AI is most certainly replacing a number of entry-level jobs, but what happens when early-career professionals are allowed to work more closely with AI instead? A new study by KPMG and the McCombs School of Business at The University of Texas at Austin found that “employees with nearly identical knowledge and skill can produce dramatically different results once AI enters the workflow,” which is data that employers should be paying closer attention to.
The roles of employees and that of artificial intelligence are often seen at odds with each other, with many employers exploring which one they intend to retain at their organization. Hiring managers have also echoed a preference for training AI over giving fresh grads a job at their organization, exacerbating pre-existing resentments around managing Gen Z workers. They may have their reasons for this attitude, but it could be hurting the organization rather than benefiting it.
AI-powered workflows have been redefining how work is done within organizations, but there are more gains to be found once organizations come to terms with not just the “where” of AI integration, but the “how” and “why” as well. There could be some key ways to not replace but redefine entry-level jobs in collaboration with AI, once businesses pay more attention to how employees can create value best.
Beyond knowledge and skills, understanding the process behind AI workflows is critical to developing early-career talent in the AI era. (Image: Pexels)
Exploring Early Career AI Collaboration and How Best to Make the Most of the Technology in the Workplace
The hiring landscape has vastly shifted in favor of identifying talent that comes equipped with AI skills to support a business in its operations. Unfortunately, many report a dearth of such skilled AI workers, even as the demand for them continues to grow. With the capabilities of AI fast evolving, many employers remain stuck on how best to direct the talent they do find, creating a chaotic bubble around the use of AI overall. The joint study by KPMG and the University of Texas at Austin offers some clarity on how best to rely on AI and shape talent around it.
The study started with an AI-only baseline to understand how the tech performed without human interventions. It then looked at 523 early-career professionals with tenures of 18 months or less at KPMG, who were tasked with working with AI agents. This was done to understand how collaboration affected results. They found three distinct profiles on interaction.
First came the AI amplifiers, who beat the AI baseline. Then the delegators who matched the AI baseline. Finally, they identified the AI apprentices, a group that fell below the baseline. Even when equally matched in critical thinking, domain knowledge, and AI literacy with the amplifiers, the apprentices fell behind.
The data shows that often, workers may perform just as well with AI as the tech does on its own, but those with a little insight into how to use the tools effectively could collaborate and surpass what the tools achieve on their own.
What the Differences in Approach Mean for the Results
AI can assist employees in early career roles, but it’s their approach to the technology that matters most. The results showed that while AI apprentices ranked better than the delegators on aspects of critical thinking, domain knowledge, and AI literacy. But when it came to AI, they often critiqued the responses, but rarely in a way that pushed for improvement in the output, and often in a way that distracted from the goal. Their approach to refinement did not come with an application of their foundational knowledge within their AI workflows.
Similarly, AI delegators scored lowest on foundational skills, but still managed to meet baseline performance. If judged by the end output, they succeeded in their role just fine, but they spent little energy on refining the results, accepting acceptable responses as is. The results hinged more on what the AI could perform rather than as a result of the employee’s intervention.
In contrast, AI amplifiers actively rewrote their processes and the AI’s approach towards resolving the problems at hand, returning to the output to refine what it had to offer. As the article in the Harvard Business Review states, “they used their understanding of AI to provide precise, task-specific guidance that enabled the system to produce deliverables that met real-world performance standards.”
What this shows is that it isn’t sufficient to work with AI and take its results at face value. Those who truly succeed in optimizing the technology are the ones who work with it in-depth and optimize its results.
The Future of Entry-Level Work Could Be Bright with the Right Training
When it comes to the use of AI at work, it isn’t enough to hire those with the most industry knowledge or familiarity with AI to guarantee results. While those elements are undoubtedly important, it is just as essential to develop their innate talent around the needs of the organization, with clarity on the process and end goal. In many cases, concentrated training and direction can be the key to turning workers into high performers.
AI careers are still a new concept, and much of what we believe is best practice could no longer be relevant to how we approach work. Workers in entry-level roles are not only likely to be AI natives, most primed to understand the tech, but also those who do not have set routines in place that might take years of unlearning.
Employers who develop their own organizational approach to AI training have the opportunity to coach employees through the best approach to artificial intelligence, ensuring they are primed to uncover the most effective strategies in navigating this nascent technology.
Prioritize Perfecting the Process as Much as the End result
Early career hires can master AI and work with its results in meaningful ways, but it also helps to understand their approach and ability to navigate the technology, and then build on it to shape their understanding of when to stop refining the results. This isn’t just about training them on perfect prompting, which is where they start the process, nor is it solely about teaching them what a good end result looks like.
Instead, much of the work here is iterative, ensuring that they have an intimate understanding of what every stage of the interaction looks like and where their existing knowledge and skill should shape results. Documenting their work process and determining why some choices are made over others is, perhaps, the best way to navigate this approach. This step could be key to unlocking their full potential, not only creating room to perfect their strategy, but also revisiting the data of high performers to better assist new early-career hires.
Although abstract in theory, such an approach, one based on continuous learning, is what the teams behind the research recommend, and there is sufficient reason to believe that this may be the best approach when it comes to working with AI. As organizations continue to invest in the tech, it will be just as important to invest in its talent, not just with financial investments but in time and energy towards identifying employee skills, strengths, and approach to the technology.
What are your best practices for shaping early career hires in the art of AI? Share your thoughts with us. Subscribe to The HR Digest for more insights on workplace trends, layoffs, and what to expect with the advent of AI.
