Artificial Intelligence and automation are rapidly transforming the global and Indian labour markets. As economies shift towards technology-driven growth, the nature of work is changing from routine and manual tasks to roles that require digital skills, analytical thinking, and continuous learning. In this evolving landscape, upskilling has become a prerequisite for employment security and career progression. However, this transition is not unfolding on an equal footing for all. Women, despite rising workforce participation, face deep structural constraints that limit their ability to adapt to an AI-driven economy. Unless these constraints are addressed, the promise of AI-led growth risks excluding a large section of the female workforce.

Structural Challenge: Double Burden of Work

• Working women continue to shoulder a dual responsibility of paid employment and unpaid care work.

• Unpaid work includes childcare, elder care, cooking, cleaning, fetching water and fuel, and emotional labour.

• Time-use surveys consistently show that even when women participate in the labour market, their domestic and caregiving responsibilities do not decline proportionately.

• Men’s participation in unpaid care work remains limited, resulting in an unequal distribution of household labour.

• This persistent double burden leaves women with limited discretionary time for personal development and skill acquisition.

Time Poverty and Its Impact on Learning

• Time poverty refers to a condition where individuals lack sufficient free time after completing paid and unpaid work.

• Women spend longer total hours per day working compared to men when both paid and unpaid work are combined.

• Due to this, women have less time for:

  • Learning new skills

  • Adapting to technological changes

  • Professional networking and career planning

• Reduced leisure, rest, and sleep further affect cognitive capacity, productivity, and long-term learning ability.

• In an economy where continuous reskilling is essential, time poverty becomes a major structural barrier to women’s advancement.

The Emerging Upskilling Gap in an AI-Driven Economy

• Women spend fewer hours per week on upskilling and training compared to men.

• This gap is most visible during prime working and caregiving years, particularly between the ages of 25 and 45.

• As AI increasingly rewards high-skill and technology-intensive roles, limited access to upskilling leads to:

  • Job stagnation

  • Occupational downgrading

  • Concentration of women in low-growth and low-value roles

• Over time, this widens gender gaps in wages, leadership positions, and economic security.

AI, Automation, and Gender Bias

• Women are disproportionately employed in sectors that are routine-based and more susceptible to automation.

• AI-driven productivity and performance systems often assume workers to be time-neutral and free from caregiving responsibilities.

• Such systems fail to recognise care-related interruptions and flexibility needs faced by women.

• As a result, algorithmic decision-making can penalise women caregivers, reinforcing existing inequalities.

• Instead of being a corrective force, AI risks institutionalising gender bias in more subtle and systemic ways.

Economic Consequences of Exclusion

• Women’s unpaid care work remains invisible in economic accounting despite its critical contribution to social and economic functioning.

• This invisibility leads to lower income growth and reduced economic independence for women.

• At the macro level, underutilisation of women’s skills results in:

  • Lower labour productivity

  • Inefficient use of human capital

  • Missed opportunities for inclusive economic growth

• Without addressing gendered barriers to upskilling, India’s demographic dividend cannot be fully realised.

Need for Policy Reorientation

• Current policy frameworks largely focus on job creation and skill supply without addressing time constraints.

• There is a need to shift towards time-based empowerment that recognises women’s time as an economic resource.

• Systematic use of time-use data in policymaking and budgeting can help identify hidden gender burdens.

• Policies must aim not only to create jobs but also to create conditions that allow women to access skill development opportunities.

Policy Interventions Required

Time-Saving Public Infrastructure

• Expansion of affordable childcare and elder-care facilities.

• Universal access to clean cooking energy and reliable water supply.

• Safe, accessible, and affordable public transport to reduce time spent on daily mobility.

Flexible and Lifelong Learning Models

• Design of modular, digital, and hybrid skilling programmes that accommodate women’s time constraints.

• Community-based and local training centres to improve accessibility.

• Learning pathways that allow skill acquisition without forcing women to exit the workforce.

Gender-Responsive Budgeting

• Integration of time-use indicators into budget planning and programme evaluation.

• Sustained investment in care infrastructure as productive economic investment rather than welfare expenditure.

Targeted Skilling Initiatives

• Focused digital and AI skill programmes for women.

• Special attention to rural women, informal workers, and women re-entering the workforce after career breaks.

• Alignment of national technology missions with women-centric skill development schemes.

Conclusion

AI-led growth holds immense potential to boost productivity and economic expansion, but its benefits will remain uneven unless structural gender inequalities are addressed. Women’s time poverty, driven by unpaid care responsibilities and inadequate social infrastructure, lies at the heart of the emerging upskilling gap. True empowerment in the age of AI requires recognising and valuing unpaid work, redistributing care responsibilities, and creating enabling conditions for women to continuously upgrade their skills. Without mainstreaming women into future-of-work strategies, India’s vision of inclusive and sustainable growth will remain incomplete, leaving a significant portion of its human capital untapped.