Inside AI Advantage: Building Confidence Through AI Practice
By Jenny Kay Pollock, Co-Founder and Co-CEO of WOMEN x AI.
Program contributors: Jenny Kay Pollock and Reut Lazo, Lead Facilitators; and Rachel Wong, Principal Learning Architect and Cohort Facilitator.
You ask an AI tool for help. The answer sounds polished, but something is off.
It missed your audience. It assumed something about your experience. Or it confidently included a detail that isn’t true.
What do you do next?
On October 2, 2026, women came together for the first session of AI Advantage, WOMEN x AI’s five-week live online learning cohort for women leaders.
The program combines practical AI exercises, peer learning, facilitator support, and a capstone project connected to participants’ goals.
Week 1 focused on AI fundamentals, prompting, context, and the judgment involved in reviewing outputs. Participants explored how to give AI clearer direction, recognize assumptions, and connect what they were learning to something they wanted to accomplish.
What helps women build confidence using AI?
Confidence grows through practice: trying an approach, examining the result, asking questions, and learning what to change.
Women entered the cohort with different levels of experience. Some were opening an AI tool for the first time. Others were already experimenting with research, professional materials, and websites.
The cohort opened with the WOMEN x AI Promise, grounding the experience in curiosity, sharing knowledge, asking questions, and learning together.
Jenny and Reut then introduced foundational concepts, including the relationship between artificial intelligence, generative AI, and large language models. Reut explained how AI systems learn patterns from data and how language models generate responses using the information available to them.
That helped frame an important distinction for participants: an answer can sound convincing and still need correction.
Understanding the basics gives you a better starting point for evaluating what a tool produces. What information did it use? What might it have inferred? Which claims need checking?
Those questions remain useful as the tools change.
Give clearer direction and examine what comes back
The session explored how a goal, relevant background, and an intended audience can guide an AI response.
A request that makes sense in your head may leave out information the tool needs. You may know who will read the result, what has already happened, and which constraints matter. The AI may not.
Participants tested prompts and considered how additional context changed the output.
Reut emphasized that this is an iterative process:
“So always double-check the output that you get. Correct it.”— Reut Lazo
An initial response gives you something to evaluate. You can identify what is useful, question what looks wrong, and provide more specific direction.
The human contribution continues after the prompt is written.
Notice the assumptions you bring
Rachel led an interactive exercise that made the challenges of giving and interpreting instructions visible.
As participants compared what they intended with what another person understood, they noticed missing details, differences in perspective, and assumptions they hadn’t realized they were making.
One participant reflected that she had described intricate details before establishing the overall viewpoint. Others noticed how broad instructions left room for very different interpretations.
The discussion brought those observations back to working with AI.
What feels obvious to you may need to be stated. A useful result depends partly on whether you have communicated the information that matters.
Working through that experience with another woman gave participants a shared reference point for the prompting practice that followed.
Check what AI knows and what it assumes
Rachel guided participants through examining the information their chosen tool held or inferred about them.
A response may draw on information you provided, an inference the tool made, or context that no longer reflects your goals. Participants explored how to identify gaps and correct descriptions that missed the mark.
For women using AI to support professional work, those distinctions matter. Relevant context can make responses more useful, but it doesn’t guarantee accuracy.
The discussion also included privacy and the importance of being deliberate about which personal or work information to share.
Choose a project connected to your goals
Week 1 introduced the capstone project that participants would develop throughout the cohort.
The possibilities varied: a LinkedIn refresh, a clearer professional introduction, a research resource, a recurring workflow, or a website.
Jenny encouraged participants to choose something that fit their interests, available time, and experience.
“We want you to pick something that’s relevant to what you’re doing and what your goals are and what you’re building.” — Jenny Kay Pollock
Examples from previous cohorts helped make those possibilities tangible. Participants saw a portfolio website created with several AI tools and heard how an alumna had developed a database to organize her job and board search.
Those examples gave women a starting point for imagining their own projects. Initial ideas could evolve as they explored tools and learned from one another.
For more examples, explore how women applied AI through a previous AI Advantage cohort.
Make your first steps with support
Trying AI on your own can leave you wondering whether an unhelpful result came from the prompt, the context, the tool, or an incorrect assumption.
AI Advantage creates space to investigate those questions.
When a participant had difficulty finding her way into an AI tool, facilitators paused to help. More experienced users also brought questions about context, assumptions, and tool choice. The session made room for both, so participants could work from their own starting points.
Women compared experiences, shared observations, and began connecting AI to their own goals.
Week 1 gave participants a foundation for continuing: experience testing prompts, a clearer understanding of why outputs need review, and an initial direction for a project of their own.
The next step was to set up a project or notebook and begin applying that learning to a goal that mattered to them.
Across the five-week program, guided exercises, prompt packs, live conversations, and facilitator feedback help participants turn experimentation into work they can build on.
What is one task or idea you would feel more confident exploring with AI if you had someone to help you get started?
Inside AI Advantage shares selected insights from WOMEN x AI’s live learning cohort.
