Traditionally, a strategy is about ways, means, and ends. In operational planning mode, this means detailing the paths, tools, and outcomes, as in a logic model.
A real strategy provides more than this. It realizes a framework, not just for describing, but also dynamically coordinating ways, means, and ends, and providing guidance when the context of your effort changes-not dictating ways, means, and ends as a result of being somehow able to predict the future. After all, the larger world is always changing, and when it does, if we are faithful to our values and our hopes, we should support our change goals by shifting the relationship between ways, means, and ends.
Dynamic coordination means that you can change anything arising from your strategy at any time if the context changes (which it always will). Operational plans only allow you to tweak a way, a means, or an end, not fundamentally alter their relationships.
I’m going to take you through 2 genuine strategies. These two strategies were actually used in the real world, and they have all the complexity and depth that we should expect from our change strategies, even if the scope of our strategy will probably be much narrower than these. I will discuss one below and then describe the other in the next post.
The Allied Strategy in World War II:
You may have heard the phrase “unconditional surrender” as a description of the Allied strategy against the Axis in WWII. This was not just a slogan to mobilize citizen support. It was a strategy that grew out of the failure of the “negotiated settlement” that had ended WWI. In fact, the belief was that the settlement had led directly to the rise of fascism in Germany, and contributed to the rise of fascism in Japan and Italy.
Unconditional Surrender had profound consequences for both the prosecution of the war and its impact on all the participants. A reasonable guess would be that twice as many people died as would have if negotiation short of unconditional surrender had been a possibility. Ditto for infrastructure destruction, the number of people who acquired life-long disabilities, and the dramatic shrinking of social capacity that occurred in the Axis states. It is unclear whether the Holocaust would have been less humanly destructive, or even more so, had a settlement been allowed in, say, 1943. The industrialization of the US and the Soviet Union were dramatically accelerated by this strategy of Unconditional Surrender, leading directly to the Cold War. The race for nuclear weapons would have been slowed had the war ended in 1943. At the same time, it is possible that Germany would have acquired nuclear weapon technology had the war been foreshortened by a settlement.
My point is that this choice of a strategy has had real, concrete consequences down through post-war history to this day.
In the first edition of Planet Medicine, by Richard Grossinger, he describes a tribal method to begin the hunt for caribou which has a fascinating connection to our modern approaches to building and maintaining consensus in the creation and implementation of complex planning (and also complex change).
The short overview of the ritual is that the shoulder blade of a previously hunted caribou is heated over a fire until cracks appear in it. The bone is oriented to map the hunting territory of the tribe, and the cracks are interpreted as the paths of caribou herds. A tribal decision about where to start the hunt is made on the basis of the information on the bone.
The ritual practice, in one form or another, has been used by many different communities. The question that Grossinger raised was why the practice survived when there is no correlation between the map and the actual location of herds of caribou in the hunting territory.
He offered two kinds of possibilities:
The ritual solved the problem of how to get started quickly on the hunt when the actual location of the herds couldn’t be determined. In this environment of significant uncertainty, it was better for the tribe to start hunting right away and gradually discover where the herds were, rather than wasting time arguing among themselves about where to start. Certainly anyone who has been in a modern meeting trying to make a decision about a problem without enough information to do so will sympathize with this very effective way of producing consensus about doing something. Perhaps the most significant deficit of our modern approach to dealing with large uncertainties is the way social, political, and financial conflicts and their negotiated resolution eliminate any useful consideration of inherent uncertainty.
The second possibility was more subtle. Because the effect of the ritual was to have the tribe start their hunt in a more or less random place in their hunting territory, it was not possible for the caribou herds to evolve a defense against the hunting plans used by the tribe. The relationship between the tribe and its food source was kept stable by the ritual, adding a measure of constraint to a truly uncertain task.
Change advocacy as a social action framework is more like hunting caribou than it is like designing and building a nuclear power plant. There is significant uncertainty in creating and implementing change in Snowden’s “complex systems”, and when humans try to manifest that change, they will, by the very nature of what they are trying to do, engage in the social construction of some fiction to begin their change work. They will use shared values and their common commitment to outcomes consistent with those values to begin engaging with the target of their effort, learning as they go, evolving a plan of change through action, and, hopefully, achieving the change they desire by adapting their action to fit the constraints and ongoing evolution of the target.
We need to accept the uncertainty of what we do and the complex context within which that uncertainty lives if we expect to enhance our change effectiveness. In an environment of uncertainty, detailed pre-action planning wishes uncertainty away through the apparent detail and concreteness of the plan, creating the arrogance of the planners of Fukushima, who thought they could afford financial and political compromise in the creation of their fictional starting consensus. Better to learn while on the path of change, than to assume that everything important is already known.
These last two posts have looked at the role of uncertainty in justifying an open, one step at a time, evolutionary approach to change. I have tried to emphasize this difference because of the astounding dominance of detailed operational planning as a method to secure a predictable change in our global culture. As useful as such planning is in creating complicated artifacts (like nuclear power plants), the failure of typical operational planning to include the lessons of an evolving environment brings immense danger with it. There is no greater danger than the assumption that because our social focus should be on the complicated artifact we are trying to create, the only relevant considerations should be the negotiation necessary to secure the beginning of the project. Social, political, and financial conflicts become the only “problems to be solved”. Everything else is in the detail of the plan.
Because of the dominance of operational planning as a substitute for a real strategy, I am going to take one more crack at framing the differences between this global convention and evolutionary change action, by discussing one of the oldest distinctions in systems theory: The difference between Open and Closed systems.
Today, we use the word strategy to describe any planned action model to achieve outcomes. But historically strategy served a different purpose than simply describing an action plan.
A strategy is a change framework designed to deal with two realities:
The uncertainty of the future
The scarcity of available resources (including funding, staff, skill sets, networking, values, etc.).
An operational plan, on the other hand, by its nature is supposed to draw boundaries and eliminate uncertainty by detailing the specific action path to outcomes before the project starts, and by applying a budgetary limit for the achievement of those outcomes.
In this first post, I’m going to talk about the difference between Risk and Uncertainty, since uncertainty is part of the reason why we need a strategy.
Risk and Uncertainty are not the same, though we tend to think that they are synonyms.
Risk is statistically calculable-which is to say, we can assign a probability to the risk because we have some source of data that allows us to estimate how likely the event is. The proper response to this risk probability is to mitigate risk, to take steps that will allow us to react quickly to reduce the impact of the event that we are worried about.
We can’t assign a probability to real uncertainty though it is common for organizations to define uncertainty as risk, so it will be seen as manageable and requiring no particular change of current behavior. If we confuse uncertainty with risk, we will end up imagining events (since we are uncertain as to what will actually happen), assigning them a fictitious probability, and developing fictitious mitigation strategies.
The perfect example of this was the Fukushima nuclear plant meltdown. In the planning for building the power plant, the corporation’s design system had discussions of how to quantify the likelihood of an earthquake that would threaten the integrity of the plant’s operation. These discussions were only incidentally about the actual risk. They were mostly about the initial costs of building the plant to mitigate the results of an earthquake since that structural mitigation would dramatically affect the cost of building the plant and the potential profit from its operation.
You can easily imagine ongoing arguments between engineers and financial planners about the right investment in earthquake mitigation. What they settled on was a model that made the construction of the plant able to withstand an earthquake that was 10 times stronger than any earthquake in Japan’s history (that is, experiential history, written history, narrative history, since there was no way to access historical evidence of earthquakes back beyond these sources of data).
Of course, the actual earthquake was several orders of magnitude greater than any earthquake in that historical record and resulted in the complete loss of plant integrity and an ongoing destruction of the area around the plant and in earth’s oceans that will continue at some level for longer than anyone currently alive will be around to see it.
Obviously, the planners for this nuclear plant had no idea what the actual risk of this event was. They concocted a model of the “risk” that would allow them to build the plant, satisfy state regulators and provide predictable financial projections to the organization and its investors. The model was entirely wrong but was financially, socially, and politically useful.
While nuclear meltdown is seldom a possibility in our change efforts, we often follow exactly the same line of reasoning that the engineers and financial planners did for the Fukushima plant.
The answer to uncertainty is not an operational approach to a mitigating a fictitious risk, but the development of a real strategy.
Next Post, Part 2: Fictitious approaches to uncertainty as social glue.
I discussed in my last post the reality that most real ongoing advocacy involves dealing with target systems in the complex realm. This means that there won’t be straightforward expert based advocacy solutions that can be easily implemented through typical logic model plans. Instead, we must find our way a bit at a time, trying change ideas, seeing what they do, and building on the efforts that produce some successful change. The description of an advocacy effort directed toward a target is more like an evolutionary process than it is what we think of as planned change. Like any evolutionary process, both the advocacy effort and the target are changing and responding throughout. Advocacy in complex systems is a learning process, not a plan execution.
I worked for Michigan Protection and Advocacy starting in 1981, and roughly half of my direct advocacy work was representing students and their families in special education issues. It was very interesting work, and the evolution of MPAS special education advocacy illustrates advocacy in complex systems.
When I first began advocating on behalf of students and families, it was very easy to win the negotiations with schools. Mostly, this was because the school districts considered special education a small part of their overall responsibilities, whereas we saw special education advocacy as one core service of what we did as advocates. We knew the laws and rules better than the schools, we had more practice at things like special education complaints, and we were expected to be well prepared when we went into the education planning meetings on behalf of a specific student, unlike the schools who were running (sometimes) hundreds of education planning meetings in a few weeks. So, we won most of the negotiations, even if they went to formal hearings. Also, the families we represented were very grateful, and we liked that. Finally, it was easy and quick for us to solve the problem ourselves without taking the time to teach the families how to advocate on their own behalf in future.
This worked well for the a few years. But at first slowly and then much more quickly, a number of things began to happen.
The first was that local school districts and ISDs began to hire attorneys for specific cases, and eventually, keep the attorneys on retainer. Especially when districts had attorneys on retainer, they used them in lots of situations, since they were paying them anyway. This meant that schools stopped making simple rules errors, and the cases themselves became more complex. In turn, this meant that advocacy required more research, more time, more layers of appeal, more meetings, and fewer victories.
The second thing that happened was that families told their friends in the special education community and we had a sizeable uptick in the number of requests for advocacy support. Also, since we had inadvertently encouraged dependence, we got the same families back year after year for representation in their education planning meetings. This produced a sharp spike each year in such requests, and, after a few years, we no longer had the time or resources to respond well to all the requests.
Which is to say, that we ran into a resource limit as a direct result of our success. This limit was not just some lack of funding. A great deal of skill is required to properly negotiate such cases successfully, and there simply weren’t enough experienced special education advocates to handle the demand. In fact, the Michigan Department Of Education estimated that 10% of the special education planning meetings each year were contentious (i.e., needed an advocate), and that amounted to ~20,000 or so meetings a year. Since a single advocate could only handle about 100 cases a year, that meant we need 200 full time advocates. That was never going to happen.
The organization had to respond to this evolutionary demand from the environment, and there were basically 3 alternatives. These 3 alternatives are roughly the same for any advocacy organization that faces a resource limit (an evolutionary challenge) in implementing their mission.
Keep Current Practice: Most organizations struggle to maintain what they see as the most important parts of their change advocacy, and to try to alter the forces in the environment that are requiring this strategic reconsideration. This approach almost never works. Even when it does, it is typically for a relatively short period. Instead, the organization becomes less capable over time, bureaucratizing its workflows in order to gain control over the demand. One of the telltale signs of this is a sharp rise in what the British refer to as “failure demand”-calls, letters and other contacts from clients asking for updates or responses where the response is that nothing has changed. Failure demand sucks up time that the organization already doesn’t have because of the resource squeeze.
Modify the Current Model: Various versions of constraining the actual response are made across mission related demand so that the organization can control demand. In the case I experienced, the core responses of individual advocacy and legal responses were kept more or less intact, but the percentage of cases considered for these two responses was reduced through the use of an issue priority system that was updated each year. These contacts were responded to by an organized Information and Referral process. I&R became the primary response for roughly 80-90% of the contacts, and the number of demand contacts has plateaued at many thousands.
New Way of Doing Business: The one discussed during the transition I experienced was to move to a community organizing model, in which organization staff would work as organizers training and providing technical assistance to families in local school districts, building up a more or less permanent cadre of special education advocates around the state.
As you can imagine, there are many potential variations of these three canonical responses to resource scarcity. There isn’t a “best” response. There is only the response that the advocacy organization chooses, which maps out the organization’s future for many years, and lays out a path of skill and capability (learning) that the organization will track until another evolutionary challenge comes along.
This is the way that dealing with complex advocacy always works. It has little in common with the pretty picture of a logic model implementation. It isn’t that plans are of no use. Rather the plans are executed inside this evolutionary environment in which the advocacy effort and the target both adapt.
Next Post: Some other ways of looking at advocacy in complex systems
In my last post, I discussed the right-hand side of the Cynefin Diagram, the Ordered Systems. Today, I’ll discuss the left-hand side of the diagram, the Unordered Systems.
We hear a lot about chaotic systems these days, but Chaos is always a short term event. Chaos produces Novel Actions, stuff we can’t predict, and our best response is to act according to our values (not our past practice), see what happens, and support the best of the novelty that appears. It is difficult to have an effective change strategy in a genuinely chaotic situation. But, engagement by your organization or group will let you learn what you can from the turbulence.
Complex systems are largely the place where advocacy groups plot and execute their change strategies. Most typical organizational planning is focused on the Complicated Realm, however, and we often try to make our interactions as part of complex systems change fit the logic and “predictability” of the complicated realm.
This is a mistake, and continuing to interpret the complex as complicated (and amenable to procedurally focused planning and execution models) is a strategic error, weakening our change strategies and constraining the possibilities for real change.
In this complex realm, your organization or group and your change target become part of a single system that is changing together or coevolving. The longer you pursue change in a particular target, the deeper the coevolutionary relationship becomes. Put simply, you change the target and the target changes you.
Part of the change in you involves the learning you acquire about your target, which in turn is largely a result of the responses that your target uses to blunt or counter the change strategy you are implementing. Ideally, your learning from this coevolutionary relationship improves the effectiveness of your change strategy for this effort and for future ones.
In advocacy organizations, people who have been around for a while and have created and experienced change efforts against a target will often recognize a pattern in a current change effort that has either appeared before or which is a variation of one they have worked on in the past. This recognition of a pattern doesn’t dictate a specific advocacy action but provides a guide for creating a customized response to the current circumstances of the advocacy effort. Interestingly, efforts (I have seen many) to identify such patterns and make them explicit so they can be shared with people who haven’t experienced them are largely failures. The pattern is best recognized and used in a real situation, and it is hard to pull out in the abstract (say for an advocacy manual); it is also hard for new advocates to grasp the importance of the pattern outside a real change situation. This is clearly different from the Complicated System where you can create a user’s manual for each component, and reuse already existing explicit information (from blueprints, procedures, experts, etc.) to solve a very specific problem.
In complex systems advocacy, you should try probing the target for its range of responses so that you can learn more about how it views your change effort, and how sophisticated its experience really is in the current engagement. Which is another way of saying that the success of your advocacy depends on your ability to learn from your current engagement with a specific target.
I know that the above is a mouthful. In my next post, I will use concrete examples from actual advocacy efforts to show how important this coevolution with our change target is, and better illustrate the weakness of a “complicated system, detailed planning, measurable outcomes” approach.
The right side of the Cynefin diagram represents ordered systems. Most of the time, even when a system is clearly unordered, we try to interpret it and change it as though it were ordered. I will go into more detail about how poorly such an approach to change can work in the next post.
The lower part of the diagram covers what Snowden calls simple systems. As a very simple example, consider a form that your organizations submits every month or every quarter because the form is required for you to maintain some employee benefit. Basically, filling in the form assures some agency of government that you are doing the same thing you were the last time you submitted the form, and the form, signed and sent, makes you liable if you aren’t doing things right.
You update each time if there is anything that is actually new. A more involved version of this same concept of a simple system would be tweaking a personnel policy because of a change in your organization or the rules regarding such policies. Most of the time, with a little effort, you can figure out what needs to be tweaked, make the changes, and be done with it.
Snowden describes these activities as “Best Practices”. There is a right way to perform these actions and once you figure it out, you can pretty readily keep doing it right. You identify the task (Sense), you connect what you Sense to a task you have more or less done before (Categorize), and you Respond with the appropriate output.
Much of day-to-day work for most people falls into this kind of system.
Systems are called complicated because they have lots of parts. Think of an airplane, like a 747, or a computer. When a complicated system is being designed, the relationships between the parts need to be rigidly constrained. We don’t want the wing of a 747 to suddenly decide that it needs to be somewhere other than remaining attached to the plane. Also, while the parts are optimized for being a part of the 747, they are not necessarily optimized for what they could be if they weren’t part of the 747. It is a bad idea to have one part that will last forever and another connected to it that fails with great regularity. In complicated systems, parts and their maintenance are often tailored to each other so that failure of the system as a whole won’t occur.
Much of the design of our organizations and change groups become complicated in this sense over time, as we get bigger and have more complicated constraints (laws, audit requirements, 3rd party contracts, etc.). These design changes are usually done because they can’t be avoided, and we tend to add the changes and make some adjustments in the rest of our organization or group to accommodate them. It is like trying to remodel the 747 while it is flying. Optimization doesn’t enter into our organizational results because we have to keep the doors open while we design and make the changes. After such changes, organizations usually go through a process of running into “pain points” that didn’t exist before the change. We muddle with these to make them work as well as we can, but some aspects of such change will show up in employee stories about the problems in the organization for years. Some will even outlive the employment of every person who participated in the original change.
If a change is particularly difficult (say, rewriting your retirement plan to make it compliant with a new Federal law), you might hire an expert to help plan and implement the change (say to draft a compliant plan which you can then tweak to fit your organization). Even though you end up with a plan, it had to be created part by part in the same way that you would design or build that 747.
This need for real design is the reason why “Analyze” replaces “Categorize”. The notion of problem solving rather than matching an exact solution is the reason why Snowden describes this quadrant as “Good Practice”. Nonetheless, understanding (making sense of) a complicated system can be achieved one piece at a time. Think of the metaphor of a “blueprint” (I’m old enough to remember when blueprints were blue). While you may not be able to get the whole of the 747 inside your head, you can go through the entirety of the plan by using the design documents and the narratives attached to them. Much of what we think of as funding proposals qualifies them as complicated systems. We are constructing them a piece at a time, and organizing them in a way that will show the proposal reviewers what we expect to happen and how we expect to make things happen. A blueprint. In fact, most strategic plans are also blueprints. This may seem obvious to you as you read that last sentence. But as I hope to show you in the next post, blueprints don’t work well with complex systems, and they don’t work period with chaotic ones.
Although we tend to think of advocacy as a kind of service, like other social services, it isn’t. The reasons why this is true have nothing to do with advocacy being “better” than other services. Rather, advocacy is different from standard services because it has a different relationship to the target of its actions than standard services. Along with this difference in target relationship, an advocacy effort uses a different nonstandard framework to make sense of a target and, for that matter, itself as an organization.
Sensemaking is a concept that developed in the 70’s, and it has gone through a variety of reformulations over the last few decades. For an advocacy organization, all the usual ways of making sense of their advocacy effort and their target certainly apply (identity, retrospection, and so on). But the relationship between an advocacy organization and the target of its change strategy requires a unique effort in sensemaking.
This is because the relationship between an advocacy organization and its change target is evolutionary. For typical social service systems, the relationship between the system and the person receiving services might well focus on changing the person, but the social organization expects to remain the same after the person has received services. Such organizations work very hard to stay the same after the delivery of services. In fact, this relentless focus on staying the same is one of the most criticized dimensions of working in social services.
I have looked for years to find a framework that respected this evolutionary relationship, and that allowed it to be understood in less abstract terms than a “coevolutionary change relationship”. I ran across the Cynefin Framework some time ago, and, although it is targeted at large enterprise and governmental initiatives, the model is a very useful one for understanding change efforts by small advocacy organizations, and informing our change efforts by making sense of targets. I am going to put together a few posts to introduce the model. This post will be an overview focused on the image above.
The first thing to recognize about what the diagram means is that it describes how we understand (make sense of) the target of our advocacy. That’s why the little area in the middle (disorder) is there. We often find ourselves facing an advocacy challenge on behalf of our constituents with no detailed understanding of how or why the particular challenge arose. In fact, initially, it is often unclear what the target of our change effort should be. We must “make sense” of the issue and potential targets to build a change strategy. The four categories that constitute the possible sensemaking, the meaning that the target has for us, with ordered systems on the right and disordered systems on the left. The red words are the actions we must take to build a useful understanding of each specific system type, and they are in the order we must take them.
I’ll give you two short videos, one very funny and the other a short, if challenging, introductory video to review before my next post:
All living systems age. Even organizations. This doesn’t mean that organizations die like human beings; but, there are lessons we can learn about how our organization evolves over time. In much the same way that our personal aging changes us irreversibly, so too does the passage of time alter the range and focus of our Mission 1 and Mission 2.
In the system theory framework, there are four general phases of “aging” in complex adaptive systems (including advocacy organizations):
Exploitation: For advocacy organizations, exploitation is a way of thinking about the universe of possibilities that exist when that organization first begins its advocacy work. It is an unfortunate fact that oppression and marginalization of devalued communities creates a very large context in which advocacy can take place. The choices that an advocacy organization makes when it begins to interact with this universe of advocacy possibilities are usually a mix of the worst problems the community faces and the immediate resources that the organization has. Regardless of what drives the choices, these initial decisions create the learning environment for the advocates. This learning environment can have a profound effect on the development of the organization and its advocacy. So, for our goals, exploitation is really learning.
Conservation: The most common way Mission 1 and Mission 2 interact is the use of M1 to generate resources that become the core of the organization’s strategy for Mission 2. In other words, the organization uses it’s core passion to generate resources to keep the organization going. “Resources” doesn’t just mean money. They include a governing board, reputation, social support, staff capabilities, and organizational infrastructure like equipment, fund-raising events, financial documents, and so on. Over time, what was done earlier in the development of resources constrains what can be done later. So Conservation doesn’t just mean saving. It also means the conservation of a path of organizational development. As useful as such an approach is, it reduces the flexibility of the advocacy organization.
Release: Release in organizational terms means the breakdown of the organization’s ability to pursue its core mission (M1). This can happen because the organization has become a bureaucracy and only pursues its own maintenance (M2), because it has altered its core mission to something that no longer inspires support, or because it has become corrupted and is only a tool or opportunity for its staff and stakeholders to exploit for their own individual ends. Whatever resources remain are “released” into the environment, and abandoned or picked up by other organizations.
Reorganization: The resources released during Phase 3 begin to reorganize themselves immediately. It is the reorganization that allows the possibility for exploitation or new learning.
In later posts, I will discuss each of these phases in greater detail. It is the case that advocacy strategy has to match not only the problems of a supported devalued community. That strategy has to fit the phase of the adaptive cycle in which the organization
Mission 1 (M1) is the core reason your advocacy group or organization exists. It is the purpose of your passion. Mission 2 (M2) is all the stuff we do to keep our group or organization going.
M1 and M2 do not necessarily complement one another or necessarily oppose one another. Their relationship is complex and changes over time, sometimes very quickly. There are a lot of reasons why their relationship is so volatile:
M2 decisions are common,essentially daily. M1 decisions are rarer, more consequential. So, over time M2 tends to overwhelm M1 even though at heart M1 is more important.
Managers are gradually socialized to privilege M2 issues over M1. When M1 and M2 oppose one another in a decision, there is a gradual inclination to favor M2 the longer a manager has worked for the group or organization. This is framed as “realism”, and often becomes the typical way managers judge all organization problems.
Boards, mostly because they have even more superficial relationships to decisions that pit M1 against M2 tend to develop the same attitude over time.
Staff are reinforced for a similar prioritization of M2 over M1 through sanction and punishment when they choose M1 over M2
Funders and regulators have all abstracted their concerns away from M1 because the M2 framework can be easily (if inappropriately) applied to any purpose. Thus, RFPs, reporting requirements, audits, and similar monitoring methods all deeply favor M2 over M1.
The cumulative effect of all these pressures is the gradual corruption of M1 over time, and a movement toward group or organizational survival as the primary fulcrum of decision making. Bureaucracies are large scale examples of the end point of this process.
Our commitment to creating and maintaining change for our community requires us to juggle two different, equally important missions, or purposes.
Mission 1 is the reason why our group or organization exists. Mission 2 is all the things we have to do to keep the group together or the organization’s doors open. We usually combine these two missions in our minds. We assume that if we do something to support one of the missions, it automatically supports the other.
In the back of our minds, though, we know this isn’t true. If our change work goes on long enough, eventually we will find ourselves sacrificing one mission for the other. Often, we believe that we have no choice. Commonly, we sacrifice our core mission (Mission 1) to keep the doors open (Mission 2) in one way or another. We may not want to do this, but it isn’t clear how we can serve both of our Mission Masters all the time.
It is true that Mission 1 and Mission 2 can pose opposite demands on us. But, the relationship between Mission 1 and Mission 2 is not as simple as that. Throughout an organization’s evolution, Mission 1 and Mission 2 pose unavoidable challenges. Sometimes the two missions reinforce one another, sometimes they interfere with one another, and sometimes (mostly) they pose complicated questions to us about the best way forward.
Some basic ideas:
A change effort that is solely focused on Mission 1 is like a firecracker. There is a lot of meaningful noise and then silence.
A change effort that is solely focused on Mission 2 is like a zombie. There is a lot of action and noise, but no meaning.
Mission 2 demands occur daily. Mission 1 demands are rarer. Since we tend to value what we do most often, Mission 2 will tend to dominate Mission 1 over time.
Hierarchical management values Mission 2 over Mission 1 both in terms of day-to-day work, and, as each new manager gradually becomes a member of management culture, in moral terms.
Boards almost always value Mission 2 over Mission 1 and view their fundamental purpose as maintaining and expanding Mission 2, with only a proforma commitment to maintaining and expanding Mission 1 9especially if that expansion of Mission 1 undermines Mission 2).
Government regulation is entirely focused on Mission 2, for all the IRS noise about educational and charitable purposes. As a former boss of mine once said, “They never get you for not doing your mission; they always get you for the money.”
Given all this, is it any wonder that organizations and groups gradually lose their focus on Mission 1?
I wish I could tell you that there is a simple way to manage the collisions of Mission 1 and Mission 2, but there isn’t. In fact, we must struggle to manage each collision as a singularly unique challenge to our commitments to our core purpose and keeping our doors open.