What the world needs now… is love and AI?

Paul LeBlanc, in his thought-provoking book “Broken,” shows how scaling removes humanity from many of our systems of care. AI might be the great unlock that would allow us to flood our systems with quality people, uplift them, and set the stage for a new era of human thriving. But what does the same pattern mean for management consulting?

 

Our systems of care are failing us

Paul LeBlanc, just stepping down now from his role as the trailblazing President of Southern New Hampshire University and into the job of a startup entrepreneur, joined me by the fireside on May 17.  Our conversation took his recent book “Broken:  How Our Social Systems are Failing Us and How we can Fix Them” as a point of departure.   In the book, which draws on his own background and personal experiences, but connects them to the systems of care we have designed, he points to positive solutions we can create, which will only be accelerated by the advent of better and better artificial intelligence software.

Firstly, he suggests that most of the systems designed to care for people – healthcare, education, addiction treatment, imprisonment and more – aren’t doing a great job.  Even worse, they are not great places to work for the people delivering the care.  And still worse is that we spend enormous amounts of money on services that simply don’t create great outcomes.  For instance, with respect to higher education, he points out that the system:

• Fails 40% of those who enter but never complete a degree;

• Saddles students with $1.7 trillion of debt, more than any other form of debt aside from home mortgages.

• Stresses out high school students worried about college admissions.

• Exploits student-athletes and pays football coaches more than anyone else on campus.

• Favors those who already enjoy prosperity and privilege.

At SNHU, Paul dedicated himself to creating an educational system that, as he puts it, “loves its students.”  SNHU is now the largest university in the United States, has successfully graduated and uplifted tens of thousands of students and is widely regarded as a role model for how to cost effectively provide life-changing learning for people who would otherwise be shut out.

The secret sauce?  Humans.

What they have learned at SNHU, and what Paul suggests could be a model for other care systems at scale, is how to put humans into these systems at critical touchpoints.  See, the problem with operating systems at scale is that scaled systems love predictability, reliability and same-ness. At the same time, humans are unpredictable, notoriously unreliable and different from one another. All too often, systems designers favor the demands of the system over the needs of the humans.

At SNHU, the human touch is provided by the student’s counselor who stays with them throughout their educational journey. It isn’t the professors who develop a deep understanding of each student and their needs – it’s the counselor who learns what makes them tick, keeps them on track, provides encouragement and challenge, gives feedback and where necessary overrides the systems to do the right thing.

There are other examples, sprinkled throughout the book, of systems that have similarly solved the problem of scale + humanity.  At One Medical, for instance, the time that patients spend with expensive clinicians is relatively limited.  But, once the doctor has offered a diagnosis and treatment – say, “lose weight and cut down on your salt intake,” the human factor kicks in.  The doc gets maybe three minutes. Then you’re in the hands of a health counselor who is going to work with you to see how you can possibly implement the doctor’s recommendations.  “What do you normally eat? Who does your grocery shopping? Oh, your niece? Let’s see if we can get her on the phone.”  Those very human conversations make the difference between a recommendation a patient may not even understand and an actual behavioral change.

Human work initiatives

Paul suggests that this model offers a solution to our broken systems.  What if you could limit the resources consumed by the expensive part of a system (doctors, lawyers, professors and the like), but vastly expand the resources available to “flood” the system with competent, well-trained, but less expensive people?

As he puts it, “we should flood our K-12 system with talented teachers and counselors and social workers and coaches and staff. We should rebuild the mental healthcare system that has completely decimated its patients.  America uses prisons as a mental health care system today.  We need to absolutely stop that with talented clinicians.  We need to create a system of affordable care and compassionate geriatric care for an aging society. We don't have one.  We don't lack a need for human jobs.”  But the problem is that we don’t want to pay for people to do those jobs or give them much respect and dignity.”

If we redesigned the systems and re-allocated the resources, he suggests, we may not save a lot of money but we would have far better outcomes. More than that, humans would have dignified, meaningful work helping others, which research has shown is one of the most reliable predictors of happiness.

A great inflection point that may make this possible

Carlota Perez, whose work you all know I greatly admire, suggests that when we have a transformative technological revolution, such as the advent of digitization and now the rollout of powerful AI systems, societies change. The old winners can become losers. The formerly outsiders can become insiders. New opportunities and ways of thinking emerge. It represents an opportunity to reinvent the way society works.  And it doesn’t necessarily cost more – compare the annual cost of keeping someone incarcerated ($42,672) with the benefit of having that same person productively employed.

Enter the arguments of David Autor, a labor economist and professor at MIT, who has long argued that automation, among other things, has devastated the American middle class.  AI, he suggests, could create a revolution that could re-invigorate middle class jobs almost exactly the way that Paul proposes.  As he puts it in a recent article, “expertise refers to the knowledge or competency required to accomplish a particular task like taking vital signs, coding an app or catering a meal. Expertise commands a market premium if it is both necessary for accomplishing an objective and relatively scarce...Expertise is the primary source of labor’s value in the U.S. and other industrialized countries. Jobs that require little training or certification, such as restaurant servers, janitors, manual laborers and (even) childcare workers, are typically found at the bottom of the wage ladder.”  As he suggests, the jobs that require expertise wax and wane depending on the state of technologies that require them.

The rise of the information age and the knowledge worker had the effect of increasing the rewards to expertise and education. Simultaneously, automation and routinization eroded the wage premiums that skilled workers could command, while many higher-skilled jobs that could be done more inexpensively overseas were shipped there.  The result was an erosion of middle-class jobs.  AI has the potential to dramatically change this equation.  As Autor says, “Because artificial intelligence can weave information and rules with acquired experience to support decision-making, it can enable a larger set of workers equipped with necessary foundational training to perform higher-stakes decision-making tasks currently arrogated to elite experts, such as doctors, lawyers, software engineers and college professors. In essence, AI — used well — can assist with restoring the middle-skill, middle-class heart of the U.S. labor market that has been hollowed out by automation and globalization.”

His core thesis for why this could happen is that “By providing decision support in the form of real-time guidance and guardrails, AI could enable a larger set of workers possessing complementary knowledge to perform some of the higher-stakes decision-making tasks currently arrogated to elite experts like doctors, lawyers, coders and educators. This would improve the quality of jobs for workers without college degrees, moderate earnings inequality, and — akin to what the Industrial Revolution did for consumer goods — lower the cost of key services such as healthcare, education and legal expertise.”

Just as the role of nurse practitioner emerged to provide patient care more affordably than could be delivered by expensive physicians, many services that now require expensive professionals could be carried out by less intensively trained and therefore less expensive alternatives.  In other words, by using AI to provide judgment and decision support, the human connections Paul envisions can become an affordable reality – entire systems of care can be redesigned with an AI front end.

So what does this mean for those who trade almost purely on their expertise?

If doctors, lawyers and professors’ expertise is likely to be democratized, what does all of this mean for the field of management consulting?  In this, I am delighted to say that I’m being joined at Valize by Kes Sampanthar, recently of BCG and KPMG, to consider what next-generation consulting will be like when getting a complex analysis done is a simple as asking a question and pushing a button.

Management consulting, similar to other areas of elite expertise, has been based upon a deep body of knowledge and the associated skills to make high-stakes decisions. AI enables the next rung down, the equivalent of nurse practitioners, the ability to not only manage complex analysis but also propose solutions.  What may happen is that the actual work can now be completed with less scarce and expensive talent.  That means the scarcity will move to relationships and trust, which is where experts will increasingly spend their time.

The economics of consulting will change in a major way.  Most professional services firms today operate on a leverage model, where more junior talent is billed out for work performed, supporting high pay for more senior people.  Think of it as a triangle, with scarce managerial talent at the top, billing out less scarce junior talent.  It isn’t hard to imagine a world in which the bottom part of the traditional consulting pyramid is no longer needed to do the number crunching and the low-end analysis. The pyramid becomes a diamond, in which AI augments and automates the work done traditionally by entry-level consultants.

A big open question for all professional services firms will be how to train and develop entry-level people when AI can do that work.  Increasingly, the work that expensive consultants will do, as David Maister would say, is more “brains” type problem solving for truly wicked problems, while more routine or “grey hair” consulting is handled by machine intelligence.

Management consulting has played an invaluable role in helping leaders in organizations meet complex and challenging situations. The evolution of AI now raises interesting and complex questions for an industry more accustomed to helping others navigate such shifts than figuring out new business models for themselves.  I am excited to be working with Kes as we explore ways in which our team at Valize can leverage AI.  Specifically, I see the ability to help firms bridge the knowing-doing gap by building capability rather than fostering dependency or treating strategic advice like a black box.

To learn more about our approach to building capability, get in touch here.