The UK’s new Women in Tech Taskforce isn’t just about fairness. It’s about building better AI, stronger products, and more productive teams across the economy.
Most UK tech teams are currently leaving billions on the table. Government research estimates the UK economy loses between £2–3.5 billion every year because women leave tech roles or the sector altogether. That’s not just a diversity problem — that’s a productivity and innovation problem, especially in AI.
This matters because AI now sits at the core of how we work: automating routine tasks, supporting decisions, and reshaping careers. If the people building that AI don’t reflect the people using it, we don’t just get unfair systems — we get worse products, weaker businesses, and slower growth.
The UK’s new Women in Tech Taskforce, launched by Technology Secretary Liz Kendall and co-led by STEMETTES founder Dr Anne-Marie Imafidon, is a direct response to that gap. The goal isn’t just representation for its own sake. It’s about building better AI, better technology, and smarter workflows for everyone.
In this article, I’ll break down what this taskforce is actually trying to change, why inclusive teams build stronger AI and more productive workplaces, and what leaders can do right now to support women in tech and AI — without waiting for policy to catch up.
Why closing the gender gap in tech is an AI productivity issue
Closing the gender gap in tech directly improves the quality of AI systems and the productivity of the people who use them.
The data is blunt:
- Men outnumber women 4 to 1 in UK computer science degrees.
- Women represent only around 22% of IT specialists in the UK.
- At current rates, equal representation in tech could be 280+ years away.
- Female-founded startups receive almost 6x less investment than male-founded ones, even though they often generate better returns.
Now connect that to AI:
- AI tools are being embedded into everyday work: from inbox triage and meeting summaries to hiring filters and financial decisions.
- These systems learn from historical data and the assumptions of their builders.
- If most of the people designing, training, and deploying AI products are men from similar backgrounds, the systems will reflect that bias.
Here’s the thing about AI and productivity: AI is only as smart as the people and perspectives behind it. Homogeneous teams ship blind spots as features. Diverse teams catch them early and turn them into competitive advantages.
When you widen the talent pool to include more women and underrepresented groups:
- Product teams spot unfair edge cases before they reach customers.
- AI workflows are tested on a broader range of real work styles and needs.
- You get tools that actually help more people work better, not just a narrow segment of users.
The Women in Tech Taskforce is effectively saying: if the UK wants trustworthy AI and a productive digital economy, it can’t do that while sidelining half the population.
Inside the UK’s Women in Tech Taskforce
The new taskforce is designed as a high-level, cross-industry group aimed at removing barriers for women across the entire tech pipeline: education, hiring, progression, funding, and leadership.
It’s co-led by:
- Liz Kendall, UK Technology Secretary
- Dr Anne-Marie Imafidon, founder of STEMETTES and newly appointed Women in Tech Envoy
They’re joined by senior leaders from across technology, engineering, unions, and grassroots organisations, including:
- Allison Kirkby (CEO, BT Group)
- Francesca Carlesi (CEO, Revolut UK)
- Dr Hayaatun Sillem (CEO, Royal Academy of Engineering)
- Sue Daley OBE (director of tech and innovation, techUK)
- Kate Bell (assistant general secretary, TUC)
- Representatives from Coding Black Females, Code First Girls, and other inclusion-focused groups
This mix matters. You’ve got:
- Big tech and telecoms: who shape infrastructure and hiring at scale
- Fintech leaders: who build AI-heavy financial products
- Engineering institutions: who influence professional standards
- Unions and grassroots organisations: who see on-the-ground barriers every day
Their remit is broad: advise government on how to remove systemic barriers across education, skills, recruitment, career progression, and access to capital.
The reality? Policy alone won’t fix the gender gap. But when policy and industry move in sync, it gets a lot harder for organisations to ignore the productivity cost of keeping women out of AI and tech.
The real barriers keeping women out of AI and high-impact tech roles
You can’t fix what you don’t name. The taskforce is targeting several intertwined problems that directly affect who ends up working on AI and productivity technology.
1. Cultural bias and stereotypes
Research from the Fawcett Society found that 20% of men working in tech believe women are inherently less suited to technical roles.
Beliefs like that don’t just sit in people’s heads:
- They influence who gets encouraged to apply for technical roles.
- They shape who gets the complex, career-making projects.
- They show up in performance reviews and promotion panels.
In AI-heavy teams, that often means:
- Women being steered towards “soft skills” or project coordination instead of ML engineering or data science.
- Fewer women in the rooms where decisions about training data, safety, and fairness are made.
Result: AI systems are designed and validated by a narrower group of people — and then rolled out to everyone at work.
2. The broken pipeline: education to early career
Men currently outnumber women 4 to 1 in computer science degrees. That gap starts well before university:
- Fewer girls are encouraged to see computing as a creative or impactful career.
- STEM choices are subtly framed as “for boys” in school and media.
- Role models are still too rare.
The UK is trying to address this through:
- The TechFirst skills programme (£187 million)
- The Regional Tech Booster initiative
- The STEM Ambassadors Programme
- The “I Belong” programme from the National Centre for Computing Education
There’s also a new national curriculum commitment to digital and AI skills for all young people. Done well, that’s a big productivity play: you get a generation that’s comfortable using and questioning AI, rather than passively accepting it.
But if girls and young women don’t feel these spaces are for them, you still lose them before they reach AI and engineering roles.
3. Funding and startup bias
Female-founded startups receive nearly six times less investment than male-founded startups.
That’s not just unfair — it shapes what kind of AI and tech products even get built:
- Fewer women-led companies building productivity tools for overlooked users.
- Less funding going into AI-driven solutions for care work, flexible work, or non-traditional careers.
- A tech ecosystem skewed towards the problems and preferences of a relatively narrow founder profile.
When you shut women out of capital, you shut out a huge chunk of potential AI innovation.
4. Retention and progression
A lot of women do enter tech — then leave mid-career. This is where productivity quietly erodes:
- Teams lose experienced engineers, product managers, and data scientists.
- Institutional knowledge walks out the door.
- Junior women see no path upwards and stop aiming for senior technical or leadership roles.
Government estimates suggest this churn alone is costing £2–3.5 billion per year. For organisations, that shows up as constant rehiring, retraining, missed deadlines, and weaker products.
The taskforce’s challenge is turning these high-level issues into specific, measurable changes across hiring practices, progression frameworks, parental policies, and funding decision-making.
Why inclusive teams build better AI and more productive workflows
Inclusive AI teams consistently build more trusted, more useful technology — and that flows straight into workplace productivity.
Here’s why.
Better problem framing
Diverse teams are more likely to ask:
- “Who could this feature exclude?”
- “Whose work style are we optimising for?”
- “Does this workflow assume everyone works 9–5 at a desk?”
Those questions change the products you ship. You move from generic “productivity tools” to workflows that genuinely support knowledge workers, parents, carers, shift workers, freelancers, and people outside the typical tech-bro persona.
Fairer, higher-integrity AI systems
BCS chief executive Sharron Gunn has argued that it’s very hard to build high-integrity, trustworthy AI when half the population is underrepresented in the teams building it.
When women are present and empowered in AI teams, you’re more likely to:
- Catch biased training data before deployment.
- Challenge assumptions about “ideal candidates” in hiring algorithms.
- Question default settings that systematically disadvantage certain groups.
Trusted AI isn’t a nice-to-have. If teams don’t trust the systems they use, they work around them, ignore them, or double-check everything manually — killing the productivity benefits AI is supposed to bring.
Products that match how real people work
I’ve seen this first-hand: when you put women and underrepresented groups in key product roles, features start to look different.
You see:
- Meeting tools that consider asynchronous workers and carers, not just people glued to back-to-back calls.
- Task automation that recognises invisible work like coordination and follow-up, which is disproportionally carried by women.
- Interfaces that assume shared devices, noisy environments, or intermittent time, not just a private home office.
This alignment with real workflows means higher adoption, less friction, and more people actually benefiting from AI at work.
What leaders can do now: practical steps for inclusive AI & tech teams
You don’t need to wait for government reports to start changing how your organisation builds AI and uses technology at work. Here are concrete moves that align with the taskforce’s direction and support the “work smarter, not harder” goal.
1. Audit who’s actually in your AI and data teams
Don’t guess — measure.
- Track gender representation across AI, data, and platform teams, not just “tech” overall.
- Look at seniority: who owns architecture decisions, safety reviews, and roadmap priorities?
- Review who gets sent to AI conferences, high-visibility projects, and customer-facing demos.
If women are missing from these spaces, your AI strategy has a built-in blind spot.
2. Redesign hiring for skills, not stereotypes
Small changes here compound:
- Use structured interviews with clear scoring rubrics.
- Standardise technical tasks and review them blind where possible.
- Rewrite job descriptions to focus on actual capabilities and outcomes, not “rockstar”, “ninja”, or culture-fit fluff.
- Offer flexible working patterns as a default, not a negotiation.
The goal is to remove as many subjective filters as possible between qualified women and high-impact roles.
3. Put women at the centre of AI governance
AI governance isn’t just a legal or risk exercise. It’s a design and ethics function — and it needs diverse voices.
- Bring women into data ethics boards, AI review councils, and steering committees.
- Make sure they have real decision-making authority, not just advisory roles.
- Involve them early in defining success metrics and “acceptable risk” for AI projects.
This directly affects how AI is used in hiring, performance review, security, and everyday work tools.
4. Invest in skills and visibility, not just pipelines
Yes, early education and graduate schemes matter. But don’t ignore the women already in your organisation.
- Fund AI upskilling programmes targeted at women in adjacent roles (product, operations, analytics) who want to move deeper into AI.
- Pair mid-career women with senior sponsors who can put their names on important projects.
- Share internal stories of women leading AI and technology projects — normalise it.
This approach turns existing employees into a powerful AI talent pool and reduces your dependence on an external market that’s already skewed.
5. Back women-led tech and AI startups
If you’re in a position to direct funding, partnerships, or pilots:
- Proactively seek out women-led AI and productivity startups.
- Build supplier diversity into your procurement process.
- Run pilots that give these teams real data and real users.
You’re not doing charity — you’re de-risking your AI strategy by backing teams that see problems others miss.
Where this fits in the bigger AI & Technology story
The Women in Tech Taskforce is more than a diversity headline. It’s a signal about where the UK wants to go with AI, technology, work, and productivity.
If AI is going to help people work smarter — not just faster or cheaper — then the teams designing those systems need to look a lot more like the people using them. That means more women:
- Studying computing and AI.
- Leading engineering and data teams.
- Founding and funding AI-first companies.
- Sitting at the tables where policy and standards are set.
For organisations, this isn’t a distant policy conversation. It’s a near-term competitive advantage. Teams that embrace inclusive tech practices will build AI tools their people actually trust and adopt — and that’s where the real productivity gains live.
The question for every leader now is simple: are you building the kind of AI team the future of work needs, or the kind of team history is already leaving behind?