In early October, the WOMEN x AI Titans Tools Council an expert-led roundtable of AI builders, founders, and operators came together once again to share what’s working in their real-world workflows. This quarter, our Council tested 39 battle-tested tools , spanning everything from research copilots and meeting notetakers to AI-driven recruiters and design generators. Each tool was explored through hands-on use, uncovering what accelerates progress and what still needs human judgment. This report captures those lived insights. No sponsors. No hype. Just the voices of women using AI every day to lead, build, and create. General Assistants Notebook LM Use Cases:  Deep dives on communication strategy (company or personal branding), automating paperwork (e.g., creating spreadsheets from multiple documents). What Works (Pros): ✅ Upload up to 50 sources per notebook. ✅ New mobile app for easy access and audio playback. ✅ Great for straightforward data extraction and compilation (e.g., creating an Excel sheet from employee forms). What Doesn’t Work (Cons): ❌ Limited podcast-voice variety (only two options). ❌ Always double-check outputs accurate but occasionally shallow. Awesome new modes released including mind mapping as shown below during our recent hands on AI Tools for Everyday Work Workshop with FEI - San Francisco Chapter. Council Members:  Jenny Kay Pollock, Meg McWilliams Link:   Notebook LM ChatGPT-4o / o3 Models Use Cases:  Strategy development, content ideation, and creative writing support. What Works (Pros): ✅ Upload background docs and websites for context-aware insights. ✅ Strong reasoning and analysis capabilities. ✅ Voice input for fast brainstorming. What Doesn’t Work (Cons): ❌ Broad prompts return generic answers, specificity wins. ❌ Outputs can vary slightly across versions. Council Members:  Jenny Wilde, Miri Rodriguez Link:   ChatGPT Claude 3.5 Use Cases:  Copywriting, message clarity, and pitch refinement. What Works (Pros): ✅ Excels at empathetic copy and tone adaptation. ✅ Keeps style consistent once trained on examples. What Doesn’t Work (Cons): ❌ Requires style training up front; underperforms without context. ❌ Doesn’t handle structured data as gracefully as ChatGPT. Council Member:  Meg McWilliams Link:   Claude.ai Lindy Use Cases:  Automating repetitive administrative tasks and managing workflows across departments. What Works (Pros): ✅ Executes multi-step routines across departments (e.g., marketing + ops). ✅ Great trial credits to explore advanced features. What Doesn’t Work (Cons): ❌ Not a “set-and-forget” tool you must configure workflows carefully. ❌ Overlap with other assistants may require integration cleanup. Council Member:  Jenny Wilde Link:   Lindy.ai Pulse by ChatGPT Use Cases:  Like an assistant for daily life reminders, tips and suggestions. What Works (Pros): ✅ Starts your day with curated context, reduces time spent gathering updates or checking multiple apps. ✅ Personalized to your patterns and connections integrates with Gmail, Calendar, chat memory if enabled. ✅ Visual-card format keeps information digestible and avoids endless scroll. What Doesn’t Work (Cons): ❌ Currently available only to Pro-subscribers (preview) and may require substantial data/app integration. ❌ Because it’s proactive and based on predictions, sometimes suggestions might be irrelevant or miss the mark. ❌ Privacy and data-connection trade-offs: full benefit requires sharing more app/data access with ChatGPT. Council Member:  Meg McWilliams Link: Pulse by ChatGPT Research & Deep Work Perplexity AI Use Cases:  Fast research, validation of sources, and surfacing reputable data. What Works (Pros): ✅ Always cites sources. ✅ Excellent for quick fact-checking and deep dives. ✅ The “Pro” version handles document uploads for targeted queries. What Doesn’t Work (Cons): ❌ Can oversimplify nuanced topics; double-check linked sources. ❌ Limited depth in multi-stage reasoning. Council Member:  Jenny Wilde Link:   Perplexity.ai Vellum / Fireworks (Maverick) Use Cases:  Automating research reports and orchestrating multi-agent data analysis. What Works (Pros): ✅ Reduces hours of manual analysis through chained agents. ✅ Highly secure hosting environments (great for enterprise). What Doesn’t Work (Cons): ❌ Verbose by default; prompt for concise summaries. ❌ Setup requires technical familiarity with agents. Council Member:  Rachel Skeates-Millar Links:  Vellum · Fireworks Dovetail Use Cases:  Synthesizing customer interviews and qualitative research. What Works (Pros): ✅ Handles large transcripts effortlessly. ✅ Summarizes insights visually with themes and tags. What Doesn’t Work (Cons): ❌ Designed for research, not strategy pair with GPT for next steps. ❌ Pricing can scale up quickly for large teams. Our recent WOMEN x AI Podcast on AI x Product had all of the PMs on the call raving about Dovetail. Tune in to find out why. Council Member:  Rachel Skeates-Millar Link:   Dovetail.com Deep Research (Multi-Model Technique) Use Cases:  Market research, competitor mapping, and multi-source synthesis. What Works (Pros): ✅ Provides comprehensive context by comparing models. ✅ Reliable for quantitative summaries (e.g., TAM, competitor lists). What Doesn’t Work (Cons): ❌ Slower processing best used for longform research sessions. ❌ Requires manual synthesis of overlapping insights. Council Member:  Meg McWilliams Links:   ChatGPT  · Perplexity.ai Note-Taking & Memory Granola Use Cases: Recording meetings, brainstorming sessions, and reflections; automatically generating summaries and follow-up emails. What Works (Pros): ✅ Records both in-person and online conversations. ✅ Syncs with Limitless and Google Calendar for full context. ✅ Allows manual additions, summaries, and follow-up suggestions. What Doesn’t Work (Cons): ❌ Currently supports only business accounts (no @gmail access). ❌ Occasional transcription lag for long recordings. Granola in Action During How Women Lead's Get On Board Week session on using AI to help you land a board seat . Council Member:  Jenny Wilde & Jenny Kay Pollock Link:   Granola.ai Notes:  Ideal for founders and operators juggling multiple meeting types. Otter.ai Use Cases: Recording Zoom calls, capturing team discussions, and producing instant meeting notes. What Works (Pros): ✅ Automatically joins calendar-linked meetings. ✅ Generates transcripts, summaries, and speaker insights. ✅ Translates across languages with impressive accuracy. What Doesn’t Work (Cons): ❌ Sometimes joins private meetings uninvited adjust settings. ❌ Overly detailed transcripts can be overwhelming without filters. Council Member:  Malinda Johnson Link:   Otter.ai Limitless Use Cases: Capturing spontaneous ideas, hallway conversations, or event notes; acting as a personal memory device. What Works (Pros): ✅ Great for recalling names, quotes, and commitments. ✅ Ideal for end-of-day reflection and voice journaling. ✅ Integrates with Granola and Notion for data continuity. What Doesn’t Work (Cons): ❌ Occasionally mishears or misspells names. ❌ Battery requires daily charging; limited onboard reasoning. Council Member:  Jenny Wilde Link:   Limitless.ai Notes:  Expected to integrate with future ChatGPT hardware. Vibe Coding / No-Code Builders Bolt Use Cases: Instantly generating websites, dashboards, or prototypes from text prompts. What Works (Pros): ✅ Creates functional web apps and dashboards in minutes. ✅ Perfect for quick internal tools or client mockups. ✅ SVGs import cleanly for logos and icons. What Doesn’t Work (Cons): ❌ PNG upload issues. ❌ Limited customization for advanced users. ❌ Mobile app prompting still inconsistent. Council Member:  Meg McWilliams Link:   Bolt.new Notes:  Jenny vibe-coded the WOMEN x AI Membership Quiz with Bolt. Lovable Use Cases: Rapidly building visual prototypes and “vibe-coded” experiences. What Works (Pros): ✅ Excellent UI speed and collaborative design view. ✅ Friendly for creative demos and investor previews. What Doesn’t Work (Cons): ❌ Limited integrations; not production-grade. ❌ Code export sometimes breaks styling. Council Member:  Jenny Wilde Link:   Lovable.dev Notes:  Frequently recommended for prototyping during live workshops. HeyBoss AI Use Cases: Quick visualization of product concepts and early app flows. What Works (Pros): ✅ Instantly visualizes rough ideas in meetings. ✅ Downloadable code for developer handoff. What Doesn’t Work (Cons): ❌ Prone to glitches with dynamic pages. ❌ Lacks backend connectivity; design-only prototypes. Council Member:  Jenny Wilde Link:   HeyBossAI.com Communication & Presentations Gamma Use Cases: Creating professional decks, iterating ideas, and collaborative storytelling. What Works (Pros): ✅ Generates full decks in minutes from a prompt. ✅ Slides are editable and easy to restyle. What Doesn’t Work (Cons): ❌ No ability to import custom templates. ❌ Formatting occasionally resets between exports. Council Members:  Jenny Wilde, Reut Lazo Link:   Gamma.app Notes:  Frequently used for WOMEN x AI workshop slides. Napkin AI Use Cases: Turning text-based ideas or posts into diagrams and frameworks. What Works (Pros): ✅ Converts long text into visuals instantly. ✅ Excellent for blog illustrations or presentations. What Doesn’t Work (Cons): ❌ Free plan limits design variation. ❌ Requires cleanup for brand colors. Example of napkin taking text explaining Private Equity terms and making a visual. Council Members:  Jenny Kay Pollock, Jenny Wilde Link:   Napkin.ai Whimsical AI Use Cases: Mapping processes, brainstorming sessions, or project dependencies. What Works (Pros): ✅ Real-time collaboration and auto-formatting. ✅ Perfect for product discussions or org-design visuals. What Doesn’t Work (Cons): ❌ Can get messy without post-meeting cleanup. ❌ Limited diagram export options. Council Member:  Rachel Skeates-Millar Link:   Whimsical.com Yoodli Use Cases: Practice presentations, pitches, etc. Members of myJob Search Mastery program are acing interviews with companies like Fastly, Google, and Amazon by practicing with this tool What Works (Pros): ✅ Real-time collaboration and auto-formatting. ✅ Perfect for product discussions or org-design visuals. What Doesn’t Work (Cons): ❌ Still less good than a human. Council Member:  Malinda Johnson Link: Yoodli.ai Founders’ Toolkit TrustCloud AI Use Cases: Helping startups streamline SOC 2 compliance and security documentation. What Works (Pros): ✅ Prebuilt templates cover most compliance needs. ✅ Offers customer-facing security pages to enhance sales trust. ✅ Simplifies policy writing for early-stage teams. What Doesn’t Work (Cons): ❌ SOC 2 is still time-consuming AI only reduces, not removes, the work. ❌ Limited support for highly customized frameworks. Council Member:  Malinda Johnson Link:   TrustCloud.ai Conversa AI Use Cases: AI-powered hiring assistant that screens candidates and flags top matches. What Works (Pros): ✅ Increases diversity and fairness by reducing bias in candidate selection. ✅ Saves time on resume reviews. ✅ Automates interview scheduling. What Doesn’t Work (Cons): ❌ Not optimized for outbound sourcing. ❌ Small learning curve for configuring custom roles. Council Member:  Malinda Johnson Link:   GetConversa.com Juicebox AI Use Cases: Outbound recruiting and LinkedIn candidate messaging automation. What Works (Pros): ✅ Personalized outreach sequences drive higher response rates. ✅ Excellent search filters for technical and executive roles. ✅ Saves hours in manual LinkedIn outreach. What Doesn’t Work (Cons): ❌ Doesn’t process inbound applicants. ❌ Limited CRM-style reporting. Council Member:  Malinda Johnson Link:   Juicebox.ai Content Creation & Thought Leadership Riverside.fm Use Cases: Recording and editing professional podcasts or interviews. What Works (Pros): ✅ AI editing removes filler words and adjusts pacing. ✅ Auto-generates show notes, chapters, and keywords. ✅ Records locally for higher-quality audio. What Doesn’t Work (Cons): ❌ Over-editing can make dialogue feel robotic keep some pauses. ❌ Early feature rollouts sometimes introduce bugs. Council Members:  Reut Lazo, Jenny Kay Pollock Link:   Riverside.fm Notes:  Used for all WOMEN x AI podcast episodes and panel recordings. Descript Use Cases: Editing internal training videos, webinars, or repurposing recordings for content. What Works (Pros): ✅ Edits video by changing text transcripts. ✅ Great for quick internal content creation. ✅ Supports brand voice adjustments through overdub features. What Doesn’t Work (Cons): ❌ AI tone correction sometimes feels flat needs final human polish. ❌ Large files may slow rendering times. Council Member:  Rachel Skeates-Millar Link:   Descript.com Custom GPTs (Hook Generator Example) Use Cases: Generating catchy marketing hooks, copy, or idea prompts tailored to brand voice. What Works (Pros): ✅ Specialized GPTs solve niche problems effectively. ✅ Easy to reuse once a strong one is found. ✅ Great for marketing, copywriting, and brainstorming. What Doesn’t Work (Cons): ❌ Quality varies, requires experimentation to find good ones. ❌ Broad prompts yield weaker results. Council Member:  Jenny Kay Pollock Link:   ChatGPT Custom GPTs Notes:  WOMEN x AI uses a Custom GPT to guide users through membership tiers. Veed Use Cases: Web video editor with lots of ai features like , smart subtitles, and an ai assitant that can find the elements you need and insert them automatically What Works (Pros): ✅ Text to voice for voice over ✅ Smart subtitles ✅ AI assistant can help you find and insert elements What Doesn’t Work (Cons): ❌ TBD Council Member:  Meg McWilliams Link:   Veed Connections & Discovery Distill Use Cases: Researching investors, founders, or potential collaborators. What Works (Pros): ✅ Aggregates data across web and social profiles. ✅ Excellent for pre-meeting preparation or event networking. ✅ Identifies shared connections. What Doesn’t Work (Cons): ❌ Occasionally confuses similar names—verify identities. ❌ Still in beta; some inconsistencies in data freshness. Council Member:  Jenny Wilde Link:   Distill.fyi Happenstance Use Cases: Finding people in your network for targeted outreach or partnerships. What Works (Pros): ✅ Great for finding warm intros (“Who in my network works at X company?”). ✅ Supports queries across multiple platforms. ✅ Helps resurface dormant contacts. What Doesn’t Work (Cons): ❌ Results depend on prompt precision. ❌ Works better with smaller curated networks. Council Member:  Jenny Wilde Link:   Happenstance.ai Coral Use Cases:  Get feedback on how you performed in a meeting and provides coaching on how you can improve. What Works (Pros): ✅ Feedback based on the goals you have for how you want to show up in meetings. ✅ Provides actionable suggestions for your next meeting. What Doesn’t Work (Cons): ❌ Have to upload a transcript. ❌ Transcript must include who you are. Council Member:  Jenny Kay Pollock Link:   Coral Gobby Goblin Use Cases: Custom intros at events. What Works (Pros): ✅ Tell Gobby your goals via text or phone before you attend the event. ✅ Finds other event attendees to intro you to before the event. ✅ Great for finding the needle in a haystack and increasing ROI for attending an event. What Doesn’t Work (Cons): ❌ Can be hard to find your match in the crowd if you haven't coordinated ahead of time ❌ Doesn't work at events that don't have Gobby embedded Council Member:  Meg McWilliams Link: Gobby Goblin HireBase Use Cases: Discovering open job opportunities that match your experience and skills. What Works (Pros): ✅ Aggregates listings from multiple job boards. ✅ Uses AI to match qualifications to job descriptions. ✅ Provides advanced search filters for seniority and location. What Doesn’t Work (Cons): ❌ Focuses on discovery, not referrals or introductions. ❌ Occasional duplicates between job sources. Council Member:  Malinda Johnson Link:   HireBase.org Agents & Workflow Builders Superinterviews Use Cases: Interview prep and personalized practice sessions for job seekers or founders. What Works (Pros): ✅ Mimics real interviews and provides instant coaching. ✅ Highlights weak spots in communication or storytelling. What Doesn’t Work (Cons): ❌ Early-stage tool—limited customization and data sets. ❌ May recycle generic feedback. Council Member:  Jenny Wilde Link:   TeamSidebar.com/Superinterviews Particle Use Cases: Daily AI and tech news summaries. What Works (Pros): ✅ Delivers concise, high-quality summaries. ✅ Good for trend tracking and staying informed. What Doesn’t Work (Cons): ❌ Limited depth beyond the headline layer. ❌ Focused mainly on technical updates, not industry context. Council Member:  Jenny Wilde Link:   Particle.News Claude Artifacts Use Cases: Creating interactive diagrams, prototypes, or UX flows with natural language. What Works (Pros): ✅ Turns text ideas into working visual prototypes. ✅ Encourages visual collaboration and quick iteration. What Doesn’t Work (Cons): ❌ Not intended for production, no export polish. ❌ Still experimental in output fidelity. Council Member:  Meg McWilliams Link:   Claude.ai Email & Inbox AI Gemini Pro for Gmail Use Cases: Drafting and refining email responses directly within Gmail. What Works (Pros): ✅ Reads context of incoming email to draft replies. ✅ Good for short and medium-length responses. ✅ Improves tone matching over time. What Doesn’t Work (Cons): ❌ Requires review and minor edits. ❌ Inconsistent for nuanced or emotional messages. Council Member:  Jenny Wilde Notes:  Available to Gemini AI Pro subscribers. Voice to Text Wispr Flow Use Cases:  You can speak instead of type across all major apps on your phone and laptop. What Works (Pros): ✅ Doesn't change your tone not your words. ✅ Tidies up your thoughts, grammar and punctation. ✅ Can hear you when you whisper so you can use it around people. What Doesn’t Work (Cons): ❌ Won’t elevate your wording or make things sound formal ❌ Need another tool if you want a more polished tone Council Members:  Jenny Wilde Link: Wispr Flow Forms Tally Use Cases:  Create a form. What Works (Pros): ✅ Generous free tier. ✅ Editor is natural and feels like writing in a doc. ✅ Has templates, custom domains, integrations, conditional logic, answer piping, and more. What Doesn’t Work (Cons): ❌ Advanced team/workspace controls and deeper analytics sit behind the paid “Pro” plan. ❌ No native mobile app ❌ Styling is less flexible than some alternatives Council Members:  Jenny Wilde Link: Tally.so Browser Dia Use Cases:  AI native browser - think chrome w/ an ai co pilot in every tab What Works (Pros): ✅ Easily move between browsing and side bar chat ✅ Research more deeply and create TL;DRs ✅ Can compare tabs and give breakdowns What Doesn’t Work (Cons): ❌ TBD Council Members:  Meg McWilliams Link: Dia Browser Comet AI Browser Use Cases:  Conversational browsing and research synthesis. Acts as both a search assistant and a browser that can summarize, compare, and act across web pages, helping users move from finding information to applying it directly. What Works (Pros): ✅ Integrates conversational AI directly into browsing summarize, cross-check, and compare open tabs instantly. ✅ Excellent for deep research, policy monitoring, or content curation; saves hours of manual tab-hopping. ✅ Built on Chromium, so it supports Chrome extensions and familiar workflows. What Doesn’t Work (Cons): ❌ Still early-stage; interface can feel cluttered or inconsistent across updates. ❌ Requires precise prompting for best results casual browsing yields weaker insights. ❌ Heavy AI processing can drain system resources on large projects. Council Members: Jenny Kay Pollock Link: Comet AI Browser Reflection Across 39 tools and nine categories, one theme stood out: AI is no longer a novelty, it’s an extension of how we think, plan, and collaborate. Each Council member brought a lived understanding of what it means to build responsibly with AI. The tools that succeeded weren’t just powerful; they respected time, context, and creativity. AI fluency is a practice, not a destination. It’s the space between curiosity and confidence where leaders experiment, share, and learn in community. Learn more about the AI Titans Tools Council and who helps select and review these tools. Learn AI in Community. Join the WOMEN x AI Membership to explore, experiment, and grow alongside other AI-curious and AI-fluent leaders.

A Glossary of Common AI Terms & Concepts by Tamara Gracon , Joanna Ridgeway , Paula Fontana , Jenny Kay Pollock and Reut Lazo . Here’s a quick guide to help you stay current in the AI era. From AGI to LLM, we’ve got you covered with clear, simple definitions of the terms you’ll hear most often in today’s conversations about artificial intelligence. 📘 Download the printable version A - C Agentic AI  – AI that can set and work toward goals on its own, making decisions and adapting with little or no human direction. Algorithm  – A set of defined steps used by a model to solve a problem or perform a task. Artificial General Intelligence (AGI)  – A hypothetical form of AI capable of performing any intellectual task a human can do (also called General AI or Strong AI). Artificial Intelligence (AI)  – Technology that can perform tasks requiring thinking, learning, or decision-making, sometimes in ways that go beyond what humans can do. Backward Chaining  – A method that starts with a goal and works backward to find supporting data. Bias  – When an AI system produces results that are unfair or skewed because of problems in the data, design, or training process. Big Data  – Large or complex datasets that traditional data processing tools cannot handle. Bounding Box  – An imaginary rectangle used in image tagging to identify and label objects. Chatbot  – A software tool that simulates conversation with users, typically through text or voice. Chaining  – Connecting AI steps or systems so the output of one becomes the input for the next, creating a longer process or workflow. Cognitive Computing  – A marketing-friendly term for AI, emphasizing human-like reasoning. Computational Learning Theory  – A field focused on analyzing and developing machine learning algorithms. Corpus  – A large collection of text used to train models in natural language tasks. D - G Data Design and Training – Process of selecting, organizing, and preparing data to train ML models effectively, ensuring it represents the problem domain and supports accurate learning. It includes tasks like data labeling, feature engineering, and data splitting. Data Lake – A centralized repository that stores large volumes of structured, semi-structured, and unstructured data in its raw form, enabling flexible access and analysis for AI and analytic applications. Data Mining  – Analyzing large datasets to discover patterns that can inform model behavior. Data Science  – An interdisciplinary field combining statistics, programming, and domain knowledge to interpret data. Dataset  – A structured collection of data used in training, validation, or testing. Deep Learning (DL)  – A type of machine learning that uses many layers of processing to find patterns in data, loosely inspired by how the brain works. Embeddings  – Vector representations of words or data used to capture relationships in models. Entity Annotation  – Tagging parts of text (e.g., names, places) to help models understand context. Entity Extraction  – Structuring data by identifying entities such as people, places, or objects. Forward Chaining  – A reasoning method that moves from known data to possible conclusions. Generative AI  – AI that creates new content such as text, images, or music based on training data. General AI  – A hypothetical form of AI capable of performing any intellectual task a human can do (also called Strong AI or Artificial General Intelligence also known as AGI). H - L Hallucinations  – Instances where AI generates information that is plausible-sounding but incorrect or fabricated. Hyperparameter  – A value set outside the model that affects how the model learns during training. Intent  – The underlying goal behind a user’s input, used especially in chatbots and NLP. Label  – The correct output associated with input data, used in supervised learning. Large Language Model (LLM)  – A neural network trained on massive text datasets to generate human-like language. Linguistic Annotation  – Marking up text with information such as sentence structure or parts of speech. M - N Machine Intelligence  – A general term encompassing all types of AI, machine learning (ML), and deep learning (DL) systems. Machine Learning (ML)  – A subset of AI that allows computers to learn from data and improve over time. Machine Translation  – Automatic translation of text between languages using algorithms. Model  – The output of a training process, used to make predictions or decisions. Natural Language Generation (NLG)  – The process of converting data into human-readable text or speech. Natural Language Processing (NLP)  – A field of AI focused on enabling machines to understand and generate human language. Natural Language Understanding (NLU)  – A branch of NLP focused on interpreting the intent, meaning, and context behind human language inputs. Neural Network  – A model inspired by the human brain, used especially in deep learning applications. O - R Overfitting  – A model’s tendency to learn the training data too well, resulting in poor performance on new data. Parameter  – Internal variables in a model that are learned during training. Pattern Recognition  – Identifying recurring patterns in data to inform AI decisions. Predictive Analytics  – Using historical data and ML to predict future events or trends. Prompt Engineering  – Crafting effective inputs (prompts) to guide LLMs in producing desired outputs. Python  – A widely used programming language in AI development for its simplicity and flexibility. Reinforcement Learning  – A learning method where models learn through trial and error, based on reward signals. S - T Semantic Annotation  – Tagging data with meaning to improve search or classification tasks. Sentiment Analysis  – Detecting emotions, attitudes, or opinions in a piece of text. Strong AI  – AI with reasoning abilities equivalent to human cognition (also known as general AI or AGI). Supervised Learning  – Training models using labeled data to predict outcomes. Test Data  – Unseen data used to evaluate a model's performance. Training Data  – Labeled data used to teach a model how to make predictions. Transfer Learning  – Using knowledge gained from one task to improve performance on another. Turing Test  – A test to determine if a machine's behavior is indistinguishable from that of a human. U - Z Unsupervised Learning  – Training models on data without labels to uncover hidden structures or patterns. Validation Data  – Used to tune model parameters and check for overfitting during training. Variance  – How much a model’s predictions change if the training data changes — high variance can mean it’s not reliable. Often linked to overfitting. Variation  – Different ways a person might say the same thing, used to help chatbots understand natural conversation. 📘 Download the printable version Keep this glossary handy as you continue exploring how AI is reshaping the way we work, build, and lead.

Community, Costumes & Claude: How AI Got Spooky This Week This weekend’s #WxAISocialSaturday was equal parts spooky, strategic, and social, thanks to our host Summer Poletti  (rhymes with spaghetti), a fractional revenue leader and GTM strategist known for transforming underperforming sales orgs and elevating women into revenue and executive roles. Summer brought her signature blend of AI-savvy and human-first leadership  to a festive Halloween-themed session full of AI art, tool recommendations, and joyful community moments. Participants were invited to share: 🎃 Their favorite spooky season traditions 🧟 An AI-generated monster 🤖 The AI tool they used most this week The thread exploded  with creativity, laughter, and some very spooky prompts. 👩‍💻 About the Host: Summer Poletti Summer is a revenue growth architect who helps scaling tech companies unlock sustainable sales. She’s also a strong advocate for women in leadership , a fan of nano bananas (Gemini users will know!), and a podcast host with a deep interest in AI-powered sales and strategy. Learn more about her here . 💬 Community Voices: AI Monsters, Favorite Tools, and Traditions This week’s thread showcased just how rich our community is with insight and  humor: “🎃 Spooky Season Tradition: Decorating the front yard with scary stuff with my teen! We’re so unserious and enact all sorts of ridiculous characters.” — Tandeep Sangra “🧟 AI Monster: I used Midjourney to create a Studio Ghibli-inspired pumpkin monster. My kid said it was too cute to be scary!” — Reut Lazo “🤖 AI Tool: Perplexity is my favorite lately for quick insights. At parties, I’m the one who magically knows all the trivia!” — Ruby Vitatoe The monster images sparked tons of conversation, and many community members got into Halloween mode by blending traditional fun (like Halloweentown  movie nights) with cutting-edge AI play. Popular tools mentioned included Gemini , Claude , ChatGPT , Midjourney , Arcade , and Copilot  — each used in creative and productivity-boosting ways. A few more community gems: “Hocus Pocus is on deck for tonight… even my husband likes it!”  — Ary Aranguiz “Riverside has been my video editing lifesaver for the podcast — the AI features are surprisingly good.”  — Jenny Kay Pollock We also had pet inspired images check out Bearington the cat: And yes, several people confessed they keep their spooky decor up well past  Halloween. 🎃 🎙 Podcast Connection: Revenue Remix Summer also gave a shoutout to the Revenue Remix Podcast , inviting WxAI community members to apply to speak on upcoming episodes, particularly on B2B growth and customer experience. Apply here  if you'd like to join the conversation! 👻 We’ll Call This One: Empowerment, With a Side of Pumpkin Spice From pumpkin-painting and spooky AI art to serious discussions about tooling and collaboration, this weekend showed what makes WOMEN x AI so powerful: a community of curious, creative, and capable women using tech to transform the world and having fun while they’re at it. 💬 Join Us Next Weekend! Didn’t get a chance to join this week? There’s always next time. Jump into #WxAISocialSaturday  next weekend and bring your voice to the table.

SAN FRANCISCO, CA — WOMEN × AI (WxAI)  launched the WOMEN × AI Podcast  to amplify the voices of women shaping the future of artificial intelligence and the movement has only grown since. What began as just two women with two computers in San Francisco has evolved into a thriving global platform, featuring founders, executives, and innovators who are building, funding, and governing the next generation of AI. A Platform for Women Defining the Future of AI AI is transforming every industry, but representation still lags behind. The WOMEN × AI Podcast  is changing that by showcasing the women leading from the front lines of innovation. Hosted by Reut Lazo and Jenny Kay Pollock, Cofounder and Co-CEOs of WOMEN × AI, the show brings authentic, practical conversations to the forefront blending product strategy, leadership, and responsible AI in every episode. What Listeners Can Expect Each conversation explores how AI is transforming different sectors with a focus on business, creativity, and leadership. Past episodes include: AI × Product  — How product teams use AI to build better, faster, and more responsibly. AI × Go-to-Market  — How founders and marketers harness AI to find traction and scale. AI × Data  — Practical frameworks for applying AI with accountability. Episodes are available on Spotify, Apple Podcasts, YouTube, and anywhere you get your podcasts. We often share short video clips and takeaways on LinkedIn  and YouTube Shorts  to highlight key moments from each conversation. Featured guests have included Annie Xu , Saumya Bhatnagar , Summer Poletti , Dr. Nancy Li, Namisha Balagopal, and Mayan May-Raz, among many others shaping the next era of AI leadership. Why It Matters The WOMEN × AI Podcast  was born from a shared belief: AI is a tool for all of us so it should be built by all of us. By handing women the mic, the podcast helps make AI conversations more accessible, ethical, and human-centered. If you have a topic you'd like us to explore on the podcast email jenny@womenxai.com .   About WOMEN × AI WOMEN × AI (WxAI)  is a global community and movement dedicated to amplifying female voices in artificial intelligence. Through podcasts, panels, and workshops, WxAI connects women who are building, funding, and governing the next generation of AI. Founded in 2025, WOMEN × AI has grown into a trusted platform for education, collaboration, and advocacy where AI-curious professionals become AI-confident leaders. Follow us on Spotify for new episodes each week as WOMEN × AI continues to spotlight the women defining the future of artificial intelligence .

By Moha Shah  Venture Capital Leader | Future of Mobility, Climate, & Insurtech/Fintech | Strategy, Innovation & Digital Transformation Generative artificial intelligence (Gen AI) continues to make headlines as technology transforms many industries and the future of work. McKinsey’s analysis across 63 use cases in 2023 forecasted that Gen AI could add between $2.6 trillion to $4.4 trillion to the global economy. Big tech firms such as Google and Microsoft, and scaling Gen AI companies like Anthropic and OpenAI are striking strategic partnerships and investments to stay ahead of the AI race. Amid the fast-paced market activity in AI, it can be challenging to detect the signal from the noise. To uncover some ground truths, I recently attended the first Boston AI Week from September 26 to October 3. It was launched by Judah Phillips , a Boston-based technology entrepreneur. There were over 100 events hosted by organizations, including the Museum of Science, MIT, Harvard Business School, GAI Insights, Glasswing Ventures, and Underscore VC. These events showcased Boston’s unique ecosystem at the forefront of AI and innovation. Boston: A Leading Hub for AI and Innovation I’ve spent most of my professional career working at Boston-based companies and studied at leading universities in the area. I’ve witnessed the transformation of the startup ecosystem, largely driven by the cross-fertilization of corporate, academic institutions, venture capitalists, startups, and civic groups. Boston often evokes images of the American Revolution, the Charles River, and top universities such as Harvard and MIT. The city is also a global hub for innovation; it consistently ranks among the top three cities worldwide for its concentration of venture capital (VC) funding. Boston ranks first in biotech, third in AI, fourth in consumer, healthtech, and software-as-service (SaaS), fifth in fintech, and sixth in hardware per Carta’s analysis of VC funding to U.S.-based startups between Q3 ‘24 to Q2 ‘25. Innovative tech companies from Meta to Moderna were founded in Boston over the past few decades. Source: Carta’s Head of Insights Peter Walker via LinkedIn Top Takeaways from Boston AI Week Boston AI Week was filled with thoughtful discussions and learning on the impact of AI. Each event organically attracted leaders from leading corporations, startups, academic institutions, venture capital firms, and civic organizations. Below are my takeaways that I captured from several events. AI Adoption Among Corporations is Real During the GAI World 2025 , a two-day conference in Boston focused on Gen AI, corporate leaders from companies such as Liberty Mutual, Bain, Apollo Global Management, J.P. Morgan Chase, and Mayo Clinic spoke about how their companies are adopting AI. For example, Liberty Mutual deployed a custom GPT application for its global employees to summarize information and receive answers to common questions. Apollo Global Management is encouraging the adoption of AI tools to drive greater operational efficiencies for its portfolio companies. The speakers and attendees at GAI World 2025 noted that the adoption of AI is real and accelerating; it is transforming the workflows inside companies and how they serve their customers. AI Literacy Still Lags Across Many Organizations During sessions at GAI World 2025 and my discussions with industry leaders at Boston AI Week, many of them reinforced the need for upskilling and AI education. Several Fortune 100 companies are embracing popular Gen AI tools, but training is required at all levels of the organization, from the C-suite to junior employees. Several stakeholders noted that having AI enablement teams or AI champions inside companies can help foster AI literacy more quickly. IT Governance and Ethics Remain Critical in the New Era of AI As with many new technologies, AI poses increased cyber risks. During a panel at GAI World 2025, several Chief Information Officers discussed the importance of having for companies of all sizes to have strong posture on AI governance, data security, and privacy as AI tools are used and deployed to users externally. Many of my conversations also raised the importance of critical thinking skills to assess the outputs of popular Gen AI tools, whether used for personal or work tasks. Additionally, discussions centered around the need for users across different industries to remain vigilant of the outputs of AI tools and AI agents in the market. Diverse Leadership Matters as AI Transforms Industries I attended Women Applying AI’s launch event and a breakfast at GAI World 2025 for women in AI. Both events attracted many women who are interested in AI for different reasons – to learn about AI’s impact on their careers, build new ventures, or invest in AI startups. Speakers emphasized that women and diverse voices are needed as builders, investors, and leaders to ensure equity, inclusion, and innovation. Many of my discussions centered on the importance of staying informed about AI developments and upskilling as AI reshapes many industries. AI Agents Will Reshape the Future of Work AI agents were a popular topic during Boston AI Week. Many questions were raised about them including: What are AI agents, and how will they reshape the future of work? AI agents are designed to perform tasks autonomously; you can check out Google’s overview of AI agents for a more in depth look at what they are and how they work. Many companies are building AI agents and deploying them. Visa , for example, envisions a future of commerce that allows AI agents to seamlessly book and pay for transactions on behalf of users. During panel discussions at GAI World, executives from companies such as Bain and Blue Cross Blue Shield of Michigan shared how they’re building and deploying AI agents across their organizations. Also, MIT Professor and MIT Media Lab Director Ramesh Raskar delivered an insightful presentation with his vision for a more secure, collaborative, and decentralized “Internet of Agents” through Networked Agents and Decentralized AI ( NANDA ). Professor Raskar also shared the chart shown below outlining three phases of the agentic web.    Source: https://nanda.media.mit.edu/ Beyond the innovations in agentic AI, many new startup ventures with AI at their core are being launched in Boston. Curious about agentic AI? Check out our 10 takeaways from our agentic AI event . Innovation Among AI-First Ventures Continues to Rise in Boston Photo by Moha Shah Boston AI Week offered fertile grounds to reconnect with or meet new startup founders. Many first-time founders and serial entrepreneurs whom I met are excited by the application of Gen AI to drive innovation. One of my favorite events during Boston AI Week was Demo Day hosted by The Next Thing (TNT), an accelerator led by Harvard and MIT students. During 16 live pitches, the students showcased AI-first ventures that they launched over the summer. TNT’s cohort comprised new ventures using AI to solve problems across diverse industries such as consumer, energy, edtech, healthcare, and manufacturing. Some student entrepreneurs who pitched at TNT’s demo day are highlighted below. Consumer | Harvard graduate student Beatriz Zanforlin showcased Coral , an AI-powered consumer application that provides users with insights and actionable feedback on their conversations. Manufacturing | MIT Sloan student Dillon Johnson introduced LineGuard, an AI-powered quality control platform to automatically detect manufacturing defects. Overall, Boston AI Week proved to be an excellent forum to learn from leaders across the AI ecosystem, connect with stakeholders from Boston’s vibrant AI community, and meet startup founders building new AI-first ventures in the city. Want to continue learning AI in community? Join WOMEN x AI's free membership .

Introduction: Why AI Belongs in the Boardroom Now Artificial intelligence isn’t just a future concern—it’s a present-day boardroom issue. From ChatGPT to AI-embedded SaaS platforms, AI is quietly transforming how companies operate, compete, and scale. Yet most private boards still lack a common language or framework to provide meaningful oversight. That’s why we created the Private Director’s AI Governance Compass . This framework helps directors cut through the hype, understand where AI risk and opportunity lie, and embed responsible governance into board practices. It requires no technical background, just strategic curiosity and a commitment to responsible leadership. What Is the AI Governance Compass? The AI Private Governance Compass  is a practical framework built for private boards. It breaks AI oversight into four key quadrants: Individual Readiness  – Are board members prepared? Boardroom Practices  – Are structures and processes aligned? Organizational Oversight  – Is internal governance working? Stakeholder Responsibility  – Are impacts and risks transparent? Each quadrant contains four components (16 in total), from ethical awareness to workforce development. It’s a heatmap, not a checklist. The goal: spark informed discussion and focus where it matters most. 👉 Download the full white paper Why This Matters Now: The AI Adoption Curve Has Tipped We are no longer in the experimentation phase. AI is embedded in employee workflows, customer interactions, and vendor tools. With regulations tightening and public expectations growing, boards must act before oversight gaps become reputational or legal liabilities. AI oversight today is like cybersecurity oversight 10 years ago. The lag between use and governance can be costly. Boards must: Understand where AI shows up in the business Know who owns strategy and risk Ensure alignment with values and law Set expectations for transparency and monitoring How to Use the Compass Each of the four quadrants includes four components: 1. Individual Readiness Role clarity vs. execution Ethics and emerging standards Innovation mindset (not just risk avoidance) Chair’s role in inclusive, informed conversations 2. Boardroom Practices Committee ownership or integration Inclusion in agendas and CEO reports AI education and fluency Legal and regulatory literacy 3. Organizational Oversight Internal strategy and reporting Alignment with laws and culture Risk management (bias, hallucination, model drift) Workforce development and upskilling 4. Stakeholder Responsibility Clear communication with customers and employees Disclosure and explainability External engagement on AI decisions Societal impact and equity From Theory to Action: Start with the Self-Assessment AI governance doesn’t need to be overwhelming. Start by scoring each of the 16 elements from 1 (strong) to 5 (needs work). Apply force multipliers where urgency exists (e.g., competitive pressure or regulatory risk). The highest-scoring items become your board’s AI priorities. This turns AI oversight into actionable items that boards can address. We recommend starting with 3 - 4 priorities and tracking over time to measure how the board's AI oversight evolves over time. 👉 View the AI Compass Scoring Sheet Practice with Real Case Studies We’ve included two real-world boardroom scenarios to help directors practice applying the compass: 1. MidTech Components:  A manufacturing firm shifting to service-based models using predictive AI tools. 2. DataFlow Solutions:  A SaaS company under investor pressure to add AI features without compromising security. Each scenario highlights governance questions and shows how the compass can guide strategy and oversight. 👉 Read the Full Case Studies What Boards Can Do Next Here’s a step-by-step action plan: Build baseline AI fluency using plain-language resources Add AI oversight to regular board agendas Define who owns AI strategy and risk in management Apply the compass to identify oversight gaps Align AI efforts with company values and ethics Track relevant regulations (EU AI Act, White House EO, state laws) Revisit and update the self-assessment regularly Glossary & Reference Tools Need a refresher on terms like agentic AI , hallucination , or model drift ? Check out our AI Glossary for Boards—a director-level resource for confident conversations. 👉 Visit the Director-Level AI Glossary Meet the Authors The framework was created by Joanna Ridgway , Tamara Berner Gracon , Paula Fontana , Jenny Kay Pollock , and Reut Lazo . As board members, operators, and investors, they built the tool they needed but couldn’t find. First launched at the Private Directors Association’s PRISM 2025  conference in Anaheim, CA. Responsible AI Starts in the Boardroom You don’t need to become an AI expert to provide effective oversight—you just need the right tools. The AI Governance Compass  equips directors to ask smarter questions, protect company trust, and lead with confidence. Explore More: Download the white paper Try the Self-Assessment Tool Read the Case Studies Blog Post Visit the AI Glossary Want to learn AI in community? Join our WxAI Membership !

Generated with Gemini This year’s SF Tech Week blurred the lines between tech, art, and community. One day we were talking about AI in law, the next about AI in style. From Wala Kasmi and Natalie Pan’s Kickoff Party on October 3 to a TechWalk in Golden Gate Park hosted by Holly Uber, Zulma Terrones, and Swagata Ashwani on October 4, the week began not with product launches but with people. Founders, investors, and creators came together to connect before the city exploded into more than 1,500 events that turned San Francisco into a living map of innovation. You could feel it in the air: optimism, curiosity, and a little chaos. SF Tech Week 2025 highlighted San Francisco as the epicenter of the global AI conversation. By the Numbers 1,500+ events hosted across the city 61 founders selected from 14,000 applicants for the Speedrun Demo Day (90% AI-focused) 1,700+ people wait-listed for corporate venture events like IBM Ventures “Scaling Over Cocktails” Estimated 15,000 attendees citywide The Human Start to a High-Tech Week Before the panels and pitch decks came moments of connection. Wala and Natalie’s Kickoff Party  gathered hundreds of builders and investors for an evening that set the tone: optimistic, collaborative, and slightly electric. The next morning, the TechWalk in Golden Gate Park offered a slower rhythm. Dozens of technologists and founders walked side by side through redwood trails, sharing ideas about ethical AI, creative experimentation, and community wellbeing. It was a reminder that innovation grows stronger when we step away from our screens. Spotlight 1: Aparti.AI Salon — AI and the Future of Law On October 7, Aparti.AI  hosted an invite-only salon at Werqwise that explored how AI is reshaping the legal world. Photo by Reut Lazo Moderated by Anna Naidis, CPO and Co-Founder of Aparti.AI , the discussion brought together Rebecca Lynn (Canvas Prime), Joshua Geffon (Fenwick & West), Esther Rosenfeld (Rosenfeld Office), and Igor Sheremet ( Aparti.AI ). Key takeaways included: Empathy and human context still matter in client service. AI can remove repetitive work, freeing lawyers for higher-value strategy. Legal judgment cannot be templated. Technology amplifies, not replaces, expertise. Courtrooms will soon run on real-time data, not delayed transcripts. The event drew legal experts, AI founders, and investors into unfiltered conversation that showed governance and innovation can coexist when the dialogue is candid and inclusive. Read our full recap of Aparti.AI → Inside the Aparti.AI Salon Spotlight 2: Fashion × Tech Showcase — Where Style Meets Algorithm On October 10, Fashion × Tech: Fashion, Consumer and Creator  brought together nearly 300 guests for an evening dedicated to creativity and commerce in the AI era. Photo by Jenny Kay Pollock Co-hosted by WOMEN x AI, the showcase featured founders, investors, and creators exploring how technology is transforming fashion and consumer culture. The night included: A panel discussion on the opportunities AI is unlocking for the creator economy. A startup showcase featuring demos from our WxAI community Maria Lence ( For Women’s Health ) and Dhivya Vijayakumar ( Velvee ), both reimagining how health and fashion intersect. Attended by WxAI community members Reut Lazo, Jenny Kay Pollock, Jasmeen Bal, Robyn White, Annie Deihl and Ruby Vitatoe on the importance of visibility and community in innovation. It was a night that embodied the theme of the week: AI is everywhere but the human element matters. Read our full recap of Fashion × Tech→ Fashion x AI Where Style Meets Algorithm Themes That Defined the Week 1. AI is becoming cultural. No longer a niche topic for technologists, AI conversations spanned law, health, art, and design. 2. Community is the new infrastructure. Decentralized gatherings, from salons to park walks, created genuine exchange far beyond corporate stages. 3. Women are at the center of the conversation. From founders demoing at showcases to moderators guiding complex panels, women shaped the dialogue about how AI can serve humanity. Looking Ahead SF Tech Week 2025 reminded us that the future of AI is not being written by one company or one sector. It is being written by communities like this one. As we carry that momentum forward, we are proud to spotlight the women and allies building the next generation of ethical, inclusive innovation. Follow WOMEN x AI on LinkedIn for upcoming event recaps, community spotlights, and stories from across the AI ecosystem. To learn AI in community, join our WOMEN x AI membership (it's free!).

Photo by Jenny Kay Pollock What happens when fashion, health, and technology share the same runway? On October 10, that question came to life at Fashion × Tech: Fashion, Consumer, and Creator, a highlight of SF Tech Week 2025. Hosted in San Francisco and co-organized by WOMEN x AI, the evening gathered nearly 300 guests for a celebration of creativity, entrepreneurship, and innovation at the intersection of AI and consumer culture. The Scene SF Tech Week Photo by Jenny Kay Pollock The event opened with an intimate networking session as designers, founders, and investors filled the room with an energy equal parts elegance and experimentation. AI x Fashion Panel Photo by Reut Lazo The agenda flowed from a panel discussion on how AI is transforming the creator economy to a startup showcase featuring women founders who are reimagining what it means to design for real lives, not algorithms. Among the evening’s featured guests were Maria Lence, Founder of For Women’s Health , and Dhivya Vijayakumar, Founder of Velvee , who both demoed products that are AI powered to help the end user. WOMEN x AI (WxAI) co-founders Jenny Kay Pollock and Reut Lazo, along with WxAI community members Jasmeen Bal, Robyn White, Annie Deihl and Ruby Vitatoe on the importance of visibility and community in innovation. Reut and Jenny supported the event and captured these insights from the front lines of AI innovation and inclusion. The Conversations The panel of women leaders spanned founders, investors, and creators. They explored how AI is unlocking new opportunities across fashion and consumer ecosystems while challenging long-held assumptions about creativity, authenticity, and ownership. 1. AI as a design partner. AI tools are helping creators prototype faster, test new aesthetics, and personalize experiences for every customer. The message: machine learning can extend human imagination, not replace it. 2. Consumer empowerment. From fit prediction to ethical sourcing, technology is giving consumers more control and transparency in their choices. 3. The new creator economy. AI is reshaping how creators monetize their work. Data, content, and design are merging into new business models where creative voices hold the power. 4. Health and fashion are converging. Innovations from For Women’s Health  and Velvee  showed that fashion can be functional, personal, and transformative. Why It Stood Out Unlike traditional panels, Fashion × Tech felt like a community gathering. Every conversation circled back to one idea: the future of AI is collaborative. Designers need technologists. Founders need storytellers. And women are redefining both. What’s Next The showcase captured what made SF Tech Week special—people from different industries building a shared vision for what technology can do when guided by creativity and care. WOMEN x AI is enjoyed seeing the excitement around AI and to support founders from our WxAI community Maria Lence ( For Women’s Health ) and Dhivya Vijayakumar ( Velvee ), both reimagining how AI can be built into products to help women. They are turning bold ideas into real-world impact. For the bigger picture, read our SF Tech Week 2025 Recap or Top Takeaways from Boston AI Week from Moha Shaw. Or dive deep to see how AI is transforming another tradition-bound field in our coverage of the Aparti.AI Salon: The Future of Law Meets AI . Join WOMEN x AI to stay connected to upcoming events, spotlights, and stories from women shaping the future of technology.

Photo by Reut Lazo What does the future of law look like in an AI-powered world? That question shaped one of the most thought-provoking events of SF Tech Week. On October 7, an audience gathered at Werqwise for the Aparti.AI Salon, hosted by Anna Naidis, CPO and Co-Founder of Aparti.AI , and supported by WOMEN x AI. Reut Lazo attended and had a great time learning at the intersection of AI and legal, in community. The evening brought together legal experts, founders, and investors for an honest conversation about how artificial intelligence is reshaping the practice of law, courtroom dynamics, and the client experience. The Setting Photo by Reut Lazo The event felt more like a dinner party than a conference. Instead of long presentations, attendees joined an open dialogue led by a panel of voices who have been shaping the intersection of law and innovation: Rebecca Lynn  – Co-Founder and Managing Director, Canvas Prime Joshua Geffon  – Corporate Partner, Fenwick & West Esther Rosenfeld  – Family Lawyer and Author, Transformative Divorce Igor Sheremet  – CEO and Co-Founder, Aparti.AI Moderator:   Anna Naidis , CPO and Co-Founder, Aparti.AI Partners including Axos Bank , SuperFunding.AI , and Tecto AI  helped make the conversation possible, reflecting how the legal and financial ecosystems are converging around AI adoption. What We Heard Each speaker shared a unique lens on how AI is already changing their work. 1. Empathy and context still matter. As one panelist put it, your lawyer is not your therapist—but empathy and human judgment remain essential. AI can process data, but only people can interpret emotion, risk, and nuance. 2. Automation is redefining the workday. Lawyers are using AI tools to summarize transcripts, prepare filings, and manage discovery. The time saved lets them focus on complex reasoning instead of spreadsheets. 3. Judgment cannot be automated. Clients hire lawyers for expertise, not templates. AI can enhance decision-making, but it cannot replace human discernment. 4. Courtrooms are becoming data-driven. Real-time transcripts and searchable case data are transforming access to justice, improving both transparency and speed. 5. Meet your clients where they are. Adoption starts with trust. Firms that communicate clearly about how they use AI are building stronger relationships with clients and regulators alike. Reflections from the Room The salon created a rare environment for open debate. Some attendees voiced concerns about bias in legal datasets and accountability for AI-generated work. Others shared optimism about how technology could democratize legal access and lower costs for underserved communities. One theme stood out: AI is not replacing lawyers—it is redefining what good lawyering looks like. Why It Matters Photo by Reut Lazo The legal sector often lags behind in digital transformation, yet it is now on the front line of AI adoption. Tools that analyze contracts, summarize cases, or predict outcomes are becoming mainstream. What remains uncertain is how governance, ethics, and education will keep pace. Events like Aparti.AI ’s salon create the space for those conversations. By bringing founders, lawyers, and investors together, they move the dialogue from speculation to strategy. Looking Ahead Aparti.AI plans to continue hosting salons where experts can explore responsible AI use in professional fields. The conversation at Werqwise proved that innovation and accountability can grow together when the right people are in the room. WOMEN x AI is proud to celebrate members like Anna Naidis  who are leading the way in creating thoughtful, inclusive spaces for dialogue around AI. Continue the conversation with our full SF Tech Week 2025 Recap , or see how AI showed up in unexpected ways at the Fashion × Tech Showcase  co-hosted by WOMEN × AI.

We’re excited to present  Joan Chepkwony , AI Engineer & Founder at  AI Sprouts Africa Group , as this week’s! Let’s dive into our interview with Joan and see how she’s using AI to create change. 1. Share your AI origin story  My journey into AI began with curiosity — and a bit of frustration. I’ve always loved how technology solves real problems, but most tools used in Africa were created elsewhere. That question — why aren’t more Africans building the tech we rely on? — pushed me to learn. Through online courses and late nights of study, I discovered how AI could transform lives here at home — in agriculture, education, healthcare, and more. Seeing how few young Africans, especially girls, had access to this knowledge, I founded AI Sprouts Africa Group to make AI education more accessible. We started with just 3 laptops, borrowed spaces, and a big vision.  Watching students in rural areas build simple AI projects reminded me that innovation begins with access. Today, my mission is to help young Africans not just learn AI, but use it to shape their future and tell their stories through technology. 2. What three AI tools have been most game-changing for you?  1️⃣ Teachable Machines  2️⃣ Scratch with ML Extensions  3️⃣ Python & TensorFlow 3. If you were just starting your AI journey today, where would you start?  I’d begin with the real-world problems I care about — exploring AI as a way to imagine new possibilities and connect with others who share that vision. Learning should be hands-on and collaborative, rooted in purpose.  From day one, I’d also focus on making knowledge inclusive, because AI’s true power lies not in complexity, but in how it enables people to shape their future with confidence and creativity. 4. Share the spotlight: Name 3+ women leading in AI we should all follow.  ⭐  Jenny Kay Pollock  ⭐  Heather Murray  ⭐  Joan Chepkwony 5. As a woman in AI, what do you want our allies to know?  True allyship means showing up — not just cheering from the sidelines. It’s about creating space where women don’t have to prove their worth twice, and where ideas are believed in.  Our experiences make technology more compassionate and relevant. If you’re serious about being an ally, amplify women’s voices, challenge bias, and help build a future where talent isn’t limited by gender but unleashed by opportunity. Want to be the next in our AI Spotlight? Fill out the AI Spotlight nomination form . It’s your chance to share your voice with the Women x AI community!

Boards everywhere are realizing that AI isn’t just a technology topic, it’s a governance topic. The decisions being made today about data, ethics, investment, and risk will shape not just valuations, but culture, trust, and long-term growth. At WOMEN x AI, we’ve been working with private directors to build tools that help boards navigate this new terrain. Below are two real-world case studies that show a glimpse into real world board level case studies. They are a great opportunity to practice using our AI Compass for Private Directors Framework. Case Study 1: MidTech Components – “Manufacturing-to-Service Transformation” A 40-year-old, family-owned manufacturing business is at a crossroads. Customers no longer want just parts—they want predictive maintenance and AI-enabled services. What’s happening: Flat financials:  $85M annual revenue, margins cut nearly in half. Competitive squeeze:  Overseas rivals 40% cheaper; 15% market share lost in five years. Customer pressure:  OEMs demanding digital solutions; predictive maintenance emerging as the norm. Board challenge:  Approve a $12M investment into AI-powered services and IoT sensors—risking a shift from traditional manufacturing into a service-based model. Governance insight: This case is all about balancing legacy vs. innovation . With no formal AI strategy and family dynamics at play, directors must weigh the risk of transformation against the risk of irrelevance. Full Case Study 1 Details: Case Study 2: DataFlow Solutions – “AI Feature Integration Challenge” A PE-backed SaaS platform faces urgent pressure to roll out AI features. Customers demand it, investors expect it, but law-firm clients raise red flags over data privacy. What’s happening: Strong growth:  $95M ARR, 25% growth, but high acquisition costs. Customer demand:  78% ask for AI features; competitors already moving. The trade-off: Third-party integration ($200K + $50K/month) = fast but risky. In-house build ($2M, 12-month payback) = secure but slow. Board challenge:  Decide between speed to market or security, with a potential 15–20% valuation hit if rollout lags. Governance insight: This case puts the spotlight on AI oversight and fluency . Directors must parse complex trade-offs in risk, regulation, and competitive positioning—with limited internal AI governance experience to guide them. Full Case Study 2 Details: Why It Matters These case studies illustrate why boards need more than “gut feel” on AI. Directors need frameworks for asking the right questions, scoring risks, and prioritizing what matters most. That’s why we created the AI Compass for Private Directors Framework  a practical tool to help boards oversee AI responsibly and confidently. 👉 Check out the full details on the AI Compass for Private Directors Framework

A Community Builder at the Frontlines of AI For more than a decade in Silicon Valley, Jenny Kay Pollock navigated the tech world. Many times as “the only” the only woman in the room, the only one without a technical background, the only voice with her perspective. Today, as Co-Founder and Co-CEO of WOMEN x AI, Jenny and Reut are changing that narrative. Captured by Natasha Renée for the Featured Founders  series, Jenny shares her journey from scrappy startup roles to building a global community designed to put women at the center of AI. Breaking into AI Without a CS Degree Jenny’s path into tech and AI was unconventional. Like her, your path doesn't need to include a Computer Science degree. “You don’t need to know what an LLM is to get started. You just need to know what problem you want to solve — and find a tool that helps you do it.” She brings this practical mindset to WOMEN x AI, showing women how to integrate AI into their workflows, no coding required. From “The Only” to Building for Everyone Jenny knows firsthand the cost of being overlooked. From speaking up and not being heard to watching her ideas taken by others, she’s experienced what exclusion feels like. “When you’re the only one at the table, it’s easy to feel invisible. But when we build community, no one has to go through it alone.” This ethos powers WOMEN x AI: a collective that doesn’t just teach AI, but actively redistributes visibility and opportunity. Scaling with Purpose, Not Burnout Jenny is transparent about the challenges of scaling a mission-driven startup. Her solution? Automation. “Look for the task you hate doing every week. That’s where AI can help. For me, it was formatting blog submissions. Now I use a free Chrome tool called Autocrat, and it saves hours.” Her lesson: automation isn’t about replacing work, it’s about creating capacity for what matters most. Check out our AI Spotlight series that is powered by the Autocrat automation. Why Community Learning is the Secret No single person can keep up with AI’s pace — and Jenny doesn’t try. “I can’t keep up, and I do this all day. But in community, we share what’s working. That’s how you supercharge your learning.” This philosophy drives WOMEN x AI’s #WxAISocialSaturday , a casual weekly LinkedIn exchange where women trade tips, tools, and encouragement. Her Deeper Why For Jenny, representation in AI is a matter of urgency. “If AI is only built by a small group of people, it won’t serve all of us. We have a chance to do it differently this time — but only if diverse voices are at the table.” 🎥 Watch: Jenny Kay Pollock on Inclusive AI, Burnout, and Community-First Tech Captured by Natasha Renée as part of the Featured Founders series From One Voice to Many Jenny’s story is about transformation from being “the only” to ensuring women everywhere have space to lead. Her work reminds us that representation isn’t optional in AI it’s essential. “We all benefit when everyone gets a seat at the table.” Check out the other amazing founders Natasha has interviewed. Ready to grow alongside women like Jenny? Become a WOMEN x AI member to learn AI in community.