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Registration & Opening Networking
Jessy Hanley VP of Ecosystem Lifecycle Marketing Intuit🤝 From Buzzword to Bottom Line: Building an AI Marketing Strategy That Actually Works
What if the reason most AI strategies fail isn’t the technology — but the fact that buying AI tools and building an AI strategy are two completely different things? We’re living in a moment where every company has an AI initiative, every deck has an AI slide, and yet many marketers are drowning in tools while struggling to produce meaningful results. This session explores what AI-driven marketing actually looks like when it works — not in a lab or a pitch deck, but in the real world. Drawing on more than seven years of hands-on AI marketing experience and 25 years in technology, it covers practical applications like targeting the right customer at the right moment, creating messaging that feels truly personal, and using smarter pricing and discounting instead of sending the same offer to everyone. The results are real, and so is the story of what it takes to get there. Because AI doesn’t transform your marketing on its own. Transformation happens when you know where to apply it, how to scale it, and what to stop doing to make room for it. You’ll leave with a new way of thinking about AI in marketing — not as another tool to plug in, but as a growth lever to use with intention. Practical. Honest. Zero fluff.
🤝 From Signals to Systems: A Builder's Guide to Community Intelligence
What does it look like when a company treats community engagement with the same rigor as product development? Not as a side project or a reporting exercise, but as a real engineering challenge with messy data, tight timelines, and users who aren't data scientists? This workshop tells the story of building a community intelligence practice from the ground up. Amazon's Jane Barker and Shreya Sarawgi walk you through two real builds: one that derived ground truth from GPS behavior data across thousands of sites, and another where a small team rapidly prototyped a tool that turned scattered community signals into a single interactive source of truth. You'll hear about the decisions that shaped each build, the frameworks that made them possible, and the tradeoffs the team navigated along the way. Then you'll pressure-test your own project against the same principles.
Executive Experience Breakfast in Executive Lounge
•Alex Gaudiani, General Manager, Surgical Gloves, Medline Industries •Anshu Bhardwaj, Senior Vice President and Chief AI Transformation & Simplification Officer, PayPal •Ashley Siegel, Head of Talent, Diversity, and Inclusion - Technology, Morgan Stanley •Bethany Eppner, VP, Head of Global Business Marketing, Fandom •Billy Hackenson, Vice President, Marketing Strategy, Planning, Operations & Head of AI Innovation, Cisco •Caitlin Harren, Director, Sustainability Solutions, Net Zero Carbon Products & Social Responsibility, Amazon •Caitlin Kalinowski, Former OpenAI, Head of Robotics •Charman Hayes, Executive Vice President, People and Capability, Technology, Mastercard •Christopher McCormick, Chief Executive Officer, Visionary Consulting LLC SFB •Christy O'Gaughan, VP of APEX/Head of US Commercial Analytics, Insights, and Decision Sciences, AbbVie •Elyssa Byck, SVP, Enterprise Partnerships & Operations, NBCUniversal •Emily Kagan Trenchard, Former SVP, Chief of Consumer Digital Services, Former Northwell Health •Hannah Yankelevich, Former SVP Stores & Merchandising, Former AKIRA •Hillary Reinsberg, Managing Director, Head of Content, Card & Connected Commerce, JPMorgan Chase •J. Bob Alotta, Executive Director, Media Democracy Fund •Jane Chen, Senior Vice President, Integrated Marketing and Loyalty Solutions, Live Nation Entertainment •Kari Fleischauer, Chief Operating Officer, Gotham FC •Kathleen Wrynn, Global Head of Digital Assets, Invesco •Kristen Nolte, SVP of Global Consumer and Small Business Marketing, Dell Technologies •Laura Spanjian, Senior Director, Global Policy, Airbnb •Lois Schonberger, Partner, QuantumBlack, McKinsey & Company •Mariko Sola, Vice President, Global Strategic Sourcing Business Planning & Operations, Center of Excellence, Visa •Marion Régnier, Partner, PwC •Megan Brown, Former Director, Data Office, Starbucks •Melissa Dudek, Partner & Director, Digital Transformation, BCG •Monica Landen, Global Chief Information Security Officer, Diligent •Monique Dorsainvil, Public Policy Director, Meta •Pam Drucker Mann, Chief Executive Officer & Co-Founder, Run-A-Muck •Rosanna Durruthy, Vice President, Belonging, Learning and Employee Experience, LinkedIn •Sarah Hoyle, Head of Global Trust & Safety, Spotify
When Software Becomes Physical: Engineering Trust in AI Systems
AI is rapidly expanding beyond chatbots and recommendation engines into autonomous systems that interact with the physical world. As AI begins making decisions that influence people, machines, and critical infrastructure, the distinction between cybersecurity and safety is becoming increasingly blurred.
The Next Leadership Skill: Communicating in Prototypes
AI is changing how products, services, and more get imagined, aligned, and built. In the near future, leaders guiding the vision will increasingly be expected to communicate ideas not just through words, decks, or requirements, but through prototypes. This session explores how AI-native prototyping is reshaping leadership: speeding up innovation and concept development, improving cross-functional alignment, and making ideas easier to test before significant time and money are committed. I will cover the shift in mindset, the emerging tools and workflows enabling it, and what leaders need to understand now in order to stay effective as product, service, and offering development becomes more visual, interactive, and AI-assisted. Attendees will leave with a practical view of why this skill matters, where it is headed, and how to begin building fluency.
AI Stewardship: How Leaders Build Trustworthy Intelligence at Scale
As AI becomes embedded across large organizations, the hardest problems aren't technical -- they're organizational. Who owns the decisions AI systems inform? How do leaders build accountability structures around tools that move faster than policy? How do you maintain trust with employees, customers, and stakeholders when the underlying systems are opaque by default? This session examines what responsible AI leadership actually looks like in practice: the governance structures, decision frameworks, and cultural conditions that determine whether AI scales well or just scales fast.
Learning to Be Bad Again: The One Skill That Survives Every Tech Reset
Every senior leader in tech is quietly asking the same question right now: am I still going to be relevant in five years? The honest answer depends less on which AI tools you adopt than on the willingness to be a beginner inside the same field where you're already supposed to be the expert.
10:00 am Sessions⚡
💻 Becoming a Better Listener: LLM-Powered Market Segmentation
Most product teams are one well-asked question away from insights that could change how they think about their relationship to their customers. This session shows how to use LLMs to segment free-text customer data (CRM notes, app reviews, support transcripts) by decision-making language and patterns, not just demographics.
Everyone's Telling PMs to Become Builders. Here's How.
A concrete, actionable framework for PMs to become builders using AI, specifically through Spec-Driven Development. The most effective form of vibe coding isn't just prompting an AI to build something, it's doing the thinking upstream so precisely that the AI can execute the vision faithfully and without hallucinations.
💻 Generative AI for Decision Intelligence: Building Agentic Analytics Systems from Business Data
AI is changing the interface to enterprise data—and with it, what analytics engineers need to design. Reliable data models and metrics still matter, but AI systems also need business context, tools, orchestration, validation, and clear boundaries around what the model can decide. In this hands-on workshop, participants will extend a traditional analytics workflow into a small decision-intelligence system. Using a realistic business scenario, we’ll connect an LLM to structured data and tools, separate deterministic logic from model reasoning, and progressively build the surrounding components needed to move from a business question to useful decision support. Key takeaways: 1. Understand how analytics architecture changes when AI becomes a consumer of enterprise data. 2. Design the boundary between data logic, business rules, tools, and LLM reasoning. 3. Build the surrounding AI layer—context, orchestration, validation, and human checkpoints—that connects analytics to decisions.
💻 Gen AI: Your Co-Buddy in the Journey of Cybersecurity
Generative AI has revolutionised every field of technology. Having a sneak peek into how it’s revolutionizing the cybersecurity right from code development to threat attacks because the code is not only vulnerable in production anymore but it’s vulnerable right from its inception of coding by developer to CI/CD pipeline to pre production. So it’s important to know how to leverage gen AI to prevent cybersecurity attacks at all phases. This talk will focus on that through real world use cases and hands on
💻 You Don't Get Promoted for Doing More Work: The Shift from Doing to Leading**
Many high-performing women in tech are taught that excellence means doing more, moving faster, carrying more weight, but that mindset can quietly stall growth. This talk shares the pivotal mindset and behavior shifts that enable the rise into executive leadership: letting go of control, redefining value, and learning to lead through others.
Redesign Real Work: Rebuilding Your Processes with AI Prompts & Agents
In this hands-on workshop, participants will do the initial, messy but critical work of redesigning one real process using AI. Rather than focusing on tools alone, the session walks attendees through how to rethink where AI fits into a workflow, including what should remain human-owned and where prompts and agents can reliably take on repeatable work. The session blends short teaching segments with individual reflection and small-group collaboration. Participants will leave with: •A better understanding of how to redesign work for sustainable impact •A vision for their "AI-ified" future-state process •An initial outline of prompts and agents to put into immediate practice •Confidence to better incorporate AI into daily work This workshop is designed for people who still think AI is either all talk or too technical to apply to their own work. This session will empower them to create new habits and change how they work.
10:30 am Sessions⚡
🤝 The Connection Cheat Code
Most people hear the word networking and immediately want to leave the room. That is because networking often feels like performing for people who might be useful someday. Real connection feels different. It starts with a shared interest, a thoughtful comment, a good question, or that instant feeling of “Oh, you get it.” This session will show the LWTSummit participants how to turn the energy in the room into relationships that last long after TechFutures. KJ Jones, Heather Combs, and a LinkedIn Creator will share ten honest and useful takes on finding your people, showing what you care about, and making LinkedIn work harder for you. The audience will not just sit and listen. They will vote, debate, try a few things in real time, and leave with connections they can actually remember.
Emerging Space Technologies for Civic Benefit and Public Interest
As the commercial space economy surpasses $626B, the New Space era offers unprecedented opportunities for advancement. This session explores how satellite communications, Earth observation, and PNT systems can foster digital equity through the lens of public interest technology. The session also examines policy and regulatory frameworks that mandate public interest obligations, and discusses how to operationalize these principles within space and emerging technologies.
💻 How to Build Resilient Teams
Tech teams are operating in a strange moment: rapid AI shifts, constant reorgs, tighter expectations, burnout, layoffs, ambiguity, and pressure to move faster with less. Resilience is no longer just an individual trait. It is something teams have to deliberately build into how they communicate, make decisions, handle change, and recover when things go sideways. In this workshop, we’ll explore what resilient teams actually look like in practice: how they create psychological safety without lowering standards, adapt to uncertainty without spinning, and build habits that help people stay effective under pressure. You will leave with concrete tools for strengthening trust, improving team communication, and creating systems that make teams steadier, clearer, and more capable in the face of change.
The Hidden Cost of Leadership: When Leading Hurts
Leadership is often portrayed as a privilege, but for many it can also be profoundly isolating, morally distressing, and psychologically damaging. This workshop introduces the concept of traumatic experiences of leadership and explores why capable leaders burn out, lose confidence, or leave leadership altogether, with particular relevance for LGBT leaders.
💻 Building AI That Helps Students Learn
A case study in applied AI for education: systems where every evaluation gets read by AI and flagged for human attention, and feedback loops that never existed now run in days instead of months, built on a governed lakehouse with LLM-powered analysis while protecting student privacy under FERPA.
💻 Architecting Production-Ready Multimodal RAG Pipelines: Building AI That Understands Text, Images & Documents
Large Language Models are no longer limited to understanding text. Today's enterprise knowledge bases contain diagrams, scanned PDFs, screenshots, charts, tables, and images that traditional RAG systems struggle to interpret effectively. In this hands-on workshop, we'll build a production-inspired Multimodal Retrieval-Augmented Generation (RAG) pipeline capable of retrieving and reasoning across both textual and visual information using modern Vision-Language Models (VLMs). Participants will learn how multimodal AI systems ingest complex documents, generate unified embeddings, retrieve relevant context across different data types, and produce grounded responses using state-of-the-art LLMs. Rather than focusing solely on model theory, we'll cover the engineering decisions that matter when deploying these systems in production including document parsing, multimodal embedding strategies, retrieval optimization, hallucination reduction, evaluation frameworks, cloud deployment, and cost-performance tradeoffs. By the end of the session, attendees will understand how to design scalable AI search applications that can answer questions about technical manuals, research papers, diagrams, charts, screenshots, and image-rich documentation. Key Takeaways * Understanding Multimodal RAG architectures * Vision-Language Models (VLMs) and how they complement LLMs * Processing PDFs containing text, tables, charts, and images * Building unified retrieval pipelines across multiple modalities * Choosing embedding models and vector databases * Evaluating retrieval quality and reducing hallucinations * Designing scalable cloud-native architectures for enterprise AI applications * Production best practices for latency, security, observability, and cost optimization This workshop is ideal for software engineers, AI engineers, machine learning practitioners, and technical leaders interested in building next-generation enterprise AI systems beyond traditional text-only RAG.
11:00 am Sessions⚡
11:00am - 11:15am | BREAK
Please note: Every session needs transition time, and some sessions may start a few minutes late. This break is intentionally built into Tuesday's agenda to bring all stages back on schedule before the next programming block. A big thank you to our sponsors:
The Operator's Edge: Why the Best Early-Stage Investors Understand the Product and the People Building It
As a former engineer turned early-stage investor, Ananya found that the best investors at the earliest stage can read both halves of a startup at once — the product and the people building it. She calls it the operator's edge. In this talk she breaks it into the technical read and the human read and shares how having built things herself shapes both.
11:15 am Sessions⚡
🤝 💻 From Yoga Mat to Corner Office: Building Sustainable, High-Performance Leadership
What does it take to lead with clarity, purpose, and wisdom in moments of rapid change? In this conversation, Aparna Bawa, Intel's Executive Vice President and Chief Legal & People Officer, joins Kamini Ramani, author of Lotus Leadership: A Modern Playbook of Ancient Wisdom, to explore how heritage, lived experience, and enduring values can inform modern leadership. It is a book Kim Scott has called yoga for business and that Publishers Weekly named an antidote to burnout. Drawing on insights from more than 30 accomplished women leaders of Indian heritage and other women of color, Ramani's Lotus Leadership framework offers a practical perspective on how leaders can build both meaningful lives and meaningful careers. The interactive conversation between Bawa and Ramani will dive deep into how leaders can nurture high-performing teams, navigate complexity with both rigor and humanity, and create cultures where people can do their best work over the long term. With concrete examples from Bawa's storied journey through Zoom's pivotal role during Covid to playing a key role in Intel's transformation journey, the session will highlight how to build sustainable success through sound judgment, self-awareness, trusted relationships, and the ability to adapt to the needs of the moment. Attendees will leave with practical ideas for leading with greater intention and for building success that endures.
How to Manage AI Agents: The New Skills and Disciplines**
Making AI agents work requires a foundational management layer currently missing from technical conversations. As knowledge workers shift from executing tasks to instructing and evaluating AI, leaders must apply strict product discipline, data-driven evaluations, and Human-in-the-Loop frameworks to manage the resulting explosion of unpredictable outputs.
How To Ship A GenAI Feature You Can Trust
Traditional software testing assumes there's one correct answer to check against. Generative AI breaks that assumption, and quality becomes something you measure rather than assert. This workshop walks engineering and product leaders through the full lifecycle of shipping a GenAI feature people can trust, with evals as the connecting thread.
Why Tell Stories When You Have AI
What do a job interview, an unlikely alter ego, and a rowing coach have to do with AI? More than you might think. Let’s explore a world where AI can create almost anything while human attention remains stubbornly finite. We’ll put the extraordinary power of AI next to one of our oldest human superpowers, storytelling, and see what happens when they collide. Do they make each other essential, or does one make the other obsolete? What still matters when everyone has access to extraordinary tools? Come find out for yourself.
From Support Function to Strategic Force: AI as Your Transformation Toolkit
Large-scale transformational initiatives are usually led by the people closest to the technology , but the people closest to the business impact are often operators, program leads, and cross-functional generalists. This talk is meant to empower all the non - technical operators. Drawing from a real case study, I'll share my framework : a practical toolkit for using AI to drive (not just assist) large-scale initiatives from within Product and Business Operations. You'll leave with concrete techniques to move from reactive support work to strategic ownership: using AI to synthesize cross-functional complexity, build executive-ready narratives fast, and run transformation programs with the speed and rigor usually reserved for technical teams. This session is for anyone who has ever felt like the "support function" in the room and is ready to lead instead.
🤝 From Possibility to Progress: Reimagining How a Global Bank Works
How do you drive meaningful innovation inside a 240-year-old institution without compromising the trust, resilience, and controls that have enabled it to endure? In this interactive workshop, Mark O'Looney, Director, shares how BNY's Process Innovation Taskforce brings together business, technology, design, and AI expertise to reimagine complex processes and turn ambitious ideas into measurable progress. Drawing on real-world lessons, they will explore how the team moves beyond incremental improvements to uncover root problems, challenge long-standing assumptions, and rapidly shape solutions around the people who perform the work. Participants will then apply the same approach to a process of their own, identifying friction, reframing the opportunity, and mapping a practical path from discovery through delivery. Attendees will leave with a repeatable framework for: • Distinguishing process optimization from genuine reimagination • Bringing multidisciplinary teams together around a shared problem • Identifying where AI and emerging technology can create meaningful value • Balancing experimentation with the controls required in a highly regulated environment • Moving from an exciting possibility to a solution that can be tested, measured, and scaled. This workshop is not about innovation theory. It is a practical look at how established organizations can create the conditions for innovation and translate new ideas into real enterprise change.
11:45 am Sessions⚡
Navin Chand Founder & CEO New Moon Ideas🤝 💻 Beyond AI Experimentation: Building Skills for Business Impact
Realizing value from AI takes more than technology. It takes people with the skills, confidence, and support to use it effectively. As Founder & CEO of New Moon Ideas, Navin Chand has a front-row seat to Microsoft’s AI transformation. In this keynote, he’ll offer a glimpse into how Microsoft helps its workforce turn skills into business results, highlighting lessons other organizations can apply to build confidence and support adoption. Navin will also draw on his own experience to explore what these shifts mean for how organizations work, learn, and lead. You’ll leave with practical ways to support employees through change, build a culture of continuous learning, and move from AI experimentation to everyday impact.
Integration: The Open Secret Behind Agentic Superpowers
Modern day agent building is powerful and fun, but sometimes enterprise agents require so many resources it can be difficult to organize and manage them. We will talk about managing your agent resources, both local and organizational, to create an efficient and effective agent stack.
Dream it, Test it, Launch it: Building ideas into apps with open source AI models
A workshop that takes you through identifying, evaluating, and implementing open source AI models for an AI application of your choice. Users will evaluate AI models against different quality benchmarks and launch their model of choice into a test product to see how it performs in a real application.
AI Hasn't Changed My Leadership. I Don't Think It Should.
There's a lot of talk about how the age of AI has changed how we lead. This isn't that talk. This speaker argues AI hasn't significantly changed how they lead, and shouldn't have to.
A Practical Framework for AI Use in High-Trust Professions**
For professionals who work with confidential information and trust-sensitive client relationships, using AI well requires more than good intentions. This interactive workshop gives participants a practical framework for evaluating AI use, spotting high-risk use cases, and making decisions that hold up legally, ethically, and reputationally.
Software Engineer With a Background in... Sales? Befriending the Ego to Not Limit Our Careers and Lives
Psst... this is for everyone who has a secret desire to try something else or become somebody else.
AI-Enhanced DFT Strategies and Test Implementation
Design-for-Test is what makes manufactured silicon verifiable. Every die that ships is screened by structural test, and the quality of that screen decides what reaches customers and what test costs across millions of units — which makes DFT one of the quieter but more consequential disciplines in the semiconductor industry. It is also full of slow but necessary work: requirements checklists, scan configuration and partition files, design-rule check logs, chain traceability, coverage debug, and failure log extraction. This talk shows where AI helps with each of them, from RTL design through to silicon failure analysis, and how the work changes when a tool can read volume, draft a first pass, and check its own output. The time saved is real — but the more important point is what does not change. Patterns are still simulated. Design checks still gate tape-out. AI shortens the path to an answer; engineers remain the gatekeepers of what is true.
12:15 pm Sessions⚡
Callie McKee Leader, Inclusive Leaders and Teams Cisco🤝 Before the Prompt: Why Trust Determines Who Actually Uses AI
Every AI rollout is secretly a trust negotiation. Will your team trust the tool? Will they trust leadership’s intentions, and do leaders trust their people enough to grant real autonomy? While companies often budget heavily for new technology, they frequently fail to budget for the trust required to implement it—which is precisely why so many transformations stall. Drawing on insights from GLAAD’s 2026 AI report, this session explores how the LGBTQ+ community’s valid concerns regarding misinformation, privacy, and bias serve as a sophisticated stress-test for modern technology. Far from being “behind” on AI, these community perspectives offer the exact kind of critical scrutiny that leaders need to navigate the AI era successfully by shifting the narrative away from ultimatum of “adopting AI or being left behind”. We will explore mindsets, skillsets and toolsets for bridging the trust gap, turning employee skepticism into strategy, and creating trusted, inclusive environments that empower AI innovation.
Beyond the Binary: Rethinking Data Centers & AI Infrastructure
The data center debate is often framed as a choice between building the infrastructure AI demands and standing in the way of progress. What might we build if sustainability, durability, community benefit, and public accountability guided those decisions from the start? We invite you to an intimate, off-the-record salon with a select group of senior leaders. Together, we’ll explore the opportunities and decisions shaping the future of data centers and AI infrastructure. Your perspective would add valuable insight to the conversation, and we would be delighted to have you in the room.
💻 AI as a Design Multiplier: Rethinking How We Work, Not Just What We Build
AI tools are everywhere, but most designers and knowledge workers are barely scratching the surface, using them for one-off tasks instead of restructuring how they work. This talk covers the shift from occasional AI use to intentional AI integration, building workflows where the tool amplifies judgment rather than replacing it.
AI Evaluation Playbook – Beyond the Score
A practical demo-style presentation for evaluating AI solutions, built for technical and product professionals, covering the critical metrics and standard framework for assessing AI applications using AI, with a hands-on approach to the LLM-as-a-judge method.
More AI Doesn’t (Have to) Mean Less Human
We’re in an era of AI that makes it possible for us to create more content than ever before—faster, cheaper, and at massive scale. We also have more data and channels than ever before to deliver personalized content. But there’s a tension many of us are feeling—especially those of us whose professional and/or personal passion is rooted in language—that as creation and delivery become more automated, content is increasingly less human. I’ll share my own experiences balancing the growing use of emerging AI with the universal need for authentic human connection. I’ll also look at the role data plays in personalization and why more data doesn’t always translate to better experiences (including my own as a Lesbian who Techs and is targeted accordingly). And I’ll share practical ways to lead with empathy while driving efficiency to deliver relevant and engaging content that make customers feel seen and understood, not just targeted. More AI doesn’t have to mean less human if we’re intentional about how we use it!
💻 Who Builds What's Next: Closing the AI Skills Gap Before It Closes the Door on a Generation
AI is reshaping technical work faster than the workforce and education systems can adapt, and the stakes are highest for the women and underrepresented technologists who were already fighting for a seat at the table. Drawing on a decade of building applied learning infrastructure with employers and universities, Dr. Judith Spitz – former CIO of Verizon and founder and CEO of Break Through Tech – will unpack how the AI talent pipeline is being rapidly redefined, how traditional classroom education is structurally insufficient to prepare the next generation of technology professionals and how we have a once-in-a-generation opportunity to build a workforce development infrastructure that works for everyone. Judith will discuss why ‘who is in the room’ matters more than ever, why access to applied, work-based learning opportunities is now a prerequisite to entry-level employment, and why these experiences are so hard to deliver at the scale needed to support widespread access to the AI economy. Attendees will leave with a concrete model of what applied learning can look like, and why building these learning opportunities at scale is one of the most critical things tech leaders can advocate for now.
How AI Empowers Undervalued Operators — And How to Build Systems That Amplify Their Strengths
AI adoption is skyrocketing, but not only because of efficiency and automation. AI can also offer qualities that many people find supportive: predictability, patience, consistency, curiosity, and non-punitive feedback. For chronically underserved technologists, particularly those navigating trauma, neurodivergence, or historically exclusionary systems, these qualities can reduce friction and create more room for experimentation, clarity, and growth.This session introduces the Kindness-as-Infrastructure Framework and explores why AI can become a valuable support system for people who have not consistently experienced psychological safety in their work environments. Through a persona journey and real-world communication workflow, attendees will examine how AI can support clearer communication, experimentation, and operational effectiveness, and how leaders can intentionally build similar conditions into their teams and systems. Attendees will leave with practical approaches for creating environments where curiosity is rewarded, communication risk is reduced, and more people have the support they need to do their best work. Key TakeawaysBy the end of this session, attendees will understand: •The Kindness-as-Infrastructure Framework: How kindness can function as a scalable system and influence AI adoption among underserved communities. •The Undervalued Operator Advantage: How AI can reduce communication risk, support psychological safety, and create more room for experimentation among marginalized technologists. •A Supportive Communication Workflow: A five-step model, Regulate → Reflect → Refine → Review → Try Again Tomorrow, for using AI to improve clarity, tone, and emotional governance in digital communication. •How to Replicate AI’s Strengths: Practical ways leaders can design workflows and team cultures that incorporate predictability, curiosity, consistency, and non-punitive feedback. •Kindness → Trust → Brand → Currency: How emotional governance can build trust and become a form of professional capital, particularly for underserved communities.
The Learning Loop: Get Better at Getting Better
The premise of the talk is that the fundamental competitive advantage in the AI era isn't intelligence; it's the speed and quality of the learning loop. I will describe the framework that captures the synergies between high-performance AI systems and organizations, particularly around the feedback, reflection and better action.
12:45 pm Sessions⚡
Policy Is Product: The Invisible Decisions That Shape Technology
When we think about building great products, we picture engineers writing code, designers crafting experiences, and product managers defining roadmaps. Rarely do we think about policy.
The New Design Surface: Prompts, Patterns, Prototypes, and the Weird New Role of Design
AI is changing design work from the inside out. This talk is a practical look at how design teams can move beyond Figma-centered workflows into AI-forward collaboration with tools like Cursor and Claude, plus an honest look at the human side: what designers are struggling with as identity feels shaky.
AI? No Thanks, Too Much Change!
Flip the script and learn how to manage the change fatigue associated with AI, using AI. This session covers organizational change management approaches to remove stress and resistance, improve readiness and adoption, and illustrate ROI, no formal authority needed.
💻 Building Context-Aware AI with GraphRAG: From Safety to Financial Independence
What does it take to build AI that actually understands the women it’s meant to help? This talk shares the story behind a conversational AI agent designed to support women on the path to financial independence, and the journey toward making it more intelligent, connected, and context-aware using GraphRAG. Part personal story and part practical exploration of AI design, this session looks at how technology can move beyond simply answering questions to better understanding context, relationships, and real-world needs. At its core, this is a story about building AI with purpose—and how thoughtful technology can help create pathways to greater safety, opportunity, and independence.
💻 Stack Overflow for Leadership: Using Engineering Solutions for Influence Problems
When your code breaks, you know exactly what to do: classify the error, map the dependencies, find the root cause, and fix the bottleneck. This workshop shows how the engineering patterns you use every day translate directly to leadership challenges, applying the debugging skills you already have to a broader problem space. You'll leave with at least three patterns you can apply to real problems on Monday.
Great Minds Don't Think Alike: Leading High Performing, Neurodiverse Teams
Technology breakthroughs emerge when diverse minds approach complex problems from different angles. With tech roles having among the highest concentrations of neurodivergent talent, teams that optimize cognitive differences gain a measurable advantage, especially critical for female leaders since neurodivergence is often hidden.
1:15 pm Sessions⚡
Friction to Flow: Building Health Tech That Sees the Communities Others Overlook**
Christine Bartle founded Carete to solve a problem she'd seen inside two of the country's largest health plans: rural and underserved communities falling through the cracks of care coordination. This session covers what it takes to build a health tech company as a woman entrepreneur, from raising capital to protecting IP as a solo technical founder.
Map It Before You Automate It: What to Automate, What to Protect
Most AI adoption starts backwards — with a tool, not a problem, and with whoever's workflow is easiest to document. This hands-on workshop teaches a mapping method for diagramming attendees' actual end-to-end workflow and pinpointing where real friction lives, then adds a second pass most teams skip: asking whose expertise doesn't show up on that map at all. Informal knowledge, workarounds, and judgment calls are often invisible in process documentation — and they're exactly what gets flattened when a team rushes to "AI-enable" a workflow. Attendees will map a real process from their own work, distinguish friction worth automating from friction that's actually load-bearing expertise, and leave with a short evaluation framework for building AI use cases that support judgment instead of erasing it. Grounded in sociological methods for studying how people actually work — not how org charts or process docs say they work — this session is for anyone who wants to adopt AI deliberately instead of reflexively.
What Data Can’t See: Building Queer Safety Into the First Line of Code
A product can work exactly as designed and still make someone unsafe. By the time a report button or block feature gets built, teams have already made hundreds of decisions about who their users are, which identities exist, what data matters, and what the system is allowed to infer. For queer communities, those assumptions have real consequences. This is a working session, not a keynote. Drawing on experience in data science, product leadership, and building She & HER for the Sapphic community, we'll look upstream of "AI bias" at the human and product decisions that shape technology before a model produces a single output. You'll leave with a five-question safety framework and a way to apply it from personas and research through data, design, implementation, and iteration: 1.Who could this feature make less safe? 2.What are we assuming about the person using it? 3.What does our data not know? 4.What happens when our system is wrong? 5.How does the user regain control? We'll run the framework live against a real product feature, then safety-test attendee examples together. Who it's for: founders, product leaders, designers, researchers, data scientists, ML practitioners, engineers, and trust, safety, privacy, and community teams. No technical background required. Bring: a laptop or tablet and one real feature, workflow, dataset, AI use case, or product decision you're working through. Nothing formal, no code, no proprietary details. Interactive session. Laptop encouraged. Bring a feature. Leave with a safety audit.
💻 Modern Authentication and the Trust Layer*
How does a service prove who it is? This talk explores the risks of long-lived credentials and how modern authentication approaches like OpenID Connect (OIDC) and identity federation enable services to access the cloud using trusted identities and short-lived credentials, with lessons from Uber’s journey.
2:00 pm Sessions⚡
Future Plan, Q&A & Lots of Networking
Join the CEO of Women Who Code for a candid conversation about rebuilding the organization for a new era of technology. From AI transformation and workforce shifts to leadership development and community impact, this interactive session will unpack the vision for what Women Who Code can become next — and how members, leaders, and allies can help shape it. The session will include audience Q&A and networking with fellow attendees committed to the future of tech.
3:00 pm Sessions⚡
Optimizing AI for Product Discovery
By default, AI chatbots tend to be overly agreeable, like a bad therapist! This can create echo chambers and validate flawed product ideas or validation during the critical product discovery phase. This talk covers how you can set up guardrails to counter this tendency, utilizing an AI Council framework which engages multiple AI personas in parallel to rigorously pressure-test assumptions, challenge premises, and strengthen strategic decision-making.
Retire the MQL: Build an Event Signal Model That Connects to Pipeline
Every event wrap-up leans on the same numbers: badge scans, MQLs, and influenced pipeline. But those numbers don't tell you which accounts showed intent, which deserve follow-up, or whether the event moved anything forward. This workshop is about building a better signal model: choosing behaviors worth measuring, turning those signals into a score, and testing the model against real pipeline outcomes. You'll leave with a framework you can adapt to your existing event stack and a clearer story to tell leadership about how events contribute to the business.
The Art of Relentless Failing: Hacking a System That Wasn't Built for You
When the standard path is blocked, you stop trying to play by the rules and start building your own doors. Drawing on a personal journey through ADHD, dropping out of university, and finding a backdoor into Microsoft, Google, and Oxford, this talk explores the Persistence Loop, a framework for treating lived experience as an advantage rather than a liability.
Creative Confidence in an AI-Driven World
At IDEO.org we’ve coined Human-Centered Design as an approach to find new solutions to complex problems. AI poses both a threat and an opportunity to the world of design— LLMs by design pull from what already exists and yet, they also allow for us to prototype faster, learn quicker, and get to tangibility more rapidly than before. How do we discern where AI pushes our designs and creative practice and where might it have us recreating problems? In this session we’ll dive deep into how AI plugs into the human-centered design process and explore which practices and approaches keep our creativity sharp, ideas novel, and designs resonant with the people and communities they’re meant to serve. HCD 101 but in the age of AI— a practical tool kit to innovation for the future we want and deserve.
💻 Beyond the Chat: WebMCP, Generative Interfaces, & Your Site
AI has broken out of the chat pane, but most websites aren't ready to receive it. WebMCP, now a W3C draft specification, gives us a standard for registering tools AI agents can invoke directly. This hands-on workshop walks through the architectural decisions required to agent-ready your site.
3:30 pm Sessions⚡
🤝 Leading Through Transformation: The Skills and Technology Strategies Shaping What's Next
Today's technology leaders navigate a period of unprecedented change, from evolving customer expectations and increasingly complex technology ecosystems to the growth of AI and the need to reinvent workflows for an agentic era. But success isn't driven by technology alone. Join Nadine Davey-Rogers, CTO, Corporate Technology Solutions, and Charman Hayes, EVP, People & Capability, Technology, for a conversation on what it takes to build and lead technology that powers economies and empowers people around the world. Learn how Mastercard's technology teams are solving large-scale engineering challenges, leveraging AI and emerging technologies, and developing the talent and skills needed to drive innovation, resilience, and continuous transformation across more than 200 countries and territories.
TechCrawl 4:00 pm–7:00 pm
Tech Crawl
Sessions are done. Now for the good part. Connect with fellow attendees and Summit partners in a relaxed, open format. No agenda, just good company.
Leadership Keynotes & Networking @ Gitano 4:00 pm–6:00 pm
💻 Moving Fast Without Losing Trust: The AI Decisions Leaders Can’t Get Wrong
Should leaders move aggressively, and where should they slow down? How do they balance innovation with security, trust, and people? And which decisions being made today could determine whether AI becomes a competitive advantage or an expensive liability?
🤝 The Human Side of AI: Technology Is the Easy Part
The harder challenge may be getting people to actually trust, understand, and use AI. How are leaders approaching adoption, skills, culture, accessibility, and designing AI around people rather than simply deploying more technology?
Designing for Everyone: AI Glasses and the Future of Accessible Technology
Wearables are changing what access looks like. AI glasses can describe a room aloud, read a menu, or caption a conversation in real time — capabilities that were specialized assistive technology a decade ago. Monique Dorsainvil sits down with Maxine Williams, Meta's VP of Accessibility & Engagement, and trailblazing model, advocate, and athlete, Kiana Vion Glanton, to discuss the transformative value of this technology and the ways in which building with — not just for — communities makes a difference.
Leadership Dinner 6:00 pm–8:00 pm
Leadership Mentoring & Networking Dinner
Reserved for Leadership ticket holders. A curated dinner with a selectively assembled guest list -- good company, real conversation. If you have access to this dinner, you will have received a calendar hold.
Registration 8:30 am–9:30 am
Registration Opens
9:30 am Sessions⚡
🤝 From Chief of Staff to Change Agent: Why the Skills Needed to Drive AI Adoption Look Different Than You Think
This fireside chat explores how professionals are moving from traditional Chief of Staff and program leadership roles into emerging AI Enablement functions across large organizations. Rather than treating AI enablement as a purely technical discipline, the conversation will examine how translating strategy into execution, driving adoption, influencing across teams, and navigating organizational change have become essential to realizing value from AI. Attendees will hear how leaders can recognize emerging opportunities, create impact before formal roles exist, and help define new functions in rapidly evolving organizations. Attendees will learn: 1.How to reframe traditionally “non-technical” experience as critical to AI adoption. Stakeholder management, change leadership, communication, and execution are increasingly central to successful AI programs. Attendees will learn how to position these existing strengths in an AI-focused environment. 2.How to build a role before an organization has named it. The conversation will explore what it means to work in an emerging function without an established playbook, reporting structure, or success metrics, and how to proactively define the scope and value of a new role. 3.How authenticity can support career reinvention. Attendees will hear a candid discussion about navigating nontraditional career paths, building sponsorship, and using authenticity to establish trust and credibility during periods of professional change. Why this matters now AI is creating new roles faster than many organizations can formally define them, and some of the people best positioned to fill those roles are already doing the connective-tissue work that helps organizations adapt to change. The ability to drive adoption, align stakeholders, reduce friction, and translate complex concepts into action often exists outside traditional engineering roles, yet these capabilities are becoming increasingly important to successful AI initiatives. For LGBTQ+ women in technology, this conversation offers a practical look at how to leverage existing strengths, advocate for emerging opportunities, and move into new areas of impact without waiting for a title, job description, or traditional career roadmap to appear first. At its core, the session addresses a question many professionals are asking now: How do you position yourself for the next generation of AI leadership roles using the skills and experience you already have?
🤝 💻 LLM: Lackluster LLM Maintenance **
Our industry has evolved to incorporate AI in our products. AI can deliver innovative new features but also presents operational challenges. This session goes into how we can identify and mitigate operational challenges with AI starting from how we design our products. Drawing from real experience building large‑scale AI‑powered features across different domains, this talk explores practical strategies for designing systems that remain stable when incorporating LLM unpredictability in software architecture. We’ll cover multiple architecture iterations and how to design products and software to build an excellent user experience. Attendees will come away with a list of questions to consider and architecture templates for building applications that leverage LLMs.
The Demo Worked. The Organization Didn’t. Understanding the Consequences Gap™ in AI Adoption
An interactive exploration of why AI implementation so often fails after the pilot succeeds, not because the technology is inadequate, but because organizations struggle to operationalize change inside complex human systems.
How to Evaluate Your Agents: Trustworthy Quality at Scale with LLM-as-a-Judge
You shipped an agent—but how do you know if it's good? When "correct" is a judgment call and outputs run at massive volume, manual review doesn't scale, and engagement metrics lag reality. We built a reusable evaluation platform that uses an LLM as the judge to turn subjective quality into an automated, continuous signal. This signal runs at three moments of truth: offline to select models and prompts, in CI/CD to catch regressions before shipping, and in production to monitor live traffic. We'll dig into what teams most often get wrong: designing a golden set that mirrors real traffic, why a representative set measures the product while a balanced set validates the judge, and how to "judge the judge" by calibrating against human labels before trusting it to gate. You'll leave with a practical blueprint for evaluating your own agents in production—and the pitfalls to avoid, from judge bias and drift to the data ratios that make or break a golden set.
🔒💻 Unlock AI Leadership Roles: The 5Ps to Land Your Dream Job
AI is reshaping leadership hiring, but most leaders don't know how to position themselves for these roles. In this session, I'll break down exactly how leadership hiring works behind the scenes at top AI companies, and what separates the candidates who land those roles from those who don't.
💻 Beyond Static Benchmarks: Trustworthy evaluation of agentic retrieval on evolving data
How do you know your AI system is actually good when there's no ground truth to compare against? This talk explores evaluating systems that generate open-ended answers over messy, unstructured data, drawing from building evaluation frameworks for RAG agents over financial datasets.
10:00 am Sessions⚡
Jessica Swetin Head of Inclusion & Responsible AI Atlassian🤝 The Soul of the Machine: How Atlassian Is Defining Who We Are in the Age of AI **
You bought or built the AI tools. You deployed them. So why are leaders still asking where the value is? Here's the reveal: the soul of the machine is human. A model is just math until a person points it at a problem worth solving and decides what to do with the answer. Technology decides what's possible—people decide what actually happens. Miss that, and you get a very expensive summary of a meeting nobody needed. Jessica Swetin, VP of Inclusion & AI Ethics at Atlassian, has lived this transformation from the inside—and she's bringing the receipts. This talk cuts straight to the human foundations AI actually runs on: psychological safety, recognition that rewards experimentation over exhaustion, and leaders willing to go first. She'll hand you the Rewiring Diagnostic—three questions that expose exactly where your foundation is cracking—and back every claim with real results and the research she’s still figuring out in public. The most hopeful part? The best version of this future isn't AI helping the people who are already ahead pull further ahead. It's AI widening who gets to build at all, closing gaps we've struggled to close for years, led by the people closest to the problem. Someone in your organization is about to decide which future you get. Come find out why it should be you.
AI Governance Is the New Cybersecurity: What Every Technology Leader Must Understand Before the Board Asks
AI adoption is accelerating faster than most organizations' governance models can keep up. Many companies are repeating the same mistakes made during the early cloud and cybersecurity eras: deploying transformative technology before fully understanding the operational, regulatory, and reputational risks.
🔒💻 Can AI Really Build an App? From Idea to Production
What if we handed AI a Figma design and asked it to do everything else? In this highly interactive workshop, we'll build a real application together, from a Figma mockup to a live deployment on AWS. The audience becomes the engineering team. Attendees will leave with practical workflows, reusable prompts, and a modern SDLC blueprint they can immediately apply to their own projects.
Build Smarter, Not Riskier: Legal Basics for AI Builders
The best tech leaders know that shipping fast isn't just a technical question — it's a legal one. Companies are looking for leaders who can spot risk before it becomes a liability, and this session will give builders the working knowledge of AI lawsuits and regulations to do exactly that. We will start with copyright, the area generating the most real-world consequences right now: billion-dollar settlements over training data, ongoing litigation that's forcing companies to disclose how their models were built, and the open question of who owns what an AI generates. From there, we'll cover the regulatory patchwork builders are now operating in, from the EU AI Act's to U.S. state laws governing AI usage. Attendees will leave with a framework for building legal risk into product decisions, and due-diligence practices that hold up regardless of how law shifts.
Smarter Debt Management: What AI Actually Changes
American consumers are carrying more debt, at higher rates, with more complexity than any previous generation -- and most are still managing it with tools that haven't meaningfully evolved in decades. This session examines how AI is changing that: what's now possible in debt optimization that wasn't before, where the biggest gaps are between current tools and consumer need, and what a genuinely useful AI-powered approach looks like versus what's just repackaged fintech.
💻 Shaping AI Agent Behavior: Why UX Must Own AI Evaluation Frameworks
As AI agents move from novelty to core product functionality, UX teams are stepping into an unexpected new responsibility: designing and managing evaluation systems that shape how AI behaves. This session makes the case for why UX is the right discipline to own AI eval -- and shows what that looks like in practice. We'll cover how to build evaluation frameworks grounded in user behavior rather than model metrics, how to identify failure modes that engineering
The Human Work of AI Transformation: Driving AI Innovation and Adoption at Scale that Works
An honest conversation with Dina Marie Pitta, VP of Complex Care at Humana, on what it actually takes to drive AI transformation inside a Fortune 100 company. Dina Marie has lived this from every angle — as a surgeon practicing medicine and seeing the system from the inside, then a consultant helping drive health care reform at the State and Federal level and now an executive/operator - building and scaling AI adoption across a massive organization. She'll share her biggest lessons learned and practical ideas for unlocking transformation in your own organization, from mobilizing senior leaders through risk/reward conversations to winning over the field practitioners doing the work. The conversation is led by Lois Schonberger, a partner at QuantumBlack, AI by McKinsey, who brings two decades experience building consumer and enterprise software as product leader in the Bay Area for many years and more recently as a Partner at McKinsey, leading end to end AI transformations and helping companies launch net new AI consumer products.
🔒 💻 Driving AI Adoption Without Breaking Your Engineering Culture
Every engineering organization is racing to adopt AI, but few are ensuring their culture survives the shift. Our rollout took longer than it might have for one reason: we refused to sacrifice the culture that keeps our engineers here. I'll share what worked for us, from building a volunteer coalition instead of issuing a mandate to prioritizing enablement over decree, along with what we got wrong. Attendees will leave with a practical framework they can adapt, including how we sequenced the first 30/60/90 days, recruited engineers to drive adoption from within, and chose metrics that measure meaningful change rather than activity.
10:30 am Sessions⚡
From Building AI to Governing AI
The AI industry once celebrated building bigger, faster systems; today organizations are asking how to build AI responsibly. Drawing from a journey from AI product leader to Head of AI Governance, this talk explores how queer, neurodivergent experience shaped the skills essential for governing AI.
AI Isn’t the Risk. We Are: The T.E.S.T. Standard for Shipping GenAI Safely
"High risk" isn't a finding, it's a feeling. The real failure mode in AI incidents is the human trust decision around it: automation bias, overconfidence, workflows that ship before anyone can explain what the AI touches. This talk introduces T.E.S.T. (Touch, Execute, Store, Trust), a practical standard for reviewing GenAI systems.
When AI Creates Enough Value to Earn Autonomy
A practical framework for connecting AI performance, risk, and ROI Many teams have moved past experimentation. The question now is how far to let these systems go. When should a human stay in the loop? When is approval still necessary? And when has a system shown enough value and reliability to take on more responsibility? This session gives leaders a practical way to make those calls by looking at business value, performance, the cost of getting it wrong, and whether mistakes can be caught or reversed. You’ll leave with a framework for deciding when to expand autonomy, when to hold it where it is, and when more controls are needed first.
You Don't Have to Type to Build: Voice, AI, and the Future of Coding
This session shares a firsthand experience as a person with dysgraphia who has relied on speech-to-text as a lifelong accommodation, and how AI-powered tools and vibe coding have fundamentally shifted what's possible for building software with voice as the interface.
Data Deception: When to Trust the Numbers
A training-style talk on common sources of data manipulation and flawed methodologies hidden in AI analysis, unpacking the most common forms of metric manipulation and how to identify them with AI agents. A reprise of a standing-room-only 2025 talk, refreshed with a focus on AI agent guardrails that prevent flawed causal inference.
From Impressions to Intelligence: How AI Is Rewriting the Advertising Playbook
Advertising is entering a new era. For years, the industry optimized around reach, impressions, and channel-level performance. As AI reshapes how consumers discover, compare, and buy, advertisers need a new operating model built around intelligence, context, and connected decision-making.
The Visibility Gap: Building the Infrastructure That Makes AI Work for Women Leaders
AI is restructuring how people, expertise, and leadership are discovered. Search is becoming AI-mediated, recommendations are becoming algorithmic, and the systems making those decisions are trained on data that overwhelmingly reflects one kind of leader. That's the problem. This session is about the fix. We'll cover how to build the digital infrastructure that makes you findable, citable, and recommendable to AI systems: what to publish and where, how to structure your online presence for algorithmic legibility, and how to think about visibility as a long-term asset rather than a marketing chore.
Fast Enough to Matter: Tooling for AI Development at Speed*
As teams race to build AI agents and iterate faster, the right tooling can make the difference between experimentation and meaningful progress. Drawing on lessons from Datadog, this session explores how teams can use better tools and workflows to accelerate development, iterate effectively, and turn ideas into working solutions.
11:00 am Sessions⚡
11:00 - 11:15 AM | BREAK
Please note: Every session needs transition time, and some sessions may start a few minutes late. This break is intentionally built into Tuesday's agenda to bring all stages back on schedule before the next programming block.
11:15 am Sessions⚡
Keri Smith Managing Director, Global Banking, Capital Markets Data & AI Lead AccentureThe Agentic Era Is Here. Are You Running It — or Is It Running You?
The organizations winning with AI aren't waiting for a perfect strategy — they're acting, learning, and scaling fast. In this high-energy spotlight talk, Keri Smith, Accenture's Global Banking and Capital Markets AI & Data Lead, cuts through the noise to show what agentic AI looks like when it actually works: real case studies, hard-won lessons, and the leadership moves that separate those driving transformation from those reacting to it. From redesigning how teams operate to embedding responsible AI at scale, Keri delivers a frank, forward-looking playbook for leaders ready to take the wheel.
🤝 💻 The Legal Landscape of GenAI with your Friendly Neighborhood Lawyer
Your team used GenAI to make the campaign. Maybe it generated the hero image, maybe it wrote the headline, maybe it just cleaned up the copy. Now someone is asking who owns that image, whether you can say "AI-powered" on the landing page, and whether the model was trained on a competitor's catalog. These questions land on marketing teams constantly, and the honest answer to a lot of them is that the law is still catching up. I am a marketing lawyer, not an AI oracle. What I can offer is a clear-eyed look at two areas where GenAI meets marketing work right now: copyright and IP in AI-generated creative, and what you are actually allowed to claim about AI in market. We will talk about who owns AI output (and when nobody does), what your agency contracts probably do not cover yet, disclosure of synthetic content, and where regulators are drawing the line on AI-washing. Come with your messiest hypotheticals. Half of this session is sharing and chatting, I don't want to just hear my voice the whole time!
Where Did Your Week Go? (And How AI Can Give It Back)
Most of us have a vague sense that we're spending too much time on the wrong things. This workshop makes it concrete. We'll start by mapping where your time actually goes, then build practical AI workflows to handle the repetitive work that's eating it. Hands-on, role-agnostic, and focused on workflows you can use the same week. No productivity hype, just less time on the stuff that doesn't need you.
🔒💻 Steady Hands, Moving Ground: Leading Engineering Teams Through AI Change Without Breaking Delivery
AI is reshaping how software gets built, and engineering leaders are caught in a real tension: move fast and experiment, but don't drop the delivery commitments the business is counting on. This talk covers holding both innovation and execution at once, guiding platform teams through rapid AI-driven change.
Leadership Just Got a New Required Skill: Hot Seat Coaching on Learning Out Loud Without Losing Authority
Most roles in 2026 require constantly learning new things. If you're leading anyone right now, you’re learning in front of them. Leadership models haven't caught up to this reality. The old path was to develop deep domain expertise, then layer leadership skills on top. That worked when advances in your domain stayed relatively constant. The current pace of change has broken that assumption. Continuous learning is now permanent, and a new core competency has emerged alongside it: the ability to learn out loud and model good learning while leading. Most of us weren't trained for this. People who build this muscle now are setting the new standard for leadership. The leader who can learn out loud and stay credible while doing it shapes the strategy. The leader who waits to feel fully qualified before speaking watches from the sidelines. In this hot seat workshop, volunteers can take the chair for live coaching focused on this exact skill. We surface the specific moments where each volunteer's expertise is real but their expression of it is holding them back, then work in real time on the language that lets them learn while leading without losing credibility. Observers track the moves alongside us and identify their own version of the pattern. Everyone leaves with a set of phrases they can use in Monday's meeting to claim authority in conversations they've been quietly ceding because they're "not the expert."
Building AI-Ready Privacy Infrastructure
The future of responsible AI will depend less on policy documents and more on infrastructure. This session examines what it takes to build privacy architecture that scales with AI innovation, including interoperable permissions and governance-by-design.
💻 Leading in the Age of AI: Three Human Skills to Shape the Future *
As we move deeper into the age of AI, technology isn’t the only thing evolving, leadership is too. With AI transforming how we analyze, create, and make decisions, the leaders shaping what’s next will need a different set of distinctly human skills to navigate increasing complexity and ambiguity. This session explores three human skills that can help leaders thrive in an AI-driven world. Through practical examples and leadership lessons, attendees will learn how to identify what truly matters, make better decisions when the answer isn’t obvious, and confidently turn those decisions into action - skills that will become increasingly valuable as technology continues to evolve.
11:45 am Sessions⚡
Niya Baxter Culture & Strategy Leader Deloitte Consulting🤝 💻 The AI Culture Crisis No One is Talking About.
Organizations are racing to implement AI, automate workflows, and increase productivity. But beneath the surface, a quieter transformation is underway: AI is fundamentally reshaping organizational culture. From redefining expertise and collaboration to changing how employees experience trust, belonging, creativity, and power, AI is forcing leaders to rethink what makes organizations human in the first place. In this thought-provoking session, Niya Baxter explores the hidden cultural implications of AI adoption and shares how organizations can balance innovation with human connection. The future of work will not belong to companies that simply adopt AI fastest—it will belong to those that intentionally design cultures where humans and intelligent systems work together effectively.
💻 What the Research Actually Says About AI at Work and What It Doesn't
Most teams adopt AI tools the way they adopt resolutions: enthusiastically, and with no way to tell if they're working. Drawing on my own peer-reviewed research in NLP, evaluation, and human–AI interaction and putting it into practice at Microsoft, this session shows how data scientists design AI workflows that survive contact with measurement. We'll move past demos to the harder questions: where AI genuinely belongs in a workflow versus where it quietly manufactures rework, how to instrument a task so you can prove (or disprove) a productivity gain rather than assume one, and how to design human–AI handoffs that catch errors instead of compounding them. You'll leave with a concrete evaluation framework, a set of recurring failure patterns to watch for, and a checklist you can run against any AI workflow your team is weighing — starting Monday morning. Delivered from the author of research that revolves around driving people's work streams with AI.
💻 10x Your Productivity: Build Your Own Agentic Workforce
If you mostly use an LLM as a chatbot or a replacement for Googling, you're only scratching the surface. The real gains come from delegating work, not asking questions. By building your own agentic workforce, and knowing which tools to use for what, you can increase your productivity by 10x across both your work and your personal life. In this session, I'll walk you through a real case study, from identifying the right problem to building the agentic workforce that solves it. We'll evolve one workflow step by step, from a simple prompt, to connected instructions, to a scheduled team of agents that verifies its own work before anything reaches you. Along the way, I'll share a curated set of resources on the tools available today and how to put them to work for you. Attendees will leave with a framework for deciding what to automate, a reference architecture for building their own agentic workforce, and a toolkit of systems and templates they can start using the same day. Whether you're an engineer, a team lead, or simply tired of re-explaining context to a chatbot, you'll walk away with a practical path from one-off prompts to agents that work while you sleep.
Make Yes Feel Possible: The Behavioral Signals That Turn Hesitation into Engagement
Everyone wants engagement. But people don’t engage just because we ask them to. They engage when the experience makes yes feel clear, easy, normal, safe, and worth it. In this interactive fireside chat, Zakiya Pope, Senior Director of Behavioral Design at TIAA, and Brittney [Last Name], Global Creative Director at Bingo Loco, explore how behavioral design and live experience design create the conditions for real engagement. From product adoption and AI trust to brand loyalty, workplace participation, and live audience energy, they’ll unpack the invisible signals that move people from hesitation to action. Attendees will leave with a practical “Yes Check” they can use to design experiences people actually want to trust, try, join, and return to.
AI Adoption That Holds: From Pilot to Business Value
Built from a standing-room workshop with the Thoughtworks CTO at TechFutures, this session gives participants a practical framework for making AI adoption stick. We'll work through how to identify where AI actually belongs in your workflows, how to sequence rollout so it creates traction instead of resistance, and how to measure whether it's generating sustained business value -- not just activity.
Engineering Didn't Adopt AI Faster. It Just Had Better Definitions
Engineering didn't get ahead on AI because engineers are inherently more ""AI-ready."" Engineering got a head start because it spent decades externalizing more of the judgment required to execute work reliably. Think about how engineering actually works. Code lives in repositories with history and version control. Schemas make structure explicit. Ownership rules define who is responsible. Tests, CI, review gates, and deployment controls force ambiguity to get resolved and make failure visible. That last part is why coding models got good first: code comes with a grader. Tests pass or they don't, and that kind of verifiable signal is a big part of why coding became such a strong domain for AI. None of it was built for AI. But when AI arrived, it inherited a foundation where the rules, ownership, and feedback loops were already explicit. This isn't only an engineering story. Customer support often reaches AI wins faster than many other functions, and not because support teams are more technical — years of SOPs, escalation paths, ticket taxonomies, macros, and decision trees already made the work explicit. The variable isn't technical literacy. It's how much of the operating judgment required to do the work reliably has already been externalized. Most business functions weren't built that way. We got very good at externalizing the outputs of judgment — decks, documents, emails, decisions — and much less of the judgment required to reproduce those decisions. The important judgment still lives in meetings, precedent, interpersonal feedback, and experienced people who ""just know."" Giving an LLM access to every document you have does not solve that, if the decision logic, ownership, precedence, and escalation rules were never made explicit in the first place. I'll share how we're tackling this at Babylist, then participants work through one real AI use case from their own organization. Rather than starting with ""what agent should we build?"", we start with the work: what does the system need to know, what is explicit versus tacit, who owns the authoritative answer, what happens when sources conflict, what actually enforces the rule, and how would anyone know if the AI got it wrong? Participants leave with a concrete assessment of one workflow and a clear view of what's missing before they scale the automation. The goal is not to document everything. It's to identify the small set of organizational judgment that needs to become explicit, owned, and enforceable for AI to execute work reliably.
💻 The Readiness Exchange
AI adoption is moving fast enough that most organizations are implementing before they've figured out governance, and governing before they've defined what success actually looks like. This session cuts through that cycle with a practical readiness framework: how to assess where your organization actually is versus where it thinks it is, how to embed AI in ways that stick across teams and roles, and how to connect adoption metrics to business outcomes your leadership will recognize as meaningful. Drawing on a real workforce transformation this year, the session includes specific examples of what worked, what stalled, and what the org wished it had built earlier.
12:15 pm Sessions⚡
🤝 Trust is Human: Leadership for the AI Era
"Trust" has become AI's most-used—and most misunderstood—buzzword. Too often, the conversation stops at can we trust AI? or how do we get employees on board? The real challenge, however, is deeper: how do we build AI systems, products, and environments that are genuinely worthy of trust? That work starts with people, not algorithms. We need to trust each other to build a world with AI that serves everyone. In this session, discover how Cisco is tackling this challenge head-on with an inclusion-driven program that brings leaders and employees together to get real about AI, learn as a community, and build the shared trust needed for the AI era.
💻 You're Already Managing. Here's How to Do It on Purpose.
We tell two contradictory stories about engineers: that they're great because they're deeply technical, and that management is soft skills. Put them together and you get the myth that great engineers make bad managers — and the assumption that moving between individual contributor, lead, and manager work means starting over each time. Zoe traces her own path from software engineer to team lead to engineering manager inside a fast-moving startup, and what she noticed along the way: the women around her weren't short on the skills leadership looks for, they just weren't seeing enough people who looked like them doing the job. That led her to look closely at what actually separates the three levels, and how much of the next job you're already doing in this one. She'll share the Triple Threat framework she built from it. Seeing precisely what you're already capable of makes you a sharper advocate for yourself, and puts you in a position to be that familiar face for someone else.
💻 AI as a Cybersecurity Multiplier: But First, Risk
This workshop helps leadership understand how they can use AI as a cybersecurity multiplier to strengthen their organization’s security infrastructure. We will explore how AI can improve cybersecurity capabilities, enhance existing security operations, and create more effective and resilient security environments. Using AI to improve security also means understanding the risks associated with its use. The focus of this workshop is to explore how AI can strengthen cybersecurity while understanding the risks you (as a leader) need to consider FIRST.
💻 What Comes After Human-in-the-Loop: Engineering the Agent Control Plane
AI agents now open pull requests, triage incidents and run migrations. Capability is no longer the constraint, verification is. In most organisations, the control plane for AI agents is still a person: someone reading diffs and clicking approve, quietly becoming the bottleneck and the single point of failure. This session is about what replaces that person; not nothing, but infrastructure. It presents four architectural patterns for building oversight into agentic systems, a decision framework for when an agent has earned more autonomy, a measurement model for whether oversight is actually improving, and a map of the problems the industry has not solved yet. It closes on a single reframe: the question is not whether the agent can act, but whether the system can explain why it was allowed to.
💻 Build Your Own AI Study Coach for Certification Prep**
Stop preparing for your cert the wrong way. In this hands-on workshop, you’ll build a working AI system on AWS that watches how you perform, identifies your weak spots, and rewrites your study plan in real time. You’ll leave with a prototype you can keep using and a pattern you can extend to any learning experience.
Where Did My Brain Go? Using AI to Navigate Perimenopause
Perimenopause can change how you think, focus, remember, and work, sometimes seemingly overnight. Brain fog, disrupted sleep, memory lapses, and difficulty concentrating can make tasks that once felt routine suddenly feel much harder. In this practical, candid workshop, a former LWT speaker shares how navigating perimenopause coincided with being pushed to become AI-native at work, and how AI unexpectedly became an essential support system for managing the cognitive changes she was experiencing. Attendees will see the tools and workflows she now uses to reduce cognitive load, capture ideas before they disappear, organize information, communicate more effectively, and manage everyday work and life. She’ll demonstrate tools including MyClaw, SecondBrain, WhisperFlow, and AI plugins, along with specific examples of how they fit into her daily routines, from work tasks to capturing a thought while driving. Rather than a lengthy live setup, the session will use screenshots and short prerecorded demonstrations to focus on what works, how it’s configured, and how attendees can adapt these approaches to their own needs. Attendees will leave with practical ways to use AI as a cognitive support layer, whether they’re navigating perimenopause themselves or simply looking for better systems for focus, memory, organization, and getting things done.
Already Inside: How Frontier AI Is Changing the Way We Hunt Hidden Attackers
The hardest attackers to stop aren't the ones trying to get in -- they're the ones already there. Threat actors with persistent access can move slowly, blend into normal traffic, and evade detection for months. Traditional security tooling was built to stop intrusion. It was not built to find someone who's been living in your environment since last quarter. Drawing on two decades of incident response and DFIR work, this session covers how frontier AI is changing threat hunting for hidden and persistent attackers: how to detect lateral movement that looks like normal behavior, how AI is accelerating the identification of indicators that human analysts miss at scale, and what a modern detection strategy looks like when the attacker already has a foothold.
💻 Who Writes the Rules? A Field Guide to AI Policy for the Builders Shaping It
AI is moving faster than most organizations' ability to govern it. Engineering teams ship new use cases every sprint, while policy and risk conversations often happen only after something has gone wrong. This session offers a different starting point: build the governance framework alongside the technology.
12:45 pm Sessions⚡
Future Plan, Q&A & Lots of Networking
Join the CEO of Women Who Code for a candid conversation about rebuilding the organization for a new era of technology. From AI transformation and workforce shifts to leadership development and community impact, this interactive session will unpack the vision for what Women Who Code can become next — and how members, leaders, and allies can help shape it. The session will include audience Q&A and networking with fellow attendees committed to the future of tech.
1:30 pm Sessions⚡
🔒💻 Redefining Success Through Career Reinvention*
This session explores what it means to rebuild a career and an identity, through personal experience transitioning from a decade-long role in healthcare as a physician assistant to software engineering, covering imposter syndrome, non-linear career paths, and practical strategies for self-advocacy and resilience.
Stop Measuring AI Features Like It's 2019
Most teams shipping AI features measure them with metrics designed for static software, optimizing for the wrong things or declaring success on features that are quietly failing. This talk offers a clearer mental model for separating AI activity from AI value.
2:00 pm Sessions⚡
Beyond the Headlines: Megan Rapinoe & Rebeca Huntt in Conversation
Megan Rapinoe has spent much of her career in full public view, as a world champion, activist, cultural figure, and one of the most recognizable voices in sports. In the new documentary Rapinoe, director Rebeca Huntt turns the lens toward the person behind that public image. In this intimate fireside chat, Rapinoe and Huntt come together to discuss the making of the film, the relationship between subject and storyteller, and what it means to tell an honest story about a life shaped by extraordinary achievement, public scrutiny, conviction, and change. The conversation will explore the choices and sacrifices behind Rapinoe's public life, what Huntt discovered by looking beyond the familiar headlines, and what both learned through the process of documenting a career and a life in transition.
The Fork In The Yellow Brick Road - Which Way To Go?
An interactive, community conversation about professional growth options and the pros and cons of degrees, certifications, and other career paths. Which options have you pursued, and why? Let's chat about your path down the yellow brick road.
💻 Thinking Agentically: The New Mindset for Building with AI
The next generation of builders won't be defined by the tools they know, but by how they think. This talk introduces thinking agentically: a mindset for designing AI systems where humans define intent, AI executes intelligently, and together they solve problems neither could tackle alone.
Rewrite the Playbook: Putting Women at the Center of the Membership Economy
Women's soccer is entering the largest attention window in its history, and women increasingly hold the buying power driving it. At Seattle Reign FC, that meant rethinking loyalty from the ground up -- moving from a transaction model to a year-round relationship built around how these fans actually engage. This session breaks down the strategic shifts behind that rebuild: how to identify the moments that drive long-term retention, how to design touchpoints that work across a non-linear fan journey, and how to make the case internally for a loyalty model that looks different from the default playbook.
💻 From Pager to Pull Request: Engineering AI Loops That Prevent the Next Incident
On-call can get overwhelming fast. This talk covers practical ways to use AI during incidents to summarize logs, pull context from runbooks, draft status updates, and think through possible causes, plus where AI can be risky: confident-but-wrong answers, unsafe commands, and missing production context.
2:30 pm Sessions⚡
🤝 💻 Drive Impact, Avoid the Chaos: How to Successfully Manage Multi-Agent Ensembles
Learn how to move beyond a simple chatbot interface to orchestrating your own ensemble of agentic workflows. By leveraging multi-agent orchestrations, you will accelerate development speed, improve accuracy and robustness, and deliver greater value. Understand the biggest risks and challenges of managing multi-agent architectures, along with the guardrails and evaluations that make implementations successful.
💻 From Research to Production: AI Model Safety in Practice
We will talk about how to safely deploy AI models in production. There are a number of risks that originate from deploying various closed source or open source models that can cause businesses to lose customers or affect the brand name negatively. For example: Models may produce unsafe outputs, they may recommend competitor brands, they may talk openly about scandals related to the company that is deploying the model etc. The talk will provide a framework on which risks to consider, how to build an evaluation framework to measure these risks and alignment strategies to mitigate these risks before deployment.
Where Human Work Creates Value: How AI is reshaping work, teams and the capabilities that will define our careers
AI is changing more than how we do our jobs. It is changing how organizations define work, structure teams and decide which capabilities matter most. This talk explores how companies are redesigning around AI, and how individuals can use the same lens to understand where their skills will create the most value, what to build next, and how to position themselves as work continues to change. Attendees will leave with: •A clearer view of where human judgment, creativity and leadership create the most value as AI takes on more of the work •A simple lens for assessing which parts of their role are likely to change, which capabilities will matter more, and where to invest in building skills •A better understanding of how AI is reshaping team structures, roles and ways of working across organizations •Practical ways to position their strengths and experience for an AI-enabled workplace •A set of questions they can use to make more intentional career decisions as work continues to evolve
The Value of a Dollar: How Blockchain is Changing What it Means to Pay
Blockchain. Tokenization. Stablecoins. Agentic Payments. Let's break it down and get up to speed on what these changes are and what they mean when you tap your credit card.
💻 AI Fluency is the New Career Currency
AI is not just transforming products, it's redefining hiring signals. Recruiters increasingly prioritize adaptability, AI leverage, and demonstrable impact over tenure alone, yet many women in tech, especially caregivers and first-generation professionals, have less discretionary time to experiment and upskill.
3:00 pm Sessions⚡
Unspoken Meaning: How Brands Can Lose Trust and How They Can Win it Back
In biotech, policy, and mission-driven institutions, credibility isn't a soft skill -- it's operational infrastructure. This talk examines how leadership teams in high-stakes sectors build and maintain trust across stakeholders with competing priorities, and what transparency, empathy, and scientific integrity actually look like in practice when the pressure is on.
Leading Through Technical Influence: Scaling Engineering Excellence through Guilds**
As organizations grow, how do grassroots initiatives contribute to an innovative and collaborative culture that pulls up great ideas? This talk shares how Bloomberg's culture led to 13 guilds, communities of volunteers around a technical area of interest that help break silos and scale engineering excellence.
Your Website Is Talking to AI: Structured Data for the LLM Era*
Modern AI systems increasingly benefit from entity-level semantics that anchor machine understanding in structured, authoritative data. This hands-on workshop explores how to use JSON-LD and Schema.org to build a rich semantic layer for your website that helps search engines, knowledge graphs, and AI systems better understand your organization.
AI as a Thought Partner: Reducing Overwhelm, Protecting Mental Health, and Working Smarter**
AI is changing the way we work, but for many it's also adding pressure, confusion, and digital noise. This practical workshop explores how AI can be used as a human-centered thought partner to reduce cognitive load and support healthier team workflows.
The Third Brain: Building AI Knowledge Systems for the Unseen Work**
Most "personal AI assistant" tools are designed for one person working alone, uninterrupted, measured by what they ship. Knowledge is something you collect, keep, even hoard. That's a great system if you're trying to outperform your peers, and if you're already the person whose work gets counted (as PRs, as shipped features, as revenue, etc). It's blind to everything else: context-carrying, coordinating, remembering who needs what and when, and the labor that holds a team together but shows up on nobody's performance review. We built a system to remedy that. In the last five months of using agents to help with this aspect of work, what surprised us most wasn't the productivity gain, it was how much invisible labor suddenly became visible when we started telling agents to pay attention to it. In this hands-on session we'll help you boot up your own personal agent - a "Second Brain" - to help you at work, using whatever AI tool is already on your laptop, fed with real context from your actual job. We'll show you what ours look like after months of use, what broke, and what we'd do differently. Then we'll peek into the frontier that we're still working on: what happens when this stops being about personal gain and evolves into a shared agent - a "Third Brain" - where context, capabilities, and power get distributed across a team instead of accumulating benefit only with whoever had the time, access, and nerve to build one on their own. You'll leave with a working agent and a strategy for how to get its effects to start compounding for you. You'll leave with a portable file that moves with you to any tool. And you'll leave with a sharper set of questions to evaluate every AI tool you use: who was this built to see and benefit, and who does it leave out? Bring a laptop if you can. If you can't, there's a QR code and you can build it on your own.
Creators as Founders: Building a Data-Informed TV/Film Studio for Underrepresented Stories
Hollywood is undergoing a fundamental restructuring as studio consolidation, AI, and changing economics redefine the path for independent creators. This workshop explores what it takes to build a resilient, founder-led studio that retains intellectual property, unlocks new sources of capital, and creates lasting economic and social value. Using Endwell Studio as a real-time case study, participants will examine an evolving operating model inspired by approaches from A24, Blumhouse, international co-productions, and philanthropic funding. The session will explore how venture philanthropy, corporate partnerships, audience validation, workforce development, international financing, and retained IP can work together to build a scalable independent media company. The workshop concludes with a screening of Endwell’s award-winning television proof of concept, Committed, followed by an anonymous audience reflection survey and facilitated discussion. Participants will experience firsthand how audience validation can generate meaningful business intelligence for broadcasters, buyers, brand partners, and impact investors. Founders, technologists, investors, and creative entrepreneurs will leave with a practical framework for thinking differently about ownership, capital, partnerships, impact, and the future of independent media.
💻 Your AI Demo Works. Now What? The 7 Decisions Between Prototype and Production **
The hardest part of enterprise AI isn’t getting a model to work. It’s deciding what should be built, proving it works well enough to trust, and designing everything around the model that allows it to operate in the real world. That gap between AI demo and AI production is where many promising initiatives stall. In this 60-minute session, attendees will follow one enterprise GenAI application from idea → prototype → production → agent, making the technical and business decisions required at each stage. Rather than focusing on a specific model, vendor, or platform, I’ll introduce a practical framework teams can use to make better AI decisions regardless of the technology they choose. The framework is grounded in my experience at AWS leading AI modernization initiatives across customers, partners, technical teams, and go-to-market organizations. I helped define what “good” looks like across approximately 10 AI modernization patterns, including criteria for assessing customer problems, grounding data, architecture and model fit, quality, latency, cost, security, and business value before investing in a solution. I also worked on the next challenge: establishing the data foundations, governance, security, integration planning, evaluation, observability, cost controls, and operational readiness required to move AI into production. More recently, that work has extended into agentic AI, including agent orchestration, tool access, controls, and human oversight. The goal isn't to teach attendees which model to choose. It's to teach them how to make better AI decisions. 0–5 Minutes | The Demo Trap We’ll start with a familiar scenario: an AI prototype performs beautifully in a controlled demo, generating excitement and an immediate request to launch it. But what happens when 10 users become 10,000? When the model accesses sensitive enterprise data? When it confidently produces a wrong answer? When latency increases or inference costs scale? And what happens when we give that same system permission to take an action? These questions establish the central challenge we'll solve throughout the session: what actually separates an AI demo from an AI system we can trust in production? 5–15 Minutes | Decision #1: Should We Build This With AI? We'll introduce our running use case: an enterprise customer-support copilot designed to answer customer questions using internal company knowledge. Before selecting a model, we'll evaluate whether AI is even the right solution using: Customer Problem → Data & Grounding → Model/Architecture Fit → Quality → Latency → Cost → Risk → Business Value Attendees will see how these dimensions interact and why successful AI initiatives often begin with better questions, not model selection. 15–30 Minutes | Decisions #2–5: The Demo Works. Are We Ready for Production? Now we'll build the prototype and then expose everything the demo hides. We'll expand the architecture into a production system: Enterprise Data → Grounding/Retrieval → Model → Orchestration → Security & Guardrails → Evaluation → Observability → Cost Management → Human Oversight We'll examine four critical production decisions: •How the system accesses and grounds enterprise data •How we determine whether outputs are good enough •How security, permissions, and guardrails are enforced •How teams balance quality, latency, cost, and scale We'll also explore practical failure scenarios. What happens when retrieval returns the wrong information? When the model produces a plausible but incorrect answer? When a user requests information they shouldn't be able to access? When model quality improves but cost or latency becomes unacceptable? The goal is to move beyond the happy-path demo and show attendees how production AI requires designing for failure, not just success. 30–42 Minutes | Decisions #6–7: Now Make It an Agent Then our stakeholder asks the question many AI teams are now hearing: “Instead of telling the employee how to resolve the issue, why can't the AI just resolve it?” Our copilot begins accessing tools: looking up an order, updating a customer record, or potentially initiating a refund. The application has moved from generating information to taking action. We'll explore how that transition changes the architecture and risk model, including agent orchestration, tool access and permissions, evaluation, failure handling, controls, and human-in-the-loop design. We'll work through a critical question: Which actions should the agent be allowed to take autonomously, which require human approval, and which should it never be permitted to take? The evaluation problem changes too. We're no longer asking only, “Did the model produce a good answer?” We're now asking: “Did the system make the right decision, use the right tool, and take the right action?” 42–50 Minutes | Apply It: The AI Production Readiness Canvas Attendees will then bring everything together using a reusable AI Production Readiness Canvas they can take back to their teams. The canvas captures the seven decisions we've made throughout the session: 1.Problem: What are we solving, and why is AI appropriate? 2.Data: What information must the system access, ground, and govern? 3.Model & Architecture: What capabilities does the use case actually require? 4.Evaluation: How will we prove the system is good enough? 5.Tradeoffs: What are our acceptable quality, latency, cost, and risk thresholds? 6.Production Readiness: What security, governance, integration, observability, and operational capabilities need to surround the model? 7.Agency & Human Oversight: If the system can take actions, where do permissions, controls, escalation paths, and humans belong? Attendees will use the canvas to pressure-test either the customer-support example or an AI use case from their own work, giving them experience applying the framework rather than simply hearing about it. 50–60 Minutes | Q&A + Real-World Application We'll close with audience questions and, where possible, apply the framework live to AI use cases raised by attendees. By the end of the hour, attendees will have watched one AI application evolve from idea → prototype → production → agent, understood how the technical and business decisions change at every stage, and practiced applying those decisions themselves. Most importantly, they'll leave with a framework they can use in their next architecture review, product discussion, or AI planning session. The next time someone says: “We should use AI for this.” They'll know exactly what to ask next.
TechCrawl 4:00 pm–7:00 pm
Tech Crawl
Sessions are done. Now for the good part. Connect with fellow attendees and Summit partners in a relaxed, open format. No agenda, just good company.
Registration 8:30 am–9:30 am
Registration Opens
9:30 am Sessions⚡
💻 Privacy and Data Protection as a Cross-Functional Competency
In the AI era, safeguarding data and cybersecurity can no longer function as an isolated, defensive vault managed by a few specialists. This session explores how organizations must transform privacy and data protection into a shared, cross-functional competency.
10:00 am Sessions⚡
💻 Architecting Cybernova: Continuous Threat Ingestion and AI Attack Surface Parsing
The modern threat landscape is defined by speed and scale. Sophisticated threat actors leverage automation to discover and exploit enterprise exposures within hours of disclosure, leaving manual vulnerability triage obsolete. Security teams are drowning in disconnected telemetry without the context needed to act quickly.
🔒💻 Stop Waiting for the Job Description: Operating like a founder inside the enterprise
AI is collapsing the value of role-defined work. The professionals who thrive won't be the ones doing their jobs well, they'll be the ones who spot a gap and create their own mandate. This talk shares the intrapreneur's playbook for identifying white space, earning institutional cover without formal authority, and treating your career as a portfolio.
💻 The Agentic TPM: Upgrading Legacy Technical Management with Kiro and QuickSuite
As AI agents reshape the engineering landscape, Technical Program Managers (TPMs) face a persistent narrative: "Unlearn everything you know to survive the AI wave." This advice is wrong. Foundational technical management practices; risk mitigation, dependency mapping, critical path analysis, and cross-functional alignment are more critical than ever. The challenge isn't our core skill set; it is our legacy toolkit. To scale at the speed of modern codebases, TPMs must learn how to port their established experience into agentic workflows.In this session, we will explore a real-world case study of how a traditional TPM framework was reinvented by integrating the agentic power of Kiro with the enterprise infrastructure of QuickSuite. This powerful tech stack allowed a lean program management function to drastically scale delivery, accelerate real-time technical learning, and maintain a high-velocity engineering output. Attendees will see firsthand how treating agentic tools as an extension of project infrastructure rather than a replacement for human oversight unlocks unprecedented leverage.By shifting the mindset from unlearning skills to relearning infrastructure tools, TPMs can move from passive trackers to active technical accelerators. Trust your learned experience, trust your infrastructure, and discover how to let agentic tools handle the cognitive load of data aggregation so you can focus on strategic execution.
💻 The Pitch That Works: Landing Media That Builds Your Brand
Getting media coverage isn't luck -- it's a repeatable process most people were never taught. This session breaks down how to pitch yourself for interviews, what producers and podcast hosts are actually looking for, how to build a media presence that compounds over time, and how to turn one booking into ten. Whether you're starting from zero or trying to level up from occasional features to consistent visibility, you'll leave with a concrete outreach strategy and the materials to execute it.
💻 Building Azure Linux: The Engineering Behind Microsoft's Linux Distribution
Most developers use Linux every day, whether it's powering cloud applications, containers, Kubernetes clusters, AI workloads, or development environments. But few engineers ever get to see what it takes to build and maintain an entire Linux distribution. In this session, we'll take a behind-the-scenes look at Azure Linux, Microsoft's Linux distribution used across cloud and infrastructure workloads, and explore how a modern operating system is built, secured, and released at enterprise scale. We'll follow the journey from a developer submitting source code all the way to signed packages, container images, and production-ready releases. Along the way, we'll discuss how the Azure Linux team uses the open-source Koji build system to coordinate thousands of package builds, automate release workflows, enforce security and compliance requirements, and maintain software supply chain integrity. Attendees will gain an accessible introduction to Linux distribution engineering while learning practical lessons about large-scale build systems, DevOps automation, open source collaboration, and modern software supply chain security. Whether you're a student, software engineer, platform engineer, or open-source enthusiast, you'll leave with a new appreciation for the infrastructure that powers today's cloud computing ecosystem.
10:30 am Sessions⚡
💻 Your AI Prototype Is Ready. Your Partner Team Isn't - AI Framework for Cross-Team Product Alignment at Scale
AI can prototype a feature in an afternoon, but at large-organization scale that speed doesn't remove the real bottleneck, it just surfaces it faster. New features depend on core components another team already owns, and building on top of them takes real conversations: roadmap reviews, security sign-off, partner-team buy-in.
💻 Leading Responsible AI in Aerospace: From Design to Flight Operations
AI is transforming every stage of the aerospace lifecycle, from design and manufacturing to maintenance and flight operations. This session examines how leaders can implement AI responsibly in safety-critical environments through governance, human oversight, transparency, and cybersecurity.
💻 Trust Before Intelligence: Engineering the Data Foundation for Agentic Analytics
As organizations embrace agentic analytics, the challenge is no longer enabling AI to generate insights, it's ensuring those insights are grounded in trustworthy, well-governed data. The real differentiator is the data platform that powers the AI agents.
💻 Where Did All the Returnships Go — And How Do We Bring Them Back?
Returnships once offered a powerful pathway for experienced professionals—especially women—to re-enter engineering jobs. Tech companies launched and rapidly expanded these programs beginning in the mid-2010s and by 2022 hundreds of programs had launched and thousands of women were hired. Then came the tech layoffs. Then the DEI backlash. And one by one, many programs stalled, shifted focus, or quietly disappeared. Let's bring them back.
💻 Designing with a Second Brain and a Second Designer
Traditional UX workflows were built for a world where thinking, synthesis, and execution happened separately. AI collapsed those boundaries. This case study shows how one designer built an AI-assisted workflow with Obsidian, Claude, and generative design tools that moves from raw thoughts to structured UX decisions, and what it takes to turn a personal system into something a whole product team can use.
💻 How SRE Accidentally Prepared Us for AI
As organizations rush to adopt AI, many discover the biggest obstacles aren't model quality or prompt engineering, it's undocumented systems and tribal knowledge. Site Reliability Engineering practices like Infrastructure as Code, observability, and runbooks were designed to help humans understand complex systems. It turns out they help AI too.
Slow Thinking, Fast Acting: Leadership in the AI Age**
The instinct in the AI age is to move fast, but the thinking that makes leadership valuable, judgment under uncertainty, the willingness to sit with a hard problem, is exactly what speed erodes. This session offers practical frameworks for leaders who want to use AI without hollowing out their teams' capacity to think.
11:00 am Sessions⚡
11:00 - 11:15 AM | BREAK
Please note: Every session needs transition time, and some sessions may start a few minutes late. This break is intentionally built into Tuesday's agenda to bring all stages back on schedule before the next programming block.
11:15 am Sessions⚡
Beyond the Tools: How People and Organizations Adapt to the Agentic Era
The human side of enterprise transformation Giving people access to AI is easy. Changing how they actually work is harder. Drawing on Yahoo’s evolution from experimentation to more structured AI programs, Chief Information Security Officer Sean Zadig will explore what it takes to build real AI fluency and help new ways of working stick. From The Paranoids’ AI Skill-a-thon, which expanded into a cross-functional program across Yahoo, to the new questions that emerge as people increasingly work alongside agents, the conversation will examine the human side of the agentic transformation: how skills and roles evolve, how organizations create room to experiment, and how security and governance must adapt without slowing innovation. Zadig will also share practical lessons for leaders and employees navigating this shift safely and effectively.
💻 Back to Building: Rediscovering Hands-On Engineering in the Age of AI
After six years in engineering leadership, this speaker returned to hands-on engineering as a Staff Software Engineer just as AI was reshaping how software gets built. This workshop explores returning to hands-on development with AI while discovering why experience, engineering judgment, and technical intuition matter more than ever.
💻 Own the Shift: Navigating Your Career in the Age of AI
AI is reshaping roles, compressing timelines, and raising the bar for what it means to be a technologist. This session distills 3 mindset shifts that separate technologists thriving in this shift from those just keeping up - backed by real signals from how companies are hiring and evaluating today. Walk away with a practical AI career toolkit you can start using immediately.
💻 How to Think AI First
AI is no longer just a futuristic concept; it is a fundamental shift in how we approach productivity and problem-solving. But having access to AI and knowing how to think and work with AI are two different things.
💻 Building Production-Ready LLM Systems with Small & Open Models
Most teams can prototype an AI demo. Far fewer know how to deploy and operate LLM systems reliably in production. This hands-on workshop focuses on the engineering realities of building scalable AI applications using smaller and open models. Attendees will explore the tradeoffs between model quality, latency, infrastructure cost, and operational complexity while working through real production-inspired scenarios. We'll cover: - Why Production AI Is Different — Where demos break, why model choice is hard, and the case for small & open models - Fine-Tuning & Results — QLoRA fine-tuning, and SLM vs. GPT-4o-mini head-to-head - Measuring What Matters — The production metrics that actually predict user experience - Building the Pipeline — vLLM, quantization, batching, chunked prefill, speculative decoding - Observability and Reliability — Considerations for keeping AI systems reliable in production - Deploy & Key Takeaways — Hosting on a budget, what to do Monday morning, and resources
💻 Agentic Software Development: The New Engine Behind Faster Hardware Launches
The best use of AI in product development is not replacing engineering judgment. It is removing the repetitive work surrounding engineering judgment. When AI agents can analyze a problem, modify software, generate tests, prepare documentation, and support deployment, the question is no longer simply whether developers can code faster. The larger question is: How should we organize and manage differently when intelligent agents become part of the engineering workforce?
Why the Future of AI Needs Creative Thinkers
In the future of AI, change, and technology, taste has become more crucial than ever in how we operate. Nowadays, things are starting to blend and look the same. From how we think through marketing campaigns from concept to execution, AI has started to reinforce stereotypes around what looks good, what looks bad, what is culturally appropriate, and what is not. Judgment is something that has shifted, and I'd like to walk through my journey of understanding that, and what taste and experience can look like when you're a creative telling stories for brands. It's about using tools like Weavy, Gemini, MidJourney, and Runway to generate toward an idea that has tension, feels relevant, and feels accessible. But it's also about challenging stereotypes and shifting perceptions through creativity. And prompting the right way AI is democratizing our ability to make, but it still needs human judgment to understand what is worth making. As technology gives us more shortcuts, it's important to remember that creativity is still at the heart of it all, and that technology ultimately has to serve and align with human dignity
Beyond the Box: Redefining Your Value When the System Wasn't Built for You
OVERVIEW Beyond the Box argues that the career path most professionals were promised—work hard, wait your turn, climb the ladder—was never built to pay off for everyone in the room, and current data shows it’s getting worse, not better. Drawing on her own experience inside top Fortune companies, Morgan B. McCombs walks through what's actually happening right now to corporate diversity commitments, promotion pipelines, and venture funding, then takes an unexpected turn: AI, usually framed as a threat, is reframed as the first real leverage point individuals have had to opt out of a system that wasn't designed for them. The talk closes with a live, room-wide exercise and a practical method attendees can use immediately to name and price their own value. WHAT TO EXPECT – A clear, current picture of how corporate commitments to diversity and advancement are trending – Why the traditional promotion ladder is structurally slower and narrower than it looks, and why waiting on it is a losing strategy. – AI reframed as leverage for independent work, backed by current data on freelance and independent income growth. – A practical, one-sentence framework for turning your own experience into a value proposition. – A live, room-wide exercise: naming a real need and a real offer to someone sitting near you, no strings attached. – The two-line thesis the talk is built around: own your value, invest in someone else’s.
11:45 am Sessions⚡
🔒🤝 Why "Safety" Is Everyone's Job
Every system a technologist ships eventually touches someone's physical world — a badge reader, a location ping, an alert routed to the wrong team, a dashboard nobody checks at 2am. The line between "digital" and "physical" risk doesn't exist anymore; it's one pipeline with two failure modes. At Intuit, our Global Safety & Security team sits downstream of decisions made by engineers who never think of themselves as safety practitioners — API rate limits, data retention windows, alert-routing logic. Each of those choices either protects someone or leaves a gap. In this session, Buddy O'Connell, Director of Global Safety & Security at Intuit, makes the case that safety isn't a department, it's an architecture decision made by anyone who touches identity, location, or access data. Drawing on two decades in law enforcement and corporate security — and what's failed and worked at Uber, Match Group, Lucid Motors, and Intuit — she'll name the specific design choices that turn a system into someone's safety net, and the ones that quietly turn it into a liability. Attendees leave with a concrete checklist for auditing their own systems against duty-of-care risk, a reference architecture for predictive risk systems, and the underlying case for treating physical security as a data engineering discipline.
The Verification Tax — A field guide to designing and measuring human + AI teams
Your team shipped twice as much this quarter. Why does everyone feel more tired rather than less? In a 2025 randomized trial, experienced developers were 19% slower on real tasks in their own repositories when using AI, and afterward still believed they had been 20% faster. Roughly 9% of their working time went to reviewing and cleaning AI output. That is the bill nobody is tracking: when agents do the producing, humans inherit the verifying. I call it the Verification Tax, and most teams pay it in overtime and silent rework rather than measuring it. This talk offers a framework for designing human and AI teams deliberately. We will walk through the LOOP: Locate the work, Operate the rhythm, Observe the tax, and Promote what has earned wider scope. We will also examine why the research keeps pointing at what your team knows, rather than what your model knows, as the largest line item on the bill. You will leave with metrics you can begin capturing at Monday's retrospective without asking anyone's permission.
💻 Predictable Beats Easy: How AI Is Rewiring SRE
AI is often pitched as a way to make SRE easier. In production, easier is not enough—AI behavior must be predictable. Using Brightspot’s AI adoption and a guided production-incident replay, this session shows how AI can collect evidence, form hypotheses, and recommend actions while operating within clearly defined limits. Attendees will learn how incident contracts, bounded permissions, human approvals, evidence preservation, and recovery verification can make AI-assisted incident response trustworthy.
💻 Code, Culture, and Change: The Human Side of AI Adoption
The biggest hurdle in the AI revolution isn't how we code, it's the human culture impacted by AI in the SDLC. While the industry moves at breakneck speed, many development teams meet it with skepticism, fear of obsolescence, and tooling fatigue. Bridging that gap requires a shift in leadership mindset, not just new tools.
💻 The Automation Audit: A Framework to Find Your Team's Highest-ROI AI Use Cases
The vast majority of generative AI pilots fail to produce measurable business impact, and I believe that's because most teams pick a tool before they've clearly defined the problems worth solving. This shift, from tool-first to value-first AI adoption, is the single biggest change separating organizations that get real ROI from those stuck in pilot purgatory. In this hands-on workshop, non-technical leaders will use a five-lens framework (People, Process, Data, Technology, Culture) to identify and score their team's highest-ROI AI use cases live in the room. They'll walk away with a completed worksheet, scored use cases, and at least one concrete action to take back to their team that will really work.
💻 The EDITT Framework: From AI aware to AI Enabled - Readying the organization for AI adoption
AI adoption is often treated as a technical challenge, but the biggest barriers are usually human, operational, and strategic. Many professionals are being asked to lead AI conversations without being engineers, data scientists, or deeply technical AI experts. This workshop introduces EDITT (Empathize, Diagnose, Ideate, Test, Translate), a practical framework for leading AI adoption through better questions, clearer use-case identification, stakeholder alignment, responsible experimentation, and business-value storytelling. Participants will work through each stage of EDITT: empathizing with the stakeholders affected by a change, diagnosing where AI can actually improve a workflow, ideating and prioritizing use cases, testing ideas through responsible, low-risk experimentation, and translating results into language leadership acts on. The session includes practical examples from enterprise automation and AI adoption, along with a use-case prioritization tool drawn directly from the framework that attendees can apply immediately in their own teams or organizations. Attendees will leave with a practical EDITT-based conversation guide, a use-case prioritization template, and a clearer understanding of how to lead AI adoption through influence, judgment, and business alignment, even when they are not the most technical person in the room. By the end of this session, attendees will be able to: 1. Empathize and Diagnose: identify the human and operational barriers to AI adoption, and pinpoint where AI can realistically improve a workflow. 2. Ideate and Prioritize: use EDITT's prioritization structure to rank AI opportunities by business impact, feasibility, and adoption readiness. 3. Test: design low-risk pilots that validate a use case before scaling. 4. Translate: present the value, risks, and adoption needs of an AI initiative to stakeholders and leadership in terms that drive decisions.
🔒💻 Making Your Codebase AI-Ready
AI-powered developer tools are rapidly changing how we write, review, and debug code, but most codebases aren't designed to work effectively with them. This hands-on workshop covers how to restructure and evolve codebases to better collaborate with AI code assistants and autonomous agents.
12:30 pm Sessions⚡
💻 Beyond the Prompt: Architecting Trustworthy AI Agents with Knowledge Graphs, Semantic Observability, and Post-Deployment Governance
Transitioning AI from a promising prototype to a reliable production-grade agent demands a rigorous architectural shift toward grounded knowledge and continuous observability. This session covers integrating Knowledge Graphs with RAG to eliminate hallucinations by providing a deterministic, semantic foundation for LLMs.
💻 Your AI Strategy Has a Foundation Problem. Let's Fix It Live.
Most AI initiatives don't fail because of the technology, they fail because no one was honest about the business before they bought it. This workshop, built around the Adaptive Intelligent Logic framework, walks through a live organizational readiness assessment to identify where AI will multiply impact versus amplify what's already broken. This workshop, built around the RoadmapIQ™ diagnostic, walks through a live organizational readiness assessment to identify where AI will multiply impact versus amplify what's already broken.
💻 The Missing Layer: Why Your Data Agents Hallucinate (and How a Semantic Layer Fixes It)
Most data agents fail for the same reason: we point LLMs at raw warehouses and hope they figure it out. They can’t. The agent picks the wrong table, joins on the wrong key, and confidently returns MRR three different ways in one conversation. By the time it does, the user has already given up waiting. It’s not a model problem. It’s a missing-layer problem, and it shows up in both the answer and the response time. This workshop is a walkthrough of the semantic layer we built at Velora using dbt, BigQuery, and a curated metadata catalog in Notion to make our data agents usable in production. We operate across three brands with three different definitions of the same metrics, so we’ll also look honestly at the governance challenges that creates and how we’re working through them. How the session flows: •The failure autopsy (10 min): Three real failures from our agents, including the wrong table, conflicting metrics, and a latency death spiral. We’ll diagnose together what layer was missing in each. •The live demo (15 min): See the same agent running against a raw warehouse versus our semantic layer, with accuracy, tool-call count, and response time compared side by side. •The framework (10 min): The checklist we use internally when scoping a new agent, including what belongs in the layer, who owns definitions, how conflicts get resolved, and what to evaluate. •Group discussion + Q&A (10 min): Whose definition wins in your organization, and how do you make it stick? What you’ll walk away with: •A production checklist for designing a semantic layer for your own warehouse •A governance model for reconciling conflicting metric definitions across teams •An evaluation approach that catches drift, hallucinations, and performance regressions •Concrete latency lessons from our stack, including where the semantic layer improves speed and where it doesn’t •The honest tradeoffs: where we over-invested, where we cut corners, and what we’d rebuild
💻 You Don't Have to Out-Code AI. Here's What to Do Instead.
Generative AI is reshaping what technical expertise looks like -- and creating a lane that didn't exist five years ago. Subject matter experts who can direct, evaluate, and collaborate with AI tools are becoming some of the most valuable people in any technical org, and women who code are exceptionally well-positioned to occupy that role. This session maps out what that career path looks like in practice: the skills that transfer, the new ones worth building, the kinds of roles emerging at the intersection of domain expertise and AI fluency, and how to position yourself for them now.
1:00 pm Sessions⚡
💻 Driving Participation through Code Reviews*
Code reviews can be so much more than an opportunity to prevent bugs. This talk looks at the different forms participation can take and how to engage the entire team in the process, whether you're a manager looking for a fresh approach or an individual contributor unsure of how to get more involved.
💻 Stop Proving, Start Positioning: Using AI to Reclaim Your Time and Build Strategic Visibility**
Capable, credentialed, and still overlooked? Many mid-career women in tech try to earn recognition by doing more — and stay stuck. This hands-on session flips that script: stop proving, start positioning. You'll learn to name and pay down your “Visibility Debt” (the gap between the value you create and the value that actually gets credited), and to use AI not as a shortcut but as a personal learning lab that gives back your time. I'll teach my six-step SUNRISE prompting method (an extension of Google's TCREI framework), show how to turn AI output into real proof of your skills, and share a practical 30/60/90-day plan to reposition your career. Attendees leave with ready-to-use prompts and a clear next step — responsible, judgment-first, and immediately actionable.
💻 Now Is the Best Time to be a Software Engineer*
AI is fundamentally reshaping software engineering, and unlocking one of the most exciting eras to be an engineer. This talk shares practical lessons from building products for small businesses and mentoring aspiring technologists, with actionable ways to think more strategically about product opportunities and grow into engineers who influence products end-to-end.
💻 The Skills AI Can't Automate: Why Emotional Intelligence Is Becoming a Technical Leader's Competitive Advantage**
As AI becomes capable of writing code and analyzing data, the better question isn't how to compete with AI, but what becomes more valuable because it exists. This interactive workshop explores the leadership capabilities becoming more important in the age of AI: self-awareness, judgment, influence, and decision-making.
🔒💻 Prototyping at the Edge of AI: Building Without a Blueprint
There's no blueprint for building at the edge of AI — only ambiguous requirements, shifting tools, and pressure to ship something that doesn't exist yet. As a Senior Software Engineer in AI prototyping, I've learned that clarity doesn't come before the work. It comes from doing it. I regularly take ideas from vague concepts to executive-level demos without stable requirements or established patterns, and this session breaks down exactly how. Attendees will leave with a repeatable framework for: •Rapidly prototyping with emerging AI tools •Breaking ambiguous ideas into small, testable components •Designing systems that can absorb constant change •Making smart trade-offs between speed and scalability •Pitching ideas that don't have proof yet The session closes with a hands-on workshop where you'll apply these concepts directly: defining a prototype strategy, identifying trade-offs, and building a demo narrative you can actually use.
💻 Rebuilding Our Product Process Around AI: What We Changed and What We're Learning
Waterfall, Scrum, Kanban, and most recently Shape Up. Over a decade my team has run all of them, and every one worked for a while and then slowly eroded, the way they all do. Then AI flipped the software world on its head, and building a feature has become dramatically faster and cheaper. So we took the lessons of what had worked over the years and what hadn’t, and rebuilt our process around AI. In a world where work moves faster than ever, process matters more — not less. This talk walks through the problems we hit along the way — the status meeting nobody wanted, the QA checklist nobody opened, the work that never got linked back to the plan — and how we solved each one as we leaned further into AI. An agent triages support tickets before an engineer ever sees them. The QA checklist compiles itself into our pipeline. Production errors are down from around a thousand to twenty. Then a live demo of the board that keeps our tight ship sailing on its own: every team’s work in flight, on one timeline, refreshed from GitHub each morning and maintained by nobody. The team stopped writing status updates, and the rest of the company stopped asking when things were shipping. Whatever size your team is, you’ll walk away with everything I share — the process, the documentation, and the prompts to build your own version. Whether you run the process or live under it, it’s yours to take and give your product team time back in their day.
2:00 pm Sessions⚡
💻 Atlus Insights: The Scorecard for AI-Native Engineering, Now on Databricks
Most companies claim their engineers are "AI-native." Almost none can prove it. Atlus Insights, built by Lumenalta, gives that claim an evidence-grounded scorecard, platform practice, execution quality, governed AI adoption, and consistency, backed by a coaching engine that tells engineers what to fix and why. In this session, Dilorom Abdullah, Databricks Practice Director at Lumenalta, Databricks MVP and Databricks Partner Champion, shows why this works materially better running natively on Databricks: Unity Gateway turns a workspace's own metadata into real-time cost, usage, and productivity visibility. Engineering leaders get a read on where AI adoption is real versus performative. The CFO's office gets a defensible number for what AI coding spend is actually returning. She'll close with the next test for the same coaching model: extending it to ramp talent from other stacks, a JavaScript engineer picking up Databricks in days rather than months, onto the platform using evidence rather than tutorials.
💻 Score It Yourself: How Reliable Is the AI That Grades Your AI?
Many leaders approve AI features they have never inspected themselves. The demo goes well, a number gets reported, and the decision rests on someone else's judgment about what "good enough" means. Once you are working at volume, nobody reads the output by hand. Another AI reads it and returns a number, and that number is what reaches the person who signs off. This workshop takes apart the numbers behind one AI product, a reading app used in grades 1 through 8, and shows where each of them can go wrong. You start as the test taker. The handout has comprehension questions that an AI wrote, printed without the stories they belong to, and you answer them anyway. Then you see how two AI graders did on the same questions with the stories withheld from them as well. Each was run three times. One answered 89.5% correctly without the story and the other 96.8%, where random guessing scores 25%, and where a professionally written reading test loses about 30 points when the passage is taken away. The questions were barely holding on to the stories they were written for, and the two graders differ from each other by more than the smaller of them found. Mason Borchard writes the software behind that product and is responsible for its teaching quality, so she is evaluating a system she maintains. The session opens up the machinery she uses to do it: the same test items each run, an unchanged version sitting next to each changed one, a grading rubric written before anything is scored, and repeat runs, because one run settles nothing. Then what the same machinery looks like when it works: a prompt change that cut sentences running past their intended reading level from 28% to 12% across 800 sentences. Then the ways a good-looking number goes wrong, each one a real result, including a run that scored 32 of 32 cells at exactly 100%, the same 12 images scored 4.92 by one AI grader and 3.92 by another, and a 20-point gain that a $2.50 experiment traced back to something the product already did, which canceled the build. In the second half you work from those score tables, compare notes with the person next to you, and decide which results you would act on and which need one more question first. The session closes on how this evaluation was itself checked, and on the two findings that check forced her to withdraw. Nobody outside her team has reviewed these questions, no student has answered them, and every grader named is an AI. The session says so. No technical background is required. You leave with a one-page guide: three questions to ask about any AI quality number, the take-the-source-away test written so you can run it on a vendor's output, and the five warning signs. By the end of the session, attendees will have practiced how to: 1. Ask for the take-the-source-away test on work that claims to be based on a document: withhold the document, and see whether the output still stands up. 2. Ask how an AI quality number was produced -- counted directly, judged by a person, or judged by another AI model -- and whether anyone checked the judge. 3. Recognize five signs that a number may not mean what it seems to. 4. Ask a team or vendor what the product did without a proposed change before crediting the change with an improvement. 5. Ask what an AI review process has retracted, as the first test of whether it can change a decision.
💻 Behavioral Science: A Cheat Code for Predictability in the Age of AI
There's a gap between what people say they want and what they actually do, and it's especially acute with AI. In this hands-on workshop, behavioral scientist Katie Dove introduces the 3B Framework (Behavior, Barriers, Benefits) for vetting product roadmaps and AI investments, drawn from experiments with companies like Google, Microsoft, and Airtable.
💻 Not a coder? Not a problem: Build simple tools that solve real problems.
There's a career path that didn't exist three years ago: the domain expert who builds. This speaker was a subject matter expert at a defense-tech company for seven years, watching internal pain points go unsolved because engineering time was reserved for revenue-generating products, until AI tools changed what was possible without becoming an engineer.
💻 From Prompt to Production: How Non-Technical Teams Can Ship AI Work at Engineering Scale ***
Format: 90-minute hands-on workshop Most AI workshops teach prompting. That's table stakes now. The real gap for marketers, PMs, and ops folks isn't "how do I write a better prompt" — it's "how do I turn an AI conversation into a system that runs reliably, integrates with the tools my team already uses, and scales past me?" I've spent the last year at a FAANG company answering exactly that for our sales org — taking people who'd never touched a terminal and getting them shipping AI-powered workflows that plug into our production data, CRM, and internal APIs. This workshop is that playbook. What We'll Cover Module 1 — Designing the Project (20 min) Most AI projects fail because they were scoped like a feature, not like an experiment. I'll walk through the framework I use: •The "boring middle" test: how to pick problems where AI actually wins (repetitive, judgment-light, high-volume) vs. where it fails (novel, high-stakes, one-off) •Writing a one-page spec: inputs, outputs, success criteria, failure modes •Breaking a fuzzy ask ("help me with competitor research") into an agent-shaped task graph •Choosing your abstraction level: one-shot prompt vs. workflow vs. autonomous agent Module 2 — From Prompt to Running System (25 min) The leap most non-technical folks never make. Live demo + hands-on: •Using Claude Code as your "engineer in a box": how to describe a system in plain English and get working code •Wiring it to real data: CSVs, Google Sheets, Slack, your CRM, internal APIs •Version control basics (git): enough to not lose your work and collaborate with real engineers •Local dev vs. deployed: when a script on your laptop is fine, and when it isn't Module 3 — Making It Run at Scale (25 min) The module nobody teaches non-technical audiences, and the difference between a cool demo and something your team actually uses: •Infra vs. app code demystified: what's the difference, and why should you care? •The 4 things that break when you go from "runs for me" to "runs for the team": secrets, scheduling, storage, and observability •Deploying to a managed platform (Vercel, Replit, AWS Lambda): live push included •Cost and rate limits: how to not wake up to a $4k bill •Guardrails: logging, evals, and human-in-the-loop checkpoints so AI mistakes get caught early Module 4 — Working With Engineers (10 min) •What to build yourself vs. when to hand off •How to write a handoff spec engineers will actually respect •Speaking enough of the language to be dangerous (auth, APIs, environments, PRs) Module 5 — Q&A + Open Build (10 min) Bring a real problem from your job. We'll scope it together. What Attendees Walk Away With •A working AI-powered tool built during the session, deployed and shareable •The one-page project spec template •A decision tree for "prompt vs. workflow vs. agent vs. hire an engineer" •A vocabulary cheat sheet for talking infra with technical teams Prerequisites: Laptop, curiosity. No coding background required. I'll help you install everything. Why Me Five years in Big Tech, with the last year spent shipping AI-powered developer tools at a FAANG company. Most recently, I led the rollout of Claude Code to our sales team — non-technical folks who are now delivering projects at a cadence that used to require dedicated engineering headcount. I've made every mistake in this pipeline so you don't have to. Why This Belongs at Women Who Code The next decade of technical leverage isn't going to be gated by whether you can write a for-loop. It'll be gated by whether you can design a system, integrate it with real infrastructure, and ship it. This workshop gives women in adjacent roles the same superpower engineers have had — the ability to turn an idea into something running in production — without a CS degree.
2:30 pm Sessions⚡
💻 AI Agent Semantic Tool Selection: Cut Errors 75% and Token Costs 89%
AI agents with many tools face a dual problem: they pick the wrong tool (hallucination) and waste tokens because tool descriptions get serialized into the context on every call. Semantic tool selection filters tools before they reach the LLM context using vector search, reducing errors by 75% and token costs by 89%.
💻 How We Solve Critical Supply Chain Problems with Data and AI
A look at how McCormick & Company is using data science and AI to tackle some of its biggest supply chain challenges — from demand forecasting to procurement decision-making to inventory, network, and plant-floor operations. Drawing on systems built and led in production, the talk explores how data-driven approaches turn slow, manual, and error-prone processes into faster, more reliable purchasing and planning decisions — and what it takes to lead technical teams that build AI solutions businesses actually trust and adopt.
💻 Beyond The Code: Skill That Shapes Who You Become
Coding didn’t give me a career path. It gave me a new way to see the world and myself. Through coding, I learned how to think: how to balance logic with curiosity, navigate complexity without getting lost, and move between the smallest details and the bigger picture. In this talk, I’ll share how those ways of thinking shaped my approach to solving problems, making decisions, and navigating complexity. While my career has evolved beyond coding, those foundational skills have stayed with me, guiding the path I’ve built across science, business, and leadership. This session invites students to see coding not simply as a technical skill, but as a powerful lens for navigating life, expanding what feels possible, and intentionally shaping who they become.
💻 Building Deterministic Record & Replay Testing for Gemini AI Agents in Python**
Prerequisites: Please follow this document (https://docs.google.com/document/d/1GgR9-TNsxLyPcuQj9vUzY3bVoedON4-OFM2rEXab2XY/edit?tab=t.0#heading=h.9urdxf3tsmds) to complete the pre-requisistes before joining the workshop. When autonomous AI agents interact with external APIs, microservices, and databases in CI/CD pipelines, automated testing quickly turns non-deterministic. Upstream API rate limits, schema drift, and volatile outputs cause test suites to fail intermittently—destroying developer trust in agent evaluation. In this 60-minute technical workshop, attendees will build an enterprise-grade Deterministic Record & Replay Evaluation Sandbox using the Google Gen AI SDK and Gemini model in Google Colab. You will implement a Python dispatch interceptor that executes live tool calls during a Record Phase, sanitizes non-deterministic arguments (timestamps and ephemeral tokens), and saves golden response. In the Replay Phase, the sandbox operates in a network-isolated environment, replaying historical fixtures to guarantee 100% reproducible agent trajectories and sub-second CI validation.
Leading in Your Own Voice: Scaling Communication With AI Without Losing Trust
AI can help leaders communicate at unprecedented speed, but when every message sounds polished, generic, and strangely interchangeable, that speed can come at the cost of trust. Your leadership voice is more than tone—it communicates your values, judgment, expectations, and how you respond when the stakes are high. In this hands-on workshop, you'll create a leadership voice guide that teaches AI how to help you communicate without pretending to be you. You'll identify the patterns that make your communication recognizable, establish boundaries around what AI can draft versus what only you should decide, and test your guide against real leadership scenarios. You'll leave with a working first draft and a repeatable process for creating Slack messages, strategy documents, presentations, and team updates that remain unmistakably yours.
