Many believe that AI has the potential to create more equal workplaces. Recent studies highlight the equalizing potential of AI for employee productivity, showing that when given access to AI tools at work, lower-skilled performers often exhibit a significantly higher relative increase in task quality and productivity compared to high-skilled performers. The reality may not be as simple as this. The emerging “AI-specific Matthew Effect” suggests that the performance gains of AI may be skewed in favor of top performers.
According to the theory, “star performers,” who already outperform at work, are likely to be the ones leveraging AI for innovation. Meanwhile, their colleagues may struggle to adapt, resulting in a widening performance gap.
This blog explores why AI’s impact on productivity may be uneven, what drives these disparities, and how leaders can ensure equitable adoption across their workforce.
How AI Potentially Drives Inequity in the Workplace
Rather than democratizing performance and reducing disparities, AI could concentrate opportunities further and exacerbate inequities in the workplace. The following are key reasons why this might happen:
Enhanced growth prospects for high achievers: Top performers, with their superior domain expertise and strategic use of AI, may be able to extract greater value from artificial intelligence. Their colleagues, on the other hand, may be less capable of pushing the boundaries of AI. As these “star performers” yield outsized AI-driven results, organizations may focus their resources, i.e., training budgets, job rotations, and tech pilots, on them, intensifying the “Matthew Effect.” It could mean greater autonomy, more recognition, and faster career advancement for top performers. Meanwhile, those whose initial AI experiments go awry may become risk-averse or overlooked entirely. ]
Prioritized access to AI training and tools: At most organizations, AI provisions are deployed in high-impact roles and core business functions. Typically, senior staff and high-potential employees are more likely to receive extensive AI training and to have regular access to enterprise-grade tools. This inequity can, in turn, deepen skill gaps in a country like India, where supply already lags. According to NASSCOM, the current availability of AI talent in India meets only 50% of the demand. Employees in the informal sector may be notably excluded due to a lack of access to formal training programs.
Greater performance gains among top talent: The impact of AI is uneven, as it does not boost productivity and efficiency equally among all users. Several studies have shown that when highly proficient workers utilize AI tools, particularly in domains such as programming, research, and analytics, they experience higher productivity compared to their less-skilled colleagues. As such, AI may deepen existing performance gaps within organizations rather than mitigate them.
Why Organizations Need to Pay Attention
The disproportionate performance advantage top performers stand to yield from AI can be damaging for workplaces and economies as a whole:
Organizational innovation suffers because only a fraction of employees are empowered to push the boundaries of AI.
If “star performers” benefit more than others, resentment and disengagement can arise.
As high performers become increasingly efficient, they may resent coworkers who are lagging. As a result, workplace conflicts may become more common.
Succession planning and knowledge transfer can become difficult.
Those with advanced capabilities may command higher compensation. Over time, these inequalities pose risks to economic mobility and overall welfare.
In India, this inequity can be damaging for specific worker groups. Employees in the informal sector are at risk of displacement. Even gender disparities can amplify this risk: according to the WEF's Global Gender Gap Report 2025, globally, only 29.4% are currently working in STEM fields. Studies also show that AI adoption is lower among women.
How Organizations Can Equitably Integrate AI for Employee Productivity
Emerging research suggests that employees who are better at capturing value (“star performers”) may reap greater performance advantages and advancement opportunities. As such, the majority of employees risk getting sidelined as opportunities and recognition get concentrated.
It is incumbent upon business leaders to take proactive steps to prevent widespread loss of morale and engagement from AI integration. The following strategies can help:
Democratizing AI training and practical upskilling is necessary. Ensuring specific employee cohorts are not left out is key to building a future-ready, resilient economy.
Traditional hiring models need to be reconsidered. HR leaders should prioritize employees who demonstrate critical thinking, curiosity, and a capacity for innovation.
Employees at all levels should have access to relevant AI tools. Organizations should promote a culture that encourages experimentation and normalizes failure. This effort is necessary to prevent risk aversion among employees and drive innovation.
Regularly monitoring performance and promotion data to identify equity gaps is necessary. Equally important is taking corrective action proactively.
Knowledge-sharing should be a standard practice within organizations. “Star performers” should be encouraged to share their learnings with coworkers and mentor and support them, so everyone benefits from AI advancements.
Conclusion
With AI, we stand on the edge of a technological revolution that could accelerate productivity, drive global expansion, and reshape entire economies. Yet, how AI tools and training are deployed across workplaces matters. Adopting AI in the absence of a clear strategy and supportive infrastructure could mean that only a select group of employees (high performers) may capture its value.
Therefore, proactive action is necessary. Organizations that promote upskilling, knowledge sharing, and inclusive growth can leverage AI to democratize performance and enhance employees' capabilities across the board.
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