Machines & Managers – Changing Leadership Equation

Reported by: |Updated: March 2, 2020

Can there be a decision-making algorithm? Or as a step ahead, can such a decision-making algorithm assume the position of a board member in a corporate?
Yes, according to a new report by McKinsey. It says Deep Knowledge Ventures, a Hong Kong VC firm, has ‘appointed’ such a decision-making algorithm on its board of directors.
Citing this as an instance of how leadership equations are changing with technology inching ahead to take over critical functions, the authors of the report, Martin Dewhurst and Paul Willmott, who are directors in McKinsey’s London office, argue: “…the advances of brilliant machines will astound us, but they will transform the lives of senior executives only if managerial advances enable them to. There’s still a great deal of work to be done to create data sets worthy of the most intelligent machines and their burgeoning decision-making potential. On top of that, there’s a need for senior leaders to ‘let go’ in ways that run counter to a century of organizational development.”

The two are, however, confident that if these two things happen – and they are sure they’re likely to – the role of the senior leader will evolve. “We’d suggest that, ironically enough, executives in the era of brilliant machines will be able to make the biggest difference through the human touch. By this we mean the questions they frame, their vigor in attacking exceptional circumstances highlighted by increasingly intelligent algorithms, and their ability to do things machines can’t. That includes tolerating ambiguity and focusing on the ‘softer’ side of management to engage the organization and build its capacity for self-renewal,” they say.
The report points out to instances of IBM supercomputer Watson, which has the potential to predict oncological outcomes more accurately than physicians by reviewing, storing and learning from reams of medical journal articles. In contrast, many organizations may have access to big data, but “the executives in these companies often find themselves beset by ‘polluted’ or difficult-to-parse data, whose validity is subject to vigorous internal debates.”

The McKinsey authors feel that as artificial intelligence grows in power, the odds of sinking under the weight of valuable insights grow as well. “The answer isn’t likely to be bureaucratizing information, but rather democratizing it: encouraging and expecting the organization to manage itself without forcing decisions upward. Business units and company-wide functions will of course continue reporting to the top team and CEO. But emboldened by sharper insights and pattern recognition from increasingly powerful computers, business units and functions will be able to make more and better decisions on their own. Reviewing the results of those decisions, and sharing the implications across the management team, will actually give managers lower down in the organization new sources of power vis-à-vis executives at the top. That will happen even as the CEO begins to morph, in part, into a chief experimentation officer, who draws from acute observance of early signals to bolster a company’s ability to experiment at scale, particularly in customer-facing industries,” they visualize.
The report predicts that in a world where artificial intelligence supports all manner of day-to-day management decisions, the need to ‘let go’ will be more significant and the discomfort for senior leaders higher.

If senior leaders successfully fuel the insights of increasingly brilliant machines and devolve decision-making authority up and down the line, what will be left for top management to do?
“A great deal,” says the report. “Asking the right questions of the right people at the right time is a skill set computers lack and may never acquire. To be sure, the exponential advances of deep-learning algorithms mean that executive expertise, which typically runs deep in a particular domain or set of domains, is sometimes inferior to (or can get in the way of) insights generated by deep-learning algorithms, big data, and advanced analytics. In fact, there’s a case for using an executive’s domain expertise to frame the upfront questions that need asking and then turning the machines loose to answer those questions. That’s a role for the people with an organization’s strongest judgment: the senior leaders,” it adds.
The report points out that the importance of questions extends beyond steering machines, to interpreting their output. There is risk of relying on technology-based algorithmic insights without fully understanding how they drive decision-making and that makes it impossible to manage business and reputational risks among others properly, it says, stressing that the potential for disaster is not small.
They further then elaborates the argument: “The foremost cautionary tale, of course, comes from the banks prior to the 2008 financial crisis: C-suite executives and the managers one and two levels below them at major institutions did not fully understand how decisions were made in the ‘quant’ areas of trading and asset management.

According to the report, an increasingly important element of each leader’s management toolkit is likely to be the ability to attack problematic exceptions vigorously. “Early evidence of this development is coming in data-intensive areas, such as pricing or credit departments or call centers, and the same thing will probably happen in more strategic areas, ranging from competitive analysis to talent management, as information gets better and machines get smarter. Executives can therefore spend less time on day-to-day management issues, but when the exception report signals a difficulty, the ability to spring into action will help executives differentiate themselves and the health of their organizations,” it elaborates.
The solution is for senior leaders to draw on a mixture of insight and inspiration; insight for examining exceptions to see if they require interventions, such as new credit limits for a big customer or an opportunity to start bundling a new service with an existing product, and inspiration as leaders galvanize the organization to respond quickly and work in new ways. Exceptions may pave the way for innovation too, say the authors, something already seen – leading-edge retailers and financial services firms mining large sets of customer data.

The authors feel while algorithms and supercomputers can give answers, they are likely to be most definitive on relatively small questions. “The bigger and broader the inquiry, the more likely that human synthesis will be central to problem solving, because machines, though they learn rapidly, provide many pieces without assembling the puzzle. That process of assembly and synthesis can be messy and slow, placing a fresh premium on the senior leaders’ ability to tolerate ambiguity,” they warn.
The report suggests that in such a scenario, it is essential for digitally-oriented managers to adopt a wide range of A/B testing to ascertain what does and does not appeal to users or customers online. Such a process will increasingly hold sway as computers gain power, with fully fledged plans of action giving way to proof-of-concept (POC) ones. And this process will increasingly enable companies to proceed without knowing exactly where they’re going.
The authors maintain that sometimes machines will provide invaluable input, but translating this insight into messages that resonate with organizations will require a human touch. “No computer will ever manage by walking around. And no effective executive will try to galvanize action by saying, ‘we’re doing this because an algorithm told us to’. Indeed, the contextualization of small-scale machine-made decisions is likely to become an important component of tomorrow’s leadership tool kit….we’re firmly convinced that simultaneous growth in the importance of softer management skills and technology savvy will boost the complexity and richness of the senior-executive role,” they conclude.

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