Return to site

Integrative Design and Innovation (idei) - A Powerful Synthesis

A Thoroughly Modern Innovation Paradigm that combines the best of

Products Research, Design Thinking, Lean Startup and Behavioral Science

· Prof Emeritus PR,LeanInnovation,DesignThinking,Products Research,idei

Let's dig deeper into the evolution of innovation paradigms, and my contribution to this field, Integrative Design & Innovation. idei strengthens innovation as a both a process and, obviously most importantly, as an output. In my previous blog post An Introduction to Products Research and Integrative Design & Innovation, I shared the diagram below that shows how I conceptualize, teach and practice the front end innovation process today.

This is the Integrative Design & Innovation (idei) framework. So, what does it integrate? Actually, lots of things, but lets start with other paradigms: idei integrates the best of Design Thinking, Lean Startup, Agile Innovation, nearly 100 years of Products Research at P&G, and Behavioral Science for Innovation.

The Integrative Design & Innovation Framework, showing how to start with innovation hypotheses, then test these in rapid proto-cycles and use behavioral criteria of choice vs. existing offerings to know when you're ready to pitch and move to market

I originated the idei framework during my time at Northwestern's Segal Design Institute as Industry Innovator in Residence in order to address key opportunities for improvement that I saw in each approach individually, and most importantly to enable student innovation teams working on real world projects to have the best possible chance of success in the classroom and in their professional lives. I taught and applied idei successfully in the Intersect class I created with Craig Sampson where the challenge was to go from idea to fundable, holistic innovation proposition in just 10 weeks. I also applied the idei framework in industry on similar time frames, with great results.

These dual crucibles of real world innovation in industry and in partnership with academia of course helped flesh out the frameworks, and further showed that the idei framework really does work to deliver big, transformative and potentially disruptive innovation propositions on fast cycles, with $100Ms to $Bs in market potential, better than the previous approaches.

What follows is a little bit deeper dive into the idei construct and the ways in which it builds on and improves the individual innovation paradigms from which it draws.

Why idei?

First, why create idei? Simply put, extensive experience doing big innovation in the modern world showed that each of the other paradigms was insufficient alone to drive big transformative or disruptive innovation successfully to market, and each could be improved via synthesis. Actually doing big, transformative or disruptive innovation is really really tough for big organizations, and the very best of each innovation approach is required to in order to have even a chance of success given the enormous social, organizational and individual human barriers that stand between today and creating a transformatively or disruptively better tomorrow. My goal was to ruthlessly combine the best of everything against the singular goal of efficacy vs. choosing one existing approach over another.

idei Explained

idei is composed of three interconnected blocks as shown in the diagram above:

  1. WHAT - Alpha Innovation Hypothesis
  2. HOW - Lean Design Thinking
  3. WHEN - Behavioral Science for Innovation measures applied at the project level.

WHAT: Alpha Innovation Hypothesis. This is the starting point in the framework, and consists of a simple statement with five parts that the team must pull together to launch the fast cycle innovation process effectively. The statement goes like this: We can improve livesand create value by doing new thing X for human group Y using enabling science and technology Z. The work of the team is then to test the components of the innovation hypothesis using the rest of the framework.

Breaking the alpha innovation hypothesis down into the 5 component parts, we have:

Alpha Business Hypothesis draws from Humans served, Business, Organization or Societal sponsor and the enabling Science and Technology required to create the innovation
  1. Improve Lives - What is the issue, and in what way will you make life for your intended audience better than today?
  2. Create Value - How will this be valuable to you and your sponsors?
  3. New Thing X - What is the thing you will produce to make life better, i.e., product, service, community, etc.?
  4. Human Group Y - For whom will you improve life? How do you define these folks, and find them for research? Hint: do NOT use demographics, rather use contexts, tasks, tensions, aspirations and decisions to be made.
  5. Enabling Science and Technology Z - What science and technology will you bring to bear in the creation of New Thing X? How much can you get from others? What must you create within the team or sponsor others to create for you?

HOW: Lean Design Thinking. The HOW part of the diagram shows the Lean Design Thinking (LDT) approach to fast cycles of innovation and evaluation. LDT is a synthesis of Lean Startup and Design Thinking, with a liberal dose of rigor pulled from Products Research. I created the Lean Design Thinking synthesis to enable innovation teams to move from raw hypotheses to fundable propositions in weeks vs. months or years. The diagram makes rapid hypothesis formation and testing via minimum viable prototypes explicit, as well as the Pivot action when needed to win consumer choice vs. current offerings.

Lean Design Thinking wraps the Design Thinking activities in fast proto-cycles of hypothesis testing, which leads to a Pitch for next phase funding or a Pivot

The notes on each LDT hexagon provide guidance re: how to focus the activity. For example, in Empathy, Tasks, Outcomes and Experiences (for the consumer) are called out as primary focus areas, and we teach and use specific state of the art research approaches to identify each of these. In Define, we focus on Tensions (found in the current approach as practiced by the humans we seek to serve) and Aspirations (of the humans, at the task and life levels), using carefully crafted research techniques to deeply understand each.

Once Tensions and Aspirations are understood and defined, the goal of the innovation work in the last three LDT activity blocks - Hypothesize, Prototype, Learn - becomes the resolution of tensions + delivery of aspirations, a very powerful and effective focus for teams that is stretching enough to inspire breakthroughs and concrete enough to enable rapid iterative progress.

Teams applying Lean Design Thinking form strong bonds of purpose which helps teams create big, game changing innovation. Building empathy together, experiencing the human responses to their prototypes together, and pitching their propositions to key stakeholders together drives a sense of a common experience and purpose for all the members, making the teams powerful advocates for the humans they are trying to serve and the best ways to serve them, no matter the background or expertise of each team member. We always teach that if you must fall in love with something, fall in love with the humans you're trying to serve and resolving their issues, but do not fall in love with a specific technology or solution! Many an innovation program fails due to unrequited love for technologies or solutions when these solutions are actually no longer the best ones for the humans served and business.

WHEN: Behavioral Science for Innovation. Under WHEN in the diagram, we bring in principles from state of the art behavioral science and business growth through innovation. Classic market research techniques are very blunt instruments when it comes to guiding innovation and must be set aside in favor of behavioral tools that by their nature illuminate the relationships between the things we do control, like the parameters of our innovation, and the response of those we're trying to serve. It is not enough that a potential user likes or even prefers your innovation, rather, they must CHOOSE it over alternatives in some kind of realistic context, or a context that produces responses with known correlations to the real world. Behavioral Science for Innovation and to some extent Behavioral Economics provides a paradigm and methods by which choice can be measured quickly and effectively throughout the innovation process. These behavioral metrics avoid many potential dead ends that one might otherwise be misled into pursuing using attitudinal measures.

Behavioral Science for Innovation uses fast cycle stimulus response testing to guide innovation, with an ultimate output of choice and adoption of the innovation.

Improving on Design Thinking, Lean Startup, Agile Innovation & Products Research

As I dove more deeply into Design Thinking in preparation for teaching and building courses at Northwestern during my Innovator in Residence tenure, I saw that, while forming a very valuable foundation for doing human centered design and innovation, the actual teaching and practice of Design Thinking could be improved in several ways. Three big opportunities for improvement stood out:

  1. More Effective Human Research. The human research techniques taught and used throughout the DT process were not nearly as strong as the behaviorally based approaches that were emerging around the world and that we were pioneering at P&G.  The DT techniques also lacked a firm grounding in the sciences that underlie modern human research techniques and human decision-making; behavioral and experimental psychology, anthropology, sociology, ethnography, neuroscience, biometrics and sensory science.  I saw a tremendous opportunity to strengthen both the theory and practice of DT by incorporating robust in-context research methods as well as some of the modern Behavioral Science techniques, anchored with a behavioral goal: to get your target users to chose your innovation over other options, and adopt the innovation on-going.
  2. More Effective Iterations.  The DT construct and practice could be further strengthened by explicitly integrating the concepts of fast cycle hypothesis formation and testing and pivoting from the Lean Startup world, and summarizing the learning in a live business model.  Innovation accelerates tremendously when each human, technical and business hypothesis is made explicit and tightly linked to both a prototype and a fast cycle, state of the art research method.  Further, the business model canvas is a much more effective way to summarize the hypotheses and learning as it focuses the learning on finding a viable and testable commercial proposition.  I use my own business model canvas for most work, dividing the canvas into three sections:  Your Proposition, Paths to Market and Paths to Volume and Profit, as the folks I work with find this easier and more effective than some alternatives, but any business model canvas can be used.
  3. Sharper Focus on Outputs.  Each of the 5 DT activities could benefit from integrating portions of the Products Research and Lean Startup paradigms to increase focus and rigor during the activity, thereby helping to clarify what is being learned in each domain.  For example, I found that adding the concept of minimum viable to the Prototype activity especially important to help teams right-size the prototyping effort for the hypotheses being tested.  Industrial and digital designers naturally want to produce beautiful prototypes, and there is a time for that, but not every protocycle, nor even most.  Similarly, technologists sometimes want to jump to fully feasible technologies to test elements of the consumer proposition or business model well before they are needed. Another example: The Define activity benefits from an explicit focus on Tensions and Aspirations of the humans we seek to serve, leading to much sharper and generally deeper human problem definition, which then sets up the subsequent fast cycle prototyping to be much more effectively focused and progress measured.

Lean Startup had big opportunities for improvement as well.

  1. More Effective Human Research.  One shared with Design Thinking was a lack of rigorous state of the art human research methods. I've seen weak human research approaches in otherwise advanced courses on innovation in academia, and most certainly in practice out in the wider world, especially the startup world. Folks who wrote the books on lean startup and innovation seem to have a blind spot with regard to state of the art human research as applied to innovation. I believe these weak approaches to human research greatly delay startups, lead to misdirection, and contribute to the high failure rate.  
  2. Smartly Balanced Effort.  Another opportunity to improve is to reduce unreasonably unbalanced innovation effort across the three critical domains of humans, science/technology and business.  I've often seen work in one domain run dysfunctionally far ahead of the others, leaving real killer issues unresolved in the other domains in the enthusiasm to move one domain ahead.  In my experience this is most frequently the science/technology effort running far ahead of the other two while the other two still contain potential show stoppers, however, this problem is not exclusive to the science/technology domain.  Another version of this issue is to call out that there is ONE killer issue and its the ONLY thing we're going to focus on, which of course puts all the effort into a single domain.  The "single killer issue" myth only seems to be the case from a distance for those not actually on the team.  Anyone really doing big innovation work knows there are multiple killer issues to address to get to market and succeed, and defining and addressing these in an efficient and synergistic way is part of doing big innovation masterfully.  YES, rigorously prioritize your hypotheses and focus on a limited set, but don't follow prioritizing off the cliff, trading common sense for guru BS.
  3. Robust Partnerships to Obtain the Best Things that Already Exist.  A third practical deficiency is failing to partner to obtain state of the art resources that already exist in the world as part of your business model and path to market vs. re-inventing these internally on the team.  I've seen so many teams create a killer issue for themselves because they don't see the entire world as their supplier of capability and things.  Once you know the critical elements for success of your innovation and business model, figure out how to get the very best of what you need that somebody else has vs. compromising on what's handy, who you know, or what you think you can do yourself.  I've also seen large corporations refuse access to knowledge and expertise when they form "startup-like" teams due to issues of turf, trust, hierarchy, or organizational politics.  Understandable, given that all organizations are made up of humans, and humans are designed to do these kinds of obviously dysfunctional things in certain contexts, but destructive to the innovation effort.  Combining the best of the world (inside or outside any sponsoring organization) with the innovation team's unique genius and agility is the way to make great things happen, and innovation teams must ruthlessly go after what they need to succeed, and get it.

And both Lean Startup/Innovation and Design Thinking can benefit from a more robust and explicit approach to discovering and defining one of the key elements of big, successful innovation strategy, Purpose, i.e., the enduring reason for being in the world, the way in which the total innovation proposition actually benefits humans, and linking each element of the innovation to this purpose while differentiating your offering from competitors.

Integrative Design & Innovation addresses all these and more via a thoughtful synthesis of paradigms.

Making idei Real - An Example Sprint from Idea to Fundable Proposition

So, what does a great front end idei application look like? The idei framework can be applied in many contexts, but I really am a front end expert, so here's a rough outline of a 2-3 month front end sprint applying all the elements of Integrative Design & Innovation that is designed to move from a preliminary innovation idea to a fundable proposition ready to move to first transactions:

  1. Form a high-level innovation hypothesis, e.g., we can improve lives and create value by doing new thing X for human group Y using enabling science and technology Z.  
  2. Recruit a small group of folks with passion to make this real.  Have a master integrator lead the team through the process (someone with a T-shaped skill set) who owns final responsibility for the integrated output of the team.  The team leader insures individual team members lead in their areas of expertise.  Team disciplines should include those with breadth as well as deep domain experts, and should be small, on the order of 5 or so total.  Everybody learns together through the sprint, so that they are ready to act together, vs. spending time and energy forcing their point of view on others.
T-shaped skill set for team leader, breadth in product and initiative design and business growth through innovation, depth in science or engineering

3. Expand the alpha innovation hypothesis into explicit human, science/technology and business, organization or societal hypotheses.

4. Create an alpha consumer-facing pitch based on your alpha hypothesis that explains why and how you will help your target users, and why they should believe you. Where high uncertainty exists in the components of the alpha innovation hypothesis, create pitch variations the reflect a range of possible solutions.

5. Immerse yourself in the world of the humans you seek to serve using modern, rigorous techniques, to both understand their reality and to pitch your high-level proposition.

6. Revise and prioritize your consumer-facing pitch and the specific human, technology and business hypotheses based on the immersive human learning. Do 5 and 6 iteratively until folks are loving your proposition and you have 2-3 (or more) potential ways you could bring the proposition to life. Having a few paths forward engages the fabulous human ability to compare things, making it more likely that you'll land on a successful approach. It's also a great way to keep a team innovating effectively. When team members disagree, have a healthy but short debate, and if this doesn't create a way forward, simply turn the alternative positions into hypotheses and test them! It's only a little overstated to say that I solve all team disagreements though prototyping and testing!

7. Craft a Living Business Model (tm) for of your consumer-facing pitches, utilizing both knowledge and hypotheses to complete the three key elements: Your Offer, Paths to Profit & Scale, and Paths to Market. Prioritize the hypotheses within the Live Business Model and work on confirming, changing or discarding the few that are killer issues or show stoppers. Revise the model and reprioritize hypotheses as you learn.

    Live Business Model Canvas has three primary components: Your Offer, Paths to Profit & Scale, Paths to Market

    8. Define minimum viable prototypes for each of the critical hypotheses contained in your Living Business Model.

    9. Do rapid protocycles to test each of the hypotheses, moving the holistic proposition and human, technology and business domains ahead, and summarize these in revised human-facing propositions and prototype business models.

    • Each domain need not move ahead in lock step, but neither should any domain run far ahead of the others unless ALL killer issues are resolved in said domain.  
    • Utilize robust human research approaches drawing from psychology, anthropology, ethnography, behavioral and sensory science.  These can all be done QUICKLY, by those knowledgeable in the craft and right-sizing the test for front end work.  And never use traditional market research techniques (like surveys or focus groups) as your primary research, it's terribly inefficient at producing the depth of knowledge and insight required for transformative or disruptive innovation, and is so blunt as to often miss the key insight entirely or to misguide the work when folks try to apply traditional interpretations of market research data to new consumer and business domains.  If market research data is available, it can be a fine secondary source of support for a hypothesis test, but not primary.
    • Summarize new insights, learning and hypotheses-testing in your business model to help make sure you're discovering and then building a commercial proposition with ever-higher probability of success.  
    • Check for progress in the two most critical human metrics,  choice and adoption, by presenting your propositions and prototypes in a realistic context and getting target adopters to choose between your proposition or prototype, and existing alternatives, in a way that incurs a cost to those making the choice.  The cost need not be monetary, especially early on, but there should be some trade-off that the person makes when executing their choice.  Choice with cost is one key component of modern behavioral science and metrics that provides MUCH greater clarity on where your innovation stands than traditional market research measures.  Remember, in the real world, it's not about liking or preferring or surveys or, heaven forbid, focus groups, it's about choosing and using!
    • Pivot in any of the domains you deem necessary to construct a) an offer that drives choice or b) a business model that is viable for launch.

    10. Pitch your idea for funding when you've done the following:

    • Discovered and defined a preliminary business model that you believe is viable, with supporting evidence for each of the critical elements that will make your model go and go better than alternatives you considered. Include a preliminary pipeline of path-to-market and path-to-profit activities and innovations that will help you eventually scale.
    • Have evidence that your solution will be chosen and adopted over alternatives, and by whom.  The initial WHO may be very specific, and broaden over time as you execute the pipeline of scale activities.  The ultimate WHO helps define the size of the prize, and is critical to showing the potential return on the innovation investment.
    • Believe you are ready to move your innovation from prototype that drives choice to a salable level of usability  so that you can conduct your first transactions with consumers.  This is what the next round of funding is for, to move from proposition, prototypes and pitch to first transactions!

    Integrated Design & Innovation can be practiced in any front end innovation context, be it big organization, startup or experiential classroom. idei loads the front end with the best possible stuff, however, idei does not address all the barriers to taking transformative or disruptive innovations to market in the big organization context. Something like the Hybrid Startup Marketplace is required to increase the pace and value of transformative and disruptive innovation for large organizations, as I wrote about here on LinkedIn.

    Similarly, idei does not cover innovation strategy per se, that is another construct that I'll write a bit more about in the future, but idei is both informed by innovation strategy, and, just as importantly, informs innovation strategy through the depth of learning across consumer, business and technology domains, some of which will abstract successfully to the strategic level and influence the cascade of choices of which an innovation strategy is made.

    While no blog post will fully cover all the key elements of something as far reaching as a new innovation paradigm, I hope this helps you understand some of the key idei principles and sparks ideas you can apply to your own innovation efforts. Always happy to discuss further, just contact me via the info below.

    Perhaps we'll practice Integrated Design & Innovation together!

    All the best, innovators!

    Robb Olsen

    July 3rd, 2019


    Copyright 2019, Robb Olsen, Author. All rights reserved.


    All content shared here-in are the views of the author. No other endorsement is claimed or implied.


    Short quotes from this article are allowed so long as they reference and link to this article and credit the author. Mentions of this article with links are also allowed. This article may not otherwise be reproduced or distributed in any way without explicit consent of the author.