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Humanizing Robo-Consulting


Wed Oct 03 2018

Humanizing Robo-Consulting

We’re now 10 months into 2018, and it’s already evident that the term of the year in consulting will be _robo_tics. Although techno-pessimists may prophesy that massive unemployment will hit skilled professionals (for example, see a report by two Oxford academics claiming that 47 percent of current American jobs are at ‘’high risk’’ of being automated within the next 20 years, or Rise of the Robots: Technology and the Threat of a Jobless Future—a vision, virtually, of economic Armageddon), the helping industry is ripe for major disruption indeed.

Numerous advisory jobs (such as medical consultants, lawyers, financial advisors, and management consultants) often involve going through a similar workflow: collecting data > analyzing the data > interpreting the results > putting forward recommendations > executing the action plan. So why not simply automate the consultative cycle and processes? In reality, artificial intelligence (AI) has already been at our service.


In what follows, I will look into four positive AI-driven cases in point, and offer some preliminary recommendations on how the robo-advice industry can become more humane.

Cases in Point

By way of illustration, let us now draw into sharp focus four cases.


IBM Watson for Oncology, by understanding literally millions of data points, helps relevant data come to light, connect distinct sources of intel, and determine personalized treatments—fast. In fact, in a double-blind study, the doctors at Manipal Hospitals discovered that Watson was concordant with the tumor board recommendations in 90 percent of all breast cancer cases. The bottom line is, providing evidence-based cancer care to patients brings confident decision-making to oncology. Essentially, doctors, spending less time looking through literature, can concentrate on what they do best—namely, deliver patient-centric care.



In a case prediction challenge, the humans—over 100 lawyers from many of London's most posh law firms—as well as the AI, a program called CaseCruncher Alpha developed by Cambridge law students, were provided with the basic facts of hundreds of Payment Protection Insurance (PPI) mis-selling cases and were tasked with predicting “yes or no” as to whether the Financial Ombudsman would allow a claim. Eventually, the AI system achieved a validation accuracy of 86.6 percent, compared to 62.3 percent for the human lawyers. Having said that, it does not necessarily imply that that legal decision prediction systems can beat human lawyers easily at predicting outcomes. Rather, it demonstrates that AI can definitely help clear legal bottlenecks within enterprises and mitigate against “performance” factors (such as memory, distractions, attention) or bias (such as racial bias, religious affiliations, political views, class, language, similarity, and more) that may show up in the judgments made by humans.


After surveying over 4,000 financial advisors and half a million of their clients, scholars revealed that vast numbers of go-to advisors committed quite a few of the classic rookie mistakes of personal finance. Surprisingly, they didn’t just put their clients through their bad advice; the study showed that the financial advisors were consistent in coming to very poor investment decisions for themselves, too. That being the case, to reduce risk with the aid of sophisticated portfolio customization and automated intelligent rebalancing and to better execute time-tested investment strategies, “finnovative” software built on brainpower (such as Alexa virtual assistants) is already on the market. Such smart high-tech can help you out with investing your hard-earned money and in your financial planning. In point of fact, Fitch Ratings estimate that it is probable that robo-advisors will keep seeing double-digit growth in assets under management (AUM) over the next several years (starting from a low base of less than $100 billion in 2016!). Indeed, an A.T. Kearney robo-adoption study forecasts that, by 2020, $2 trillion will be managed under robo-advisors.

Strategic Management

As published in a recent report, the U.S. market for corporate advice is more than $50 billion—and almost of all of that advice is both human-based and high-priced (as an industry snapshot, for research findings and insights into management consulting, see, for example, here, here, here and there). Certainly, some extremely talented and trusted management consultants can offer a particular client competent, highly-personalized, strategic advice, and they surely profit from this. However, no matter how good at researching and presenting data consultants are, AI may be able to do it better—and cheaper—soon. In fact, in Prediction Machines, three Toronto-Rotman professors view the rise of AI “as a drop in the cost of prediction,” and when AI gets framed as “cheap prediction,” it has implications for strategizing. As the unexpected holds back strategy, more accurate and reliable prediction generates possibilities for novel business models to compete. Perhaps, in the not-too-distant future, CEOs could be asking, “Alexa, how do we best solve the puzzle of sustaining growth while creating value?” rather than offering burnt sacrifices to the plethora of big consulting firm gods.



Ultimately, the fundamental role of the consultant is to methodologically co-create value and improve the client’s condition and performance. Naturally, a consulting assignment is successful only if the client is demonstrably better off after the engagement. Furthermore, one must remember consulting must be professional, ethical, sustainable—and unbiased. Last but not least, constant strategizing is critical.

To conclude, five central recommendations for making robo-consulting more humane are:

  • Welcome the new positively—evangelize and bring AI into your consultative cycle, processes, and architecture. As a matter of fact, build “high-performing human-robot teams” (HPHRT), in which robo-advisors and human advisors reinforce one another and work synergistically to create enhanced organizational performance. When implementing HPHRT, try to make it as easy, attractive, social, and timely as possible.

  • Embrace the digital—increase an enterprise-wide automation capability. It seems that the technology required to achieve next-gen digital advice is already here; it’s just not fully utilized. Craft a strategically integrated service automaton program first—support from senior management will be critical.

  • Develop super-flexible talent and organizational structures that enable human resources to retain and sharpen their expert know-how, while being able to nimbly move between diverse teams and projects. As an experiment, for instance, find out if your risk engineer is able to work with your people operations person, and whether they can both work well with, say, drone technologists.

  • Nurture emotional intelligence across the board—this will guarantee against “robotic” robo-consulting. In other words, consulting is as much about mindsets as skill sets. Regarding L&D methods, to build and sustain high-quality connections at work, use L&D experiences based on drama and theatrical activities, team coaching, behavioral coaching, and mentoring.

  • Purposefully _un_learn—learn how to strategically unlearn old dominant logics that kill the ability of human advisors in the organization to drive organic radical innovation.


Further Reading


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