[This article was written by Arnaldo Pellini from Capability and Vanesa Weyrauch from P&I and it first appeared in Helvetas Mosaic, a quarterly publication exploring new trends and fresh ideas of international development. Subscribe to receive more articles like this in the future.]
It is easy to get carried away with the promises of technology when we read about the Fourth Industrial Revolution, or 4IR.
In an earlier Mosaic article, Knowledge Systems and Policy Innovation in the 4IR, we wrote that, according to the World Economic Forum there is a good chance that by 2025 we will have: 1 trillion sensors connected to the internet; the first 3D printed car in production; the first government replacing the census with big-data sources and analytics; and artificial intelligence performing 30 percent of the corporate audits in the world. Jamie Susskind describes the mind-boggling possibilities offered by nanotechnology, with nanorobots able to swim through our bodies delivering targeted drugs, or the staggering increase in the number of people connected to the Internet, from 400 million in 2000 to an expected 4.6 billion by 2021. He writes that we are entering “a digital lifeworld characterized by machines that are equal or superior to humans in a range of tasks and activities; technology that is embedded in the physical environment in which we live; and digital technology that more and more records human activities as data and processes it through digital systems”.
There are risks of course, for example David Wallace-Wells writes: “Five years ago, hardly anyone outside the darkest corners of the internet had even heard of bitcoin; today, mining it consumes more electricity than is generated by all the world’s solar panels combined, which means that in just a few years we’ve assembled a program to wipe out the gains of several long, hard generations of green energy innovation.” Another example is OpenAI, the Elon Musk-backed non-profit set up to responsibly push the boundaries of what is possible with artificial intelligence. It has developed an AI system which generates coherent paragraphs of literary or news text (go to 19’45” of the podcast) which is so sophisticated that OpenAI has decided not to release it fully to the public because of the real risk that in the wrong hands it could generate very plausible fake news, spam or reviews.
Reading about technologies of the future gives the impression that the technological changes we are witnessing have a life on their own. They promise a bright future of efficient production and an economic growth path that is at last within the natural limits of our planet. As argues by Susskind, these technologies are not exogenous forces over which we have no control. There are people behind these technologies and governments will need to strengthen leadership and develop human capital so that they are able to govern the techno-digital transformation in a way that leaves no one behind.
The challenge for results-based management approaches
In our discussion paper, State Capability and Policymaking in the Fourth Industrial Revolution, we argue that the challenge for government as we approach the 4IR is to keep up with the pace of change and understand the likely social and economic impact of technological innovation to be able to regulate it. This represents a huge challenge for middle and low income countries, since policymakers are expected to resolve interdependent policy challenges that imply a high degree of uncertainty while facing significant capability and institutional barriers such as for example limited budgets, decision-making processes organized in silos, weak IT and knowledge management structures, and low investment in evidence generation.
At the same time, the technological changes that are emerging will change the way governments work and policies are decided. Processes that investigate specific problems, design the necessary policies and regulatory frameworks, and deploy them through top-down systems will struggle in this new technology-driven context facing increasing disruption. In the near future, government institutions will have to adopt policies that govern techno-digital transformation by testing technologies and experimenting to ensure they benefit their citizens on the whole, while trying to avoid intensifying inequalities. Agile forms of government will be needed to help regulators and legislators continuously adapt to a new fast-changing social and economic environment, without stifling innovation. This is likely to challenge the role of central government agencies as local institutions may be quicker in adopting and using new technologies and interacting with citizens. Policymaking and decision making are likely to become more decentralized and concentrated in new areas called mega-regions, which combine cities and metro areas and are increasingly powering today’s world economy.
This poses a challenge to the way today’s development initiatives, particularly around governance reforms and public policy, are designed, planned and implemented. Pablo Yanguas, in his Why We Lie About Aid, writes that everyone involved in public policy knows that definitive results are rare, and yet the vast majority of development initiatives are designed to follow a linear results-based logic of input-output-outcome-impact. Most of the evaluations commissioned today by development partners and implementing organizations are asked to verify this results chain.
If we accept that governance systems will fundamentally change as we enter the 4IR and that governments have to start preparing today, then we may need to rethink the way we design and implement development initiatives. This is particularly true for initiatives that aim to support the development of leadership and human capital capabilities of future generations of civil servants, policymakers and researchers to drive these processes.
What are the implications for development programming?
Program design: The emerging literature on adaptive development provides some interesting ideas about how programs can be more open to the uncertainty of outcomes and results, and how to build a more experimental approach into governance initiatives that will increasingly deal with it. The suggestions are to invest time and resources into developing relationships with local partners and discovering common interests around problems. Digital technologies and platforms can help with that. This can help focus on solving problems that are owned, debated and defined by local stakeholders and partners, and which are not predefined. In some cases it can be about identity solutions (also called positive deviances) that partners have been able to develop despite bureaucratic constraints they face daily, and which document and support those initiatives.
Investing in acquiring a good knowledge of the political economy of the space and context in which the development initiative operates can help to design policy solutions that are politically feasible and not just technically sound. To do so, it is important to work with local innovation leaders committed to testing new governance solutions. These are individuals whose leadership is not a side effect of their position in a formal hierarchy (e.g. their job title), but rather a side effect of the respect, appreciation and trust they receive from their peers as well as being lifelong learners, a fundamental skill required by the pace and depth of changes in the 4IR.
Program implementation: test new forms of knowledge co-production to inform governance innovation and policy experiments through greater access to and use of digital technologies to link a wider range of stakeholders as we suggest through the knowledge system pentagram in our paper. Linking, for example, researchers from universities with policy research organizations; or professional knowledge by technical experts and civil servants with citizen knowledge. Through an experimental approach the need for blending different forms of knowledge and apply a more interdisciplinary approach to knowledge generation becomes more prominent. So does the need to develop physical and digital spaces for collaboration that enable testing of solutions, learning, and building on what works while dropping solutions that do not. Program funders can support this adaptive process by allowing a space for experimentation, acceptance that some experiments may fail, and investments in learning to help decide on which solutions to support.
Program teams: An experimental and politically informed program implementation approach requires a program team with the capacity and skills to do this. This involves either finding individuals with experience in adaptive management, demonstrated capacity to understand and collaborate with local leadership, and the ability to support experimental approaches, or investing the necessary financial resources and time in building skills and knowledge within the team and providing them with the space required to maximize the experience they bring with them.
Impact, replication and scaling up: Every development program is under pressure to replicate and scale up sustainable approaches and solutions. But what does sustainability and scaling up mean for a program adopting adaptive and experimental approaches to testing solutions? Replication and scaling up can refer to the uptake of adaptive and experimental principles by government partners to explore the opportunities and challenges that new technologies bring to governance, social and economic systems. It can be useful to explore the opportunities provided by the principles of innovation diffusion, which state that innovation emerges through initiatives designed and implemented by a small number of innovators. The tested solutions are then gradually taken up by a group of early adopters, followed by a larger group of adopters. In this context, innovation leaders can be catalysts for change from within a policy community. The scaling up can be accompanied by investment in documenting the successes of the partners more than those of the project, even though the two may be interlinked. In our opinion, it is a subtle but important difference.
Governance and policymaking process will be different in the 4IR. They will rely more than today on digital technologies and in the co-production of new forms of knowledge, within areas that bridge innovation, research, higher education, and local and professional knowledge.
These changes will not emerge overnight, they will evolve incrementally. Governments have to start preparing today human and governance capabilities that will be required in this imminent future to take advantage of the changes that are emerging and minimize the possible negative outcomes. Similarly, project and programs aimed at collaborating with national governments to support these change processes will need to evolve their approaches to design, implementation, and evaluations of results. In this article we have provided some initial ideas on how to do so.