Redefining Talent in the Age of Generative AI: How to Identify, Attract and Enhance Skills for the Future

[Original title: Impostor Polymaths Wanted: New Frontiers of Talent in the Age of Generative AI (modified on Nov 15, 2024)]

In the age of generative AI, we neither know the talent we have nor the talent we need.

John Wanamaker used to say “Half the money I spend on advertising is wasted; the trouble is I don't know which half”.

In the age of generative AI a similar expression could be coined: “half of the talent I have I don't need. The problem is I don't know which half to give up” And you probably don't have to give up any talent, just to sort out and redefine how talent is measured in your company.

1. Looking for talent in the age of generative AI

Let me set the baseline so anyone reading this article can grasp the scale of the problem. Take the talent selection process in any company.

Studies show that interviewers make decisions in the first few minutes based on unconscious bias and not on objective qualifications. With more than 180 cognitive biases, the human being is a jumble of unconscious subjective decisions. Is hiring talent in my company really a data-based process, free of cognitive biases? If we add to that the fact that the average time to fill a vacancy is around 40-50 days, it puts the figure in the thousands of euros a year in lost productivity with every vacancy. And all of the above without taking into account the true cost of a bad hiring process, which is sometimes estimated at 30% of the annual cost of the person hired (U.S. Department of Labor [1]).

On top of that, companies still resort to old-fashioned hiring techniques, drawing up shortlists of candidates for each new position posted. Now, admittedly, with the help of elaborate word-matching solutions or automated screening-out criteria from job boards, in order to reduce the number of candidates to interview for a given role. And back to the start with every new selection process, giving up the economies of scale from the learning accumulated in the previous recruitment process.

The game is a perfect example of low performance (a textbook under-performance process).

If we also add to the equation the fact that most companies have DEI policies (diversity, equity and inclusion), the play becomes more complex still. And all this without including the vector of generational or age diversity (which few companies, if any, have solved yet).

And now let me make the final play more complex by introducing the impact of generative AI. Is it possible that more than 60% of workforces could be cut if we assume generative AI's ability to replace human cognitive functions? I'm only citing an order of magnitude with this McKinsey article [2].

"Current generative AI and other technologies have the potential to automate work activities that absorb 60 to 70 percent of employees' time" —McKinsey Digital

The result is the breeding ground for a perfect storm in companies' HR and talent-acquisition functions.

Below I'm going to list a series of questions that no HR director could possibly know how to answer at this moment: "reflections on the impact of generative AI on talent acquisition":

If there is a potential for AI to substitute cognitive functions, what is the impact of generative AI on each and every one of the departments in my company?
How many of the job openings I'm posting right now could go unposted if we ran a study of AI-augmented skills across all the departments in my company?
How many of the units that have asked me to look for talent outside the company could have the vacancy filled internally if I had an inventory of AI-augmented skills in the company, contextualized for each unit-department of the company?
Should I trust the requesting units to look for talent outside, given the impact on company morale, or should I rather be in a position to make use of internal talent?
What is the relationship between current and future skills once you strip out the impact of generative AI? How do these skills relate to each other for each department/unit in my company?
How much talent (and money) am I losing by not creating a personalized cumulative learning process based on the relationships between skills for different positions, departments and projects?

Let's break the challenge down. They say the best way to eat an elephant is one bite at a time.

Let's start at the beginning and do the exercise of reflecting on the future of work and its impact now that we live in the age of AI, in the age of sustainability, in the age of diversity, in the age of…

2. The Age Of Generative Impostors

This post sets out to reflect on the future of work and the impact on how we measure the skills needed to compete in a reality dominated by augmented humans. If you want to go deeper into the steps needed to implement generative AI in your company, I recommend reading this other post (A Roadmap To Help Companies Lead Good AGI Practices).

If we want to find the perfect combination of skills in the age of AI, of sustainability, of diversity, etc., we would have to find a broader spectrum of talent now that AI can do much of the dirty work. Isn't it more human to let machines do what machines know how to do better than humans? Should we have a human take care of complex mental calculations when we can let a computer solve the problem easily and accurately? What would happen if those skills were now not only mathematical calculations but also: Classification, Design, Writing, Programming, Translation, Conceptualization, Research, Ideation, Analysis and Benchmarking, …? Every verbal-linguistic, logical-mathematical, visual-spatial or even musical cognitive function could become a candidate for AI improvement. Howard Gardner, at 80, should revise his theory of multiple intelligences, if he isn't already doing so right now with the help of generative AI.

We humans now have the ability to create code without having studied computer science or computing. We have the enormous pleasure of creating magnificent works of art with just the right combination of words. We have the good fortune of writing beautiful compositions in prose without having read in depth on the subject. We have the ability to solve complex analyses without having reached the necessary analytical competence or without having cultivated the necessary mathematical thinking. Aren't all of us humans who use generative AI true impostors in the age of generative AI?

So then, welcome to the age of impostors.

In truth, I don't think this is the time of impostors. Impostors have always existed throughout history. This, however, is the era of the absolute creation of augmented humans. Everyone is capable of creating anything.

Output in every field will grow exponentially. We will witness unexpected creations in completely disconnected and unimaginable domains. The news will fill up with strange and bizarre new inventions as a result of our infinite capacity to combine multiple domains of knowledge (Note: I promise to finish my article on the responsible use of generative AI based on the principles of Buddhism), all of it thanks to our AI-augmented intelligence.

I prefer to swap the concept of impostorhood for that of endless creativity. Welcome to the Age of Absolute Creation.

Now we all speak the language of data and the language of computers, because generative AI has given us the richness of communicating with computers using natural language. But is our organization ready to create value with its current stock of talent? Have we adapted the hiring and retention of the necessary skills to today's reality? What are the competences of the future in an era in which nobody has to learn code to talk to machines (up to a point)? Starting with developers, according to GitHub more than 90% of them already improve their development capabilities with the support and help of generative AI.

3. In search of new polymaths

And in the era of unlimited, inexhaustible creativity, what we need is a new professional profile capable of ingesting any contribution from any outside domain and creating a brilliant new solution to a still unsolved problem. And maybe, just maybe, those people already populated planet Earth and we forgot that they were already around us, and we called them polymaths (RAE: 1. m. and f. A person with great knowledge of various scientific or humanistic subjects.).

I read this article [3] about an Italian fisherman so in love with his stretch of sea and so involved in marine conservation that, in trying to fight bottom-trawling fishing practices, he came up with the idea of creating a museum of giant sculptures that would get in the way of the trawl nets. To that end he got in touch with artists and sculptors from all over the world who wanted to join his crusade. The result, a megalithic museum on the seabed. Artistically beautiful, tremendously creative and valuably effective.

Perhaps we need more of a new breed of professionals capable of combining a strange mix of different domains of knowledge that switch on our capacity to translate curiosity into creativity. People capable of combining mathematical thinking, humanistic approaches and creative ideas so that environmentally respectful solutions emerge and write a new chapter of a new and good world. Perhaps a better world is no longer necessary; a good world would be enough.

In the age of AI we could resume the old search for Renaissance sages and polymaths. Or perhaps, in the absence of polymaths and sages, what we need is a diversity of thinking so that creative solutions emerge. Maybe that way we solve the challenge of finding talent in the age of AI, the age of sustainability and the age of diversity.

What we undoubtedly need is to stop looking outward and start looking inward again. We need more people willing to grow from the inside out than from the outside in. We need more inner explorers who celebrate the glory of coexisting with others. And fewer human beings set against each other and led by populist leaders. Of that we already have plenty.

In the age of AI, in which we all have access to immeasurable knowledge thanks to easy-to-use interfaces and large language models, the beauty of evolution will no longer be in the answers, but in the questions. If we bring together the right diversity, we will be able to generate a question factory. Let humans do what they know how to do best: questions and creativity. And let machines do what they know how to do best: computational depth, and specialist, repetitive access to knowledge.

“Computers are useless. They can only give you answers.”— (Attributed to) Pablo Picasso

The way our organizations look for, measure and retain talent has been completely altered by the disruption of generative AI. We began the article with the paradox about the half of the talent we don't need. Perhaps it isn't a problem of quantity, and it all depends on which of the stakeholders is the focus of the strategy when making that decision. If we only work for the shareholder, I already know what the impact of AI will be. And it will not be sustainable, and even less diverse.

This is such a paradigmatic change that it will take us years to adjust our companies to this new reality. What is clear is that skills and processes have to be reviewed by default and periodically over time. There are authors who talk about a framework in which the dialogue between machines and humans [4] dynamically redefines the processes of any company. And it may make sense given the learning pace of all natural language models. Any skills analysis from twelve months ago has been left obsolete by the power of that same model today.

In my judgement, whatever the process or the framework, every company must start by:

training all employees on how to talk to / interact with / ask generative AI (the value of a human using their own language generatively)
creating an observatory of good practices (the value of scaling and harnessing collective creativity)
assessing risks and a responsible-use model (defining how to use and when not to use generative AI)
setting up working groups and validating results with groups of experts in each subject, (the experts in each subject will accelerate the learning of our models)
Developing talent analytics, inventorying and taxonomizing skills, driving data graphs to connect skills taxonomies, departments and projects (skills datagraphs + skills ontology).

And now try applying it to your company and "act as an experienced head of human resources and talent with broad experience recruiting and retaining talent and ask...": What is your hiring strategy based on? How have you managed to attract the right competences and cultivate the perfect combination of talent resources to create value in your company so far? How are you going to do it in the future?

Review each and every one of the job postings from the last 12 months and admit that you have been looking, wholly or in part, for the wrong set of competences. Or let me rephrase it: have you been looking for competences and skills of the past to build a different and uncertain future?

Perhaps if we stop calling it Artificial Intelligence and start calling it Augmented Intelligence everything will turn out a little easier.

Welcome to the age of augmented Creation. And there is still work to be done.

[This article has been 100% conceived and written by a human]

Sources for further information:

Superstaff. “Counting The cost of hiring a new employee” Dec 2024 https://www.superstaff.com/blog/counting-the-cost-of-hiring-an-employee/ Last visit on Mar 18, 2024
McKinsey Digital, Jun 2023. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier#introduction Last visit on Mar 18, 2024.
National Geographic “Off the coast of Italy, a radical approach to battling illegal fishing: a seafloor sculpture museum” Feb 2022. https://www.nationalgeographic.co.uk/environment-and-conservation/2022/02/off-the-coast-of-italy-a-radical-approach-to-battling-illegal-fishing-a-seafloor-sculpture-museum Last visit on Mar 18, 2024.
HBR. Paul Baier, David DeLallo, and John J. Sviokla: “Your Organization Isn’t Designed to Work with GenAI” Feb 2022 | https://hbr.org/2024/02/your-organization-isnt-designed-to-work-with-genai Last visit on Mar 18, 2024.

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