Why all the gap between superstars and all will enlarge

Copyright © HT Digital Streams Limit all rights reserved. Matthew Call, The Wall Street Journal 5 min Read 13 Oct 2025, 07:19 am Ist Decades of research shows that individuals with high status get a great credit for work similar to those of low status employees. (Pixabay) Summary tension on the workplace and resentment will rise as top performers more than everyone else benefits from artificial intelligence instruments. But there are things that companies can do to level the playing field. The conventional wisdom is that artificial intelligence will level the playing field to employees, giving the average employees the tools to shine as bright as the superstars. My research suggests that this conventional wisdom is wrong. I believe it is the superstars that will draw most out of AI and the gap between top performers and everyone will increase. While this can be good news for superstars, it is problematic for companies, because AI-reinforced performance gaps will strengthen the tension and resentment of the workplace that stars can sometimes create, which undermines the team’s cohesion-and ultimately impairs the collaboration work that drives the success of the business world. Organizations that do not address this can find their best talent more difficult and their remaining employees more difficult to motivate. How expertise strengthens AI benefits, think of your own organization. When a new tool arrives that promises to make everyone more productive-advanced Excel features, sophisticated customer relationship management systems or powerful analytical platforms-which actually master it first? It is usually the superstars who dive deep, discover hidden capabilities and find creative applications that no one else has thought of, while the average employees tend to stick to basic functions. Ai follows the same pattern as every other workplace instrument: The superstars are the first to embrace it. In addition, research shows that stars also use their ‘domain expertise’-it means to withdraw their in-depth knowledge of a topic or business-to fundamentally more value (and to catch more mistakes) from AI systems than average achievers. Imagine a star consultant working to bring a new product or service to the market. Instead of asking AI to ‘analyze this market’ and receive generic insights, the star uses years of experience to ask more nuances and targeted questions about competitive dynamics, regulations and obstacles. Stars’ deep expertise will lead them to better refine the commands or questions they give AI, rather than accepting the first output. This leads to more useful and accurate results. In addition, research finds that employees with more expertise than their peers are significantly better to accept AI recommendations when they are correct and, more importantly, reject it if they are wrong. Stars have another benefit: they generally work more systematically, which means they are more organized and considerate in how they approach tasks compared to the average worker. Research finds that it is exactly the types of people who get dramatically better results from AI instruments than those who just dive randomly. AI instruments respond best to clear, structured input – exactly what stars naturally deliver through their organized work habits. Extra credit The way managers treat superstars only exacerbate their benefits. According to my research, the reputation and status of top achievers gives their autonomy and discretion in their work. This means that stars are more likely to dive in and start experimenting with AI immediately. While average employees are waiting for official guidance or follow the company-approved templates for fear of errors, stars will test boundaries, discover creative applications and build personalized workflow long before their organizations catch up. If an AI experiment goes sideways, it’s more likely to get a pass – or at least the benefit of the doubt. Then there is the question of getting credit. Decades of research show that individuals with high status get great credit for work similar to those of low -status employees. This indicates that when AI aid is invisible – what it is often – observers are likely to fill the gaps on the basis of what they already believe about the employee. Stars will get the benefit of the doubt: their AI-enhanced work becomes proof of their excellent judgment and strategic thinking. Average artists have the opposite assumption: If the work is exceptional, AI should have done so. This creates an evil double tire for average employees. They are less equipped to utilize AI strategically, but even if they manage to deliver excellent AI-assisted work, they are unlikely to get the recognition of the career advanced that comes with it. Sometimes just the suspicion of AI engagement is enough to reduce how others view their contributions. How to level the field, what can companies do to prevent AI stars from turning into an untouchable class? I suggest three things: • Encourage everyone to experiment with AI. While stars are quietly building personal AI workflow, most employees await official guidance that may never come. Smart leaders need to create ‘AI Sandbox’ time where all employees can test tools without being afraid of making mistakes, and to establish cross-training programs connecting the average achievers to early employees. More importantly, they need to invest in AI literacy training that goes beyond the use of the basic tool to include rapid engineering, output evaluation and strategic task delegation. The goal is not to eliminate the expertise benefit of Stars, but to learn the teachable skills that can level the playing field. • Distribute the knowledge. Since AI responds best to clear, detailed input, leaders must train average achievers to adopt employment habits that enable them to draw most out of AI. This means providing templates for organizing information and creating shared collections of effective AI directions, strategies and use cases. Rather than letting stars end up their discoveries, sharing knowledge makes a standard practice. If one employee discovers an effective AI workflow, catch it and spread it over teams, so it becomes something that everyone can use rather than the secret advantage of one person. • Redesign of employee evaluation systems to take into account Ai-augmented work. The prejudice that gives stars out of proportion to AI-assisted work will only worsen if not marked. To solve this, companies need to make clearer guidelines on AI disclosure. They must develop evaluation criteria that are fairly assessed that AI-supported work, regardless of the existing status of the artist. And they must train managers to acknowledge when prejudice can skew their assessment of an employee’s performance. Consider implementing ‘AI transparency’ practices where teams share how they use AI instruments, making the help visible rather than hidden. Without these systemic changes, AI runs the risk of creating a two-level workforce where a small group seizes the most opportunities and all left behind. Matthew Call is an associate professor in the Department of Management at the Texas A&M University’s Mays School of Management. He can be reached at [email protected]. Catch all the business news, market news, news reports and latest news updates on Live Mint. Download the Mint News app to get daily market updates. More Topics #Kartic Intelligence Read the following story