Tag Archives: regulation

Are We Doing Social Innovation Wrong?

Are We Doing Social Innovation Wrong?

GUEST POST from Geoffrey A. Moore

The Volume Operations business model kicks in when you have hundreds of thousands of users and goes up from there. 100,000, for those of us who are not math majors, is 10 to the power of 5. Uber-successful volume ops businesses operate at 10 to the power of 9 and up—millions of users or customers. But if you are a start-up, you are looking at 10 or maybe 100. How do you get from here to there?

The key thought to keep in mind is the old chestnut “what got you here won’t get you there.” That is, whatever operating model you have, keep in mind it can scale to two exponents but never to three. That means for every two exponents you have to change operating models, which likely means you have to change executive leadership in order to go forward.

To illustrate this idea, I’d like to focus on the non-profit sector and ask the question, what would it take to really solve for any widespread social problem? Homelessness was the first one that came to mind, but hunger is another obvious one, drug addiction a second, street crime a third. They are all seemingly intractable issues that, despite the best intentions of a whole raft of people, and regardless of how much funding is supplied, stubbornly resist any sustainable improvement.

The question I want to address is not what programs would work—because I actually think a whole lot of programs would work—but rather, how could we organize to deploy these programs successfully at scale.

Following our principle of what got you here won’t get you there, we need a ladder of operating models that can take us, exponent by exponent, from 10 to the power of 1 to, say, 10 to the power of 7. What might that look like?

Scaling Social Innovation

Consider this a straw man, a place to start, something to edit. It conveys a key lesson from the high-tech sector, namely that the fastest way to kill a disruptive innovation is to race to scale by skipping over one or more of these “exponential steps.” It just doesn’t work. There are too many emergent factors at each new level you must learn to cope with in order to succeed. The only reliable way to scale is to ratchet your way up this staircase, adapting your systems and operations as you go.

Unfortunately, that’s not what politicians do. They want to make a big impact right away. That means they start everything on one of the upper stairs. Driven by impatience, they ignore the dynamics of adoption and demand mass deployment from the get-go. They think the problem is simply one of getting enough funding. It’s not. It’s one of operational innovation. Scaling prematurely simply wastes the funding. And then when programs do flounder, as they inevitably will, they blame it on execution when in reality they simply did not do the hard, time-consuming work of building up their foundation step by step from below.

One of the implications of this framework is that social services should be incubated in the private sector where freedom from regulatory constraints supports agile innovation. But as they scale, the importance of regulatory oversight increases and more communal engagement is required. The goal should be to keep this oversight as local as possible as long as possible, doing as much as we can to empower the people delivering the service itself. Once that operating model solidifies, then, and only then, is there a proper foundation for scaling to state and federal programs.

Today, we do not lack the empathy to support social services. Nor do we lack the funding. But we are failing nonetheless. We can do better. We need to do better.

That’s what I think. What do you think?

Image Credit: Pexels

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.

Unintended Consequences.  The Hidden Risk of Fast-Paced Innovation

Unintended Consequences.  The Hidden Risk of Fast-Paced Innovation

GUEST POST from Pete Foley

Most innovations go through a similar cycle, often represented as an s-curve.

We start with something potentially game changing. It’s inevitably a rough-cut diamond; un-optimized and not fully understood.  But we then optimize it. This usually starts with a fairly steep leaning curve as we address ‘low hanging fruit’ but then evolves into a fine-tuning stage.  Eventually we squeeze efficiency from it to the point where the incremental cost of improving it becomes inefficient.  We then either commoditize it, or jump to another s-curve.

This is certainly not a new model, and there are multiple variations on the theme.  But as the pace of innovation accelerates, something fundamentally new is happening with this s-curve pattern.  S-curves are getting closer together. Increasingly we are jumping to new s-curves before we’ve fully optimized the previous one.  This means that we are innovating quickly, but also that we are often taking more ‘leaps into the dark’ than ever before.

This has some unintended consequences of its own:

1. Cumulative Unanticipated Consequences. No matter how much we try to anticipate how a new technology will fare in the real world, there are always surprises.  Many surprises emerge soon after we hit the market, and create fires than have to be put out quite quickly (and literally in the cases of some battery technologies).  But other unanticipated effects can be slower burn (pun intended).  The most pertinent example of this is of course greenhouse gasses from Industrialization, and their impact on our climate. This of course took us years to recognize. But there are many more examples, including the rise of antibiotic resistance, plastic pollution, hidden carcinogens, the rising cost of healthcare and the mental health issues associated with social media. Just as the killer application for a new innovation is often missed at its inception, it’s killer flaws can be too.  And if the causal relationship between these issues and the innovation are indirect, they can accumulate across multiple s-curves before we notice them.  By the time we do, technology is often so entrenched it can be a huge challenge to extract ourselves from it.

2.  Poorly understood complex network effects.  The impact of new innovation is very hard to predict when it is introduced into a complex, multivariable system.  A butterfly flapping its wings can cascade and amplify through a system, and when the butterfly is transformative technology, the effect can be profound.  We usually have line of sight of first generation causal effects:  For example, we know that electric cars use an existing electric grid, as do solar energy farms.  But in today’s complex, interconnected world, it’s difficult to predict second, third or fourth generation network effects, and likely not cost effective or efficient for an innovator to try and do so. For example, the supply-demand interdependency of solar and electric cars is a second-generation network effect that we are aware of, but that is already challenging to fully predict.  More causally distant effects can be even more challenging. For example, funding for the road network without gas tax, the interdependency of gas and electric cost and supply as we transition, the impact that will have on broader on global energy costs and socio political stability.  Then add in complexities supply of new raw materials needed to support the new battery technologies.  These are pretty challenging to model, and of course, are the challenges we are at least aware of. The unanticipated consequences of such a major change are, by definition, unanticipated!

3. Fragile Foundations.  In many cases, one s-curve forms the foundation of the next.  So if we have not optimized the previous s-curve sufficiently, flaws potentially carry over into the next, often in the form of ‘givens’.  For example, an electric car is a classic s-curve jump from internal combustion engines.  But for reasons that include design efficiency, compatibility with existing infrastructure, and perhaps most importantly, consumer cognitive comfort, much of the supporting design and technology carries over from previous designs. We have redesigned the engine, but have only evolved wheels, breaks, etc., and have kept legacies such as 4+ seats.  But automotives are in many, one of our more stable foundations. We have had a lot of time to stabilize past s-curves before jumping to new ones.  But newer technologies such as AI, social media and quantum computing have enjoyed far less time to stabilize foundational s-curves before we dance across to embrace closely spaced new ones.  That will likely increase the chances of unintended consequences. And we are already seeing the canary in the coal mine with some, with unexpected mental health and social instability increasingly associated with social media

What’s the Answer?  We cannot, or should not stop innovating.  We face too many fundamental issues with climate, food security and socio political stability that need solutions, and need them quite quickly.

But the conundrum we face is that many, if not all of these issue are rooted in past, well intentioned innovation, and the unintended consequences that derive from it. So a lot of our innovation efforts are focused on solving issues created by previous rounds of innovation.  Nobody expected or intended the industrial revolution to impact our climate, but now much of our current innovation capability is rightly focused on managing the fall out it has created (again, pun intended).  Our challenge is that we need to continue to innovate, but also to break the cycle of todays innovation being increasingly focused on fixing yesterdays!

Today new waves of innovation associated with ‘sustainable’ technology, genetic manipulation, AI and quantum computing are already crashing onto our shores. These interdependent innovations will likely dwarf the industrial revolution in scale and complexity, and have the potential for massive impact, both good and bad. And they are occurring at a pace that gives us little time to deal with anticipated consequences, let alone unanticipated ones.

We’ll Find a Way?  One answer is to just let it happen, and fix things as we go. Innovation has always been a bumpy road, and humanity has a long history of muddling through. The agricultural revolution ultimately allowed humans to exponentially expand our population, but only after concentrating people into larger social groups that caused disease to ravage many societies. We largely solved that by dying in large numbers and creating herd immunity. It was a solution, but not an optimum one.  When London was in danger of being buried in horse poop, the internal combustion engine saved us, but that in turn ultimately resulted in climate change. According to projections from the Club of Rome in the 70’s, economic growth should have ground to a halt long ago, mired in starvation and population contraction.  Instead advances in farming technology have allowed us to keep growing.  But that increase in population contributes substantially to our issues with climate today.  ‘We’ll find a way’ is an approach that works until it doesn’t.  and even when it works, it is usually not painless, and often simply defers rather than solves issues.

Anticipation?    Another option is that we have to get better at both anticipating issues, and at triaging the unexpected. Maybe AI will give us the processing power to do this, provided of course that it doesn’t become our biggest issue in of itself.

Slow Down and Be More Selective?  In a previous article I asked if ‘just because we can do it, does it mean we should?’.  That was through a primarily moral lens.  But I think unintended consequences make this an even bigger question for broader innovation strategy.  The more we innovate, the more consequences we likely create.  And the faster we innovate, the more vulnerable we are to fragility. Slowing down creates resilience, speed reduces it.  So one option is to be more choiceful about innovations, and look more critically at benefit risk balance. For example, how badly do we need some of the new medications and vaccines being rushed to market?  Is all of our gene manipulation research needed? Do we really need a new phone every two years?   For sure, in some cases the benefits are clear, but in other cases, is profit driving us more than it should?

In a similar vein, but to be provocative, are we also moving too quickly with renewable energy?  It certainly something we need.  But are we, for example, pinning too much on a single, almost first generation form of large scale solar technology?  We are still at that steep part of the learning curve, so are quite likely missing unintended consequences.  Would a more staged transition over a decade or so add more resilience, allow us to optimize the technology based on real world experience, and help us ferret out unanticipated issues? Should we be creating a more balanced portfolio, and leaning more on more established technology such as nuclear? Sometimes moving a bit more slowly ultimately gets you there faster, and a long-term issue like climate is a prime candidate for balancing speed, optimization and resilience to ultimately create a more efficient, robust and better understood network.

The speed of AI development is another obvious question, but I suspect more difficult to evaluate.  In this case, Pandora’s box is open, and calls to slow AI research would likely mean responsible players would stop, but research would continue elsewhere, either underground or in less responsible nations.  A North Korean AI that is superior to anyone else’s is an example where the risk of not moving likely outweighs the risk of unintended consequences

Regulation?  Regulation is a good way of forcing more thoughtful evaluation of benefit versus risk. But it only works if regulators (government) understand technology, or at least its benefits versus risks, better than its developers.  This can work reasonably well in pharma, where we have a long track record. But it is much more challenging in newer areas of technology. AI is a prime example where this is almost certainly not the case.  And as the complexity of all innovation increases, regulation will become less effective, and increasingly likely to create unintended consequences of its own.

I realize that this may all sound a bit alarmist, and certainly any call to slow down renewable energy conversion or pharma development is going to be unpopular.  But history has shown that slowing down creates resilience, while speeding up creates instability and waves of growth and collapse.  And an arms race where much of our current innovative capability is focused on fixing issues created by previous innovations is one we always risk losing.  So as unanticipated consequences are by definition, really difficult to anticipate, is this a point in time where we in the innovation community need to have a discussion on slowing down and being more selective?  Where should we innovate and where not?  When should we move fast, and when we might be better served by some productive procrastination.  Do we need better risk assessment processes? It’s always easier to do this kind of analysis in hindsight, but do we really have that luxury?

Image credit: Pixabay

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.

Four Ways Governments Can Accelerate the Digital Transformation of Their Economies

Four Ways Governments Can Accelerate the Digital Transformation of Their Economies

GUEST POST from Art Inteligencia

In today’s digital world, governments have a critical role to play in accelerating digital transformation. As technology continues to evolve, governments must find ways to embrace and apply new technologies, while also ensuring that their citizens have access to the most advanced digital services.

To ensure success, there are several key steps that the government should take.

1. Governments Should Invest in Digital Infrastructure

By investing in the infrastructure necessary to support digital transformation, the government can create a platform for innovation and adoption of new technologies. This includes things like high-speed broadband, 5G networks, and cloud computing capabilities.

2. Governments Should Provide Incentives to Spur Digital Adoption

This could come in the form of tax breaks, grants, and other incentives to organizations that are investing in digital transformation. This will help create a climate of investment and innovation, which will in turn help accelerate the transformation process.

3. Governments Should Create a Supportive Regulatory Environment

This means creating laws and regulations that are conducive to digital transformation, such as data privacy and security laws. This will help ensure that organizations can safely and securely adopt new technologies and services.

4. Governments Should Invest in Digital Literacy and Education

By investing in digital literacy and education, the government can ensure that citizens have the tools and knowledge necessary to take advantage of the digital transformation. This can include programs such as coding boot camps and digital literacy courses for adults.

Conclusion

By taking these steps, the government can create an environment that is conducive to digital transformation and help accelerate the process. In doing so, the government can ensure that its citizens have access to the most advanced digital services and technologies, and that organizations can take advantage of the opportunities that come with digital transformation.

Image credit: Pixabay

Subscribe to Human-Centered Change & Innovation WeeklySign up here to get Human-Centered Change & Innovation Weekly delivered to your inbox every week.

Parallels Between the 1920’s and Today Are Frightening

Parallels Between the 1920's and Today Are Frightening

GUEST POST from Greg Satell

It should be clear by now we are entering a pivotal era. We are currently undergoing four profound shifts, that include changing patterns of demographics, migration, resources and technology. The stress lines are already beginning to show, with increasing tensions over race and class as well as questions about the influence technology and institutions have over our lives.

The last time we faced anything like this kind of tumult was in the 1960s which, much like today, saw the emergence of a new generation, the Baby-Boomers, that had very different values than their predecessors. Their activism achieved significant advances for women and minorities, but also at times, led to tumult and riots.

Yet the changes we are undergoing today appear to be even more significant than we did then. In fact, you would have to go back to the 1920s to find an era that had as much potential for both prosperity and ruin. Unfortunately, it led to economic upheaval, genocide and war on a scale never seen before in world history. We need to do better this time around.

Panics, Pandemics and War

A Wall Street crisis that threatened the greater economy and led to sweeping legislation that reshaped government influence in the financial sector was prelude to both the 1920’s and the 2020’s. Both the Bankers Panic of 1907 and the Great Recession which began in 2007 resulted in landmark legislation, the Federal Reserve Act and Dodd-Frank, respectively.

Continuing in the same vein of eerie parallel, the 1918 flu epidemic killed between 20 million and 50 million people and raged for more than two years, until 1920, when it finally got under control. Much like today, there were social distancing guidelines, significant economic impacts and long-term effects on educational attainment.

Perhaps not surprisingly, there was no small amount of controversy about measures taken to control the pandemic a century ago. People were frustrated with isolation (it goes without saying that there was no Netflix in 1918). Organizations like the Anti-Mask League of San Francisco rose up in defiance.

The years leading up to the 1920s were also war-torn, with World War I ravaging Europe and the colonial order increasingly coming under pressure. Much like the “War on Terrorism,” today, the organized violence, combined with the panics and pandemics, made for an overall feeling that society was unravelling, and many began to look for a scapegoat.

Migration, Globalization and Nativism

In 1892, Ellis Island opened its doors and America became a beacon to those around the world looking for a better life. New immigrants poured in and, by 1910, almost 15% of the US population were immigrants. As the 1920s approached, the strains in society were becoming steadily more obvious and more visceral.

The differences among the newcomers aroused suspicion, perhaps best exemplified by the Sacco and Vanzetti trial, in which two apparently innocent immigrants were convicted and executed for murder. Many believed that the new arrivals brought disease, criminality and “un-American” political and religious beliefs, especially with regard to Bolshevism.

Fears began to manifest themselves in growing nativism and there were increasing calls to limit immigration. The Immigration Act of 1917 specifically targeted Asians and established a literacy test for new arrivals. The Immigration Act of 1924 established quotas which favored northern and Western Europeans over those of Southern and Eastern Europe as well as Jews. The film Birth of A Nation led to a resurgence of the Ku Klux Klan.

Scholars see many parallels between the run-up to the 1920s and today. Although nativism these days is primarily focused against muslims and immigrants from South America, the same accusations of un-American political and religious beliefs, as well as outright criminality, are spurring on a resurgence of hate groups like the Proud Boys. Attorney General Merrick Garland has pledged to make prosecuting white supremacists a top priority.

A New Era of Innovation

As Robert Gordon explained in The Rise and Fall of American Growth, prosperity in the 20th century was largely driven by two technologies, electricity and the internal combustion engine. Neither were linear or obvious. Both were first invented in the 1880’s but didn’t really begin to scale until the 1920’s.

That’s not uncommon. In fact, it takes decades for a new discovery to make a measurable impact on the world. That’s how long is needed to first identify a useful application for a technology and then for ecosystems to form and secondary technologies to arise. Electricity and internal combustions would ignite a productivity boom that would last 50 years, from roughly 1920 until 1970.

For example, as economist Paul David explained in a highly cited paper, it wasn’t the light bulb, but in allowing managers to rearrange work in factories, that electricity first had a significant effect on society. Yet it was in the 1920s that things really began to take off. Refrigerated rail cars transformed diets and labor-saving appliances such as the vacuum cleaner would eventually pave the way for women in the workforce. The first radio stations appeared, revolutionizing entertainment.

Today, although the digital revolution itself has largely been a disappointment, there’s considerable evidence that we may be entering a new era of innovation as the emphasis shifts from bits to atoms. New computing architectures, such as quantum and neuromorphic computing, as well as synthetic biology and materials science, may help to reshape the economy for decades to come.

A Return to Normalcy?

Not surprisingly, by 1920 the American people were exhausted. Technological change, cultural disruption brought about by decades of mass immigration, economic instability and war made people yearn for calmer, gentler times. Warren G. Harding’s presidential campaign touted “a return to normalcy” and people bought in.

Yet while the “Roaring Twenties” are remembered as a golden age, they set the seeds for what came later. Although the stock market boomed, lack of regulation led to the stock market crash of 1929 and the Great Depression. The harsh reparations imposed by the Treaty of Versailles made the rise of Hitler possible.

The 1930s brought upon almost unimaginable horror. Economic hardship in Europe paved the way for fascism. Failed collectivization in the Soviet Union led to massive famine and, later, Stalin’s great purges. Rising nativism, in the US and around the world, led to diminished trade as well as violence against Jews and other minorities. World War II was almost inevitable.

It would be foolish beyond belief to deny the potential of history repeating itself. Still, the past is not necessarily prologue. The 1930s were not the inevitable result of impersonal historical forces, but of choices consciously made. We could have made different ones and received the bounty of the prosperity that followed World War II without the calamity that preceded it.

What we have to come to terms with is that technology won’t save us. Markets won’t save us. Our future will be the product of the choices we make. We should endeavor to choose wisely.

— Article courtesy of the Digital Tonto blog
— Image credit: Pixabay

Subscribe to Human-Centered Change & Innovation WeeklySign up here to get Human-Centered Change & Innovation Weekly delivered to your inbox every week.

The One Movie All Electric Car Designers Should Watch

Ford Mustang Electric Cobra

by Braden Kelley

In 2011 a Ron Howard comedy was released starring Kevin James, Vince Vaughn, Winona Ryder, Channing Tatum, Jennifer Connelly, and Queen Latifah. The film was called ‘The Dilemma’ and it was a very funny buddy comedy focused on commitment and marital infidelity. But today, we’re focused on one of the subplots that makes ‘The Dilemma’ a movie that every electric car designer should watch. The subplot highlighted a solution to the silent problem with electric vehicles and one of the barriers to widespread adoption.

Vince Vaughn and Kevin James’ characters are best friends and partners in a small auto design firm. The two have recently been given an opportunity to pitch an eco-friendly car to Dodge. One of the main features of this car is that it looks like a muscle car and it sounds like a muscle car, but it’s actually an electric car. Here is a video clip in German that I found on YouTube that shows their sound triumph:

Besides being like large golf carts, electric cars are also INCREDIBLY dangerous to pedestrians and cyclists at low speeds because they’re nearly silent. In addition to being dangerous, electric cars also sound boring.

Electric cars are so dangerous because of their silence, some governments are mandating that they make sounds at least while backing up – you know, those annoying beeping sounds.

Even the cool 1,500 horsepower equivalent electric Ford Mustang Cobra pictured above sounds really boring when it shoots off the line in its promo video going down the drag strip.

Designers, why can’t you implement more interesting, more exhilarating sounds like those in the video before we’re all forced to buy electric vehicles?

They could easily be designed to fade away as the vehicle reaches speeds of around 30 miles per hour and wind and road noise starts to become sufficient to give pedestrians and cyclists a fighting change.

What say you?

Image credit: Slashgear.com


Accelerate your change and transformation success

Subscribe to Human-Centered Change & Innovation WeeklySign up here to get Human-Centered Change & Innovation Weekly delivered to your inbox every week.