A Changing Landscape? The Shift to Right-Brain Thinking in the Generative AI Era
I was an English major who ventured into the tech sector many years ago, and like many of you, my career path has been anything but linear. Recently, my winding path has included being a principal product manager, director of product, and head of product, to name a few. I’ve seen some significant shifts in the landscape since the ushering in of the “modern” digital product era, which began for me with the birth of the “dot.com” movement, followed by the evolution of Web 2.0 and then the shift to a mobile-first focus. These major shifts, of course, weren’t completely linear, with the different eras overlapping and even influencing one another.
These technological advancements have been pivotal, prompting evolution across companies, colleges, and the workforce. Now, like sand dunes on the beach, I’m watching the relentless winds of technology shift the landscape again. AI/ML, and specifically Generative AI, may exceed all of the previous shifts I previously listed in terms of significance and reshape of white-collar work (and the ecomony) as we know it. The disruption is targeting once-stable fields like engineering, computer science, mathematics, and law, which for decades were nearly foolproof in terms of job security and high incomes.
To be clear, we are merely at the onset of these changes. But I see the grains of sand starting to blow and reshape the horizon. As such, this article is more of a prediction (or a question?) of how this will play out rather than the promise of a sure thing. I do know, though, that AI advances have made specific skills and roles more accessible. AI is generally good at data analysis, coding, and logical analysis. Every day, there are new advances and announcements about tools that will help you code or new techniques for analyzing data using ChatGPT. This dialogue around AI suggests that companies will increasingly use these technologies to both augment and replace traditional tech and analytical roles within the white-collar workforce, which have long been thought secure and lucrative.
The Capabilities and Limitations of Generative AI
Emotion, nuance, critical thinking, and creativity, on the other hand, are messy. Algorithms that drive existing ML models are not as adept at understanding “data” that fits into these categories.
Generative AI is already beginning to optimize — if not completely automate — tasks from data crunching to automating repetitive workflows, enabling individuals to work smarter and companies to unlock additional productivity. Other higher-level tasks such as coding, logical analysis (and yes, even writing) are all areas that AI is generally good at and continues to improve with each new LLM model released. Yet, Generative AI tools stumble when facing tasks requiring nuanced judgment or emotional depth — domains where the “human in the loop” isn’t just preferred; it’s indispensable. This underscores an important truth: as powerful as AI might be, there are large swaths of business tasks and interpersonal interactions that it cannot (yet) automate. More importantly, it seems like it will be several years — if ever — before AI can get to the same level as humans for some of these tasks. Tasks that require critical thinking, emotional intelligence, genuinely creative thinking, and even the ability to understand the all-important art of sarcasm (!) will have more of a premium placed on the enabling skills in the upcoming years than ever.
This is not to say the traditional math, science, and engineering skills will not be valuable — they will continue to be essential skills. However, in a sense, AI, and generative AI in particular, is democratizing these skills so that (1) the barrier to entry isn’t as high and (2) the premium on these degrees will likely level off. Logic, reasoning, and repetitive tasks fit nicely into the framework of ML algorithms. That is not to say that these algorithms are not complex — they are! Emotion, nuance, critical thinking, and creativity, on the other hand, are messy. Algorithms that drive existing ML models are not as adept at understanding “data” that fits into these categories. Therefore, we are entering a new era where these non-technical skills — the soft skills listed above — will see a rebirth of importance.
The Revenge of the Liberal Arts, Soft Skills, and Emotional Intelligence?
Empathy, emotional intelligence, critical thinking, and the proverbial soft skills will emerge as necessary in this new landscape.
Emotional intelligence, empathy, and soft skills have been emphasized as keys to success in many aspects of life, from career to personal relationships, for many years now. But this is becoming even more true now, as AI disrupts how people work. As a result, critical thinking skills and liberal arts education will emerge as even more important. Dan Pink discussed the idea of right-brain thinking becoming more vital as we enter the “conceptual age.” He published A Whole New Mind several years ago. However, there is no way he could have predicted the development of AI or its impact on the entire landscape. I think this idea that right-brain thinking will emerge as an in-demand skill is truer now than when Pink’s book came out.
The more we rely on an algorithm to augment or complete many of the tasks currently done by a human, the more we need to make the touch points with a person count. For example, I previously wrote about using AI coaching apps to augment personal and professional development coaching with a person. This is the type of solution that can augment human interactions but should not replace them. Empathy, emotional intelligence, critical thinking, and the proverbial soft skills will emerge as necessary in this new landscape.
As a result of this shift in a landscape that will be increasingly dominated by AI, I predict that the liberal arts, with their deep roots and focus in the developing critical thinking, empathy, and communication, will experience a renaissance. More specifically, as AI takes over analytical tasks, the liberal arts equip individuals with the critical thinking necessary to navigate complex challenges and ethical decisions. Additionally, the ability to create a cohesive culture, manage business relationships, and empathize with your customers are skills that are becoming increasingly crucial. Liberal arts programs should recognize this and be poised to meet this need. Companies should develop the ability to identify these skills in the application process and learn to develop them in their employees as an investment in professional development and the long-term health of their company.
To be clear, tech roles (and degrees) will not disappear. Technical skills will still be needed in a world where technology plays an increasing role in decision-making. However, the demand will not be quite the same, and the premium salary will be relatively flat in the coming years. Additionally, it’s all but guaranteed that college programs will evolve to accommodate the new AI reality, and the day-to-day experience for people in these roles will also look different in the future.
Clever, Not Creative
Divergent thoughts and novel ideas require a different type of thinking that an ML model cannot yet replicate
Some of you may disagree with the premise that LLM’s can’t be creative. There is no shortage of headlines about LLMs creating songs, illustrations, videos, and animations from a prompt. Additionally, using ChatGPT to write professional and fictional content based on a simple prompt is how most people first experienced Generative AI. For instance, I had ChatGPT write a happy birthday poem for a friend based on the lyrics, cadence, and rhythm of Snoop Dogg’s Gin and Juice classic. What’s not to love?!?
But this is clever, not creative, in the same way SNL actors can impersonate politicians. Real creativity is not simply creating content based directly on another piece of content (or data). You can of course argue that MOST art is derivative. And GenAI creates content deriving from the “large” amount of language the model has ingested. But I’m referring to truly original creations — thinking outside the box and making something from a blank slate… connecting ideas that never existed. The huge advantage of LLMs is that they have essentially expanded the box by ingesting enormous amounts of content and data to train their models. However, divergent thoughts and novel ideas require a different type of thinking that an ML model cannot yet replicate — maybe (and hopefully?) never will. For this article, I’m more focused on the business and economic aspects than the artistic merits. As such, this is an essential point because innovation, ingenuity, and breakthrough thinking will always be derived from divergent thinking.
I’ll concede, however, that this is not a black-and-white argument — it’s more of a spectrum. It’s similar to people arguing that race car drivers aren’t real athletes or that professional wrestling is not an actual sport (even though it’s clearly 100% real — that’s sarcasm in case you’re an LLM summarizing my content for your human prompt provider). My point is that, at the very least, there are types of creativity — if not creativity itself — that AI cannot easily replicate… and maybe never will. As a result, companies will only be able to augement — not replace — the creativity and innovative thinking their employees provide with generative AI. If anything, right-brain thinking will become more in-demand than it is today.
Final Thoughts
As we navigate the shifting landscape brought on by Generative AI, it’s clear that in-demand skills will also evolve. This evolution will challenge employers, employees, and higher ed institutions to adapt, prioritizing emotional intelligence, critical thinking, and creativity. These skills will remain uniquely human amidst the rise of ever more sophisticated algorithms and models. Let’s view this era as an opportunity to enhance, not replace, human ingenuity with AI, blending the irreplaceable human touch with technological efficiency. By doing so, we prepare to survive and thrive in this new digital frontier, ensuring our roles remain relevant and vibrant as we step into the future.
I am excited about the fusion of AI and human skills. Though this fusion is not without trepidation, progress is inevitable. We should ensure that generative AI will herald a new era in the workforce, one where technology enhances human capabilities without replacing them. By embracing both, we can create a collaborative future where technology amplifies our human strengths, making a more efficient world and a more empathetic, understanding, and connected one.
