Labcraft is a book which illustrates ways in which labs cultivate change through experimentation and collaboration. The labs themselves are part of an emerging family of hybrid organizations which create dialogue, cross-pollinate perspectives, and create space for new things to emerge.
The book was co-authored by 12 different lab leaders/facilitators of which most had not worked together before. They were brought together and produced the book in four days using the Book Sprint methodology. The authors believe that we are living in times of fundamental transition in the way we organize our societies and economies. Furthermore, they believe that there’s an abundance of untapped energy, ideas, and potential that can be leveraged to address the big challenges of our times. The book itself demonstrates what is possible when people join forces in new, innovative, and experimental ways. The authors combine their individual experiences with a willingness to represent a multitude of voices and perspectives. Together they convey an invitation to create spaces and initiatives for innovation and collaboration everywhere.
The book is published under Creative Commons Attribution-Non Commercial 4.0 License. Below are excerpts (in italics) from Part III of the book, where the authors take a closer look at strategies for generating and accelerating emergent innovations. This is the part which I find most interesting. However, the book overall is well worth reading and I highly recommend the book to all who are interested in collaborative approaches!
Excerpts from Labcraft, Part III: Lab Strategies, pp. 64—114.
Seeing the Bigger Picture
Many of our labs start with stepping back and trying to understand the landscape in which we are operating, and the interconnections between various players, rules, and stakeholders. Social systems are vast and complex, and individual organizations can sometimes forget that. As intermediaries and conveners of diverse stakeholders, our labs have an opportunity to make a larger part of the system visible to all, or to help that system see itself better. … Seeing the whole system helps people and institutions that are normally immersed in it to see the forest rather than the trees. When people are able to broaden their view, even just a bit for a short period of time, and look at the system as an observer, an “aha” moment is much more likely to arise. And for any of us, it can be empowering to realize that we aren’t the only tree trying to change the forest. Finally, mapping the system can help us identify the emerging alternatives, and any barriers to their entry. …
The practice of cultivating connections is an enormous part of our labs’ work, which begs the question: connections between whom and what? Our labs often bring together people who don’t—or can’t, or won’t—normally engage with one another. We facilitate interactions between actors from differing sectors and fields, divergent backgrounds, and distinct, frequently antagonistic factions in our societies. Often, we do this to uncover commonalities. These engagements commonly end with a remarkable alignment of needs, challenges, and aspirations. And—equally valuable—a shared understanding of points of divergence and conflict often emerges. It’s essential to build understanding of opposing perspectives, and build depth and strength of relationships. … Over time, these are the some of the most important learnings our labs have found crucial to cultivating connections:
- Create safe spaces. In labs, participants can come and hear views they don’t normally align themselves with; be honest about the challenges they face; and show up as individuals, not the organizations they represent.
- Take a birds-eye view. Find the parts of the system that don’t understand each other, and look for people doing similar work in different systems. These stakeholders usually learn the most from each other.
- Unexpected connections can be the most obvious ones. Sometimes it seems to make sense that two people should work together. And yet, for many reasons stemming from hierarchies and organizational and disciplinary silos, they just don’t. At the same time, some pairings or groupings that seem unlikely can become the most fruitful. Don’t take any connection for granted; you might miss some excellent opportunities.
- Don’t wait to be asked. Often we’re not given the mandate to convene, but do it anyway.
- Pull experience from everywhere. We build unexpected connections across disciplines by bringing together ideas like social enterprise, personal development, marketing, design, advocacy, and education.
Staying Close to People
Another aspect of our lab approach is to work directly with ordinary citizens and users—people. Our goal is that decisions within a system emerge from the authentic experience of end users, and the professionals that represent institutions “on the ground,” such as teachers and nurses. Staying close to people isn’t just good practice, it’s about respecting the rights and agency of users to influence processes that impact them. … Some of our key learnings as we aim to stay close to people are:
- Get immersed. The best make-or-break observations often come when the observer is embedded in the target systems and with target beneficiaries. There’s no substitute for being there. … You can also build design skills in people who are already immersed in the context.
- Just asking might not get you what you need. Interviews are invaluable, but they’re only one tool in the observer’s tool kit. Users may lack the schema or even language to communicate their needs. And often they are so intimately familiar with—and invested in—existing narratives about a problematic situation, they may not be best positioned to see what would be clear to a third-party observer.
- Build for the hardest to reach. …
- Positive deviance is powerful. Staying close to users helps us fight the assumption that we must make a new thing. Instead, we commonly find that elements of the overall challenge have already been addressed by community members who successfully developed their own local solutions that deviate from the mainstream way of dealing with the problem. Hence, our efforts are better spent iterating upon and scaling these existing bottom-up solutions than reinventing the wheel.
Experimenting and Prototyping
The notion of experimentation figures prominently into how we as labs identify and conceive of ourselves. What’s a laboratory, after all, without experimentation? We don’t just use experimentation in order to develop new solutions; it’s in our DNA. The concept of experimentation in the “hard” sciences is widely understood to involve these steps: look at the evidence; propose a hypothesis that explains that evidence; create a trial that tests the ability of your hypothesis to confirm, predict, or explain the evidence; and use the results of your trial to refine your hypothesis. … Our approach to experimentation looks considerably more like the natural sciences than a cursory glance might reveal. … We create hypotheses … We translate our hypotheses into prototypes for new or improved solutions … We test those solutions through their application, often in the form of pilots or trials with users. And we use the results of our tests to iterate and to inform the creation of still-better solutions. And we develop our own strategies and programs through a trial-and-error process of experimenting and prototyping. The prototype emerges as a central feature of our approaches. … It’s vital to experimentation that we introduce some thing you can test—something real that can succeed or fail, that can go off the rails, that can have unintended outcomes, that can break! That test allows us to learn. There’s a ton of great thinking out there on prototyping, so we’ll say only this: prototypes are disposable. Create them quickly and cheaply to make your thinking tangible, get it into the hands of users and stakeholders to test it, and throw it out when you’ve extracted what you need to know in order to make a better version. Iteration is what we do with that learning: we take our lessons from trials and pilots and feedback loops built around our prototypes, consolidate them into a refined hypothesis, and build a new and improved version of that prototype. … Some of the things we’ve learned over time about experimenting:
- Know what you’re trying to discover. There’s a lot to be said for insights that emerge from pilots, and even more to be said about being open to being surprised. But our experience suggests that our efforts are best served when we define from the outset what we hope to learn from a pilot or trial.
- If it isn’t working, stop doing it. This may sound obvious, but continuing on with something when it’s clearly not working happens more often than you might think in almost every type of organization. One of the key aspects of rapid-cycle prototyping is that you simply stop doing something when you realize it’s not working, learn from that, and move on.
- Don’t take it personally. Labs take risks, so failure will happen. … Make sure the culture in your organization genuinely supports the notion that things won’t always work, and backs up the individuals who lead experiments.
- Be strict about learning. Experimentation isn’t a substitute for deeper learning. There’s no point in failing for the sake of it. It’s crucial that no matter how much you may want to forget a failed experiment, you reflect after every activity that went wrong on what went well, what didn’t, and what you’d do differently.
Enabling Change Agents
One of the most important principles underpinning our practices is “go where the energy is.” We find pioneers and help them get their work done better and faster. Change agents can come from anywhere. … They can be highly skilled or completely fresh. They may have solid institutional backing—or none. The core philosophy here is that the people ultimately best suited to make change in the system are the people who are actually in that system or those impacted by it—those who live and breathe it every day. … Some important learnings about supporting change agents:
- Create communities of change agents. Participants can learn from each other as well as from you, and they’ll have a support system that remains long after you stop facilitating.
- Nurture accountability. Where possible, help people who are personally committed to your cause and are truly motivated to learn. People who are “told” they have to participate can be hugely disruptive to the process. Ownership can only be taken, not given.
- Be realistic about timeframes. Genuine capacity-building takes a long time. From the start, set expectations regarding impact.
- Acceleration is quicker that incubation. If you incubate concepts, rather than strategies or businesses, expect that many ideas or projects won’t get off the ground. Accelerators that support scalability for pioneers who are already innovating produce much faster results.
- Be clear about what you hope to achieve through capacity building, and let your objectives inform your efforts. …
Power and Labwashing
How do we navigate the power dynamics between institutions and labs? Central to our craft is the ability to play the game while changing it. … As a result, we constantly walk a tightrope between challenging the status quo and asking radical questions using unconventional methodologies—while not alienating our own supporters and critical stakeholders. Swaying too far one way might make us irrelevant, while moving too far the other opens us to critique of “labwashing” important issues. An exercise that superficially looks like a lab process, but really only touches the surface and avoids really challenging the status quo, actually diverts scarce resources from where they could make a greater difference. To be successful, we need to “take our own medicine” and critically reflect on how we walk this tightrope. Becoming co-opted by power players and structures in the existing system is one of the greatest risks we face. When do we become so immersed in the game that changing the rules becomes a secondary goal? What we’ve seen across our labs is that these tensions, paradoxes, and questions arise constantly and must always be addressed seriously.
Tracking Fuzzy Impact
… All labs are real-life examples of how institutions and civil society can work together in more human, democratic, and creative ways. But the reality is that many of the people we depend upon for our survival—those who help resource us—are waiting for us to explain in clear and measurable terms the difference we’re making. For some aspects of our labs’ work, this demand is straightforward; in other areas it’s significantly more challenging. … It’s inherent to the mission and culture of most innovation labs that we stay open, not draw quick conclusions, and adapt—not begin with assumptions and narrow the possibilities. … So there’s an inherent contradiction between the predictive modus operandi of the existing institutions we work in or with, and the emergent approach that our labs use to innovate. … As labs, we see that a more general exploration of the problem will allow us to understand the nuances and opportunities within the problem space, and help us to define it differently. When we do that, we’re more likely to arrive at a breakthrough. We create different levels of impact. Some are tangible, some intangible; some are direct and some indirect. … In one way or another, all our labs create impact. And in the best cases, that impact is directly measurable. … Many of our impacts are less tangible, and yet no less real. One way to understand these less tangible impacts of labs is to distinguish between four levels of impact: …
- [Impact at the Level of] The Lab Itself
Creating and running a lab is in itself an outcome that we shouldn’t ignore. … The value of this work lies in expanding the climate of ideas. It creates connections and breathes diversity into systems caught in the trap of “no alternatives.” Capturing the impact of these activities in a clear narrative [is] one of our challenges. A poignant question might be “what wouldn’t have happened if the lab didn’t exist?” …
- [The] Spinoffs [Generated]
Many of our labs have created new labs that focus on other themes or challenges, using a similar methodology. …
- [The] Innovations and Innovators [Cultivates and Supported]
A third level of impact comes in the form of developing new solutions, policies, technologies, business models and products (the innovations), and through building the capacity of change agents (the innovators). The involvement of change agents in our programs can range from intensive retreats and long-term incubation projects to participation in a one-day event … This aspect of tracking participants is a real struggle, as resources are scarce and capturing the impact of the many participants who pass through all of our programs is impossible.
- [Emerging] New Narratives
A fourth level of impact created by labs is the cultivation of new meta-narratives—the stories through which we understand society and detect opportunities for change. We act as hubs in networks of changemakers and emerging innovations, and from that unique position we can see the new stories emerging in between seemingly diverse projects and ideas.
We’re all experimenting with [finding new] ways to keep track of our impact. …
Our labs each occupy a specific position in between the old and the new, between massive challenges and emerging alternatives. These alternatives are sometimes small, sometimes even seemingly irrelevant, yet are impossible to ignore, especially in the long term. Stuck systems produce various kinds of urgencies, and one of the most prominent ones is the constant impulse to grow. … The key challenge here is to find a way to grow our impact without becoming the same rigid system we’re trying to transform. Can we work at scale and still be nimble? Or does scale imply compromise?
What our labs seem to have in common is a search to find new ways of spreading, replicating and diffusing. … We open-source our processes so others can use them, build on them, adjust them to their own contexts, and drastically improve them. …