As exponentially advancing digital technology transforms so much of work and the world, questions inevitably arise about the place of the human being. Some warn of a diminishing role for the human, such as in decision-making, starting perhaps with the simple tasks now performed by chatbots, but soon enough in more creative problem-solving. It is easy to imagine technology’s superhuman powers – its advanced algorithms, deep learning networks, and other AI strengths, its Blockchain dynamics, big data processing, and so forth—taking us inexorably toward the Singularity Ray Kurzweil envisions.

The questions are all the more urgent given the rate of change around us. It’s true that the future is always uncertain – remember the old quip, “prediction is difficult, especially about the future” – but today we are faced with heightened, and seemingly increasing, turbulence. Many look to technology to provide the confidence needed to navigate through uncertainty.

Yet there is a countercurrent emerging that calls for reasserting the human role. The experience of cities and nations responding to the COVID-19 crisis has emboldened these voices, as it highlights the human creativity and judgment essential not only to balance competing social and ethical priorities but to accomplish scientific breakthroughs and overcome logistical challenges. The same countercurrent is rising inside organizations where “data-driven” decision-making so often falls short of the sound judgment that, however tainted by cognitive biases, combines science with common sense.

As economic, fiscal, cultural, and political crises escalate in the wake of pandemic, the tension between the technocratic and the humanistic forces is reaching a breaking point. The former see a time of upheaval as an opportune moment to effect a large-scale “reset” to a system currently flawed in many ways. The latter reject any such revolutionary redesign as inimical to human nature which craves, as Peter Drucker put it, a balance between “change and continuity.” Which is the best way forward, and how can we ensure that it prevails? 

Leading thinkers at our 2021 Forum will grapple with important questions including but not limited to the following:

• Must there be a human imperative at the core of organizations? How would we define it? What threatens it most today? How could good management serve it better?
• Forced to make decisions under highly dynamic conditions, should organizations rely more heavily on data and analytics? What are the risks of moving away from human judgment?
• Do we need better ways of discovering truth and thinking through the complex issues of our time? What insights should we take from philosophy, psychology, and other realms to prepare our minds for the age of AI?
• What should we hope for—and fear—in the aftermath of a year of remote working? Will less in-person contact become the norm? How might human beings as social animals and community builders respond?
• What lessons can we take from the Covid-19 crisis about the clashing perspectives of scientific experts, policymakers, business leaders, and ordinary citizens and workers—and how they should be prioritized or integrated to best serve the needs of humanity? 
• As in every time of upheaval, some today say we should not let “a serious crisis go to waste.” But is seizing the chance to enact sweeping change a humane impulse? What can we learn from the history of sudden revolutions, whether political, cultural, or organizational?
• Central to the human condition is the ability to learn from evidence and experience—both our own and others’. How is it, then, that human organizations prove so resistant to collective learning? How do we stop making the same mistakes?
• What changes to management education would better equip managers with the knowledge and competences they need today? Are there useful models to be found in how other professions are mastered?
• Peter Drucker insisted that to be a change leader, an organization must also “establish continuity internally and externally.” But that human-friendly balance he advised means nothing to a computer. Is it still valid as a principle for management?
• So much of recent human achievement has resulted from growing capabilities in administration and leadership that the past hundred years have been called “the management century.” How can we extend that run and make even greater progress in the future?

This article is one in the “shape the debate” series relating to the 13th Global Peter Drucker Forum, under the theme “The Human Imperative” on November 10 + 17 (digital) and 18 + 19 (in person), 2021.
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Or, just as bad, you’ll trust your instincts on a matter where a straightforward data analysis would expose how off-base your understanding is.

Mistakes like this happen all the time, because different kinds of human effort require different kinds of knowledge. This is no novel claim of our own — it’s only what Aristotle explained more than 2,000 years ago. He outlined distinct types of knowledge required to solve problems in three realms. Techne was craft knowledge: learning to use tools and methods to create something. Episteme was scientific knowledge: uncovering the laws of nature and other inviolable facts that, however poorly understood they might be at the moment, “cannot be other than they are.” Phronesis was akin to ethical judgment: the perspective-taking and wisdom required to make decisions when competing values are in play — when the answer is not absolute, multiple options are possible, and things can be other than what they are. If you’re a farmer designing an irrigation system or a software engineer implementing an agile process, you’re in the techne realm. If you’re an astronomer wondering why galaxies rotate the way they do, you’re in the epistemic realm. If you’re a policymaker deciding how to allocate limited funds, you’re in the phronesis realm.

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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The reason that Aristotle bothered to outline these three kinds of knowledge is that they require different styles of thinking—the people toiling in each of these realms tend toward habits of mind that serve them well, and distinguish them from the others. Aristotle’s point was that, if you have a phronetic problem to solve, don’t send an epistemic thinker.

But imagine that you’re a leader of a large enterprise that has challenges cropping up regularly in all three of these realms. There are plenty of techne problems as you work to adopt effective methods and tools in your operations. You also have epistemic challenges; anything you approach as an optimization problem (like your marketing mix or your manufacturing scheduling) assumes there is one absolutely right answer out there. And firmly in the realm of phronesis would be anything you label a “strategic” matter — decisions on mergers and new product launches, for example, involving trade-offs and recognizing that the future holds various possibilities. As a leader presiding over such a multifaceted organization, it’s a big part of your job to make sure the right kinds of thinking are being marshaled to make those different kinds of decisions. This means that you personally need to have some facility with all the different modes of thinking — at least enough to recognize which one is the best fit to a given problem, and which people are particularly adept at it.

That’s all the more true for the largest leadership challenges in the modern world, those that are scoped so broadly and are so complex that all these kinds of thinking are called for by one problem, in one facet or another. Think, for example, of a corporation facing a liquidity crisis. Its leaders need to marshal epistemic expertise to discover the optimal resolution of loan covenants, issuance restrictions, and complex financial instruments — and the phronetic judgment of where short-term cuts will do least damage in the long run.

This brings us to the Covid-19 global pandemic and the challenges it has presented to leaders at all levels — in global agencies, national and local governments, and businesses large and small. To be sure, almost all of the world was blindsided by this catastrophe and early missteps were unavoidable, particularly given misinformation at the outset. Still, it has now been 10 months since patient zero. How can the devastation still be running so rampant — and have segued, unchecked, from deadly disease to economic disaster?

Our diagnosis, not as medical experts but as students of leadership, is that many leaders stumbled in the fundamental step of determining the nature of the challenge they faced and identifying the different kinds of thinking that had to be brought to bear on it at different points.

In the early weeks of 2020, Covid-19 presented itself as a scientific problem, firmly in the epistemic realm. It immediately raised the kinds of questions to which absolute right answers can be found, given enough data and processing power: What kind of virus is it? Where did it come from? How does transmission of it happen? What are the characteristics of the worst-affected people? What therapies do most to help? And that immediate framing of the problem caused leaders — and the people they influence — to put enormous weight on the guidance of epistemic thinkers: namely, scientists. (If one phrase should go down in history as the mantra of 2020, it is “follow the science.”)

In the U.K., for example, this translated to making decisions based on a model produced by researchers at Imperial College. The model used data collected to date to predict how the virus would spread in weeks to come (quite inaccurately, unfortunately). At the frequent meetings of the Scientific Advisory Group for Emergencies there was one government official in attendance, and early on, he tried to inject some practical and political considerations into the deliberations. He was promptly put in his place: He was only there to observe. Indeed, members expressed shock that someone from the world of hashing out policy would try to have influence on “what is supposed to be an impartial scientific process.”

But the reality was that, while scientific discovery was an absolutely necessary component of the response, it wasn’t sufficient, because what was happening at the same time was an escalation of the situation as a social crisis. Very quickly, needs arose for tough thinking about trade-offs — the kind of political deliberation that considers multiple dimensions and is informed by different perspectives (Aristotle’s phronetic thinking). Societies and organizations desperately needed reliable processes for arriving at acceptable balances between factors of human well-being too dissimilar to plug into neat equations. Pandemic response was not, as it turned out, a get-the-data-and-crunch-the-numbers challenge — but since it had been cast so firmly as that at the outset, it remained (and remains) centered in that realm. As a result, leaders were slow to begin addressing these societal challenges.

What was the alternative? What should a great leader do in such a crisis? We believe that the right approach with the Covid-19 pandemic would have been to draw on all the relevant, epistemic knowledge of epidemiologists, virologists, pathologists, pharmacologists, and more — but to ensure that the scope of the problem was understood as broader than their focus. The tendency of the epistemic habit of mind is to go narrow, into pockets of science where it is possible to arrive at absolute, can’t-be-otherwise answers. The right approach would have been to factor those contributions into what was understood from the outset to be a sprawling, complex system of a challenge that would also call on holistic thinking and values-balancing decisions. If leaders had from the outset framed the pandemic as a crisis that would demand the highest level of political and ethical judgment, and not just scientific data and discovery, then decision-makers at all levels would not have found themselves so paralyzed — regarding, for example, mask mandates, prohibitions on large gatherings, business closures and re-openings, and nursing home policies — when testing results proved so challenging to collect, compile, and compare.

We admit we are painting with a broad brush here, undoubtedly some leaders balanced competing priorities and managed the calamities of 2020 more effectively than others. Our objective here is not to point fingers but simply to use the extremely prominent example of Covid-19 to underscore a fundamental but under-appreciated responsibility of leadership.

Part of your job as a leader is to frame the problems you want people to apply their energies to solving. That framing begins with comprehending the nature of a problem, and communicating the way in which it should be approached. Calling for everyone to weigh in with their opinions on a problem that is really a matter of data analysis is a recipe for disaster.  And insisting on “following the science” when the science cannot take you nearly far enough is a way to paralyze and frustrate people beyond measure.

This ability to size up a situation and the kinds of knowledge it calls for is a skill you can develop with deliberate practice, but the essential first step is simply to appreciate that those different kinds of knowledge exist, and that it’s your responsibility to recognize which ones are called for when. Aristotle’s efforts notwithstanding, most leaders haven’t thought much about realms of knowledge and what problems they can solve. Expect that to change as enterprises, and societies, take on increasingly complex and large-scale challenges — and leaders are increasingly judged on the thinking that goes into them.

This article was originally published at HBR.

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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Some, for example, are inspired by Thomas Piketty, who seems to have singlehandedly set a new agenda for economics research. This group focuses on reducing the disturbing inequalities in individuals’ incomes and wealth.

Others, like the Legatum Institute, think of prosperity less in financial terms and more as overall well-being, and focus on measuring and growing all its components in societies around the world.

A third group takes a more managerial approach; and that’s the one we want to focus on here. When Eric Beinhocker and Nick Hanauer took on the topic, they put it this way: “Prosperity in a society is the accumulation of solutions to human problems.” By emphasizing solutions as the engine of growth, Beinhocker and Hanauer wanted to cast capitalism as a force for prosperity (as the system that churns out the most constant stream of superior ones). But their way of thinking about prosperity also offers direction for any managers who want to work harder to make the world better off: your mission is to imagine, develop, and launch more life-improving solutions, especially the kinds of goods and services that improve ordinary people’s lives. Businesses have a variety of social responsibilities, but the essential one—and the main reason that private enterprise is given license to operate—is to innovate.

We’d like to add a wrinkle to Beinhocker and Hanauer’s argument. If we’re thinking about prosperity in broad terms, then we should also recognize it isn’t just the solutions themselves that improve quality of life – it’s also engagement in the act of solving. Participating in the satisfying work of innovating enriches lives by endowing them with purpose, dignity, and the sheer joy of making progress in challenging endeavors. Imaginative problem-solving is part of human nature. Participating in it is essential to the good life – and no elite minority should have a monopoly on that.

So this raises the question: How do we enable more people to get involved in solving problems? Every person is capable of creative thought and action. Great managers know how to tap that superabundant resource, and they recognize that pooling creative energy usually accelerates progress. Many minds make lighter work.

But for this to happen broadly, more organizations need to recognize that their innovation mandate is not just to design new products and services, but also to redesign how work gets done.  The digital age gives us a tremendous opportunity to do that – but also comes with its own challenges and risks. How businesses continue to develop and deploy information and communications technologies will profoundly affect whether prosperity is inclusive or exclusive. At their best, today’s increasingly capable machines enable and empower people to collaborate more effectively, and they make learning from experience scalable. Collaborative platforms allow people to combine their measurements and observations of large-scale phenomena (such as water quality), while advances in machine learning, artificial intelligence, and sheer computational power extend the powers of human intellect just as earlier technologies amplified human strength.

But at their worst, smart machines have the potential to marginalize human contributions, automating cognitive work and leaving society with, as Bill Davidow and Michael Malone vividly phrased it, “hordes of citizens of zero economic value.” The situation creates huge responsibilities for politicians, educators, executives, and others to manage the transition and the hardships that may come with it.

We find ourselves, therefore, at an important crossroads. The technologies our species is developing might either hold the keys to unlocking human potential — or to locking it up more tightly than ever. Indeed, they could even transform what we think of as human potential, given the startling new combinations of technological and human capabilities being devised. (No need to wait for Elon Musk’s Neuralink. As DARPA’s Arati Prabhakar has described, the merging of humans and machines is happening now. )

Clay Christensen likes to remind innovators of the importance of remembering the essential “job to be done” by their offerings – what is it that customers “hire” your product or service to do for them? In that spirit, what is the “job to be done” by the practice of management itself? What is the job that society needs to get done that it turns to competent managers to do? Increasingly, that job is not only to produce better goods and services more efficiently, but to organize individuals to collaborate and create together in unprecedented ways.  The business leaders who get that job done will be those who make the most of human potential, and manage to make prosperity inclusive.

This post is the first in a series leading up to the 2017 Global Drucker Forum in Vienna, Austria – the theme of which is Growth and Inclusive Prosperity.

Originally posted on https://hbr.org/, 3 April 2017.

About the authors:

Richard Straub founded the nonprofit Peter Drucker Society Europe after a 32-year career at IBM. He is on the executive committee of the European Foundation for Management Development, is Secretary General of the European Learning Industry Group, and serves the IBM Global Education practice in a strategic advisory role.

Julia Kirby is a senior editor at Harvard University Press and longtime contributor to HBR‘s pages. Her newest book (May 2016) is Only Humans Need Apply: Winners and Losers in the Age of Smart Machines, with Tom Davenport. Follow her on Twitter @JuliaKirby.

Many jobs include tasks that can and will be automated, but by the same token, almost all jobs have major elements that — for the foreseeable future — won’t be possible for computers to handle. Our advice therefore can’t boil down to a clear “avoid careers in a, b, and c” or “apply for jobs x, y, or z.” And yet, we have to admit that there are some knowledge-work jobs that will simply succumb to the rise of the robots. They are just too thoroughly composed of work that can be codified into standard steps and of decisions based on cleanly formatted data. A perfect example has just come up in the news. The headline as the Wall Street Journal writes it is this: “Financial Firms Turn to Artificial Intelligence to Handle Compliance Overload.”

Compliance, of course, refers to a company’s obligation to prove that it is following the rules spelled out by government regulators. In a financial services firm, that includes constant monitoring of possible money laundering, transactions subject to sanctions, or billing fraud, and preparedness for “know your customer” checks. All these are now being done, WSJ’s Ben DiPietro reports, by machines equipped with natural language processing systems.

But compliance with regulations isn’t only demanded of banks. Compliance professionals work in every kind of business – from health care companies challenged by legislation to food companies under a regulator’s watchful eye to airlines obliged to track anti-terrorism data. Job growth in the compliance category has far outpaced most fields in the past decade – but virtually all of its recordkeeping and communication is crying out for automation.

Compliance is ripe for automation because it is both rule-based and data-intensive. The more rules there are to follow, the more employee behavior there is to monitor, the more customer and employee transactions there are generating data—the more you need automated software to monitor compliance. The U.S. Congress or the European Union can throw all the regulations they want at banking and other industries, but politicians and bureaucrats are no match for today’s cognitive technologies. It’s hard to imagine complying with all the compliance regulations in some industries without automated help.

Not all the jobs in compliance will go away—often computers only suggest a likelihood of rule-breaking, leaving it to a person to investigate further before acting on that red flag—but many routine and information-intensive tasks will be taken away from human workers. There will undoubtedly be layoffs. Compliance workers will either be looking for work or lonelier at work, and that stinks. (And by the way, we sympathize with the fact that, just two years ago, people didn’t see this coming. The WSJ for example reported as recently as 2014 that the future was “very bright for anyone entering into compliance as a career.”)

At the level of a national economy, however, how much should we protest this particular line of labor dislocation?

Last fall, we had the pleasure of participating in the Global Peter Drucker Forum (an annual meeting of the minds in Vienna becoming known as the “Davos of management”) and we therefore spent some time brushing up our Drucker. One chapter of his work we found particularly interesting was about the “Entrepreneurial Society” that policymakers should be working harder to shape. Writing in the early eighties, Drucker was especially concerned about one major drag on entrepreneurial activity: the high cost of following ever more onerous regulations. He writes of “that dangerous and insidious disease of developed countries: the steady growth in the invisible cost of government”:

It is a real cost in money and, even more, in capable people, their time, and their efforts. The cost is invisible, however, since it does not show in governmental budgets but is hidden in the accounts of the physician whose nurse spends half her time filling out governmental forms and reports, in the budget of the university where sixteen high-level administrators work on “compliance” with governmental mandates and regulations, or in the profit-and-loss statement of the small business nineteen of whose 275 employees, while being paid by the company, actually work as tax collectors for the government, deducting taxes and Social Security contributions from the pay of their fellow workers, collecting tax-identification numbers of suppliers and customers and reporting them to the government, or, as in Europe, collecting value-added-tax (VAT).

Drucker’s complaint is that, in a world sorely in need of new solutions, these overhead costs constitute serious opportunity costs: “Does anyone, for instance, believe that tax accountants contribute to national wealth or to productivity, and altogether add to society’s well-being, whether material, physical or spiritual?” He points out that by forcing companies to devote people to such jobs, governments are misallocating “a steadily growing portion of our scarcest resource” – that is, well-educated human intellect — to “essentially sterile pursuits.”

Drucker thought of one solution to propose (we’ll let you read the chapter if you’re curious) but even he conceded it would never be accepted. Now, however, thirty-plus years later, another one is presenting itself. Artificial intelligence, by doing the sterile work of compliance, might support more entrepreneurial innovation without any compromise of the public interest.

When we talk about how smart machines should be deployed in workplaces, we constantly emphasize the importance of augmentation rather than automation. Employers, we insist, should implement cognitive computing solutions not so that they can make do with fewer people, but to enable their people to take on bigger challenges and have greater impact than they did before. Applying smart machines to the work of compliance has the potential to augment human work on an epic scale. By freeing up humans to work on more value-creating projects, it can promote the entrepreneurial society and enable the innovation that is our best hope of enhancing human well-being.

About the authors:

Julia Kirby is a senior editor at Harvard University Press and longtime contributor to HBR‘s pages. Her newest book (May 2016) is Only Humans Need Apply: Winners and Losers in the Age of Smart Machines, with Tom Davenport. Follow her on Twitter @JuliaKirby.

Thomas H. Davenport is the president’s distinguished professor in management and information technology at Babson College, and cofounder of the International Institute for Analytics. He also contributes to the MIT Initiative on the Digital Economy as a fellow, and as a senior advisor to Deloitte Analytics. Author of over a dozen management books, his latest is Only Humans Need Apply: Winners and Losers in the Age of Smart Machines. 

The two are not as incompatible as it might seem. Colvin’s point in the earlier book was that talented people always succeed in the context of a system, and it’s hard to rate talent independent of its context. As a result, stars usually get more credit for their successes than they’re due. (Boris Groysberg’s research backs this up by showing how the high performance of stars in various fields turns out not to be portable when they are recruited away by other employers.)  Indeed, it’s often a well-designed system that makes someone valuable; the best systems are able to get “A” results out of “B” players. If you can build that kind of system as an enterprise, there is no reason to break the bank recruiting superstars or otherwise allow the top percentiles of your talent to walk away with “winner takes all” rewards.

As a follow-up, however, the point Colvin is underscoring in the new book is that the effective organizational system isn’t just a mechanistic one of capital investment. It’s a human system that relies heavily on unique human capabilities. So collectively, human talent is not overrated; it is extremely valuable. That’s an important truth to assert in an era when smart machines are taking over so many tasks that were in the past human contributions, including not only manual but increasingly knowledge work.

Colvin’s primary argument is that there are some unique human capabilities, like empathy and storytelling, that will keep people employable even as automation chips away at the content of most jobs. He further contends that, even in areas where machines do match or exceed human capabilities, there will still be an insistence that certain tasks and decisions remain in the hands of humans.  In courts of law, for example, we humans will not stand to be judged by non humans. When we arrive in a medical office to hear a diagnosis, or pay to be entertained by either comedy or drama, we’ll demand it come from someone who shares the human condition. The claim sounds plausible, though Colvin offers it as more of a prediction than an assertion with any empirical backing.

The question is: who is Colvin trying to convince that humans are underrated? To a large extent, he’s speaking directly to us humans, who may well lack confidence that we can continue to provide a superior value proposition relative to advancing technology. Colvin assures us we can, if we stop trying to win the race with the machines and instead run our own race, drawing on the strengths we have that cannot or will not be programmed into computers. The last lines of an article he excerpted from the book express it very nicely:

Staking our futures to our profoundest human traits may feel strange and risky. Fear not. When you change perspectives and look inward rather than outward, you’ll find that what you need next has been there all along. It has been there forever.

In the deepest possible sense, you’ve already got what it takes. Make of it what you will.

But it isn’t enough to convince ourselves and our fellow worker bees – who are eager to be convinced in any case. The reason Colvin’s argument is important is because he is speaking through the megaphone of Fortune magazine to the real audience that has to be convinced: the management community. In our highly competitive economy, managers may be too easily seduced by the apparent advantages of automation. In relentless pursuit of lower costs and greater throughput, they might miss the fact that advantages in storytelling, judgment, and other human strengths are much harder for competitors to replicate.

Our hope is that many managers will be persuaded by the instructive examples Colvin offers. His favorite, Southwest Airlines, certainly doesn’t lack for press about its positive organizational culture and cheerful customer-facing employees, but the example makes a more nuanced point about the contribution of people in a capital-intensive business. Southwest operates in an industry that has long been obsessed with asset utilization as the key to competitiveness. And making the minute-by-minute decisions required to maximize asset utilization is unquestionably done better by smart machines.

But optimizing asset utilization isn’t enough to sustain a competitive advantage. The problem with it is that, once smart machines are built to solve problems in asset efficiency (or indeed any area of operations) they very rapidly spread and become pervasive across an industry. Therefore, they cease to provide a competitive advantage. In airlines, for example, seat pricing and crew scheduling optimization systems are practically part of the woodwork. Like ATMs in banks, they gave their originators a fleeting advantage but quickly resolved into a new normal. What they did not and will not create is an enduring competitive advantage.

For that, you will always need good people. And you need a system that engages them and allows what is unique and valuable about individual people to be leveraged – not a system that compels people to perform standardized acts in the same way and therefore commoditizes them as undifferentiated human resources.

This is Southwest’s advantage, and the lesson other companies should take away from it. Invest in the machines, but don’t expect them to reduce your reliance on people. Business isn’t chess; smart machines can’t win the game for you in the long run. The best that they will do for you is to augment the strengths of your people – you know, all those people you are at risk of underrating.

Byine:

Tom Davenport and Julia Kirby are working on a book exploring the increasing automation of knowledge work and how workers can respond (forthcoming, spring 2016, HarperCollins). Tom Davenport is the President’s Distinguished Professor of Information Technology and Management at Babson College, the co-founder of the International Institute for Analytics, a Fellow of the MIT Center for Digital Business, and a Senior Advisor to Deloitte Analytics. Julia Kirby is editor at large at Harvard Business Review. Follow her on Twitter @JuliaKirby.