February 11 marks the International Day of Women and Girls in Science, a United Nations observance aimed at closing persistent gender gaps in STEM education and careers. A decade into the global push for inclusion, participation numbers have improved across sectors. Yet in 2026, the central question has shifted.On the occasion, author and philanthropist Sudha Murthy wrote on X, “Science does not belong to any one gender. It belongs to those who are curious, patient, and willing to learn.” Encouraging girls to pursue science with confidence, she added that every girl who feels drawn to science should follow that instinct without hesitation.Her message reinforces the foundational goal of the day: equal access. But access alone is no longer the defining benchmark.Artificial intelligence now shapes healthcare systems, financial markets, education platforms and public governance. Inclusion in this era must be measured not just by entry into STEM, but by influence over systems that increasingly determine social and economic outcomes.Who designs AI systems? Who validates them? Who governs risk? Who is accountable when they fail?
The progression gap in an AI-first economy
For many women in STEM, the barrier is no longer entry. It is advancement.“In an AI-first world, the real challenge for women in STEM is no longer entry, but progression,” says Susnata Singh, Director, Global Services, Fiserv.As AI reshapes enterprise systems, leadership requires system-level thinking. Singh explains, “Leadership is defined not only by depth of expertise, but by the ability to connect systems, apply technology to ambiguous problems, and translate innovation into responsible, scalable impact.”Influence increasingly depends on owning platforms, shaping architecture and being accountable for outcomes. When women remain concentrated in execution roles without exposure to large-scale system ownership, representation does not translate into authority.
Inclusion at the level of governance
AI systems are built on data, assumptions and ethical trade-offs. Governance decisions determine how bias is mitigated, how privacy is protected and how accountability is enforced.“Access to opportunities is merely the tip of the iceberg for women in STEM. Career longevity is shaped by who gets to lead projects with real-world impact and build cross-domain credibility,” says Rekha Nair, CHRO, Tredence.She argues that AI capability cannot be developed in isolation from high-stakes work. “AI literacy is not built in a vacuum or a classroom; it is honed through judgment labs, being close to high-stakes decisions, ethical trade-offs, and tangible outcomes.”The shift from theoretical training to applied influence is critical. “In this new era, the most critical skill isn’t just understanding the algorithm, it’s the human judgment that governs it,” she says.Real gender inclusion therefore includes representation in AI councils, ethics boards, system validation teams and enterprise risk discussions.
Healthcare and the cost of exclusion
In life sciences, AI is accelerating drug discovery, diagnostics and personalised treatment pathways. The composition of teams building these systems has direct implications for research outcomes.“As demand accelerates for both pharmaceutical and digital skills, the future of innovation in life sciences depends on who we enable to lead it,” says Mrinal Duggal, Head of Hyderabad Global Hub, Sanofi.He states that consciously hiring women leaders across data science, computational biology and AI is “a strategic imperative, not simply a diversity metric.”In high-stakes sectors such as healthcare, diversity influences design assumptions, dataset interpretation and risk modelling.
AI literacy and ethical deployment
As AI adoption scales, literacy must extend beyond technical proficiency to governance capability.“Women leaders in STEM are accelerating health impact by pairing scientific excellence with inclusive leadership,” says Amarpreet Kaur Ahuja, Country HR Director, AstraZeneca India.She emphasises that AI systems must be “anchored in high-quality, FAIR data; governed for safety, privacy, and security; used ethically to augment, not replace, human judgment; and deployed with clear accountability.”These guardrails require cross-functional fluency, not siloed expertise. Building such fluency early strengthens long-term readiness among future professionals.
Culture, sponsorship and structural backing
Entry and upskilling alone cannot guarantee sustained advancement. Organisational culture and sponsorship determine whether women move into strategic influence roles.“The evolving role of women in STEM demonstrates the power of generosity and collaboration in action,” says Shriya Dutt, Senior Director, People Organization and Chair, Corporate Social Responsibility – BMS Hyderabad.She notes that moving “beyond representation to sustained enablement—through visibility, sponsorship, and inclusive cultures” strengthens innovation ecosystems.
Redefining the benchmark
International Day of Women and Girls in Science 2026 invites a more rigorous benchmark for progress.Real gender inclusion in AI can be assessed through representation in system architecture, ownership of large-scale deployments, participation in governance frameworks and sustained advancement into senior technical leadership.Participation was the first milestone. Structural authority is the next.As the role of AI in the process of making decisions in various sectors increases, inclusion will apply in the following areas: the formulation of the rules, the evaluation of the consequences, and the accountability.The question that faces us in 2026 is not that women appear in STEM. It is that they are impacting the technology that is, in turn, impacting society.
