We’ve worked with digital computing since well before WWII. Early apps were calculations for code-breaking, ballistics, accounting, and project management. We developed specialists for the technologies involved. As technology got easier to use, incremental steps led to the digital or information age.
However, the key challenges for the next decades will be different: transformational, not just incremental. While technology will remain the enabler, success will depend more on human behavior and change management. We will need new knowledge and a new mix of leaders.
Let’s look at the big picture.
1. What’s similar: Value through feedback and analysis
To improve any system, we need workable knowledge of causes, effects, and uncertainties.
To get such knowledge quickly, accurately, and more efficiently, we developed automated ways to collect, store, communicate, and calculate data. Progress was difficult, but we soon produced better calculations and got better at code-breaking, ballistics, and other such work.
Then, as we built more powerful computers, we developed new applications. If we could improve how we made calculations, could we also improve how we used them? Could better analysis lead to better action and results?
We made substantial progress here as well, with results shaped more by human behavior than technology alone. Value came from answering questions such as ‘Where can we gain from…
- Transparency and Accountability – How can analysis create knowledge and incentives for suppliers, customers, workers, and overseers?
- Specialization and Scale – How can digital networks help us coordinate across complex tasks and people? Think of interactions smoothly harmonized in support of services, industries, and jurisdictions.
- Automation – How can we apply digital tools to more activities? Think beyond individual calculations to extensive progress with machine learning, speech to text, identification of what’s in a picture, and autonomous control for cars, trucks, buses, and other vehicles.
Value creation in the future will critically depend — as it long has — on feedback and analysis.
2. What’s different: More powerful computers, bigger value chains, more disruption
Through “Moore’s Law,” digital productivity has doubled roughly every two years. What many people fail to recognize, however, is that — over the past 50 years — instructions executed per dollar have increased 34 million times. More important, 33 of those 34 million came in the last decade, with another 235 million coming in the next six years. [1]
Cloud computing, artificial intelligence, machine learning, blockchain networks, and other technologies will offer digital power far, far greater than the computing that got us to the moon in 1969.
As a result, many new tasks will be automated. Equally important, tasks will be linked into larger and digitally redesigned value chains. Greater scale will be key, moving:
- From tasks to processes,
- From processes that could be absorbed to those requiring workers to find new jobs,
- From changes in jobs to changes in work units and institutions,
- From institutional to industry-wide and jurisdiction-wide change, and
- From change managed through internal authority and cultures to change requiring external negotiations, new relationships, and new regulations.
Half of today’s jobs may be disrupted over the next decade or so. [2] Unfortunately, we often approach digital transformation like the “technology problems” of old. We are not yet organizing for the disruption and scale of what is coming. If we have learned one thing from our experience with COVID-19; however, it should be that large-scale disruption is possible and requires solid public and private sector leadership grounded on solid evidence and analysis.
3. What’s to be done: New problem-solving and participants
In most governments, digital planning focuses on incremental adjustments for the technologies, staffing, budgets, and operations needed for the next few years. In contrast, transformation focuses on disruptive and largely external innovations for the longer term. In both cases, priorities are (or should be) determined by the degree to which gains outweigh survivable losses. But transformation requires more work on:
- New technologies – Spotting when and how to get started. What priority should be given to analysis, research, and development on big data, natural language processing, 5G, blockchain, quantum computing, etc.? What deserves general attention vs. checking up with similar government agencies vs. consulting projects vs. hands-on investments?
- Cross-boundary standards and value chains – What negotiations and innovations are needed to improve coordination across functions within the agency, agencies within the jurisdiction, and/or external services or jurisdictions (industry-wide coordination)?
- Loss avoidance – What could be done to protect against transformational threats to institutions, services, jurisdictions, or larger communities? What should stakeholders be worried about, and what could be done to solve those problems?
Estimating and influencing stakeholder responses requires working individually and collectively with diverse sources. Engagement should include:
- Internal Participants (frontline stakeholders, middle managers) – Insights for resolving confusion and conflict will be essential. Understanding and overcoming barriers to change requires considerable time, effort, and agility.
- External Participants (senior executives, allies, and opponents) – Transformation involves new relationships and negotiation. Influence through command and control is limited. Risks can be high, and opportunities rare but the possibilities are extremely important.
- The Public (and politics) – Many technology innovations are small and proceed without much public visibility or understanding. Transformation, however, demands visibility and a greater need for public support. These must be developed through negotiations, communications, and results that work both logically and emotionally.
Digital power has recently created a world where the transformation of institutions and societies remains risky, yet increasingly possible and often essential. Success will require understanding what’s different and what we should do. Perhaps most critical will be deeper engagement by non-technology people: those from the front lines, middle management, senior executives, overseers, and the public. Transformation needs leaders who are courageous and well-informed.
[1] Over 56 years, productivity doubles 28 times, becoming 228 = 268.4 million times more powerful than it was at the beginning. Over 50 years, or 25 doublings, productivity is 225 = 33.5 million times what it was at the beginning. Over the next 6 years then, productivity will grow from 33.5 to 268.4 million times more powerful than it was at the beginning, multiplying the initial productivity an additional 234.9 million times.
[2] Multiple extensive research and academic studies support similar estimates. See https://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf and https://www.mckinsey.com/featured-insights/future-of-work/jobs-lost-jobs-gained-what-the-future-of-work-will-mean-for-jobs-skills-and-wages
Digital Progress: What's Changed and How Should We Respond?
We’ve worked with digital computing since well before WWII. Early apps were calculations for code-breaking, ballistics, accounting, and project management. We developed specialists for the technologies involved. As technology got easier to use, incremental steps led to the digital or information age.
However, the key challenges for the next decades will be different: transformational, not just incremental. While technology will remain the enabler, success will depend more on human behavior and change management. We will need new knowledge and a new mix of leaders.
Let’s look at the big picture.
1. What’s similar: Value through feedback and analysis
To improve any system, we need workable knowledge of causes, effects, and uncertainties.
To get such knowledge quickly, accurately, and more efficiently, we developed automated ways to collect, store, communicate, and calculate data. Progress was difficult, but we soon produced better calculations and got better at code-breaking, ballistics, and other such work.
Then, as we built more powerful computers, we developed new applications. If we could improve how we made calculations, could we also improve how we used them? Could better analysis lead to better action and results?
We made substantial progress here as well, with results shaped more by human behavior than technology alone. Value came from answering questions such as ‘Where can we gain from…
Value creation in the future will critically depend — as it long has — on feedback and analysis.
2. What’s different: More powerful computers, bigger value chains, more disruption
Through “Moore’s Law,” digital productivity has doubled roughly every two years. What many people fail to recognize, however, is that — over the past 50 years — instructions executed per dollar have increased 34 million times. More important, 33 of those 34 million came in the last decade, with another 235 million coming in the next six years. [1]
Cloud computing, artificial intelligence, machine learning, blockchain networks, and other technologies will offer digital power far, far greater than the computing that got us to the moon in 1969.
As a result, many new tasks will be automated. Equally important, tasks will be linked into larger and digitally redesigned value chains. Greater scale will be key, moving:
Half of today’s jobs may be disrupted over the next decade or so. [2] Unfortunately, we often approach digital transformation like the “technology problems” of old. We are not yet organizing for the disruption and scale of what is coming. If we have learned one thing from our experience with COVID-19; however, it should be that large-scale disruption is possible and requires solid public and private sector leadership grounded on solid evidence and analysis.
3. What’s to be done: New problem-solving and participants
In most governments, digital planning focuses on incremental adjustments for the technologies, staffing, budgets, and operations needed for the next few years. In contrast, transformation focuses on disruptive and largely external innovations for the longer term. In both cases, priorities are (or should be) determined by the degree to which gains outweigh survivable losses. But transformation requires more work on:
Estimating and influencing stakeholder responses requires working individually and collectively with diverse sources. Engagement should include:
Digital power has recently created a world where the transformation of institutions and societies remains risky, yet increasingly possible and often essential. Success will require understanding what’s different and what we should do. Perhaps most critical will be deeper engagement by non-technology people: those from the front lines, middle management, senior executives, overseers, and the public. Transformation needs leaders who are courageous and well-informed.
[1] Over 56 years, productivity doubles 28 times, becoming 228 = 268.4 million times more powerful than it was at the beginning. Over 50 years, or 25 doublings, productivity is 225 = 33.5 million times what it was at the beginning. Over the next 6 years then, productivity will grow from 33.5 to 268.4 million times more powerful than it was at the beginning, multiplying the initial productivity an additional 234.9 million times.
[2] Multiple extensive research and academic studies support similar estimates. See https://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf and https://www.mckinsey.com/featured-insights/future-of-work/jobs-lost-jobs-gained-what-the-future-of-work-will-mean-for-jobs-skills-and-wages
Jerry Mechling, Ph.D
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