In the age of generative AI, learning won’t end with graduation. According to the WEF’s 2023 Future of Jobs report, 23% of current jobs are expected to change by 2027, with 69 million new jobs created and 83 million eliminated; meanwhile, 44% of workers’ core skills are expected to change in the next five years. And 81% of organizations surveyed plan on investing in learning and on-the-job training programs. It’s no wonder that “curiosity and lifelong learning” and “resilience, flexibility, and agility” are among the top ten most important skills organizations will be looking for in the coming years, according to the WEF.
Employees won’t merely need to be upskilled; they’ll need to be reskilled, and on a massive scale. Some employees will have to change their occupations entirely. Asa recent reportfrom the Digital Data Design Institute at Harvard’s Digital Reskilling Lab and the BCG Henderson Institute shows, reskilling is a strategic imperative for companies—both as a part of their employee value proposition and as a means of accessing the talent necessary to maintain competitive advantage.
With the coming of generative AI, employee upskilling and reskilling programs have become a strategic imperative. Fortunately, GenAI—for all the disruption it creates—can also be a powerful tool for organizational learning.
BCG U has created a prototype generative-learning platform, called BCGenie, that uses generative AI models to deliver a personalized, adaptive learning experience at scale. Integrating existing technologies, proprietary LLMs, and client-specific content, BCGenie can help organizations maximize ROI in upskilling and reskilling programs by providing employees with self-paced learning and hyperpersonalized, up-to-date content—all on demand and with materials tailored to an individual’s preferred style of learning.
To learn more about BCGenie, and about BCG U’s organizational and employee development programs, click
here.
Learningwhento use GenAI is just as essential as learning how to use it. In a recent experiment conducted in partnership with Harvard Business School, MIT Sloan School of Management, the Wharton School at the University of Pennsylvania, and the University of Warwick, BCG found that the use ofGenAI improved performanceby 18% on tasks for which the technology was well-suited; for tasks it had not yet mastered, however, performance declined by an average of 23%.
And while nearly all subjects saw a boost in performance when using GenAI for creative ideation, our experiment found that the relatively uniform output of the technology can reduce a group’s diversity of thought by 41%. What’s more, roughly 70% of survey participants expressed a concern that, over time, extensive use of GenAI may negatively impact their creative abilities. Organizations will need to be mindful of GenAI’s tendency to produce homogenous results, as well as the potential effects of technology on their employees’ professional identities.
Learn More About Gen AI
Learn More About Gen AI
Generative artificial intelligence is a form of AI that uses deep learning and GANs for content creation. Learn how it can disrupt or benefit businesses.
Scaling artificial intelligence can create a massive competitive advantage. Learn how our AI-driven initiatives have helped clients extract value.
No Shortage of Other Risks
As students, educational institutions, workers, and organizations learn how to use GenAI and other AI tools, they’ll need to keep a host of additional challenges in mind. Among them:
Plagiarism isn’t the only peril.We will, of course, need to build new tools to help teachers identify GenAI-generated content and ensure that proper sources are cited; at the same time, teachers themselves will need to determine which uses of GenAI are permissible and which constitute cheating. But GenAI presents other educational risks, too. Its powerful analytic capabilities could unintentionally create a “predictive ceiling” for some students, limiting their upward trajectory. And on a larger scale, access remains a challenge: schools with greater resources are more likely adopt GenAI tools quickly, which could widen the gap with schools that face funding shortages.
Children are especially vulnerable.Just as AI holds extraordinary potential for advancing education, it presents an equally profound risk of compounding thedangers children already face online,包括欺凌,不恰当的内容,和digital addiction. Companies must build safety and privacy protections into any AI-powered software intended for use by children.
Bias is an ever-present concern.AI systems are only as reliable as the data on which they are trained; AI outputs could therefore potentially reflect the biases inherent in the underlying data. Some facial recognition programs, for example, have had difficulty recognizing the faces of people of certain ethnic backgrounds because the data used to train the AI omitted any images of people of those ethnicities. As GenAI systems become more prevalent and powerful, humans will need to be taught to recognize such biases and correct them.
Machines are not infallible.GenAI has shown a well-publicized tendency to produce misinformation. Even so, humans may be reluctant to question the outputs GenAI and other AI systems produce, especially as the technology grows more sophisticated. Regardless of how impressive these systems become, humans must remain in the loop, ensuring that all AI tools are deployed responsibly and that their outputs are accurate.
As we continue to work with and learn from AI, new risks will surely emerge—as will new questions about what the technology means for the future of human intelligence. We’ll need to keep asking those questions, and applying our empathy and judgment as we pursue the answers. If we do so, AI’s expanding capabilities can create new opportunities to expand our own knowledge and creativity.
