Inflection Point

Well, the election is over. You could say that we’re stepping into “interesting times.” I used to work with a CEO in the corporate world who talked a lot about needing to prepare for “right-angle turns” — changes in the environment where you simply couldn’t see around the corner, but you still needed to be prepared to act and adjust, act and adjust. That pretty much sums up, I think, what’s probably in store for us in the year ahead, regardless of your politics. The one thing you can predict is that what worked last year may not work now. It’s a time to re-think assumptions.

From a learning perspective, this change in the national political landscape provides a significant opportunity, if not a mandate, to bump up your game. That’s where the field of Emergent Learning really shines. Emergent Learning is literally about learning that emerges from tackling the real-world challenges in your day-to-day work — how to make thinking visible to each other and test it out, then bring what you learn back to your colleagues to help refine your shared thinking, and so on. It’s especially valuable when you can’t take your assumptions for granted.

My partners and I have spent a lot of years of research and practice in environments full of right-angle turns, in both the corporate sector and the social sector. We’ve also been long-time students of research into how complex systems adapt. The confluence of these has led us to advocate that, if you really want to adapt as quickly as possible, you need two things:  1) really strong line of sight, so that everyone knows what success would look like, and, at the same time — and this is important — 2) to make sure that everyone has as much freedom to experiment as humanly possible. It’s not a time for putting all of your eggs into a single strategy. Times like these in particular call on the wisdom of all of us — to stay the course in terms of your big goals and, at the same time, to be very deliberate about experimenting with new moves.

This also means is that it’s not a time to defer to a single source of expertise. In a complex environment, no one person holds enough perspective to be able to come up with a complete solution on their own. It really does “take a village.” At 4QP, we talk about the importance of seeing each other as ‘experts in equal measure.’ That it is about all of us, ideally including your grantees and partners, learning our way through these potentially challenging times together. This is hard to do, but essential in an unpredictable environment like the one we are facing in 2017.

It also means that our normal cycles for strategy, for grantmaking, for evaluation, are far too long to be useful in complex environments. I wrote a research report a few years ago, “A Compass in the Woods” that described this problem. Like ants looking for food, we need to be rubbing our antennae together a lot more often.

May you succeed in turning the unknowns that lie ahead into truly transformative opportunities to make a difference in the lives of the people you serve.

As we launch our new sponsored research project, Exploring Emergence in Complex Social Change Initiatives, we realize that the field of work on which it is based might be new to many of our colleagues. While much has been written about the field of Complex Adaptive Systems, much of it is written for scientists and often incomprehensible to non-scientists. Here is our attempt to define this important work in layman’s terms.

A complex system consists of many, diverse parts, all of which interact with each other and, in so doing, create patterns that are more sophisticated than any one part operating on its own. Human languages are capable of an endless variety of meaningful communication, using a fairly small set of letters and punctuation marks, with some rules about how to combine them into words and how to combine words into sentences. Snowflakes are formed in beautiful patterns, all of which are made through the random interaction of ice crystals. Both snowflakes and human communication are endlessly varied, without having to be consciously designed in advance. Human beings are also examples of complex systems. We are composed of many, diverse cells, each of which have limited capabilities. But through the cells’ many interactions, the behavior we are capable of is endlessly rich and complex. If our cells did not interact, if ice crystals did not adhere to each other, this quality of rich behavior would be impossible. These richer patterns of behavior are said to “emerge” from these many random interactions.

As a system, we are also adaptive. Unlike a snowflake, our collection of cells is capable of having a goal — survival, reproduction, comfort, wealth — and to adapt to feedback from our environment in order to achieve it. The same can be said for our immune system or an ant colony. Collectively, through the constant interaction of individual entities, or “agents,” as they are called by complexity scientists, the larger system of which they are a part, is capable of responding to our environments in ways that take us closer to a goal than any individual agent would be capable of on its own.

A complex adaptive system is non-linear. It can be distinguished from a machine, which exists because it had a designer who could predict in advance how a particular combination of components would operate together to produce a specific behavior. Complex adaptive systems are not predictable in the same way. Researchers at the Santa Fe Institute, an important center for complexity science, have done fascinating research using complex adaptive systems as a frame, for example, to understand how cities and economies behave.

As we describe in the announcement to our research on Emergence, the social sector has begun to understand that the systems it hopes to impact are also highly complex, which suggests that we need to think differently about what it takes to achieve the kinds of impacts we aspire to create.

For example, complexity scientists emphasize that agents in a complex adaptive system behave according to simple rules and it is through the simplicity of those rules that rich patterns of behavior emerge. A frequently cited example is how birds flock. The rules that generate that behavior are very simple and do not include identifying a leader, famously modeled by Craig Reynolds in his BOIDS simulation. The corollary to that, described by Stephen Johnson in his book Emergence, is especially relevant to philanthropists: “Emergent systems can grow unwieldy when their component parts become excessively complicated. Better to build a densely interconnected system with simple elements, and let the more sophisticated behavior trickle up.”

We anticipate that our research will help us understand better how to think about the process and benefits of emergence and help funders, grantees and other partners in the social sector understand what they can do to improve their impact as they work to achieve complex goals in complex systems.

For more information on Complex Adaptive Systems, we recommend Emergence by Steven Johnson as a comprehensible and enjoyable description of how this theory can be applied to understanding everything from ants to brains to cities to software design. A Hidden Order by John Holland gives an in-depth description of the principles of complex adaptive systems theory.

I am just back from the 2015 Center for Effective Philanthropy (CEP) Conference, where Phil Buchanan invited Patti Patrizi and me to bring together — for the first time — our many collective years of thinking and doing work in the realm of Emergent Strategy and Emergent Learning.

Patti talked about the great article she and colleagues Liz Thompson, Julia Coffman and Tanya Beer wrote for Foundation Review in 2013, “Eyes Wide Open: Learning as Strategy Under Conditions of Complexity and Uncertainty.” She talked about how, in complex environments, conventional strategic processes first developed for more predictable environments lead to common “traps.” Philanthropic leaders search for certainty through simple, linear logic models; become overly dependent on measures that are developed too quickly to actually measure anything of importance; and, perhaps as a result, “outsource” learning to experts like us, at the expense of respecting their own good thinking.

She and I agree that, in complex and evolving environments, as she described it, “strategy cannot be engineered a priori” because no one has the crystal ball it would take to completely predict a future that is yet to unfold in complex ways; and, finally, that it requires trial and error by a whole community — i.e., an emergent strategy — to achieve real social impact. She shared the great story of Robert Wood Johnson Foundation and their effort to address end-of-life care. After investing in one big bet and visibly failing, they humbly acknowledged that they were going to need to learn their way to a solution. After lots of smaller experiments, including some stops and starts, and through collaborations with other partners like Open Society Foundations, they ultimately helped spur and grow the now successful field of palliative care — a result that no one could have engineered in advance.

As we were putting our talk together, I was reminded of my favorite example of emergence (in the complexity science sense). I love this example because it is a great big experiment in emergence that all of us not only know about but in which we are all active participants.

This is how I described it in our session:

“Steve Jobs was brilliant, but if the iPhone could only ever do those things that Jobs and his team thought up themselves, it would not be the powerful tool we use today. In essence, they designed a platform, which created a whole new field of play, which is itself emerging. Because all of these designers are learning simultaneously how to design for it and users are learning how to interact with it, our collective ecosystem is making it possible for designers to create even more innovative apps that no one could have thought of, much less being capable of designing or using, even two years ago. And no one person can predict how we will use them two years from now.”

Imagine creating that kind of social impact from your investments.

For readers who are familiar with the tools of Emergent Learning, you will know that we talk about them as a “platform.” This is intentional. Just as how Apple designed the iPhone created a “field of play” that resulted in the emergence of powerful new apps, we intend for the tools of Emergent Learning to help foundations set the stage for the emergence of powerful new solutions to complex social problems.

In our session, Patti and I challenged foundations to have the courage and humility to shift how they approach their initiatives. We challenged them to shift from funding, say, “Education and Displacement” initiatives with complex frameworks and expert models they expect grantees to implement, to asking a Framing Question like, “What will it take to ensure that displaced youth living in refugee camps achieve the same level of education as their peers?” A question like this invites to the table the collective wisdom and experience of everyone in the system; it gives everyone the latitude to experiment with solutions simultaneously — like the community of mobile app developers — to see what rises to the top and becomes the foundation of true social impact.

— Marilyn Darling

2013 was a stand-out year for us at 4QP.

Our first annual Emergent Learning Community of Practitioners completes their year-long Certification Program later this month. As we predicted, their experiences and perspective have expanded our own understanding and practice of the craft.

Our rich conversations over the year reminded us of the theory in which this work is rooted. We had the opportunity this fall at the Systems Thinking in Action Conference to talk about one of those roots: Complex Adaptive Systems theory.

We often say that there are two ways to think about the “Emergent” in Emergent Learning:

Most immediately, Emergent Learning is learning that emerges from the work itself, in the course of doing the work, with as little overhead as possible, so that groups of people accelerate their results together by being deliberate about testing their thinking in real time.

On a more macro level, complex behavior (think “mastery”) emerges from lots of individual interactions among “agents” (think “people”). We talk about “rubbing our stories together.” These interactions generate rules that make it possible to interact in more catalytic and sophisticated ways to achieve results in very dynamic environments.

We owe a debt of gratitude to John Holland, a pioneer in the field of Complex Adaptive Systems theory, for validating our intuitions about why and how this happens, and what it takes to adapt as quickly as what’s called for by the environments in which we operate. (We recommend his book, A Hidden Order, for its description of how this process works.)

You can see us describe Holland’s ideas and link them to Emergent Learning and its principles and tools in this video from the STIA conference.

Our wish for you is that 2014 brings you many wonderful opportunities to learn something new and empowering!

-Marilyn, Heidi and Jillaine

If you work with us for more than 10 minutes, you know that we are big fans of action hypotheses – those “if/then” statements about what we expect to happen if an action is taken.

I see them everywhere. Or, to be more accurate, I see mostly half of a hypothesis everywhere. In a recent Boston Globe editorial (9/5/13), Harvard Economist Edward Glaeser observes that “Crime rates fall when there are more police officers. Boosting the number of cops in neighborhoods helps ensure that crime stops long before an arrest.” You may agree or disagree, but by putting out his argument so clearly, the reader is invited to do just that – to walk around in the idea and try it on for size.

But I also see hypotheses in strategies and action plans; in outcome statements and theories of change and logic models and “lessons learned.” And that’s where I see half-hypotheses most often:  “Ensure that all decisions are data-driven.” Sounds good, but why? “To be successful, initiatives must establish a high level of community engagement.” Successful in what way? “We need to strive for equality.” What would that look like and what is it going to take to get there?

Half-hypotheses like these can cause a lot of grief for people trying to achieve big, complex change goals in environments with lots of moving parts. People can think they agree about what “high community engagement” looks like or what it’s supposed to achieve, but really be working from very different playbooks. Half-hypotheses can shift the definition of “success” to be about completing a task, rather than achieving an outcome. Half-hypotheses can result in the over-institutionalization of “best practices” (e.g., data-driven decision-making). The “then” is simply assumed to be good in all situations. These assumed best practices can take lots of time to implement and can sometimes make it difficult to explore outside of the boundaries into creative territory.

In the world of action – where we do something because we expect a result, hypotheses are a fundamental building block of our thinking process. We couldn’t operate without them. You can argue about whether something is a mid-term outcome or a short-term result; an input or an output; a vision or a mission, a strategy or a tactic. But to us, it’s all hypotheses all the way down. If you look at the world that way, then learning how to use hypotheses well is a very simple and elegant way to improve your ability to think strategically and take effective action – especially when it involves working as a team with other people or organizations.

So the next time you hear a “we must…” or “we need to…,” ask what that will help us accomplish (to get at the “then” part of the hypothesis). If you hear people asking you to get behind a big, audacious goal, ask what it will take for us to get there (to get at the “if”).

People solving social problems in innovative ways naturally want to know that their time and grantmaking dollars are making a difference and that their efforts moving forward will produce an even better payoff. Hence the investment in evaluation. But social innovation and evaluation can be uncomfortable bedfellows. Traditional evaluation fails social innovation on three counts:

1. It evaluates results against outcomes and indicators established at the beginning of the initiative. But the pace inherent in innovation requires that people adjust their thinking as the work unfolds. When this happens, the initiative and the evaluation can diverge, making activities related to the evaluation feel out of sync and constraining.
2. Because evaluation reports are typically written for audiences external to/separate from the actual work, it insists on a high level of fidelity of data. As a result, such reporting too often excludes important but non-linear, non-triangulated outlier results that the people doing the work need to consider as they adjust and improve. Reports then risk being “white washed” and too generalized to be of much value.
3. Finally, the traditional cycle of annual or bi-annual reporting is too slow to inform the dynamic environments in which innovative change agents operate. Reports come too late to help track and test thinking at defining moments when it would be most useful.

What’s the alternative? We and others have been working on this problem. The field of Developmental Evaluation is devoted to re-thinking evaluation in complex and dynamic social change initiatives. But we at 4QP have been thinking about this question from a completely different context that we believe has some lessons for the field.

Several years ago, 4QP partner Marilyn and her former colleague Charles Parry had the opportunity to study an urban police department’s adoption of New York’s CompStat model. CompStat relies on very simple trend data regarding a bucket of crimes –burglaries, car thefts, aggravated assaults. Every three months, each district leader would discuss their district’s trend data with peers and the commissioner. If burglaries were up, they were expected to do their best to understand why and talk about what they planned to do to address the problem. They knew that, a few meetings later, they’d be in front of their peers again and they wanted to be able to demonstrate that their thinking and actions succeeded in improving the trend line.

Meanwhile, their peers were free, when the need arose, to “steal” and refine these innovations to improve trend data in their own districts. This impressive, self-reinforcing platform for learning as an institution made room for humility and curiosity, even in the face of accountability and competition. (An unexpected result included requests by beat cops for better data and analysis tools.)

This story illustrates how people on the ground can strengthen their capacity to produce results by reflecting deliberately on very simple and frequent data reporting, which both stimulates and captures outlier innovations. It helps them strengthen their thinking and, therefore, their capacity to produce results in the future, even as their environments change.

Our big takeaway? Evaluation of social innovation should focus not just on accountability for results, but for surfacing and testing the thinking that produced results. It is that capacity to think through how to achieve outcomes in complex and dynamic situations that will ensure greater payoff in the future.

This fast-cycle learning is what we aim to support with Emergent Learning. It is not easy. But when everyone around you is doing this kind of quick, fit-for-purpose reflection on results, innovation starts to become “just how we do our work here.”

4QP and Tanya Beer of the Center for Evaluation Innovation will be co-facilitating a discussion of this topic at this fall’s American Evaluation Association conference. Please join us.

I haven’t been to the JFK Presidential Library in Boston in a long time. I was inspired to make a visit recently to see a special exhibit, “To the Brink: JFK and the Cuban Missile Crisis.” I’ve always been intrigued by what the key players were thinking during one of the closest calls in our nation’s history.

I had read a Boston Globe article this fall about the Cuban Missile Crisis that had me thinking: Is it more important to make sure that the lessons we learn are the “right” lessons, or is it more important that we put to use whatever lessons we have been able to gather?

In this Boston Globe article (10/21/12), Jordan Michael Smith described how JFK formulated his response to the Cuban Missile Crisis by recalling, and insisting that all of his Navy officers read, The Guns of August. The book described how WWI came to pass even though no one wanted a war. “Every country on the continent miscalculated, underestimating the economic and military costs of a potential war, the likelihood of one breaking out, the possibility of a single event spiraling out of control, and their opponents’ willingness to fight.”

Kennedy used that lesson to reign in his Joint Chiefs of Staff who recommended a full scale attack and invasion of Cuba. History tells us that it was the right decision. But it turns out that that lesson was wrong. Subsequent research has shown that Germany did, indeed, want the war to happen. As Smith observes, “past events are so complex and so specific to their contexts that they don’t necessarily yield a single correct lesson.” He goes on to draw the conclusion that “the value of history to leaders depends more on who applies it than on how well they really grasp the past.”

This story reinforces at least a couple of important principles we hold dear in Emergent Learning:

1. It’s hard to learn good lessons post-facto from big, complex events or pieces of work. Everyone comes to their own conclusions, based on their perspective and biases.
2. Lessons get learned when they get used, not when they get written down.

Our clients often ask “How important is it to conduct an After Action Review immediately, while the experience is fresh?” The obvious answer is “the sooner the better.” But our answer is more nuanced: If you will only give yourselves an hour or two to engage in learning from the past to improve future performance, we would prefer that you spend that time reflecting just before the next piece of action than after the last one – especially if there will be a long time gap in between.

We have clients who tell us about running what seems like the same post-mortem conversation with teams year after year. They bemoan the fact that their organizations don’t seem to be able to break through and learn the lessons that have been so clearly identified.

Ultimately, more and better learning happens by applying what may be sketchy recollections of past events than by compiling a really complete and accurate analysis and report that sits on the shelf and never gets used. Even if the lesson is wrong or the story is off the mark, as Smith suggests about The Guns of August, it leads you to ask a question you may not have thought of otherwise and to see and consider an idea that may not have otherwise been on the table.

Entrepreneurs are in the opportunity business. They like nothing more than to find a problem no one else has been able to solve and to create a business by being the first one to solve it. They are usually not satisfied to solve a run-of-the-mill problem. They are looking for real opportunities to create a breakthrough…the next “killer app.” Where other people might see the status quo as an immovable object, they see it as a platform. From that perch, instead of seeing a hundred reasons why something won’t work, all they see are opportunities to make something different happen.

People aiming to create social change can benefit from more of this kind of entrepreneurial vision. Emergent Learning is, by definition, very opportunistic, in the sense that it asks you to very deliberately use your calendar and your to-do list to create opportunities to try something new. In that sense, Emergent Learning is entrepreneurial. It helps “social entrepreneurs” think outside of their current assumptions about what’s possible.

In Emergent Learning, one of the first questions we ask is this: What one challenge or problem, if you were able to tackle it to the ground, would make the biggest contribution to achieving your goals this year? And the next question we ask is this: What are the opportunities in your calendar where you could try out new solutions?

One of our big critiques of so many designed “learning” activities is that they have no connection to opportunities. They are out-of-step with what’s on people’s plates. In the world of Emergent Learning, no opportunities = no learning.

The research of Saras Sarasvathy about what makes entrepreneurs entrepreneurial points to four basic principles of entrepreneurship, which were described in Just Start (Harvard Business Review Press, 2012). In our own words, they are:

1. Start with the means at hand. Don’t wait until the conditions are right (because they will never be right.)
2. Calculate the acceptable loss. What will it take to play the game and how can risk be syndicated?
3. Bring others along. Get good at sharing the goal in a way that moves people to support it.
4. Build on what you find. Embrace everything that happens as another doorway to success. And look for abundant and rapid feedback to inform your next step as the work unfolds.

In Emergent Learning, the real opportunity of opportunities is that they start with the means at hand – stuff in your calendar that is going to happen anyhow, and create “affordable loss” opportunities to test ideas incrementally in rapid cycle practice fields that involve important partners and stakeholders.

Unlike entrepreneurs, large organizations have a pernicious tendency to teach people to think that a problem can’t be solved until the conditions are right; to look outside of themselves for solutions to complex problems– to shift the burden to a hero (“expert”) to come in and propose what are often big, often unnecessarily bureaucratic solutions.

What we have found is that if a group of people can begin to see that it might actually be possible to solve a big, “unsolvable” problem and they band together around a compelling goal, then, like the entrepreneur, they may suddenly find themselves standing on a platform and all they see from that point forward is opportunities to make that happen.

So why are we called Fourth Quadrant Partners? If you look at an Emergent Learning Table, you will see that the name of the fourth quadrant is “Opportunities.” That’s where good ideas get tested out… “where learning turns into results.”

Great question! I have been working with the World Bank since last October on this question. I gave a presentation on “Lessons (Not Yet) Learned” to a not-so-eager World Bank audience on the topic back then. You can watch the talk at:

http://worldbankva.adobeconnect.com/p3lrvdho1hr/

That month, the new president of the Bank, Jim Yong Kim, made a call for the international development community to get better at learning from failure:

“Historically, development institutions have been reluctant to acknowledge failures, much less analyze them publicly. How can we avoid repeating past mistakes, if we don’t talk about those mistakes openly? I want the World Bank Group to set an example for the development community.”  — Jim Yong Kim, 10/9/12, World Knowledge Forum, Seoul, Korea

Following that, I moderated an internationally webcast session for President Kim on the topic in December. I have worked on several events since then, including an EL Table “deep dive” conversation to dig into one of several common cause of project failures and identify solutions on-the-ground and for the institution.

This month, an internal World Bank Community of Practice on “Knowledge Hubs” is hosting a month-long conversation on the subject. They asked me to contribute a blog about what it takes for huge institutions to learn from failure. I share that posting here with their approval and encouragement:

This may seem like a controversial example, but I thought it would be a useful conversation-starter to share a story from my first-hand research on an organization that has been through the kind of transformation that some large international development institutions hope to make.

I once befriended a Lt Colonel in the US Army. He was a smart guy. He had grown up in the Army and seen it transform from a “yes sir, how high sir” institution to one that, by and large, valued the humility it takes to learn from failure. One impetus for this shift was the Vietnam War and the Army’s eagerness not to have to learn that lesson ever again.

What I learned from my friend was quite remarkable. He was a cavalry officer. One of his favorite sayings was this: “If you give me a soldier who comes to me with a stellar record, but keeps riding the same horse he came in on, I’m going to see that his career goes nowhere. But if you give me a young soldier who is completely green but is willing to learn from his mistakes and I see the learning curve going upwards, I’m going to make sure he gets promoted.”

That sentiment was not unique to my friend. While there are still lots of places in that huge institution where “no news is good news,” the cultural norm has changed to one where talking about failure is not a career-ender. But it’s not just a soldier’s willingness to talk about failure that earns him or her credit – it’s his or her ability to learn from that failure that earns points. The Army’s After Action Review process, which got started back in the 1980’s, trained soldiers to be able to say, with humility, “Here is what I did today that I think was successful and here’s why. Here is what I did today that I think was a failure and here is what I learned from it and what I’m going to do differently tomorrow.”

We conducted research on the evolution of the After Action Review (see “Learning in the Thick of it,” Harvard Business Review, July/Aug 2005), the primary tool the US Army uses to learn from experience (both failures and successes). We sat down with three 20-year-old soldiers and asked them, “What if the Army told you to stop doing After Action Reviews?” Their response was immediate: “That would be the stupidest decision the Army ever made.”

How did this huge institution transform? It did not take place over night. And as we all know, like with any large institution, it is not a uniform success. Importantly, it has also taken the retirement of generations of leaders to transform as an institution.

The transformation took hold because units of soldiers on the ground started to perform to a standard that was head and shoulders above their peers, not because senior leaders suddenly changed their own behavior.

In a sense, what made the transformation possible was the message that came from leaders that “we need to change and you need to help us do it.” Even if those in command at the time didn’t really understand what they were asking for, they knew well enough to ask for it.

On the other end of the command chain, those soldiers in the field knew they needed to change, for the sake of the soldiers to their left and right. Lives were at stake, and they could not afford to take the stance that “I’ll do it when I see my boss doing it.”

So that’s my very simple answer to a very big question: Huge institutions don’t as a rule transform because they get driven from the top down or because senior leaders suddenly, magically, change their behavior. They transform because brave people inside of the institution see that they could do better and that they must do better, and are willing to take a risk to experiment with something different. And the people they report to are willing to let them try something new, even if they don’t quite understand what new looks like.

We would be very interested in hearing from others who have seen similar kinds of transformations in other large institutions.

Human beings are incredibly smart and resourceful.

Every single day, we use language to construct sentences we’ve never uttered before.

Many of us drive heavy machinery at 60 miles per hour for years in rivers of other human beings in heavy machinery, without accident.

We are capable of mastering golf and chess and dancing.

We navigate from place to place; we master cooking; we build careers.

We learn how to raise our children a day at a time.

We do it by experimenting over and over until we have a deep understanding of the lay of the land (the road, the golf course) and the tools of the trade (the kitchen, the chess pieces) and how to use them in every conceivable situation. We learn some of these things from the pros, but we also learn new solutions by these experiments – things we never before imagined or were taught.

The field of Emergent Learning sprouted from an observation made by Marilyn Darling many years ago: While we each have a remarkable capacity to learn as individuals, when we try to learn together, the quality is often radically different. Especially when people are working with different agendas, different personal histories and different information, she observed that the result is sometimes not much better than what her cat learns: “I got burned on the stove, so I’m never going back into the kitchen.”

But we do sometimes break through. Maybe it’s a courageous leader who is willing to name what the Emperor is wearing. Maybe the pain of “learning the same lesson over and over” reaches a threshold, or something else just makes the stars align, but people sometimes experience an inflection point where the horizon opens up and they feel free to voice new questions and contrary ideas. A few fortunate groups find ways to sustain that new level of learning. The feeling that comes with that sea change is exhilarating.

The field of Emergent Learning has evolved out of a very simple but infinitely challenging question: What does it take to create those inflection points – where the quality of what we are capable of learning together around the things that really matter to us matches the quality of mastery we are able to reach as individuals? What does it take to sustain learning at that level? Emergent Learning is about what it takes to learn from our work, in the course of doing it; about what it takes to create a heartbeat of insight that increases our capacity to produce the results we aspire to in often very complex and changeable environments.

In Inflection Point, we want to explore what creates these qualitative shifts in what’s possible. We will share what we have learned in our work and research and new questions that are emerging. We invite those of you who have experienced an inflection point to share your stories.

Please join us.

Heidi, Jillaine, Marilyn