Harold V. Langlois

At this moment in time, in order to address the challenges that financial advisors face as new technologies in the area of smart machines and AI come on line, it is important to identify what actions need to be taken to secure the future of the advisory business. Historically the industry’s major value proposition focused on providing advice through the distribution of well-designed financial products that allowed for the construction of investment portfolios that reflected a client’s tolerance for risk-adjusted returns. For the Advisor, the rules of engagement were based on open architecture, a competitive landscape, and exponential growth within the industry. The field force became well-trained, affable, and capable of explaining how complex products benefited specific individual circumstances utilizing insurance instruments and appropriate asset allocations.

While some of these assumptions remain intact, there are several forces at play that are rapidly altering how advice will be delivered. Since the primary recipients of these services are ultra-or high-net-worth individuals, one needs to factor in their changing landscapes as they enter retirement and begin to transfer that wealth to a new generation of millennials who are completely conversant in digital connectivity. As Thomas Davenport described in a December article in Harvard Business Review, “While the traditional Wall Street firms (those that still exist) haven’t yet embraced the “robo-advisor” concept for their high-end clients, automated advice is becoming pervasive at the lower end of investing. Vanguard, Charles Schwab and Fidelity have all taken steps in that direction, and startups like Betterment, Wealth Smart, and Personal Capital are pursuing millennial customers with money to invest. The capabilities already exist for high-end versions of robo-advice, and a few banks like UBS have begun to explore them. Investment advice is complex, data-intensive, and rapidly changing, so it seems very likely that there will be substantially fewer investment advisors in the future.” We need only look at what has taken place in stock trading. Davenport goes on to note that “Most trading jobs have been taken over by servers running trading algorithms.”

When we add to this mix the DOL’s Fiduciary Rule that comes on-line this April, we shouldn’t be surprised to read in December’s Wealth Management Magazine that a panel of experts reported that digital advice could change the future of the financial planning industry. While the panel considered a few alternative scenarios, they indicated that advisors should be most concerned about the advances in machine learning that could make human advice obsolete.

In order to develop a plan that addresses these pressing realities there needs to be a strategy that focuses on partnering with these new digital support systems to facilitate the delivery of comprehensive and timely advice that incorporates both the complex dimensions of human decision making with “state of the art” technological support. This should never become a question of one system becoming the sole solution.  Rather, the advisor needs to benefit from AI’s computational and predictive efficiencies in order to leverage that knowledge, and at the same time they need to improve their interpersonal skills for sustaining trustworthy relationships. Through this integrative orientation Advisors should be able to provide individualized advice in the form of value added judgement that incorporates substantially more benefits than the sole reliance on reading algorithmic recommendations.

Scholars are now referring to the rapid development of smart machines as the second industrial revolution. When we look back on the first wave of industrialization, the major beneficiaries were those early movers who built industrial empires and amassed great wealth. Their technology forged corporate cultures and allowed for the integration of human capital with machines that streamlined production and increased efficiencies. Throughout the 20th Century innovation in technology reduced human labor, and by the beginning of this millennium we entered the age of the “Knowledge Workers.”

As we scan the economic horizon it’s quite evident that “platformization” is rapidly crowding out service and retail entities that were once solely occupied by brick and mortar firms. High tech platforms, such as Uber and Amazon, are redefining how services are delivered, and senior researchers at IBM and Microsoft are now suggesting that within the next ten years over 40% of the work done by people today will be handled by smart machines.

So, as our society moves toward adopting smart machines with their rule-based algorithms, workers must have more opportunities to learn skills that are uniquely “human.” In my own case having taught graduate courses in change management for several decades, I have repeatedly observed the rapid iteration of the “next new idea” generated by the publishing industry.  However, along with remaining abreast of the never-ending waves of new approaches, I have also altered my orientation towards teaching from one that explored prescriptive concepts to one that emphasizes the process of reflection. Today, my approach focuses on helping individuals articulate their values by engaging in a dialogue about where they are today and where they might like to be in the not too distant future. By using the classroom as a place to be mindful of both yourself and others, they are able to concretize how their abilities and skill sets need to be improved. This includes learning to collaborate and becoming an active listener with the capacity to ask better questions. It’s about questioning the efficacy of one’s assumptions and thriving in a complex world by coping with a fair amount of necessary anxiety as the impetus to remain current.

So, in moving forward, our strategy should encompass expanding the linkages between the advisory business and resources within the academic community. It is possible to create more flexibility within the workday by taking advantage of smart machines to streamline analytic and predictive work, in order to allow advisors more time to assist clients with judgmental decisions that have high levels of complexity and uncertainty. As the advisor continues to incorporate what he/she learns from these insights, the process becomes a dynamic collaborative where new knowledge is woven into the fabric of their practice. This iterative activity is the grist for learning how to design an approach to the integration of new technologies, and to maintain a vigilant orientation toward the evolution of the industry.

Read more

Harold V. Langlois

To my recollection I don’t remember making a conscious decision to design my career with the intent of remaining relevant in the academic community while pursuing significant leadership positions in the financial industry. Given my personal preference to opt for influence over power, I developed supportive relationships with colleagues who would eventually occupy senior leadership positions in both national and regional firms. Before the role of “coach” had been coined outside of the sports world, the work that gave me the most satisfaction focused on building teams that were collaborative and driven by mutual trust.

From the outset of my academic career I had been coaching Board Chairmen, Chancellors and University Presidents well before I became immersed in the world of finance. The challenges of coaching requires one to restrain from taking a position or expressing an opinion that would insert yourself into the organization’s political arena, and thereby blur the boundaries of your responsibilities. To be effective and helpful, it’s necessary to have open and candid conversations with the leadership in order to help these decision makers reflect on their mindset by asking questions that inject alternative perspectives into the discussion. This entails learning to ask challenging questions in order to leverage their intuition. This is not about offering advice. Rather, it’s about constructing a relationship predicated on knowledge, understanding and trust.

Once in a while when I think that things are slightly out of focus I play one of those old Ginger Rogers and Fred Astaire movies. One is quickly mesmerized by the flawless execution, complete fluidity, and their seamless performance. With all that talent in motion it’s inevitable to find yourself “Putting on the Ritz.”

Read more

harold-01Over the past 35 years from both a consulting and teaching perspective I have observed that these two professions have altered their focus from prescriptive problem solving to one that incorporates building new skills that address the challenges of working within complex dynamic systems. With quantum increases in both the amount and velocity of information generated by new technologies, it’s become more difficult to create probability models that rely upon historical correlations. Rather than relying on prior experiences that no longer reflect conditions that have rapidly morphed, we are now designing learning approaches that foster adaption and reconfiguration.


When incorporating a learning model whose drivers are probing, reflecting, and adjusting, there are some counter-intuitive approaches that produce better results. The first thing to resist is becoming caught up in the frenetic activity of adapting to rapid change. Instead of speeding up in order to get out in front of what appears to be taking place, it’s important to build the discipline of slowing down in order to create the clearest picture of what you are facing. Avoid generalizations with their broad-based assumptions, and look for activities that act as outliers. This process of deconstruction allows you to work through the predisposition of relying on prior understandings of how things once worked. It avoids the tendencies to look for similarities that are reflections of previous conditions.


This approach rejects the traditional cyclical planning orientation of going from a stable system to one of significant organizational reconfiguration and then returning to a re-invigorated stable model. Instead, by embracing an ongoing stream of emergent opportunities that become evident as the organization maintains a constant vigilance on incorporating multiple adaptions and innovations into its core guidance system, it strives to embrace a day to day adaptive approach. Emergence is driven by engaging all of the significant players in team-driven activities that learn how to benefit from collaborating throughout the entire system. CMS is committed to helping organizations and individuals develop and refine the skill sets that are necessary to make this emergent process both successful and meaningful. This is not a process that you can roll out. Rather, it’s an orientation toward learning that incorporates the subtlety and nuances of each individual and system.

Read more