The polymath mind in the AI era
Where are we heading?
Five months ago we set up this Substack. We have been writing about the impact of AI on knowledge work and the knowledge worker since the beginning of 2022, and the more we thought about it, the more content we shared, the more we presented at conferences – the more we felt we needed a ‘home’ … somewhere to start a dialogue and feed the debate.
Our Substack is intended to be a forum in which we continue to develop our own understanding of AI’s capabilities and precisely what human skills will be needed in the future to work alongside AI, and at the same time sharing this with colleagues and inviting debate.
Why ‘The Polymath Mind’? This is not an egotistical nod to a belief in our own capabilities, but rather an expression of the kind of skill set that knowledge workers will need to have in the future to work alongside AI. They will need to display the skills of a Polymath.
In this post we are going to review the issues we’ve been addressing and the future direction we will be taking.
Chat GPT - that inflection point in history
About 9 months before ChatGPT 3.5 arrived on the scene in such a dramatic way we, as business researchers, scenario planners and management consultants, realized that AI was a technology that would very quickly, and massively, change our world and that of other knowledge workers. In a paper we wrote in early 2022 and presented at a conference in September of that year[1], we suggested that a total transformation of the nature of work was a ‘plausible possible future’.
Where we landed in this debate was that although clearly we did not have a crystal ball, there were sufficient signals to suggest that AI would not just be playing an enabling role, but fairly quickly become a substitute for many knowledge worker tasks. We took the view that AI’s potential to dramatically transform the structure of work, as we know it, should be given attention now as an urgent priority, and not put on the back burner or relegated to the ‘too-difficult’ box.
We have seen nothing since then that has changed our point of view. What we didn’t anticipate in early 2022 was the speed of AI’s roll-out nor how quickly it would become embedded in the zeitgeist.
As the influence and widespread adoption of the latest AI models became apparent, it was clear that various schools of thought were emerging. There were/are those who argue that AI will boost growth and productivity and create an environment in which we can flourish – new jobs will replace those that are lost. AI will be the latest and greatest enabler of human endeavour. This is ‘the rising AI-tide will raise all boats’ school of thought.
At the other end of the spectrum are those who see AI replacing virtually all jobs, which may or may not be a good thing.
“There will come a point where no job is needed […] you can have a job if you want to have a job for personal satisfaction, but AI will be able to do everything” Elon Musk
As we began to get our heads round the power of generative AI and reflect on the skills human beings would need to work alongside it, we soon identified seven fundamental power skills where we believed humans would enjoy an advantage in the AI era. We won’t repeat each of the seven power skills here as we have written about them at length in other posts[2], but essentially there were three fundamental defining features.
First, there is the ability of us humans to be the ‘wide angle multi-disciplinary lens’ - the holistic sense maker who can bring together lots of different sources of information into a coherent picture. We of course recognized the power of AI to assimilate masses of information but felt that human beings would still have the edge in connecting dots in a bigger, disparate, picture. We would be able to add all sorts of subtle observations and nuances about what it is to be human in a way that still eludes a non-sentient AI.
Secondly, we could see how AI would very quickly be doing all of the heavy lifting around conventional deductive and inductive thinking. So, we felt that going forward the emphasis should be on human beings’ ability to operate alongside AI applying ‘abductive reasoning’. This is all about knowing how to make informed decisions where there are high levels of uncertainty and much imperfect information, and a need to apply high levels of human-informed-intuition.
And thirdly, there were skills around humans’ ability to inspire and lead with creativity. This centres on the ability of humans to tell powerful compelling stories about human emotions and make things happen in a compassionate way.
So why did we choose the polymath concept as the big core unifying idea that summarized our seven power skills? The essence of what we considered to be the superpowers that knowledge workers would do well to acquire in the AI era seemed to align well with what the experts in the ‘polymath field’ were saying about what constitutes a polymathic personality.
Dr Michael Araki, of the University of Louisville, talks about polymathy, not only as the accumulation of broad and profound knowledge, but also the formulation of useful connections between different bodies of knowledge.
Polymathic personalities are often referred to as akin to that of a ‘renaissance person’ who can combine their cognitive prowess and creative skills to integrate and interpret knowledge drawn from a wide spectrum of disciplines into new and exciting big groundbreaking ideas.
We believe that the ‘polymath mind’ label serves as a way of summarizing our seven power skills and suggesting to our audience that going forward, those with these polymathic skills would be in the ascendancy. The new polymath is a breed of thinker who is able to see the big picture, whilst also understanding how to fuse the emotional and more so-called rational thinking in making sense of the world.
Have we revised our Point of View – thinking - about how to maximize the potential of AI over the last few months?
Our fundamental position has not changed.
We hear that the future belongs to those who will ‘upskill‘, but what does that actually mean. How and where should they be upskilling? In reality, it isn’t just about sharpening up thinking skills and being a bit more creative and adding a little bit of edge to what we’re currently doing. We are now staring down the barrel of a fundamental overhaul of what constitutes valuable skills which may lead you to a very difficult place as a society.
We feel that government, some universities and many schools are not currently giving sufficient attention to what we believe will be the dramatic change needed to deliver the new skill set that will be in needed of the next few years to work alongside AI. If anything, our position has hardened as we continue to be surprised that, as more and more evidence emerges about the need for a paradigm shift in what and how we should be teaching the skills needed to work alongside AI, little public acknowledgement of the need for massive and urgent change seems to be in evidence.
“We need to boost our skills to make ourselves AI-ready! […] People should not be worried about the impact of AI on jobs because education reforms will boost skills. […] That is my answer in a nutshell, that's why I don't want people to be worried, because we are building a world-class education system." Rishi Sunak
But there has been no meaningful national discussion about what the world of work and the AI-shaped workplace will look like, which must surely be a precursor to the redesign of any education system. A system which must include not only schools and colleges but the university sector as well. So, in our view many institutions and organizations seem to be caught in the headlights - they have frozen into inaction and are not taking this issue sufficiently seriously enough. [3]
However there have been certain areas where we felt the need to qualify some of the earlier points we have been making.
Firstly, we have taken care to stress the fact that our idea of the power of the polymath mind is very much set in the context of knowledge workers and it is not a universal panacea for people working outside of this particular sector. Nevertheless, there still leaves us with a big challenge as it is estimated that of the 3.4 billion people employed globally, it is believed that up to ~1 billion (30%) are knowledge workers (International Labour Office, Geneva, 2023)[4].
Second, we have softened and refined our tone since the earlier posts where we were giving out a sense that to survive in the AI era ‘all’ knowledge workers would need to be polymaths. We should have been clearer in explaining that this was a top end of the range aspiration, and that not every knowledge worker will need to aspire to be a fully paid-up polymath. But most, if not all, knowledge workers will need to adjust some aspect of the current skill set to operate effectively in the AI infused workplace.
Clearly there will be roles where people can slot in doing specialist AI related jobs in a way that needs some technical adjustment to their current skills, but not require a full-on reprogramming to be the total polymath article armed with all our seven power skills.
But we need to add the rider that not everyone will be equipped to make the transition to the new AI-shaped knowledge workplace.
And thirdly, we’re beginning to look more closely at the subtle dynamics of how the arrival of generative AI will impact the structure of jobs. We’re beginning to realize that working out who will be the winners and the losers is a little bit more subtle and sophisticated than at first sight.
There are going to be some surprising winners and losers in the ‘game’ of workplace snakes and ladders vis-à-vis who benefits, and who loses out in the AI job era. There are going to be disappointed knowledge workers, who might not have expected to find themselves in a losing position, given their education and expertise. But there are also going to be some winners who leap up the career ladder in a way that they might not have expected prior to the arrival of generative AI.
We have recently published an article in ESOMAR’s Research World magazine painting a picture of how AI may totally restructure the shape of the labour market over the next 5 years or so.
But back to this Substack post, the bottom line is fundamentally that we are continuing with our argument that we need to urgently revisit the issue of the skills now needed to work successfully alongside AI.
What kind of response are we picking up from the kind of messages we have been putting out?
At a high-level we can classify the kind of responses and conversations that seem to be emerging to and around our Substack posts and conference presentations into the following three categories
First, there are those who realise that these new superpowers are urgently needed, who agree that going ‘full polymath’ is the way to provide those much-needed human skills that will sit on top of what AI will be able to deliver?
They don’t see us as alarmist in calling for a massive ‘drains-up’ review of exactly what skills need to be taught in schools and universities, and welcome the call for an urgent review of what a ‘plausible possible future’ may require: a world that needs to massively revamp the current educational paradigm.
Second, there are those who feel that our worries about the future of work and the need for a paradigm shift in education are over-done. These are the AI optimists … who argue that it will all work itself out in the end. They can see how without going full polymath and acquiring the seven power skills, knowledge workers will still be doing extremely nicely in the AI era because they have been able to master specific technical skills and can work comfortably alongside AI in this way.
We of course have no problem with this: there’s a lot of evidence emerging to show that those in this category have a valid point of view. But the debate here will center on the degree to which one believes that the technology remains an enabler and does not become a substitute.
Third, we have those who seem to fall into the Scarlett O’Hara ‘we will worry about that tomorrow’ box.
Scarlett, a character in Gone with the Wind, who when faced with challenges and concerns said, ‘I can’t think about that right now. If I do, I’ll go crazy. I think about that tomorrow’.
This group seem to be in denial about the impact of AI and are simply kicking the can down the road in the hope that AI won’t be such a big deal or somehow it will resolve itself whilst they have their collective ‘head in the sand’. Without going down a rabbit hole of speculation it is perhaps worth musing on what lies behind this Scarlett O’Hara view of the world. There seem to be three fundamental reasons as to why people will take up this position:
There is a deep-seated inability to cope with fundamental change on the scale that AI is signaling. Their mindset is inextricably fixed in a way that they just can’t begin to engage with the debate.
Next there are those who deep down grasp the magnitude of the arrival of generative AI, but who are locked into roles and functions within large organizations and institutions - and are part of complex industrial type processes - such that they just cannot see how this oil tanker can be turned around quickly enough to deliver the polymath skills that the authors here are arguing for.
And we have sympathy with this. If you’re running a university, it is not easy to change the MBA curriculum, pivot and move in a different direction. And if you’re in a secondary school how do you go about changing the A-level syllabus to start dealing with some of the issues we are raising here. And if you’re a government minister how do you start talking about some of these issues we are raising in an election year without frightening the horses - why not just do what we said earlier and put this into the ‘too difficult box’.
And finally there are those who understand that an avalanche of change is coming their way but are simply suffering from AI/information overload. They get the case for a more radical overhaul of the skills we need to survive with AI but just cannot get their heads around how you actually begin to build strategies, programs and processes for teaching these polymath skills and embedding them in organizations.
And this last point neatly brings us to where the authors are now with their thinking about the polymath mind concept. We are now beginning to focus on delivery issues.
On building the polymath mind
Our top-level position is that we remain committed to supporting an exciting new AI era, where we start thinking of AI as our ‘second brain’, almost an alternative intelligence - as our trusted copilot.
As a starting point we need to concede where AI will be better than us thereby allowing us to put the focus on those silkier empathetic power skills that characterize what it is to be human - the skills that differentiate us from a non-sentient AI. So, we’re now turning to ways in which these the power skills can be taught and embedded in organizations.
And here, our thinking is informed by the fact that this is not a just case of rocking up to a hotel conference room to run some breakout sessions, using a standard training deck and a few exercises and flip charts in the vague expectation that overnight, we create a new breed of polymaths. Clearly the task of teaching such an intellectually demanding role as the polymath in its own right is a major challenge (more from us on this later in upcoming posts)
In addition, it’s clear that even when the pedagogy for teaching polymath skills has been mastered, this can only be executed against a backdrop of an organizational structure, processes, leadership style and culture that is welcoming to the polymath concept and committed to embedding it as the golden thread that runs through the organization.
With this in mind, the authors have developed a program that is part consultancy, part change management and part capability development which essentially has three strands.
First, we start by looking at the organizational structures, processes, data platforms and the leadership styles now in place and assess how sympathetic these are as the platform upon which to start building the new AI/human polymath interface.
Secondly, there is the training piece where we share our expertise on how you build polymath skills and start getting people to believe in their ability to step up to the plate and take their skill set to that next level and unleash their true creative potential.
Then finally we focus on how to embed these organizational ways of working and power skills within the organization: ensuring the appropriate people, working with well thought-out processes, have the relevant skills and are beginning to apply these in a productive way in the right settings.
If you’re interested in discussing with us how we might work with our own organization in order to take your key team members’ skills to the next level do get in touch.
[4] https://sdgs.un.org/sites/default/files/2023-05/B59%20-%20Berg%20-%20Automation%20hits%20the%20knowledge%20worker%20ChatGPT%20and%20the%20future%20of%20work.pdf
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