Guest blog written by Peter Harrington
Recently I have been engaging with a government in a middle-income country which is using PDIA methods to tackle some of their thorniest public problems. As one would expect there have been successes and failures, and lots of learning. But in addition to this, leaders in the government want to ‘embed’ PDIA permanently into the way their government works. This raises some interesting questions for PDIA – can the methodology be institutionalised as part of the way a government works? If so, what does this look like? And what is required to make that successful and sustainable?
The government has decided that it wants to embrace more innovative methods of getting things done, and governments through the ages have had similar ambitions. There has been a recognition that existing ways of working leave some problems unsolved, and a good place to start is to understand why this might be.
Governments by their nature function in routines. These are established pathways which, to a greater or lesser degree depending on the government’s capability, serve a range of functions in maintaining the operations of the state. These functions are well established but can also change, adapting to new conditions and new social or economic needs through shifts in policy, legislation, new services and new ways of working. As such governments can be understood as complex adaptive eco-systems which are themselves embedded in and interacting with a larger complex adaptive system: wider society.
As functioning (or dysfunctioning) systems, governments get an awful lot of things done. To borrow an analogy used by Matt Andrews, the amount of coordination it takes to make a streetlight switch on at a specific time each day is actually quite extraordinary. But as systems whose configuration is a response to and reflection of their societal context, governments are by their nature unable to solve every problem through their regular operation. Some problems are complex, and/or emergent, and require different types of know-how to solve. Some of these problems may arise directly because the machine of the state is configured in such a way as to neglect, exacerbate or even cause that particular issue. Think of a robot vacuum cleaner: they are pretty smart these days, programmed to trundle around your house hoovering the floor space and capturing a lot of dirt. When it’s done the floor looks pretty clean, but anyone with a Roomba knows that it can’t catch dirt gathering in the hard to reach corners, and it may have even pushed extra dirt into some of those corners. Getting into those nooks and crannies requires something different – probably a human, using a different tool.
These hard to reach corners are likely to some of the most tricky problems for governments to solve – because they are entrenched, because the know-how to solve them does not exist yet, and perhaps because they are to some extent a product of the configuration of the system itself. We call such problems complex or ‘wicked’, and they have some typical characteristics where we see:
- Little or no agreement on the definition of the problem (owing to multiple values, perception, and perspectives)
- Few or no clear solutions to the problem owing to the wide array of possible solutions and trade-offs associated with each
- Few easily identified causes or authority due to the problem having multiple potential causes, jurisdictions, stakeholders and regulator or implications.
Over the years governments have tried various ways to tackle problems like this. Task forces, delivery units and tsars are all examples of creating something outside of the routine system to tackle a problem which the system has failed to address, failed to anticipate or perhaps created due to unintended consequences of policy. Like these other examples, PDIA is a non-routine approach to tackle problems which the existing, routine system cannot. Unlike the others, it is tailored for the conditions of complexity and uncertainty about the problem and the solution. It takes these characteristics into account, forming teams who are authorised to first unpack and understand a complex problem, break it down and then crawl the design space in tight iterative cycles to look for solutions. Doing this builds capability and know-how to solve complex problems.
To do this, PDIA needs to disrupt existing institutional logics, mindsets and routines – challenging assumptions about both the problem and solutions. It is a way to stimulate or enable positive deviance. This brings us to the question – if the state is a machine that does lots of things well but is often ill-equipped to solve problems characterised by complexity, and if PDIA is a slightly disruptive, ex machina way for the state to solve problems that routine operations cannot, can PDIA ways of working become part of that machine? Can you embed and consistently reproduce positive deviance? And if so, what are the necessary ingredients for this to be effective and sustained over time?
It is worth observing that strictly speaking, PDIA is not ex machina in that it depends on concerted and consistent work by teams of people who are native to the system in which the problem is embedded. But the actual process of PDIA does sit outside of the work routines of those team members – it is a temporary focusing of attention and resources (mainly time) on solving a problem using a method tailored to complexity and uncertainty. It typically happens on top of people’s existing work, and to work it usually requires special authorisation.
A second observation is that a PDIA approach is not required to implement all the things that governments do. Most government activity is subject to relatively low uncertainty and therefore can be well delivered using routine techniques. Not all problems are complex. We do not need to substitute PDIA in to deliver routine activities like issuing passports, provided the passport system is fit for purpose.
With these observations in mind, there are at least two ways of conceptualising what it means to institutionalise PDIA. The first way seeks to establish PDIA as an add-on to routine delivery processes – an additional tool in the government’s toolkit to be used when appropriate, when the problem calls for it. In other words, adding a new gadget onto the Roomba that enables it to reach into the corners when needed. This is roughly the approach being taken by the government I have been working with.
The challenge in this approach is what this looks like in practice, and whether it can be done without losing PDIA’s potential and need to disrupt established mindsets and ways of working. PDIA exists to enable governments to step outside of the ‘business as usual’ rut that delivery often falls into. So a government must be careful not to make PDIA ‘business as usual’ lest it lose its value in disrupting institutional inertia. What is critical here is to recognise that PDIA is not just a different way of working but a different way of thinking. As such it cannot and will not survive or thrive in manuals or procedures, because bureaucratising it would rob it of efficacy. It will only be of sustained value to a government as a tool if it resides in a critical mass of people who share an innovative mindset and understand how to deploy the method. A government could create a central PDIA Unit staffed with people who know and understand the method, authorised by people with a commitment to using PDIA when appropriate, and this unit could then triage and respond to requests from across government to facilitate a PDIA process. But ultimately the heart of using such methods effectively will be a community of practice across the eco-system who understand the methods, and who share a commitment to solving complex problems and working in an adaptive way.
This brings us to the alternative idea of what institutionalising PDIA means, which is as a vector for a deeper and more radical change in the way that government thinks and acts. In some ways, institutional change is part of the theory of change of PDIA – scaling through the diffusion of new ways of thinking and greater problem-solving know-how. And once a community of practice reaches critical mass across an eco-system, a tipping point can happen where the eco-system becomes generally more open to novelty, where success is a more effective route to legitimacy than isomorphic mimicry, and where leadership is oriented towards value creation. This scenario is therefore about a more wholesale culture change towards an adaptive and learning-oriented public sector, as well as higher tolerance and even facilitation of positive deviance. It is not incompatible with the first scenario, but goes much deeper and further. The question of instigating and sustaining such wholesale change across the public sector is the subject of a vast literature, beyond what can be summarised here. But there is an unmistakable promise of change that goes far beyond tools and methods.