We are living in the future, one in which artificial intelligence can take over many of our tasks—and maybe even our jobs. Thriving in this new age requires that we engage productively with new technologies, but who is managing, supporting, and setting a strategy for how human workers and technology will interact in the workplace? According to Mercer’s recent “2024 Global Talent Trends” article:
"40% of executives predicting AI will deliver gains of more than 30%. Yet, three in five (58%) believe tech is advancing faster than their firms can retrain workers, and less than half (47%) believe they can meet this year’s demand with their current talent model."
That 30% expected gain is more complicated than it may appear on the surface—workplace applications of AI define a jagged frontier that is variable and specific, depending on how business challenges, employees’ innate skills, and the uneven capabilities of AI models intersect with one another. There is one undeniable truth, however: the pace of change driven by AI is rapid already, and it appears to be accelerating. Businesses are struggling to keep pace, while forward-looking employees are trying to upskill to keep up with the change, and maybe also wondering if they might simply be working themselves out of a job.
Keep the Pace
In truth, the fact that merely 58% of executives believe that technology is advancing faster than internal training processes begs the question: are the other 42% truly prepared for the future, or are they simply overconfident?
Earlier this year, three major LLM (large language model) AI releases were announced in the space of 24 hours. There are now highly-capable AI tools to assist workers in the generation of text, image, audio, and video, with new tools announced nearly every day. No corporate learning and development department is structured to keep pace with this rate of change.
There is significant risk in this scenario: businesses falling behind in the marketplace; employees lacking the skills to succeed within their roles; executives in possession of a strategy but struggling to implement it throughout the organization. AI is challenging us all to adopt new skills and adapt to new realities at a rate that can feel uncomfortable.
There is a way forward, however, and it involves first looking back to the year 2006.
The Power of Mindsets
In her book, Mindset: The New Psychology of Success, psychologist Carol Dweck introduced the concepts of fixed and growth mindsets. People with a fixed mindset believe that abilities are mostly innate and interpret failure as the lack of necessary basic abilities. People with a growth mindset, on the other hand, believe that abilities can be developed through dedication and hard work.
When confronted with difficult challenges, people with a fixed mindset are more likely to see these obstacles as insurmountable barriers, doubt their ability to overcome them, and even avoid engaging with them. People with a growth mindset, meanwhile, are more likely to view difficult challenges as opportunities to learn something new.
In the case of AI, an employee with a predominantly fixed mindset might look at ChatGPT and think, “That’s not what I’m good at.” AI, however, is a technology that requires patience and persistence; its rewards are significant, but they require exploration and experimentation. A growth mindset can equip employees to keep at it, working with these new tools and paradigms until they unlock innovative solutions.
No one has 100% of one or the other mindset; we are all a mixture of both, but one mindset tends to predominate over the other. Organizations are similar—at times every business will operate from a fixed mindset basis, treating some employees as more innately talented and capable than others, while in other respects it will operate from a growth mindset and work to train and develop their teams.
Talent attraction, though, has traditionally leaned more into the one than the other. We evaluate candidates largely based on the role they are performing in the workplace today and what they’ve achieved in the past. These qualities are retrospective, largely fixed, and don’t reveal much about how poised a candidate is to learn quickly and continuously, not just at the outset but for the rest of their career.
Bringing a Growth Mindset to Bear
Businesses like Microsoft take a different path. They work with employees to foster a culture of continuous learning and maintain a growth mindset. By allowing fresh insight to flow from the bottom up as well as the top down, these businesses have a better chance to be nimble and responsive in times of rapid change. They create the opportunity to succeed where competitors may struggle.
So, how can talent leaders do the same? Here are three specific steps to take:
- First, take a measure of how your employees currently feel about the pace of change. Are they feeling excited or overwhelmed? Do they feel empowered to explore new opportunities for the business? Ask about these things in your pulse surveys, and then look for opportunities to celebrate and empower your grassroots innovators.
- Second, take a measure of the candidate marketplace. Ask about growth mindset in your candidate surveys, and then–once you have the results–see if you can spot trends in the data that will allow you to target and attract growth-mindset candidates.
- Third, filter these learnings down to your recruiters and out to hiring managers. Place a greater emphasis on qualities like curiosity and resilience when choosing the candidates who will advance through the process. It may be that your best choice for the long run is a candidate who is innately curious and comes with a supercharged capacity for learning.
Growth is no longer an option—it’s an imperative, especially when it comes to AI. Our world is changing rapidly, and we will change with it, one way or the other. Fostering a growth mindset, in our businesses and in ourselves, gives us the best chance to continue to thrive through whatever twists and turns the future may bring.
*Note: The image at the top of this article was generated by DALL-E 3.
