Women Need to Lead AI Adoption, Not Just Keep Up With It
We keep telling women to get comfortable with artificial intelligence (AI). We talk far less about what our workplaces have done to make that possible.
I used to feel a little guilty about automating parts of my work. Then I got stuck trying to code an assessment tool, used AI agents to help me build one, and it now lives on my website. That changed how I see my own capacity. It also made me ask who gets the time, training and permission to have that experience.
Recently, I had two conversations that sharpened the question. One was a LinkedIn Live with Judith Tait of Tait AI , an AI strategy and governance advisor, on AI literacy for women leaders. The other was two days at the Human + AI Leadership Lab, hosted by Skills for Change, with my Cohort 1 colleagues. The Lab covered risk, readiness, critical literacy and a PlaySpace session on keeping movement and play in leadership when we are tied to screens all day. Judith named fear and a sense of not belonging as major barriers for women. I agree, and I think the barriers are also structural.
Here is what the data says
Men (85%) are more likely than women (72%) to feel confident using AI tools at work, according to a Robert Half Canada survey reported by Benefits Canada.
In the same survey, 38% of men report formal AI training, compared with 29% of women.
28% of men are given time during work hours to learn and experiment with AI, compared with 21% of women.
Statistics Canada, as reported by HR Reporter, found men more likely than women to use generative AI at work across every occupation exposure category.
The gap in confidence follows the gap in support. Here are four barriers and the fixes I see working.
Barrier 1: Learning that depends on spare time
When AI training is optional, unscheduled or after hours, it favours people with the most flexibility. Caregiving and heavy workloads mean many women learn last.
Fixes that work:
Protect paid learning time during work hours.
Build AI training into onboarding and ongoing professional development.
Start small, as Judith suggested: 15 minutes a day with one tool.
Barrier 2: No leadership clarity on how AI is used
Without approved tools and a clear policy, people use personal accounts for work. Judith called this shadow AI, and it puts organizations and clients at risk. McKinsey's State of Organizations 2026 survey of more than 10,000 leaders names regulatory, ethical and legal risk, and change management, among the top barriers to AI adoption. Those are governance gaps, which is why a written policy and a named owner matter.
Fixes that work:
Provide approved tools in one place.
Write a short AI policy and explain the reasons behind it.
Tell clients how you use AI, and where you do not. Trust requires transparency.
Barrier 3: Biased data with no owner
AI amplifies what we feed it. In my ethical AI workshop, I ask the same question about two different neighbourhoods and we compare the answers. The bias shows up fast. Judith's phrase was "garbage in, garbage out."
The stakes are highest in hiring. Stanford researchers studying four million applications screened by one vendor's algorithm found that 26% of applications from Black applicants and 15% from Asian applicants went to positions where the tool showed adverse impact. Adverse impact means a group is recommended at less than 80% of the rate of the most-favoured group. The paper also notes how concentrated the market is: more than 60% of the Fortune 100 use HireVue's algorithms, a different vendor from the one studied. When many employers lean on the same few tools, one tool's bias can follow a candidate from company to company. My colleague Nooreen Rahemtullah breaks down the research in We Read the Stanford Study on AI Hiring So You Don't Have To.
Fixes that work:
Map your data rivers (i.e. every source of information feeding your AI tools, such as internal documents, your CRM, public web content and vendor data), and name an owner for each.
Audit the data going in, and keep a person checking every output. If a tool cites a report, read the report, because a hallucination (i.e. a confident answer not based on fact) can sound reasonable.
Know what should never go in. Client confidential information stays out unless a client asks for a specific task. For First Nations data, the OCAP® principles (Ownership, Control, Access and Possession) set the standard. OCAP® is specific to First Nations, and the CARE principles (Collective Benefit, Authority to Control, Responsibility and Ethics) speak to Indigenous data more broadly. If an Elder shares feedback, it does not go into a chatbot.
Barrier 4: A narrow table
If AI decisions are made by a small group, the tools and policies will reflect that group, and the people who do not see themselves in it will opt out.
At the Lab, Heather Watts, Principal of First Peoples Group, led a session called Grounded in Connection, which invited us to consider technology through Indigenous ways of knowing, including the Two Row Wampum and the Dish With One Spoon. It was a reminder that technology is not neutral simply because it is new. How we build relationships with technology, knowledge and one another matters.
Fixes that work:
Form an AI committee with people from different roles, levels and identities, much like an employee resource group (ERG).
Let that committee plan rollouts, build training and act as peer educators.
Start with one business problem, map the process, and decide where AI fits. AI amplifies chaos, so sometimes you need a clearer workflow, not AI.
What leaders can do this quarter
Leaders have to walk the walk. If you hand AI to someone else, you cannot set direction or notice what is missing. Here is a 90-day starting plan for human resources (HR), diversity, equity and inclusion (DEI), and executive leaders.
Month 1: Clarity
Name an owner for your data and your AI outputs.
List every AI tool in use, including personal accounts.
Month 2: Capability
Block 15 minutes a day to learn one AI tool yourself.
Protect paid learning time for your team and launch your AI committee.
Month 3: Accountability
Publish a one-page AI policy.
Audit one process or data source for bias, and track who has access to training.
Build movement and play into how your team works, because leadership does not have to happen sitting down.
Women do not need more confidence alone. They need better systems, and leaders willing to build them.
Let Us Support You
Curated Leadership runs a workshop on ethical AI use that helps teams build literacy, set guardrails, and keep people at the centre. For HR, DEI and executive leaders at large employers ready to lead AI adoption inclusively, email hello@curatedleadership.com or book a discovery call. You can also connect with me on LinkedIn or learn more about me.
Listen to the Curated Conversations podcast for more on inclusion and belonging.
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