AI Will Eliminate Roles—Upskilling Is Not Optional
I attended the Workhuman Live conference in May, which brings together US and international leaders to discuss how to build human-first workplaces each year. Ironically, every keynote and session I attended discussed the role of AI in the workplace—how leaders should respond to it, sell it, and utilize it. Last I checked, AI didn’t count as human.
Following are three key takeaways that seek to reconcile increasing reliance on AI while optimizing human/organizational success:
AI will replace many entry-level jobs, and capable organizations should invest in re-skilling and up-skilling individuals where possible.
In contrast to the mid-90s where many behemoths ignored the advent of online shopping, every company now realizes the importance of leveraging AI, but most organizations don’t know how to harness it to drive results.
Successful individuals (and their organizations) will be those who can learn and adapt quickly, rather than those with years of experience.
We will explore all three takeaways, but let’s focus on the first one for this blog post.
AI is already replacing entry-level jobs; within the past week, Amazon, Accenture, Goldman Sachs, and JP Morgan Chase announced impending layoffs or hiring slowdowns due to AI-driven efficiencies, following last month’s decision by Salesforce CEO Marc Benioff to cut 4,000 roles. These layoffs don’t even consider large-scale priority shifts by companies like Microsoft, which eliminated 9,000 jobs to increase investment in AI in July.
Recent reports that job growth is slowing, layoffs are increasing, and consumer sentiment is low suggest that the economy and job market are rough. Based on my experience, one of the first things to get cut during downturns is training and development.
In the event organizations do not have the budget or desire to re-skill or up-skill, what should leaders encourage individuals to do?
Audit current skills: consider what you already do well, what parts of your job could be/should be automated, and where the gaps are (ie: domain knowledge, relationships, communication, critical thinking)
Understand AI basics: learn to work with AI, including what it can/cannot do and what biases/pitfalls may exist (ie: data quality, societal or historical biases, etc.)
Learn complementary skills: study how to interpret data, how to frame problems and execute solutions precisely (ie: how to define questions, interpret outcomes, implement solutions, and identify edge cases), and how to be a better leader through empathy, clear and inspiring communication, and managing change
Jump in: practical experience helps, and you are not behind. Try out new tools, experiment with using AI in your work, read articles, and participate in online forums. For extra points, take structured courses like those offered by Coursera/Stanford or edX.
Next up: How to use AI to actually drive results.
