Female representation in AI is a win-win situation for businesses
We're entering the era of AI success, as AI shifts from experimentation to being a driver of true enterprise ROI. But to guarantee success at scale, in a way that is trusted and mitigates bias, we must also combat the issue of female representation in AI.
International Women's Day 2026 is centred around the themes of collaboration – a value that should play a central pillar in the development of AI. As we move away from AI being an 'emerging' technology, the industry needs to share knowledge, learnings and resources to ensure all enterprises can drive success in the sector. Female voices must be part of this conversation.
Unfortunately, there's still a significant representation imbalance in the AI industry. According to 2024 analysis, women make up less than a quarter of AI talent globally - a number that drops to less than 15% at senior executive levels. LinkedIn data found that while in 2018 only 23.5% of those who listed AI engineering skills in their profiles were women, the number had risen to 29.4% in 2025. While we're seeing small steps forwards, we still have a long way to go to ensure women are equally represented in AI.
So how can we establish better representation in the sector – and what impact will it have on the AI models of tomorrow?
Building fairer AI systems
As AI agents are increasingly used to automate tasks and shape major decisions across businesses and governments, attention must turn to the driving force behind them: the AI models themselves. The outputs of these models, and therefore the decisions they make, rely directly on the data they are trained on.
Picture this: if AI models are shaped by the viewpoints of the engineers that build them, how can we avoid bias when an AI engineering team is made up entirely of men? The AI algorithms and systems that are created in that environment risk being inherently skewed due to the limited perspectives developing them. And if that is the case, what can we do to ensure the decisions they shape are representative of the female half of the population?
To avoid in-built bias, the data that AI models are trained on must be representative of a range of diverse demographics – including a balance of female contributions. Having more varied perspectives shaping AI will improve the outputs from both an equality standpoint as well as the quality of AI outputs overall.
Strengthening AI trust through representation
Trust lies at the heart of AI success - both trust in the models themselves, and trust in the teams behind them. Beyond development, AI will only succeed in real world scenarios if people trust the systems and accept them into their working routines.
Having diverse representation in AI development helps to build this trust in the AI ecosystem. It reassures businesses and individuals that the AI systems and models have been designed and tested through a variety of lenses. Embedding inclusion in the AI development lifecycle will reduce blind spots and help create AI that is more credible and widely accepted.
Inspiring inclusive innovation within AI
If we are to push AI forward on its successful trajectory, we must match its implementation with innovation. To inspire creative and innovative thinking, we must have a broad range of perspectives and ambitions shaping the future of AI.
Having more female voices present in the development and deployment of AI will bring previously overlooked priorities and use cases to the forefront. This could lead to breakthroughs in areas such as women's health or fairer hiring processes. These perspectives stem from lived experiences and help expand not only what AI can do, but also the people it can serve.
So not only will having more female voices present drive more inclusive and impactful innovation, but it will also improve the quality of AI models overall, while strengthening trust, credibility and creativity. It's a clear win-win situation. For businesses looking to get ahead, female representation in AI development should be a strategic priority.