Why Do 87% of Women Leave Tech Within a Decade?

· Source: AI Magazine · Field: Business & Management — Human Resources & Workforce Development, Corporate Strategy & Leadership · Depth: Fundamental Awareness, quick

Summary

A UK-focused study commissioned by Akamai reveals that structural barriers are causing a significant exodus of women from the technology sector, with 55% departing within five years and 87% within a decade. The research, which surveyed 1,500 women (1,000 who left tech and 500 who returned), identifies company culture (52% lacking belonging, 40% seeing no gender diversity in leadership) and inflexible working hours (56% citing work-life balance issues) as primary drivers. This talent migration impacts critical areas like cybersecurity, where diverse teams are essential for robust defense systems. While 15% of women left the job market entirely, others transitioned to finance, education, professional services, and healthcare. Nearly four in ten (39%) expressed willingness to return to tech for better salary, work-life balance, and career progression, with 37% specifically seeking flexible working arrangements.

Key takeaway

For CTOs and VPs of Engineering aiming to build resilient teams and foster innovation, your organization's retention of women in tech is critical. The high attrition rate, particularly within the first decade, directly weakens cybersecurity and limits diverse perspectives essential for AI development. You should prioritize structural changes, including implementing flexible work policies and actively promoting women into leadership roles, to create an inclusive culture that encourages long-term tenure and attracts returners.

Key insights

Structural barriers, not ambition, drive women from tech, impacting innovation and cybersecurity.

Principles

Method

The study surveyed 1,500 UK women (1,000 who left tech, 500 who returned) to identify reasons for departure and conditions for return, focusing on culture, work-life balance, and career progression.

In practice

Topics

Best for: CTO, VP of Engineering/Data, Executive, HR Professional, Director of AI/ML, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI Magazine.