AI is rewriting the rules of work, but not everyone is being written into the story equally. As organizations race to automate, optimize, and augment, new technologies promise productivity and opportunity, yet also risk widening gender inequality.
Credera’s AI Gender Gap report finds that women are disproportionately exposed to job automation and underrepresented in the design of the systems driving it. The result is a widening divide: women are twice as likely as men to hold roles at high risk of automation, yet less likely to use or shape the tools transforming those jobs.
The data delivers a stark warning. Administrative support, customer service, and data entry—roles where women comprise 70% to 90% of the global workforce—are confronting automation exposure rates as high as 80%. Alarmingly, a 2024 analysis of 39 AI-driven organizations revealed that female employees are twice as likely as their male counterparts to risk having their roles eliminated by algorithms.
Meanwhile, adoption gaps intensify the crisis. Women are 20% less likely than men to use generative AI tools and remain underrepresented among visitors to the largest AI platforms. Even when given equal access, women are still 13% less likely to adopt these tools. These factors, compounded, urgently create an alarming equation: a higher threat of job loss, combined with lower participation in the pivotal technologies transforming the workplace.
The Feedback Loop of Bias
Without action, these trends risk deepening disparities. Organizations, policymakers, and technology leaders must prioritize equitable AI adoption, representation, and education. Now is the moment to champion inclusion and ensure everyone is written into the story AI is shaping.
With too few women in AI and data science, these biases often go unchallenged. Today, women comprise only around 22% of the global AI talent pool, falling to under 14% in senior executive roles. The consequences are circular: underrepresentation in the workforce leads to biased data and design, which, in turn, reinforce the structures that exclude. Researchers call this phenomenon a bias feedback loop. Unless diversity is built into every layer of AI development – from training data to team composition – the systems built to optimize human decision-making risk amplifying human prejudice instead.
Diversity isn’t only a social imperative; it’s a design principle. Teams that include a broader range of perspectives are more likely to question assumptions, test for bias, and build technologies that serve everyone. In the context of AI, inclusion becomes a form of quality assurance.
Inclusion by Design: A Framework for Action
At Credera, we believe inclusive AI doesn’t happen by default – it happens by design. Our approach is grounded in a practical framework:
- Leadership: Inclusion must be championed at the highest levels. Leaders set the tone, own the agenda, and model transparency.
- Design & Data: Inclusion is embedded from the outset. We ask: Who is represented? Who is missing? Whose experiences are shaping the solution?
- Governance: Ethical oversight and clear accountability are non-negotiable. Fairness and transparency must be built into every decision.
- Capability: AI literacy and confidence are developed across the organization, not just among technical experts.
- Evaluation & Assurance: Systems are tested for real-world impact, not just technical performance.
Yet frameworks alone are not enough. It is the everyday behaviors – leading visibly, stepping forward with confidence, staying curious, being an ally, and creating opportunities – that transform inclusion from aspiration to reality.
From Awareness to Impact: What Works
Meaningful progress requires moving beyond rhetoric to tangible action:
- Visible Leadership: Senior leaders must participate directly in bias reviews, design sessions, and upskilling initiatives.
- Valuing Diverse Perspectives: Diversity is not a “nice to have” – it is essential for building robust, effective systems and commercial success.
- Curiosity as a Tool: Asking who is in the room, who is missing, and what assumptions are being made is fundamental to inclusive design.
- Sponsorship and Amplification: Leaders use their influence to open doors, credit others, and ensure a range of voices are heard.
- Investment in Capability: Building AI literacy and confidence, especially among those most at risk of being left behind, is critical.
Inclusion is not only a moral imperative; it is a strategic advantage. Organizations with diverse leadership outperform peers in innovation, risk management, and engagement. For the Omnicom community, the opportunity is clear: by embedding inclusion into every stage of AI development and deployment, we can lead the industry in building technology that works for everyone.
Shaping the Future—Together
AI has the power to accelerate progress – or to entrench inequality at digital speed. Which future we build depends on the choices we make today. The promise of AI will only be fulfilled when everyone is enabled to shape it.
If you are passionate about building inclusive AI or are interested in practical frameworks and collaborative opportunities, I invite you to connect. Together, we can ensure that the future of artificial intelligence is one in which everyone can participate and thrive.