Reese Witherspoon says women need to catch up on AI. The real story is more interesting.

This week, Reese Witherspoon posted a video to Instagram that went viral. She told her followers that women's jobs are three times more likely to be automated by AI, yet women are using AI at a rate 25% lower than men and announced she was going to learn, and take her audience with her. The reaction was immediate and split: some galvanised, many pushing back hard.

The underlying data isn't wrong. At the everywoman in Tech Forum this month, a panel chaired by Cheryl Razzell, Head of Specialist Solutions Architecture at AWS, found that AI adoption sits at around 80% among men in UK workplaces, compared to just 59% among women. What Witherspoon's framing misses, and what the panel addressed directly, is what's actually behind the gap.

The gap that isn't a capability story

The explanation most frequently offered for women's lower AI adoption - that women are less comfortable with technology - doesn't hold up under scrutiny. Tina Diamond, Director of Cloud and AI Platforms at Microsoft UK, argued that AI amplifies existing expertise rather than replacing it: "AI to data scientists is like a scalpel for surgeons."  The implication for founders is direct. The more you already know about your sector, your clients, your operations, the more useful AI becomes. You are not starting from zero.

What is driving the gap, the panel suggested, is something more cultural. Much of it comes down to an anxiety of perception. Women generally face higher scrutiny for not being experts in their fields, and the idea of using AI as a "shortcut" carries more baggage. Marine Rabeyrin of Lenovo identified a tendency in women to feel they must prove competency before experimenting. That instinct, to understand something fully before committing to it, is not weakness. It shapes how adoption happens when it does happen.

What careful adoption actually produces

Women are 38% more likely than men to have ethical reservations about AI, a sign of thoughtfulness that may nonetheless slow adoption. The Lean In research framing this as a "headwind" is worth questioning. Founders who enter AI adoption with ethical awareness and governance instincts are not behind, they are building something more durable.

Joanna Haslam of Snap Finger Click described how early AI experimentation raised immediate data security concerns, leading to internal controls and clearer prompting discipline. The discipline that follows from caution, tighter prompts, clearer parameters, better governance, is exactly what separates useful AI integration from chaotic experimentation.

The UK government's own AI Adoption Research found that among businesses facing barriers, ethical concerns are considered the most significant obstacle, ahead of cost and regulatory uncertainty. That finding applies across business sizes. For smaller founder-led firms, where one bad data decision can be material, caution is not a bug.

What this means for founders

The useful reframe is not "how do I catch up?" but "how do I make this work for what I actually do?" A YouGov survey of UK university students found that only 27% of female students were using AI tools at least weekly, compared to 43% of their male peers. That gap narrows when the use case is concrete and the value is immediate. The same dynamic applies in business.

In 2023, women's use of generative AI was just half that of men's. Over the past year, the proportion of women adopting it has tripled, outpacing the 2.2x growth rate seen among men, rapidly narrowing the gap. That trajectory matters more than the current snapshot.

What you can do

If you're already using AI in your business, the question worth asking now is whether you've moved from experimentation to workflow. A tool you use occasionally delivers a fraction of the value of one that's embedded in how you work every day. Pick the function that drains the most time - proposals, meeting notes, first-draft content - and build a repeatable process around it rather than a collection of ad hoc prompts.

If you haven't started yet, the panel's message is worth sitting with. The expertise you've already built is not a reason to hesitate, it's what makes the tools more powerful for you than for someone starting from scratch. You bring the context. The AI handles the volume.

And if governance has been the sticking point - concerns about data, client confidentiality, accuracy - that's not an obstacle to starting. It's the foundation of doing it properly. Sources

1. everywoman in Tech Forum panel, chaired by Cheryl Razzell (AWS) — Computing.co.uk, April 2026. computing.co.uk/feature/2026/how-to-close-ai-gender-adoption-gap

2. Reese Witherspoon, Instagram post — Variety, April 16 2026. variety.com/2026/tv/news/reese-witherspoon-ai-jobs-women-1236723992

3. Lean In, Women and AI: The Gender Gap in AI Adoption and Recognition, 2026. leanin.org/research/ai-women-gender-gap-data

4. YouGov / Datawrapper Blog, AI gender gap survey, UK university students, January 2026. datawrapper.de/blog/ai-gender-gap

5. SheAI, Women in AI: Key Statistics, 2026. sheai.co/blog/women-in-ai-key-statistics

6. UK Government / DSIT, AI Adoption Research (IFF Research and Technopolis Group), February 2026. gov.uk/government/publications/ai-adoption-research