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.