The scene opens the day of Andy’s interview with Miranda. Unruffled, she observes all of the Runway fashion magazine employees scrambling when they learn that Miranda will be arriving in the office earlier than anticipated. They are rushing around clearing their desks, changing shoes and applying more makeup before she walks through the doors. Though we haven’t even properly met Miranda yet, this scene shows us how she’s perceived with frantic anticipation, anxiety and fear by the people around her — a rather ominous effect. As the film unfolds, Andy grapples with her own feelings of inadequacy, anxiety, and resistance to the business of fashion and finally embraces her position as an assistant at Runway by seeking to understand and gain mastery of her role and industry. This is the film, “The Devil Wears Prada,” starring Merryl Streep as Miranda Priestly and Anne Hathaway as Andy Sachs.

Much like Miranda — albeit a cheeky illustration — the hype is in overdrive about the recent artificial intelligence (AI) breakthroughs, especially generative AI tools such as Bard, ChatGPT, OpenAI among many. Its ubiquitous nature has created a distraction that is perceived with frantic anticipation of something ominous to come. The problem facing many learning leaders today is a disorienting dilemma of pressing on with conventional methods of fulfilling learning needs colliding with being tentative about exploring generative AI’s rapid capability to create content, adaptive development plan, knowledge discovery with chatbots and more. This is prompting questions about learning and development (L&D)’s current state of relevance and value proposition in relation to generative AI. The hope is that this leads to unlocking our non-computable superpower — emotional intelligence (EI) to drive a much-needed L&D transformation.

Optimizing for EI: The Non-Computable Superpower

AI has made remarkable progress in recent decades. AI is a set of algorithms that enable machines to learn, analyze data and make decisions based on that knowledge. In layman’s terms, it is the claim that human intelligence can be so precisely described that we can actually create a machine to simulate it. Generative AI is a type of AI that describes algorithms that can create new content, including audio, code, images, text, simulations and videos. EI is non-computable and the intersection of cognition and emotion. It is the capacity for us to be self-aware of our own feelings and emotions, and those of others, and use that awareness to navigate a plethora of social situations and conflicts. Therein lies the fashionable opportunity in this distinction between EI and AI.

We should think about what humans can do that AI can’t (at least today), which is to optimize for EI. However, it seems that most L&D organizations are not thinking this way. In fact, only 17% of organizations were making significant investments in worker training and development to support their AI strategies.

In his groundbreaking book, “Non-Computable YOU: What You Do That Artificial Intelligence Never Will,” renowned engineering and computer science professor Robert J. Marks II wrote that “…tomorrow’s AI, no matter what is achieved, will be from a computer code written by human programmers. Programmers use their creativity when writing code. All computer code is a result of human creativity….” And creativity is linked to EI.

However, most training organizations do not promote, recognize and reward creativity. Which bears out in recent research that found only 9% of humans are more creative than the best AI chatbots albeit low-level creativity. Humans still have an edge in dealing with more complex problems that demand higher levels of creativity.

The AI Revolution Is Here

The L&D field is at an existential inflection point in its history, given the exponential acceleration of the AI-driven economy. Ready or not, the generative AI revolution is upon us, driving intense changes and rewriting the rules. According to industry analyst Josh Bersin, with about 10.3 million jobs open, about 8% (800,000) will immediately be impacted by advancements in AI. These jobs won’t go away, but they’ll be upgraded and enhanced by these systems over time. (And there are lots of new jobs like “Chatbot trainer” now being created.)

It’s clear that an intentional focus on evergreen L&D challenges, such as defining and scaling global employee development, determining informal learning effectiveness and improving learner engagement, are proving to be the way to maintain relevance in today’s AI-driven economy.

Since the rules of L&D are being rewritten anyway, why not push the boundaries of learning through intentional collaboration with generative AI to keep up with the revolutionary arc of this evolving technology? Much like Andy in “The Devil Wears Prada,” we must progressively tap into the non-computable essence of being human — EI — and start asking the right questions focused on creativity, curiosity and compassion.

How can you determine which questions to ask? Consider the following to jump-start your thinking:

  1. Curiosity. Curiosity is the intrinsic motivation for exploration, learning and creativity. It begs the question: What are the most pressing challenges for your organization? This means while there will be a range of challenges this AI-driven economy has created a seismic shift in business expectations for L&D.
  2. Compassion. Compassion is essential to maintain connection and sentience. This begs the question: How are you thinking about job extinction? This means, given the demand for causal learning impact and speed at the point macroeconomic business pressure may compel automation and generative AI solutions.
  3. Creativity. Creativity is and has been an evergreen skill of the future of the 21st-century and there have been attempts to merge AI creativity. It begs the question: What are your L&D functional skills gaps? This means as practitioners, we must assess our collective skills and reimagine new ways to assess and accelerate upskilling and reskilling and explore techniques where AI can enhance creativity to formulate even more novel ideas.

Conclusion

Much like Miranda’s expectations of her staff, the media’s hype has overly inflated frigid expectations of generative AI and the fate of humans. This has set off a perpetual scramble fueling human’s feelings of inadequacy, anxiety and resistance. Though, what isn’t hype are the staggering predictions that technology could displace an estimated 400 million workers worldwide, 15% of the total workforce, by 2030, according to McKinsey. While the media may dress AI up as the devil, maybe at least generative AI’s capability to enhance human-sentient capabilities is symbolic of wearing Prada — a premium brand of AI and EI making the planet a better place.

Finally, the good news is that U.S. policymakers and technology companies are working together to start conversations on how the evolving generative AI tools can be used safely and constructively. As an L&D professional, consider: What would it look like to convene as an industry to discuss how generative AI can be used responsibly to enhance measurable employee growth, interactive learning in the flow of work, and elevate the value proposition of the L&D field? It’s a lot to consider, but by coming together, L&D professionals can help drive an ethical and responsible AI-driven future of work.

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