Bringing together innovation, learning and people
There are many variations of the innovation cycle. The popular PDCA Cycle (plan, do, check, act) is one example. Another is the five-step process of identify, prototype, develop, validate and scale, used in the Kumbhathon we talked about in the June 2015 Future of the Future column on “pop-up” cities. That was how organizers of the world’s largest religious festival were able to prepare for an influx of more than 40 million pilgrims by using low-cost, rapidly deployable technologies for locating lost children, reducing water pollution and other essential services.
In a different corner of the universe, a sizable cottage industry is built around personality and behavior profiles such as Myers-Briggs. You’ve probably taken a few of them yourself and know whether you’re an INTJ (Introverted, iNtuitive, Thinking, Judging) or some other combination of letters and categories.
Then there are learning cycles, like the OODA loop (observe, orient, decide, act), which is widely used in military circles. Or there’s the new, improved version of Bloom’s Taxonomy with its progressive stages of remembering, understanding, applying, analyzing, evaluating and creating. Throw in Edward de Bono’s “six thinking hats” and you’ve got an array of tools that will keep your management team in workshops and your favorite hotel chain in business virtually forever.
Let’s say that after being immersed in all of those different styles, you’ve come to the realization that you tend to be autocratic, your HR person is empathetic, and your VP of R&D, well, you’re still not quite sure. How does that help you strategically plan, develop and market your next-generation product or service? Odds are it doesn’t. The real question is, how do you bring innovation, learning, people, planning and execution together in a coherent way?
One way is to start combining those tools in ways that:
- put some structure to the typically unstructured innovation process,
- identify what behavioral traits work best at each stage in the process (and which ones don’t), and
- help you reorganize or even rebuild your team so your people are in the place within the cycle that fits them best.
Going through that process will not only help speed up your time-to-market, but also help you keep better track of what works and what doesn’t, so you don’t waste precious time and resources repeating mistakes and reinventing the wheel.
We’ve identified six stages that must be present if rapid innovation is to occur successfully on a sustained basis. We’ve named those stages ADIIEA, which stands for: automation, disruption, investigation, ideation, expectation and affirmation. It is the result of decades of research, taking into account dozens of different organizational learning and change management frameworks and methodologies.
You can certainly use your own set of stages, especially if you already have an established process in place. What’s important is that you are not only clear about what goes on in each stage, but also, more importantly, that you are able to manage the transition from one stage to another. That is why we call it a change cycle. Here are the six stages:
Automation is home plate. You need it for stability, productivity and efficiency. And you probably know the behavioral types appropriate for this stage—people who are good at delivering proficiency and control.
You can’t remain stuck in automation for very long. If you don’t shake things up, someone else will. That’s where disruption comes in. You know who those people are too, but do you know where they are? Are they in the right place in your organization, or are they off in some distant corner, feeling frustrated and passing that frustration along to their co-workers?
After disruption comes investigation, along with its focus on analysis and discovery. They are always asking “why?” But they don’t have the same sense of urgency as their neighbors in the disruption stage. In fact, the mindset typically is “this will never work, and here’s why.”
Next comes ideation, where design and planning occur. Here the mindset shifts to “this could work.” People at this stage are visionaries, passionate in their beliefs about what’s possible. That passion is often shared by the occupants of the next stage—expectation—who have the skill and tenacity to bring a vision to fruition.
Finally, in the affirmation stage, the innovation is endorsed, evangelized and given authenticity by people who are looked up to as trusted, authoritative sources. It’s at this point where the mindset shifts to “this really does work.” Now you’re ready to transition to full adoption. Which brings you back to automation, and the cycle repeats.
Putting it all together
Two questions to ask yourself are where in this cycle do you predominantly operate and whom do you work closely with on either side of you in the cycle? That second question can both expose and alleviate much of the discontent and anxiety in organizations.
If you’re a walking idea generator and your organization lacks an established change cycle, you are likely frustrated, saying things like “nobody ever listens to my ideas.” But how closely and how often do you interact with people ahead of you in the cycle, in the investigation stage? Or after you, in the expectation stage? Do you even know who those people are?
To unclog the innovation pipeline and accelerate the process, you need to know which associates you should be collaborating with in moving from “won’t work” to “could work” to “does work.” That’s where leadership comes into play. Stitching all of those elements together means clear direction, communication and commitment from your executive team on down.
Most business models offer prescriptive steps with clearly established boundaries. Sense-making and hypothesis-testing are discouraged. “Check-the-box” compliance is encouraged and rewarded.
But prescriptive approaches don’t work very well when crossing boundaries. At the cross-points, emergence needs to be not only permitted but also supported. That means promoting a culture of non-deterministic thinking—a willingness to keep on trying and failing until you get it right, and learning all you can along the way. Then when you finally do get it right, you need to have the courage to disrupt everything and start over.
That can only happen when all stages in the cycle are aligned, the right people are in place at each stage, and the boundaries between stages are open and fluid. Welcome to the brave new world of collaborative disruptive innovation.