These months have proven to be emblematic of the dangers of a hyperconnected world. Coronavirus cases continue to grow and grow fast, and asymmetries rise around the world at a pace we may have not imagined when 2020 started. Yet the digital nature of our hyperconnected world may prove to hold some of the critical solutions needed to scale novel approaches to the problems associated with the pandemic. The issue is not the virus alone: it’s more about the reactions to the virus such as information on the resources we need to allocate or an understanding of the wider consequences for businesses trying to respond.

Drucker Forum 2020

This article is one in the “shape the debate” series relating to the fully digital 12th Global Peter Drucker Forum, under the theme “Leadership Everywhere” on October 28, 29 & 30, 2020.
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AI solutions

Among these digital systems, few are being more heralded more than AI-powered solutions. Despite its current novelty, AI is anything but new. The marked increase in experimentation in the pandemic, and the ensuing interest from governments and corporations alike, represents a new global conversation on AI.

Novel cases of AI use are quickly spreading across international media, such as rapid assessment of patient scans at scale for improved covid-19 detection, improved accuracy for global case tracking and prediction, wide review and collection of online articles relevant for awareness and assessment, and advanced chemical analysis to assist vaccine creation. Want some examples? From the BlueDot’s predictive awareness to Alibaba’s AI diagnostics ranging to transportation with the Hong Kong Mass Transit’s autonomous robotic cleaners and the herald of health-care AI with Boston Children’s Hospital’s HealthMap program, these programs have demonstrated a superior form of utilization of machine learning. Also noteworthy are DeepMind’s AlphaFold as well as the Center for Disease Control and Prevention’s assessment bot to finish with Facebook’s social network safety moderating. The icing on the cake comes from application with inherent ethical norms, such as BenevolentAI’s drug screening program.

The marked increase in experimentation in the pandemic, and the ensuing interest from governments and firms alike, represents a new state of affairs in the global conversation on AI.

As overwhelming this list of applications is, it demonstrates a broader public hope and commercial awareness for the increasing potential of AI as a fundamental piece of the modern technology landscape.

Reality before experiments are scaled

But a dose of reality is needed as the demand for experimentation grows into a demand for scaling. As not all problems demand AI solutions, nor are all existing AI solutions up to the task of many highly uncertain problems, so not all organizations are advanced enough to effectively deploy and leverage such solutions without creating second-order effects. While solutions at scale are needed, and new practices and means are in place to experiment, we need to be sure that organizations looking to put these experiments into play have a thorough understanding of what the “job to be done” really is. As with most transformations, such agendas are sometimes less about the technology than the culture, work, and mental models being changed such that new productivity, opportunities, and social advancement is actually achieved and made sustainable.

AI concerns

This concern extends to the question of how national  and municipal governments look to leverage these emerging technologies to help improve the speed, scale, and sophistication of responses to high-impact, low-probability events like large-scale systemic shocks. Whether it is governments looking for strategic investment for AI competency or for firms looking for proven AI applications, similar concerns need to emerge. For a more mature conversation, we need to move from what we want AI to do towards a more real, deeper conversation on what we need from AI in order to respond to crises while not generating a fundamental, deeper vacuum of rights.

We need to go further, as despite the innovativeness of those cases mentioned, broader strategies are needed for engaging with foresight into the principal and value-driven challenges brought on by AI. This will include creating the means for effective conversations on, amongst other things,  whether to sacrifice privacy to ensure health-care capacity, whether data ownership should be private or publicly managed, and whether the potential inequality from some applications outweigh the benefits.

What is the value of AI? As states look to AI to reshape their post-pandemic response, we need to have hard conversations on what the value of AI really is. All of this begins with a real appreciation for what AI can and cannot do when subjected to the demands of operational improvements at scale. These conversations need to happen together, and now to build better frameworks of use. Otherwise the huge potential of these technologies will be to no avail for the betterment of society when we need it the most.

About the Authors:
Mark Esposito Co-Founder & CLO, Nexus FrontierTech, Professor, Bestselling Author, Advisor to National Governments. 
Terence Tse is professor of finance at ESCP Business School and co-founder of Nexus FrontierTech. 
Josh Entsminger is a doctoral student in innovation and public policy at the UCL Institute for Innovation and Public Purpose.

This article is one in the “shape the debate” series relating to the fully digital 12th Global Peter Drucker Forum, under the theme “Leadership Everywhere” on October 28, 29 & 30, 2020.
#DruckerForum

Lately, we have received quite a number of requests asking us to explain further why artificial intelligence (AI) and robots are unlikely to put humans out of work soon. It may be a contrarian position, but we are definitely optimistic about the future, believing that the displacement of labor won’t turn out to be as gloomy as many are speculating. Despite the endless talk on the threat of machines to human jobs, the truth is that, while we have lost jobs in some areas, we have gained them in others. For instance, the invention of automatic teller machines (ATMs), introduced in the 1960s, ought to have eliminated the need for many bank employees in the US. Yet, over time, the industry has not just hired more staff, but job growth in the sector is, in fact, doing better than the average.

So, why is this? The answer can actually be found in Hollywood movies. In the 1957 film Desk Set, the entire audience research department in a company is about to be replaced by a giant calculator. It is a relief to the staff, however, when they find out that the machine makes errors, and so they get to keep their jobs, learning to work alongside the calculator. Fast forward to the 2016 film Hidden Figures. The human ‘computers’ at NASA are about to be replaced by the newly introduced IBM mainframe. The heroine, Dorothy Vaughan, decides to teach herself Fortran, a computer language, in order to stay on top of it. She ends up leading a team to ensure the technology performs according to plan.

Facts and not fantasies

These are not merely fantasies concocted by film studios. Granted, realistically, many jobs, especially those involving repetitive and routine actions, may succumb to automation for good. But the movies above do encourage us not to overrate computers and underrate humans. Delving deeper into this, we believe there are several elements that underpin this message.

• Only humans can do non-standardized tasks. While traditional assembly line workers are set to be replaced by automation, hotel housekeeping staff are unlikely to face the same prospect any time soon. Robots are good at repetitive tasks but lousy at dealing with varied and unique situations. Jobs like room service require flexibility, object recognition, physical dexterity and fine motor coordination; skills like these are – at the moment at least – beyond the capabilities of machines, even for those considered intelligent.

• Machines make human skills more important. It is possible to imagine an activity – such as a mission or producing goods – to be made up of a series of interlocking steps, like the links in a chain. A variety of elements goes into these steps to increase the value of the activity, such as labor and capital; brain and physical power; exciting new ideas and boring repetition; technical mastery and intuitive judgement; perspiration and inspiration; adherence to rules; and the considered use of discretion. But, for the overall activity to work as expected, every one of the steps must be performed well, just as each link in a chain must do its job for the chain to be complete and useful. So, if we were to make one of these steps or links in a chain more robust and reliable, the value of improving the other links goes up In this sense, automation does not necessarily make humans superfluous. Not in any fundamental way, at least; instead, it increases the value of our skill sets. As AI and robots emerge, our expertise, problem-solving, judgement and creativity are more important than ever [3]. For example, a recent study looks into a Californian tech startup. Despite the company providing a technology-based service, it finds itself to be growing so fast that, with the computing systems getting larger and more complex, it is constantly drafting in more humans to monitor, manage and interpret the data [4]. Here, the technologies are merely making the human skills more valuable than before.

• Social aspects matter. Perhaps one of the most telling lessons learnt from underestimating the power of human interactions can be found by looking at Massive Open Online Courses (MOOCs). Until recently, it was widely believed that the rise of digital teaching tools would make human teachers less relevant, or even superfluous. However, that was not found to be the case with MOOCs. Instead, they have shown that human teachers can be made more effective with the use of digital tools. The rise of hybrid programs, in which online tools are combined with a physical presence, has only partially reduced the number of face-to-face hours for teachers, while freeing them up to be more involved with curriculum design, video recording and assessment writing. Ultimately, it is this combination of human interactions and computers that champions [5].

Closer together
There is simply no reason to think that AI and robots will render us redundant. It is projected that, by 2025, there will be 3.5 million manufacturing job openings in the US, and yet 2 million of them will go unfilled because there will not be enough skilled workers [6]. In conclusion, rather than undermining humans, we are much better off thinking hard about how to upskill ourselves and learn how to work alongside machines, as we will inevitably coexist – but it won’t be a case of us surrendering to them.

References:
[1]  Bessen, James. Learning by Doing: The Real Connection Between Innovation, Wages, and Wealth. Yale University Press, 2015.
[2] This is called the O-ring theory or principle, which was put forward by Michael Kremer in 1993. The name comes from the disaster of the space shuttle Challenger in 1986, which was caused by the failure of a single O-ring. In this case, an inexpensive and seemingly inconsequential rubber O-ring seal in one of the booster rockets failed after hardening and cracking during the icy Florida weather on the night before the launch.
[3] Autor, David. “Why Are There Still So Many Jobs? The History and Future of Workplace Automation”, Journal of Economic Perspectives, 29(3), 3-30, 2015.
[4] Shestakofsky, Benjamin. “Working Algorithms: Software Automation and the Future of Work”, Work and Occupations, 44(4), 2017.
[5] Tett, Gillian. “How robots are making humans indispensable”, Financial Times, December 22, 2016. https://www.ft.com/content/da95cb2c-c6ca-11e6-8f29-9445cac8966f
[6] Manufacturing Institute and Deloitte, Skills Gap in US Manufacturing, 2017. https://www2.deloitte.com/us/en/pages/manufacturing/articles/skills-gap-manufacturing-survey-report.html

About the Author:

Mark Esposito is co-founder and Chief Strategic Officer at Nexus FrontierTech, a leading global firm providing AI solutions to a variety of clients across industries, sectors, and regions.

This article is one in a series related to the 10th Global Peter Drucker Forum, with the theme management. the human dimension, taking place on November 29 & 30, 2018 in Vienna, Austria #GPDF18

The article first appeared in LinkedIn Pulse

We stand at a watershed moment for society’s vast, unknown digital future.  A powerful technology, artificial intelligence (AI), has emerged from its own ashes, thanks largely to advances in neural networks modeled loosely on the human brain.  AI can find patterns in massive unstructured data sets, improve performance as more data becomes available, identify objects quickly and accurately, and, make ever more and better recommendations and decision-making, while minimizing interference from complicated, political humans.  This raises major questions about the degree of human choice and inclusion for the decades to come.  How will humans, across all levels of power and income, be engaged and represented?  How will we govern this brave new world of machine meritocracy?

Machine meritocracy

We need to travel back 800 years: January 1215 and King John of England, having just returned from France, faced angry barons who wished to end his unpopular vis et voluntas (“force and will”) rule over the realm. In an effort to appease them, the king and the Archbishop of Canterbury brought 25 rebellious barons together to negotiate a “Charter of Liberties” that would enshrine a body of rights to serve as a check on the king’s discretionary power.  By June they had an agreement that provided greater transparency and representation in royal decision-making, limits on taxes and feudal payments, and even some rights for serfs. The famous “Magna Carta” was an imperfect document, teeming with special-interest provisions, but today we tend to regard the Carta as a watershed moment in humanity’s advancement toward an equitable relationship between power and those subject to it.  It set the stage eventually for the Enlightenment, the Renaissance and democracy.

Balance of power

It is that balance between the ever-increasing power of the new potentate – the intelligent machine – and the power of human beings that is at stake.   In a world in which machines will create ever more value, produce more of our everyday products with reducing human control over designs and decisions. Existing work and life patterns are changing forever.  Our creation is running circles around us, faster than we can count the laps.

Machine decisions

This goes well beyond jobs and economics: in every area of life machines are starting to make decisions for us without our conscious involvement. Machines recognize our past patterns and those of allegedly similar people across the world. We receive news that shapes our opinions, outlooks and actions based on inclinations we expressed in past actions, or the actions of others in our bubbles. While driving our cars, we share our behavioral patterns with automakers and insurance companies so we can take advantage of navigation and increasingly autonomous vehicle technology, which in return provides us new conveniences and safer transportation. We enjoy richer, customized entertainment and video games, the makers of which know our socioeconomic profiles, our movement patterns and our cognitive and visual preferences to determine pricing sensitivity.

As we continue to opt into more and more conveniences, we choose to trust a machine to “get us right.” The machine will get to know us in, perhaps, more honest ways than we know ourselves — at least from a strictly rational perspective. But the machine will not readily account for cognitive disconnects between that which we purport to be and that which we actually are. Reliant on real data from our real actions, the machine constrains us to what we have been, rather than what we wish we were or what we hope to become.

Personal choice

Will the machine eliminate that personal choice? Will it do away with life’s serendipity? Will it plan and plot our lives so we meet people like us, and thus deprive us of encounters and friction that forces us to evolve into different, perhaps better human beings? There’s tremendous potential in this:  some personal decisions should be driven by more objective analysis, for instance including the carbon footprint for different modes of transportation, integrating this with our schedules and socio-emotional needs, or getting honest pointers on our true talents when making partner choices, or designing more effective teaching plans for diverse student bodies.

Polarization

But it might also polarize societies by pushing us further into bubbles of like-minded people, reinforcing our beliefs and values without the random opportunity to check them, defend them, and be forced to rethink them?  AI might get used for “digital social engineering” creating parallel micro-societies.  – imagine digital gerrymandering with political operatives using AI to lure voters of certain profiles into certain districts years ahead of elections or AirBnB micro-communities only renting to and from certain socio-political, economic or psychometric profiles.  Consider companies being able to hire in much more surgically-targeted fashion, at once increasing their success rates and also compromising their strategic optionality with a narrower, less multi-facetted employee pool.

Who makes judgements?

A machine judges us on our expressed values — especially those implicit in our commercial transactions — yet overlooks other deeply held values that we have suppressed or that are dormant at any given point in our lives. An AI might not account for newly formed beliefs or changes in what we value outside the readily codifiable realm. As a result, it might, for example, make decisions about our safety that compromise the wellbeing of others based on historical data in ways we might find objectionable in the moment. We are complex beings who regularly make value trade-offs within the context of the situation at hand, and sometimes those situations have little or no codified precedent for an AI to process.  Will the machine respect our rights to free will and self-reinvention?

Discrimination and bias

Similarly, a machine might discriminate against people of lesser health or standing in society because its algorithms are based on pattern recognition and broad statistical averages. Uber has already faced an outcry over racial discrimination when its algorithms relied on zip codes to identify the neighborhoods where riders were most likely to originate. Will the AI favor the survival of the fittest, the most liked or the most productive?  Will it make those decisions transparently? What will our recourse be?

Moreover, a programmer’s personal history, predisposition and unseen biases — or the motivations and incentives their employer — might unwillingly influence the design of algorithms and sourcing of data sets. Can we assume an AI will work with objectivity all the time? Will companies develop AIs that favor their customers, partners, executives or shareholders? Will, for instance, a healthcare-AI jointly developed by technology firms, hospital corporations and insurance companies, act in the patient’s best interest, or will it prioritize a certain financial return?

We can’t put the genie back in the bottle, nor should we try – the benefits will be transformative, leading us to new frontiers in human growth and development. We stand at the threshold of an evolutionary explosion unlike anything in the last millennium. Explosions and revolutions are messy, murky, and fraught with ethical pitfalls.

A new charter of rights

Therefore, we propose a Magna Carta for the Global AI Economy — an inclusive, collectively developed multi-stakeholder charter of rights that will guide our ongoing development of artificial intelligence and lay the groundwork for the future of human-machine co-existence and continued more inclusive human growth.  Whether in an economic, social or political context, we as a society must start to identify rights, responsibilities and accountability guidelines for inclusiveness and fairness at the intersections of AI with our human lives. Without it, we will not establish enough trust in AI to capitalize on the amazing opportunities it could afford us.

About the authors:

Dr. Olaf Groth, Ph.D. is CEO of Cambrian.ai, a network of advisers on the global innovation economy for executives and investors. He serves as Professor of Strategy, Innovation & Economics at Hult International Business School, Visiting Scholar at UC Berkeley’s Roundtable on the International Economy, and the Global Expert Network member at the World Economic Forum.

Dr. Mark Nitzberg, Ph.D. is Executive Director of the Center for Human-Compatible AI at the University of California at Berkeley.  He also serves as Principal & Chief Scientist at Cambrian.ai, as well as advisor to a number of startups, leveraging his combined experience as a globally networked computer scientist and serial social entrepreneur.

Dr. Mark Esposito, Ph.D., is a socio-economic strategist and bestselling author, researching MegaTrends, Business Model Innovations and Competitiveness. He works at the interface between Business, Technology and Government and co-founded Nexus FrontierTech, an Artificial Intelligence Studio.

When CE models are combined with IoT, internet connected devices that gather and relay data to central computers, efficiency skyrockets. Because finite resource are depleting, the future economy is destined to become more circular. The economic shift toward CE will undoubtedly be hastened by the already ubiquitous presence of IoT, its profitability, and the positive public response it yields.

Unlike the linear economy which is a “take, make, dispose” model, the circular economy is an industrial economy that increases resource productivity with the intention of reducing waste and pollution.  The main value drivers of CE are (1) extending asset use cycle lengths (2) increasing asset utilization (3) looping/cascading assets through additional use cycles and (4) regeneration of nutrients to the biosphere.

The Internet of Things is the inter-networking of physical devices through electronics and sensors which are used to collect and exchange data. The main value drivers of IoT are the ability to define (1) location (2) condition and (3) availability of the assets they monitor. By 2020 there are expected to be at least 20 million IoT connected devices worldwide.

The nexus between CE’s and IoT’s values drivers greatly enhances CE.  If an institution’s goals are profitability and conservation, IoT enables those goals with data big data and analysis. By automatically and remotely monitoring the efficiency of a resource during harvesting, production, and at the end of its use cycle, all parts of the value chain can become more efficient.

When examining  the value chain as a whole, the greatest uses for IoT is at its end. One way by which this is accomplished is through reverse logistics. Once the time comes for a user to discard their asset, IoT can aid in the retrieval of the asset so that it can be recycled into its components. With efficient reverse logistics, goods gain  a second life, less biological nutrients are extracted from the environment, and the looping/cascading of assets is enabled.

One way to change the traditional value chain is the IoT enabled leasing model. Instead of selling an expensive appliance or a vehicle, manufacturers can produce them for leasing. By embedding these assets with IoT manufacturers can monitor the asset’s condition; thereby dynamically repairing the assets at optimal times. In theory the quality of the asset will improve, since it is in the producer’s best interests to make it durable rather than replaceable.

Many sectors are already benefiting from IoT in resource conservation. In the energy sector, Barcelona has reduced its power grid energy consumption by 33%, while GE has begun using “smart” power meters that reduce customers power bills 10-20%. GE has also automated their wind turbines and solar panels; thereby automatically adjusting to the wind and angle of the sun. In the built environment, cities like Hong Kong have implemented IoT monitoring for preventative maintenance of transportation infrastructure, while Rio de Janeiro monitors traffic patterns and crime at their central operations center.

Despite the many advantages there are numerous current limitations. Due to difficulty in legislating for new technologies, Governmental regulation lags behind innovation. For example, because Brazil, China, and Russia do not have legal standards to distinguish re-manufactured  products from used ones, cross-border reverse supply-chains are blocked. Reverse supply chains are also hurt by current lack of consumer demand , which is caused by low residual value of returned products. IoT technology itself  generates major privacy concerns. Questions arise like: who owns this data collected? How reliable are IoT dependent systems? How vulnerable to hackers are these assets? Despite the prevalence of IoT today, with 73% of companies invest in big data analytics, most of that data is merely used to detect and control anomalies and IoT remains vastly underutilized. Take an oil rig for example, it may have 30,000 sensors, but only 1% of them are examined. Underutilization of IoT in 2013 cost businesses an estimated 544 billion alone.

Even with these current barriers, because of the potential profits and increased social performance, the future implementation of an IoT enhanced CE is bright. As government regulation catches up and technology improves, recycling and conservation will become more profitable and reverse supply chains  can proliferate. When the ownership of collected data is finally clearly defined by laws, then the interoperability of IoT data can take hold to increase its efficiency.  Estimates are that the potential profits from institutions adopting CE models could decrease costs by 20%, along with significant waste reduction.

Problems can be, and need to be, overcome, once the need for a more inclusive and fairer solution becomes the imperative, and the old capitalist model is reimagined into something that gains buy in through all societies, such that institutions gain long term benefits and regain much of the trust they have lost in their quest for narrow gains. Corporate life needs to be much more win-win and much less a zero sum game to ensure its, and societal, long term survival.

About the author:

Mark Esposito is a socio-economic strategist and bestselling author, researching MegaTrends, Business Model Innovations and Competitiveness. He works at the interface between Business, Technology and Government and co-founded Nexus FrontierTech, an Artificial Intelligence Studio.

EDIE has already shown itself to be of value in the circular economy. For instance, the sharing economy, one of the most successful branches of the circular economy, has roots in EDIE. Airbnb, the tech company that created a marketplace for short-term housing space in people’s homes and properties, was able to begin scaling its business through the San Francisco-based incubator YCombinator, a member of the Silicon Valley-area EDIE. YCombinator helped provide Airbnb with seed money as well as make introductions within the EDIE and teach the founders how to pitch to investors in the area. Similarly, the car-sharing company Uber started out with RocketSpace, another startup accelerator in the Silicon Valley EDIE. Car-sharing, which has enabled us to go from car-as-commodity to car-as-transportation-service, keeps congestion down and makes it possible to spend fewer resources on making more cars through offering excess capacity of existing cars.

So what should any party interested in the circular economy do when it comes to participating in an EDIE?

For startups and established businesses: Partner with the Ellen MacArthur Foundation. 

One of the leading organizations on circular economy thinking, the Ellen MacArthur Foundation, functions as the nucleus of an EDIE by providing easy channels for business organizations to connect and collaborate. By approaching the foundation as a partner, businesses can get the right introduction with potential business collaborators. For example, as a partner with the foundation, the Dutch company Philips was able to pilot a new model in which Philips managed the lighting for RAU Architects. Rather than RAU Architects buying light bulbs and hiring maintenance men, the firm had Philips manage lighting, or what they called selling light as a service. Founder Thomas Rau said at the time, “I told Philips, ‘Listen, I need so many hours of light in my premises every year. If you think you need a lamp, or electricity, or whatever – that’s fine. But I want nothing to do with it. I’m not interested in the product, just the performance. I want to buy light, and nothing else.”[1] The end result was that Philips helped the firm reduce energy usage by 55% through motion detectors and remote systems management, LED lights and leveraging sunlight. RAU Architects pays for the service and maintenance, while Philips maintains ownership and control over its parts, which enables the company to repair and retrofit its products as necessary.

For individuals and managers: Leverage technology.

Reusing, recycling, repurposing and regenerating means that no single nation can do it on its own; at the same time, it’s not possible to always be in the same room. Not only that but the circular economy concept is still in the process of spreading; hence, participants must utilize all that technology has to offer—modern communication that allows for real-time contact—to find potential partners and collaborators, wherever they may be. The Ellen MacArthur Foundation has capitalized on this notion by hosting the annual Disruption Innovation Festival. Much of the three-week-long event takes place via the Internet through live streams, webinars, Google Hangouts, and digital archiving to discuss relevant topics and bring like-minded entrepreneurs and business leaders together. The most recent festival featured more than 500 speakers across 270 sessions and 20,000 participants from 181 countries.

For policymakers and government officials: Support and nurture growing circular economy EDIE. Forming government-sponsored networks, think tanks, and forums targeted at inventors, entrepreneurs, intrapreneurs, investors, researchers and academic will promote confidence and trust in a national commitment toward fostering innovation and circular economic principles. Creating interactive tools will also encourage parties to explore the networks, seek out new partners and ideas, and offer ideas to others.

With the right support, bringing circular economy entrepreneurs and intrapreneurs together with EDIE, a demonstrated model for promoting innovation and economic growth, is a path toward creating a realistic and sustainable tomorrow.

About the author:

Mark Esposito is Professor of Business & Economics at Grenoble School of Management & Harvard University’s Division of Continuing Education

[1] Ellen MacArthur Foundation. “Selling Light as Service.” https://www.ellenmacarthurfoundation.org/case-studies/selling-light-as-a-service.

The EU needs to invest in an entrepreneur-driven innovation ecosystem to foster recovery and new prosperity. It must shift its core understanding of entrepreneurship away from a profit-driven model and the industrial conglomerates, toward a model driven by value-seeking disruptive entrepreneurs, which are able to generate new products and markets and naturally new jobs. They’re different from small business entrepreneurs because they have different modus operandi: rather than merely seeking profit or accounting balances, they seek out market failures, overturn existing networks and structures, and create value and new markets. They innovate to break open and redefine entire spaces in the economy, and then existing blue chip corporations follow into these new and enlarged markets with greater growth expectations. The innovation driven entrepreneurs define a new gravitational pool of innovators, incubators, business angels and crowd-funders. It is an ecosystem in the way these actors interact, exchange and create jointly.

One classic example is of Henry Ford and his Model T. It was the lower cost, mass-produced version of the automobile, not the invention of the automobile, that upturned the standard mode of transportation of the time, the horse-drawn carriage. Europe needs this special breed of entrepreneurs because they help create disruptive innovation and economic resilience by offering new growth potential where old growth has slowed.

Building an entrepreneurial ecosystem requires action from multiple stakeholders, including governments, corporations, start-ups, private equity, research labs, and universities. We find that the following steps may be necessary for policymakers to initiate and foster the facilitation of such an environment:

• Remove bureaucratic obstacles that block their flows, like high taxes and rigid labor laws in order to achieve serendipitous collisions of ideas, IP, capital, and talent. This would help reduce the financial risk for entrepreneurs and investors to enter into new ventures that have the potential to flourish and lift up the economy.
• Establish links between public servants with research labs, startups, and corporations, in order to create dedicated think tanks, designed to serve the ongoing dialogue among entrepreneurs and the territories.
• Test ideas for government intervention and implement large-scale initiatives to jumpstart entrepreneurial activity through beta version, prototypes and pilots, capable of raising the learning cycle and the opportunities arising from a more systemic integration of ideas.

One clear example of the above recommendations can be found, for instance, in Denmark’s innovation lab, MindLab, is one example of this kind of innovation think tank which nicely gels stakeholders of the same system. MindLab worked with highly skilled professionals in Denmark to develop a social network that would create the right incentives for highly skilled workers to stay in Denmark instead of leaving the country. This is achieved though a collaborative model among parties.

The next step would be to develop a tool that measures and connects entrepreneurial activity. For instance, in the U.S., innovation labs like MIT’s Media Lab created a visual network called Macro Connections to connect communities with research data and methods. The tool allows actors to seek out others with similar research in order to facilitate innovation, share information, and pursue collaborations.

Through more accommodating laws, collaboration, and technology, policymakers can bring researchers, entrepreneurs, and public servants together to identify opportunities for disruptive economic change to better Europe. Innovation ecosystems, when generated by the interface of policy, innovation and institutions for collaboration help economies withstand drastic economic shocks and financial bubbles.

About the authors:

*Mark Esposito is Professor of Business & Economics at Grenoble School of Management & Harvard University’s Division of Continuing Education

Terence Tse is Associate Professor of Finance at ESCP Europe Business School

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