By Romila Mattu, Senior Practice Director, Cloudera APAC Professional Services
This year’s International Women’s Day theme, ‘Give to Gain,’ is a reminder that investing in women’s advancement at work delivers returns for everyone. Diverse teams broaden talent pipelines, improve decision-making, and build workplaces where people are more engaged and more likely to stay. Inclusion is not a parallel diversity initiative. It is a business growth strategy. Organisations that intentionally widen access to opportunity build stronger innovation pipelines, deeper talent benches, and more resilient operating models.
Yet women face a double exposure risk in the AI economy: underrepresentation in high-growth AI roles, and overrepresentation in functions most vulnerable to automation. According to an EY report, 42.6% of India’s STEM graduates are women, outpacing several developed nations, their participation in STEM jobs remains disproportionately low. Despite initial workforce entry, women often face stagnation at mid- and senior-level positions, with representation at the leadership level significantly dwindling.
This is important to address in 2026, a crucial year in the development and implementation of Agentic AI. In fact, IDC predicts that by 2027, half of enterprises will be using AI agents to redefine how humans and machines collaborate. As these systems increasingly influence business-critical decisions, organisations need to assess blind spots in who builds, tests, and governs them.
When women are missing from AI development, the impact compounds
AI systems inherit the assumptions of the environments that build them. When development teams skew toward a single demographic, bias doesn’t only show up in datasets. It can also appear in which problems are prioritised, how success is defined, which edge cases are tested, and what risks are accepted. In the agentic era, autonomy raises the stakes: small weaknesses in data, design, or oversight can be amplified once decisions are made at scale.
True inclusion means having diverse voices shape product direction and decision rights and not just representation in organisational charts. Practically, this means auditing datasets for representation gaps, testing models for unequal outcomes, stress-testing edge cases, and involving a diverse panel of human reviewers throughout the AI lifecycle.
Innovation strengthens when inclusion is embedded into operating systems. Organisations that treat representation as a strategic input rather than an afterthought build AI systems that are more context-aware, adaptable, and aligned with real-world complexity. In competitive technology environments, that alignment becomes a differentiator.
Additionally, governance is what makes these practices consistent. India’s AI Governance Guidelines provides a framework that balances AI innovation with accountability, and progress with safety. It represents a strategic, coordinated, and consensus-driven approach to AI governance.
Workforce readiness and inclusion are central to ethical AI deployment
Ethical AI cannot be separated from who participates in building it. India’s AI economy is expanding rapidly, but representation gaps persist in the very roles shaping it. According to the NASSCOM India Tech Sector Strategic Review 2025, while India’s technology workforce continues to grow and AI-related roles are among the fastest expanding segments, women’s participation drops significantly in advanced digital and emerging technology positions, particularly at mid-to-senior levels.
This imbalance is not merely a talent pipeline issue, it is an AI risk issue. When women are underrepresented in high-impact technical and decision-making roles, blind spots in problem framing, model testing, data selection, and governance structures become more likely. Inclusive workforce development is not a parallel HR initiative but a safeguard against systemic bias in AI systems.
HR therefore needs to shift from a supporting role to a strategic one, ensuring reskilling, job transitions, and inclusion plans are designed from the start, rather than retrofitted once technology is already embedded. Ethical AI also cannot be outsourced to a model. It requires human judgment and accountability throughout the AI lifecycle, stress-testing edge cases, auditing datasets for representation, testing for unequal outcomes, and involving diverse reviewers throughout development and deployment.
Restructure how work gets valued in the AI era
As AI becomes embedded across core business functions, coding ability is no longer the sole marker of technical contribution. Engineers need business acumen, communication skills, and the ability to collaborate across functions because responsible AI depends on context and judgement, not just models.
This shift can create opportunity for underrepresented groups, including women, if organisations update what they recognise and reward. Programs like Women Leaders in Technology (WLIT) create forums where women and allies connect, learn, and support leadership pathways. Women must see themselves reflected in leadership before that path feels accessible.
When women are given resources, opportunities, and authority in AI development, organisations gain better AI systems that work for everyone. In the agentic era, diversity in leadership and oversight should be treated as part of AI risk management.
Organisations that formalise cross-functional approaches, create transition pathways, and recognise emotional intelligence as technical capability will build better AI and advance gender equity. Equity does not dilute excellence. It expands it.
