Women, work and the design of an AI transition

Written for: For an employer or learning lead planning an AI transition with women in the workforce

If you are responsible for introducing AI at work, begin with the tasks people perform rather than a list of job titles. The evidence on occupational exposure describes where work may change; it does not predict that a particular person will lose her job. Your implementation choices shape whether workers gain useful support and a voice in the change.

The case for AI learning is stronger when it addresses the work women actually do and the opportunities they can realistically reach.

Exposure is not a forecast of job loss

The ILO’s March 2026 research, using microdata from 84 countries, finds greater generative-AI exposure in female-dominated occupations than in male-dominated occupations: 29% versus 16%. Exposure identifies tasks potentially affected. It is not an observed redundancy rate and does not establish that every worker in an exposed occupation will experience the same outcome.

Make the transition concrete

For a professional or founder, the practical question is which parts of the work will change and what capabilities remain necessary. A useful learning session maps a real workflow, identifies where AI may assist and tests the human decisions that still matter. That exercise can reveal a need for better domain knowledge, communication or quality control alongside tool familiarity.

Leadership belongs in the programme

Participants should have opportunities to explain a proposed change, question its assumptions and influence how it is introduced. Someone who operates a tool without a voice in the process may gain speed but little agency. A programme that develops both practical competence and the ability to present a business case can better support emerging leaders.

Keep participation possible

Predictable scheduling, accessible materials, realistic practice time and private opportunities to ask questions can make delivery more usable. These are programme-design choices, not findings claimed from the ILO study. The Foundation should test them with its actual participants and record what helps people continue, rather than assuming one format works for everyone.

Report outcomes honestly

Separate participation, learning, sustained adoption and career or business outcomes. Include attrition and the reasons people could not complete a programme. Describe the follow-up period and avoid assigning every later success to the intervention. A credible account of what changed, for whom and under which conditions is more useful to funders and participants than an inflated headline.

Sources and review

• International Labour Organization

Reviewed 2 October 2026. Analysis and proposed programme design: The Pipeline Foundation and Ladies Entrepreneurship Club. No partnership with a named research provider or programme is implied.

Map the work with the people doing it

Invite staff to describe a normal week: which tasks are repetitive, which require judgement and where mistakes carry a high cost. Mark possible AI assistance separately from decisions that need human accountability. Test a narrow workflow with volunteers, provide time to learn and make a clear route for reporting errors or bias. Do not treat unapproved personal data as convenient test material.

Evaluate who benefits and who absorbs new checking work. Track task quality, review time, access to training and whether staff can influence the process. Include people who cannot attend a particular training slot and make adjustments based on their feedback. Report what changed, for whom, over what period and what remains uncertain. This gives leaders and funders a practical account of the transition without turning a broad exposure estimate into a claim about individual job outcomes.