development process is an iterative way to develop an offering that has already been defined.  What is not known is the best way to develop the offering, because it is the first time it is being developed.  The future is uncertain, and this process acknowledges that as long as progress is made toward a defined goal, the correct steps will be taken at that time.

This also points out why many companies may fail to develop breakthrough innovations even though they are working with iterative processes while they develop new offerings.  Ultimately, development processes of any type are best used to develop defined offerings.  You can't "develop" your way to a breakthrough.  That can only be done by understanding what motivates the market, and translating that motivation into criteria the development team can work with - whether they iterate it or not.

Last year I wrote a post about getting multidisciplinary teams right.  I've recently been asked to speak about building teams for innovation and put together this slide that elaborates on the ideas I wrote about.  Later I'll get into more detail about selecting the right people to work on innovation teams, but for now I'll just say that in my opinion, the most important differences between people who naturally fit better in innovation teams, vs development teams, are these:  People who are successful working in innovation teams (in this case I called it Opportunity Definition Teams), have a problem-posing mindset, as opposed to a problem-solving mindset. They also are synthesizing information and drawing connections between seemingly unrelated disciplines and ideas.  Problem solving mindsets favor analyzing information, and breaking down problems into manageable parts.