It has been hard to escape discussions on the impacts of AI, a technology that seems like it has sprung from the pages of science fiction. The rapid development of large language models (LLMs) and generative AI seemingly promises a cornucopia of potential benefits that many companies are eager to avail – productivity, efficiency and the unlocking of entirely new workflows (and workforces). However, with some of the hype cycle around AI fading away, the question of what AI adaptations will help companies maximise their integration of the technology could not be more critical right now.
For too long, corporate investment in women has been measured by the number of training hours delivered or certifications completed. In an AI-driven economy, that definition is no longer enough. Real investment means giving women access to the ideas, technologies, leaders and communities shaping the future while those conversations are still unfolding. It means creating spaces where an engineer learns from an AI researcher, where a product leader challenges assumptions alongside a founder, and where emerging leaders develop the confidence to ask better questions, think more strategically and return to work equipped not only with new skills, but with a broader perspective on what is possible. At AnitaB.org India, I have had the privilege of witnessing this transformation through Grace Hopper Celebration India, Asia’s largest gathering of women and allies in technology. Every year, it brings together the people shaping the future of technology across deep technical learning, leadership conversations, emerging technology tracks and cross-industry collaboration, creating an environment where women don’t just keep pace with change, but are empowered to lead it. As the next edition of Grace Hopper Celebration India 2027 brings this ecosystem together once again from January 20 to 22, 2027, it presents organizations with a timely opportunity to move beyond intent and invest meaningfully in the women who will shape the future of AI. Because investing in women’s AI capabilities is no longer about representation alone. It is about building the talent, leadership and perspectives that will determine how AI transforms the future of work.
There is no one answer when there are so many variables, but one obvious place to start is investment in women’s AI skills. A recent Gartner report titled ‘Upskill or Fail – Prepare for AI-Driven Workforce Transformation’ indicated that companies that fail to upskill their employees will not benefit from AI-driven RoI benefits that will accrue when AI process re-engineering impacts kick in in 2028. Upskilling is the key to productivity, not layoffs.
The argument for investment in women’s AI skills is therefore quite simple – when a technology is so disruptive, upskilling requires a maximisation of workforce potential by expanding to untapped demographics, and when the technology is so surrounded by hype, it will benefit the demographic most critical of claims around its value – women.
The rose glasses are off
Since the public launch of ChatGPT, the entire world has been caught in the first stage of the Gartner Hype Cycle – the peak of inflated expectation. AI is a technology that people misunderstand and mistake as exhibiting human-like intelligence. The allure of realising this science fiction trope of robots and droids through an exponential buildout of compute has led to billions of dollars in data centres, trillion-dollar projections; all with the promise that endless compute would lead to continuous exponential growth in the ‘intelligence’ of the models. However, we may be moving into the second stage of the Gartner Cycle, which is the trough of disillusionment.
Many may already be aware of these developments but may not know the full story. They may know that a big part of the cool-off towards AI has been the public reluctance to the buildout of data centres that frontier LLM models need to operate. They may even know that much of the opposition is community-led as the public becomes more aware of the environmental impacts of data centre water and power consumption on neighbourhoods. What they may not know is that many of these movements are led by women, as UN reports show women bear the brunt of the impact of environmental degradation.
Inclusion matters
This gendered divide is also found in polling and evidence around AI technologies themselves. PEW research shows that women are more likely to view AI negatively, across teenagers and AI experts. 35% of men believe AI helps their productivity while only 25% of women think the same. Similarly, women are much more likely to think AI will negatively impact them personally (33% vs 17% who think it will be positive) compared to men, who are much more marginally inclined to think they will be impacted positively (29% vs 27%).
This inherent scepticism is why gender diversity in AI upskilling should be the catalyst for optimizing a company’s AI roadmap. A critical approach that is not swayed by the hype of future promises will be the lynchpin maintaining operational continuity through a period of transition. A PNAS Nexus study by Northeastern University and Caltech Researchers revealed that when the impact of AI is characterized by uncertainty, women are roughly 11% more likely to perceive that the risks of AI outweigh its rewards.
Some may characterise this caution as a liability, but there is a long history of unbridled optimistic tech myopia resulting in expensive, redundant tool deployment with little impact on productivity, often coming with catastrophic consequences. The reality may be different. A University of Tokyo study found that women executives are more likely to invest in AI, achieve productivity gains using AI, and generate higher revenue using AI than men. Women’s critical and risk-sensitive mindsets can ward against hype-led overextensions by ensuring that AI is applied only where it is genuinely necessary and creating high value. By integrating women into AI development and deployment teams, companies can insulate themselves against costly strategic overreach and steer investments toward concrete, high-utility use cases.
The path is clear but difficult
The argument for focusing on AI upskilling for women therefore seems quite clear. Women are statistically more likely to deeply scrutinize AI-generated content for underlying algorithmic bias, factual inaccuracies, and compliance alignment – all abilities useful beyond productivity, as they can help protect brand reputation from costly mistakes. Women will be a part of how we move down the Gartner Hype Cycle from the trough of disillusionment to the slope of enlightenment and plateau of productivity.
However, the path is not easy, as there is ample evidence to suggest systemic barriers to AI use for women, such as bias during productivity or hiring assessments. Any measure to upskill women in AI will have to be intentional in not just providing training but addressing these issues to create an environment where all employees, not just women, can thrive to their full potential. But the benefits are clear. Diverse teams don’t just use LLMs to become faster at work in the short run; they use them to work more accurately, effectively and ethically, helping companies adapt to the AI revolution in the long term.
