AI Builders Global Conference 2026

Speakers

• Sara Golon - Senior Customer Solutions Manager at AWS

  Sara Golon is a Senior Customer Solutions Manager at Amazon Web Services (AWS), where she partners with enterprise organizations to accelerate cloud adoption, AI innovation, and modernization initiatives. Before joining AWS, she spent more than two decades leading technology programs and delivering large-scale IT solutions across complex enterprise environments, giving her a deep understanding of both business and technical challenges. Sara is passionate about helping customers transform bold ideas into practical, scalable solutions and lasting business outcomes.

• Jasmin Charlotte Diaz - Operations Manager at WomenTech Network

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• Saish Sali - Senior Software Engineer / Tech Lead at Netflix

  As Netflix's machine learning footprint grew across dozens of teams and business verticals, models, features, datasets, and pipelines became increasingly siloed — effectively black boxes that even ML practitioners inside the company struggled to discover, understand, or reuse. Answering a simple question like "which experiments are running this model?" or "which models share these features?" required manual archaeology across disconnected systems.This talk covers how Netflix addressed this problem by building the Model Lifecycle Graph, a metadata-driven architecture that models ML entities — models, features, pipelines, experiments, and datasets — as interconnected nodes rather than isolated pipeline stages. Powered by a real-time Metadata Service (MDS), the graph enables lineage tracing, impact analysis, and cross-domain discovery at scale, turning ML metadata into a first-class infrastructure concern rather than an afterthought. I'll walk through the architectural decisions behind the graph model, the real-time ingestion design that keeps metadata fresh as ML assets change, and the tradeoffs of choosing a graph-oriented approach over traditional pipeline-centric tooling. Related blog post - https://netflixtechblog.com/democratizing-machine-learning-at-netflix-building-the-model-lifecycle-graph-5cc6d5828bb1

• Karina Mikucka - Sr Director, Digital Business Solutions at Foundation Medicine

  Enterprise AI presents a paradox. Eighty-eight percent of organizations now use AI in at least one business function, and controlled studies demonstrate meaningful productivity gains on individual tasks. Yet only 39 percent of organizations report any enterprise-level EBIT impact, and most remain unable to scale AI beyond isolated use cases. Most explanations blame the technology, the data, or the talent. This talk argues that the gap is structural: AI compresses the production side of decision loops while often leaving the learning side unchanged. Organizations generate analyses, recommendations, and outputs faster, but the feedback that reveals whether those decisions were correct may remain slow, fragmented, or invisible. Meanwhile, standard metrics such as adoption and time saved measure only half the loop, while rework, downstream consequences, and the potential erosion of human judgment emerge later, often in someone else’s budget. Grounded in practitioner experience, including leading a Microsoft Copilot deployment that reached 83 percent weekly active usage among enabled users, this session gives leaders three practical tools: a metrics audit that separates activity, output, and outcomes; a method for classifying decisions by feedback speed, causal clarity, reversibility, and consequence; and a graduated autonomy ladder that increases AI authority only as evidence and control maturity accumulate. When an organization acts faster than it can learn, acceleration becomes instability.

• Hemang Upadhyay - Strategic Product Leader at LG Electronics North America

  Most organizations approach agentic AI as a model-selection or prompt-engineering challenge. In commerce, the more persistent problem is whether the agent has decision-grade information it can safely act on. Drawing on enterprise work across product information management, digital experience platforms, intelligent search, and AI-enabled B2B/B2C commerce, this session presents a practical framework for turning fragmented product data into a trustworthy foundation for AI agents. I will show why an agent can sound intelligent while still making poor recommendations, and why the failure usually begins upstream: missing attributes, conflicting sources of truth, unclear policy rules, stale availability, or no mechanism to express confidence and escalate uncertainty. Attendees will learn a five-part “trust layer” for agentic commerce: establish authoritative data contracts; define decision-critical attributes; constrain actions with policy and permissions; evaluate recommendations against realistic buyer scenarios; and observe failures so the system improves rather than silently repeats mistakes. The session will also cover the difficult realities teams encounter in production—legacy data inconsistencies, competing business ownership, incomplete taxonomy, and the pressure to launch before data is perfect. Rather than waiting for a flawless foundation, attendees will leave with a practical method to prioritize the data and controls that matter most for the customer decision at hand.

• Rashi Agrawal - Head of AI at Hinge Health

  Every leader I know is making high-stakes AI decisions on incomplete information. Which tools to standardize on. Where to invest. Who to hire. What to sunset. And the models, the market, and the tools are all changing faster than any planning cycle you have. That's the fog. It doesn't go away by waiting. This talk is about how to lead when the ground keeps moving. Not with more research or bigger frameworks, but with a set of decision principles you can actually use: what to commit to, what to defer, how to make reversible bets, and how to protect your team's judgment when everyone above you wants certainty you don't have. You'll walk away with a leader's toolkit for the AI fog: a framework for classifying decisions, a rhythm for revisiting them as the landscape shifts, and permission to lead honestly through uncertainty instead of pretending it away.

• Andrea Coifman - Director of Software Engineering, AI Platform at NASDAQ

  The enterprise demand for Generative AI is rapidly evolving from conversational interfaces to action-oriented systems. Users don’t just want to chat with AI; they want it to perform tasks. This session details Nasdaq’s strategic journey from a static Retrieval-Augmented Generation (RAG) platform to a proactive, agentic ecosystem capable of executing complex workflows. We will explore how adopting the Model Context Protocol (MCP) catalyzed this transformation by providing a standardized framework for integrating enterprise tools. Attendees will learn how this approach enabled us to move beyond theoretical concepts to deliver tangible value, turning our GenAI platform into a dynamic “do-bot”. The presentation will showcase how we enhanced the user experience by creating seamless, in-conversation interactions that replaced cumbersome traditional interfaces. This talk provides a proven model for building an agentic foundation that positions an organization at the forefront of the GenAI landscape, supported by key results in platform adoption and productivity.

• Sowmya Manjanatha - Director of software engineering at Oracle Health

  Energy-Aware AI: Rethinking AI Platforms for a Sustainable Future The rapid adoption of AI is driving unprecedented energy consumption. Data centers are consuming record levels of electricity, with global demand projected to reach 133 gigawatts by 2026, driven largely by AI workloads. At the same time, major technology companies continue to invest hundreds of billions of dollars in expanding AI infrastructure and data center capacity. While AI has dramatically lowered the cost of creating digital assets, it has also led to significant overproduction and duplication of content. The ease of generating text, images, code, and other artifacts encourages repeated computation with little visibility into its energy cost. Today, AI users understand token consumption, but have virtually no awareness of the energy required to produce those tokens. I believe this needs to change. Energy awareness should become a first-class concern of AI platforms, where every AI operation has a measurable energy footprint. Just as cloud platforms made compute, storage, and network usage visible, future AI platforms should expose energy consumption as a fundamental metric alongside latency, throughput, and token usage. Achieving this vision requires more than operational monitoring—it demands a fundamental architectural rethink. Most AI platform components today, including retrieval-augmented generation (RAG) pipelines, vector databases, transactional databases, and NoSQL stores, were built on architectures designed for traditional applications. AI transformations are stored, retrieved, and processed much like conventional data, without optimizing for the unique compute, network, and storage characteristics of AI workloads. The next generation of AI platforms must embed energy optimization into the lowest levels of processing. This may require rearchitecting core platform components—including databases, storage engines, retrieval systems, and inference pipelines—to minimize compute, data movement, and storage overhead while maintaining accuracy and performance. This talk explores two key themes: Energy-aware AI — moving beyond token-based metrics to quantify AI processing in terms of energy consumption and making energy visibility a core platform capability. Rearchitecting AI platforms for sustainability — examining how AI infrastructure, data storage, retrieval engines, and processing pipelines can be redesigned with energy efficiency as a primary architectural principle rather than an afterthought. As AI becomes foundational to nearly every industry, improving model efficiency alone will not be sufficient. Sustainable AI requires rethinking the architecture of the platforms that power it, making energy optimization a fundamental design objective from the ground up.

• Christie Green - VP of Customer Success at Nebius

  I've bought enterprise technology from inside Bank of America, Wells Fargo, and Lowe's. I've consulted on it from inside Slalom, watching a wide range of companies get adoption right and get it wrong. And now I implement it — leading Customer Success for an agentic infrastructure company through an acquisition. Most people talking about enterprise AI adoption have sat in one of these three seats. I've sat in all of them, and the same pattern holds from every angle: the projects that succeed aren't the ones with the most capable model. They're the ones where someone insisted on proof from day one. This talk is what that pattern looks like in practice. The goal isn't another AI adoption framework, it's a practical model, built from three different seats at the same table, for measuring whether an agent is actually working in production.

• Alicia Retzlaff - Senior Leader, Customer Solutions at Amazon Web Services (AWS)

  Alicia Retzlaff is a seasoned technology leader with 20+ years of experience driving business transformation and customer success. Currently a Senior Leader at AWS specializing in ISV, Data & Analytics, and GenAI solutions leading a team of cloud transformation strategists covering a $5B+ annual portfolio. She previously held director roles at Very Good Security and Upland Software. Alicia combines strategic vision with authentic leadership to create sustainable business success through people-centered approaches and innovative solutions. Alicia holds 3 AWS technical certifications - Data Engineering, Cloud Practitioner, AI Practitioner.

• Rittika Jindal - Principal engineer at Thomson Reuters

  Rittika Jindal is a Principal Engineer at Thomson Reuters on the Content Innovation team, where she designs and ships production AI agents for legal products including Westlaw and Practical Law. With more than 15 years of engineering experience spanning databases, data lakes, and AI systems, she now focuses on the hard parts of taking agents to production: evaluation, MCP server design, and observability. She is a recipient of the AI Kiran Trailblazer Award, an active organizer of hackathons for women in tech, and a certified yoga teacher and mountaineer.

• Kirya Francis - Chief Inclusion Impact & Sustainability Officer at Omnicom/Flywheel Commerce Network

  As artificial intelligence rapidly scales across enterprise commerce and data platforms, traditional, reactive compliance checklists are no longer enough to protect global brands from algorithmic bias and cultural friction. To build a truly resilient, inclusive AI strategy, organizations must pivot from passive lectures to proactive, gamified simulations that lower internal defenses and build genuine human trust. This session provides leaders with a practical blueprint for creating an "Inclusive Sandbox"—demonstrating how interactive learning and data transparency can bridge the gap between high-level tech governance, cultural trust, and long-term commercial velocity.

• Pimmi Malhotra - Head of Cloud Office, IBM CIO at IBM

  Most AI initiatives start with technology. Ours started with a simple question: Why are talented people spending so much time navigating process instead of solving problems? Across organizations, experts are buried in approvals, handoffs, data gathering, status updates, and repetitive operational tasks. We set out to remove that friction by building AI agents that could gather context, orchestrate workflows, recommend actions, and accelerate decisions while keeping humans in control. The journey wasn't smooth. Some solutions delivered immediate value. Others failed because we focused on technology before trust, automation before adoption, and intelligence before user experience. Along the way, we learned that successful AI transformation is far less about models and far more about people, governance, and change management. In this keynote, I'll share what we built, the outcomes we achieved, the mistakes we made, and the lessons learned moving from AI experimentation to real-world impact. Attendees will leave with practical insights for identifying high-value AI opportunities, avoiding common pitfalls, and scaling AI responsibly across their organizations. Because the true promise of Agentic AI isn't doing work faster, it's enabling people to spend more time on the work that matters most.

• Riddhima Sejpal - Senior Staff Software Engineer at Instacart

  Vision-Language Models (VLMs) are transforming computer vision, but building accurate, scalable multimodal applications requires more than simply sending images to an AI model. In this session, I’ll share how we designed a vision-language AI pipeline that analyzes shelf images captured by Instacart Caper Carts to identify products and generate structured product intelligence. We’ll explore the key design decisions that helped us improve accuracy and scale product identification across multiple retailers. I’ll also share practical lessons in prompt engineering, designing multi-stage AI workflows, and improving the reliability of multimodal AI systems. Whether you’re building AI-powered retail solutions or other vision-based AI applications, you’ll leave with practical design patterns and engineering techniques for building robust multimodal AI systems.

• Jane Austin - SVP design at Contentsquare

  Context and culture are the two variables that decide whether AI in an organisation compounds good work or compounds confusion: Stanford's Digital Economy Lab published a study called "The Enterprise AI Playbook," looking at 51 internal AI transformations to explain why overwhelming majority of enterprise GenA I investments show no measurable return. They found that companies using the same technology and the same use cases still had transformation timelines ranging from weeks to years. They ruled out the tools as the cause and said the variable was org design and culture: How ready it was to change. Whether its processes could absorb a new way of working. Whether leadership was actually behind it. Whether the culture tolerated failed attempts along the way. were they able to change their operating model around the AI Culture is the judgment that decides what to do with what the system produces. Taste is experience plus strategy plus human intelligence, and it forms through the act of making: designing, shipping, getting critiqued, getting it wrong in ways you can eventually name. That's what lets someone direct an AI system with precision. It's also what lets a team catch confident nonsense before it ships. Culture is what makes AI transformations work - I will talk about how we are building this culture at contentsquare and give advice on what is working, and how to make your culture work for you Context is the structured, current account of what's actually true in a business: the data model, the source of truth, the decisions already made, the constraints that haven't changed. AI amplifies whatever context it has access to. Rich context makes an AI system's output something a team can trust and reuse. Thin or scattered context makes speed the fastest way to spread a wrong answer. Contentsquare's AIx group is building a context layer that works the same whether the tool is Claude, Copilot, or whatever comes next. Governance is built into the layer itself: who wrote something, what stage it's at, whether it's been signed off. Context delivers better, faster and cheaper AI outputs. And I will talk about the interplay of both and how to create a culture that build context I'll walk through how the two work together in practice: a five-stage prototyping model that puts a human judgment gate between an AI-generated hypothesis and anything that ships, and a front-matter governance model that stops "looks true" from becoming policy. Attendees leave with the five-stage model and the front-matter governance approach, plus one question to bring back to their own teams: when someone hands you AI-generated work, can they tell you exactly what's wrong with it, or only that something feels off?

• Amira Youssef - Chief Digital Transformation Officer at Ex Microsoft || Ex Bupa

  Chief Product Officer and AI Transformation Executive with 20+ years building AI-powered products, scaling global product and engineering organizations, and leading enterprise transformation across Microsoft , AI startups, and Bupa . As Chief Digital Transformation Officer at Bupa, led a 300-person Product, Engineering, AI, and Technology organization, owning enterprise product and AI strategy while leading enterprise transformation. Previously held senior product leadership roles across Windows, Azure, and Azure Edge AI, building products at scale. Recognized for building high-performing product and engineering organizations, launching AI-powered products from 0→1, and delivering measurable business transformation at scale. 2025 Chief Product Officer Awards winner, 2026 AI & Digital Transformation Leader nominee , and keynote speaker on AI, Product, transformation and leadership.

• Lori Fena - Co-Founder at Personal Digital Spaces

  AI governance usually stops at the policy document. The harder problem is building consent and data rights into the access layer itself, so terms are set, verified and enforced at the moment an AI system reaches for content. This session draws on a working model: the voluntary privacy framework that brought more than 90 percent of the commercial web into compliance in 18 months, without a mandate. Lori Fena, who built that framework as the founder of TRUSTe, walks through what made voluntary consent infrastructure reach adoption then, and what an open, machine-readable consent layer looks like for the agentic AI era now, where the entity requesting access is a bot and enforcement has to happen in production.

• Anubha Gaur - Executive Director of AI Engineering at Quest Diagnostics

  Most enterprises don't struggle to build AI pilots, they struggle to operationalize AI at scale. As organizations race to adopt Generative AI and Agentic AI, they discover the limiting factor isn't model performance. It's the absence of an engineering operating model that lets AI be delivered, governed, and improved continuously across a regulated enterprise. In this session, I'll introduce Enterprise AI Engineering an emerging discipline that turns isolated AI experiments into repeatable business capabilities; through the lens of building it from zero inside a large, regulated healthcare enterprise. I'll walk through three moves I had to make at once, not in sequence: scaling an enterprise AI coding assistant with usage data that proved real adoption instead of shelfware licenses; designing a governed enterprise agent platform with compliance built into its foundation from day one which is what let a high-risk, customer-facing use case go live safely alongside lower-risk internal ones; and standardizing an AI-native SDLC so every future initiative, has a repeatable, governed path to production. Rather than chasing the next model or tool, this session challenges leaders to rethink the engineering systems - platform engineering, governance by design, agent lifecycle management and developer experience that actually determine whether AI creates lasting enterprise value.

• Lovina Dmello - senior infrastructure software engineer at Nvidia

  Production ML breaks the assumptions security frameworks were built on: learned weights have no natural trust boundary, behavior is probabilistic, and GPU multi-tenancy opens brand-new attack surfaces. So where do real-world ML systems actually get breached? Drawing on a survey of 139 papers, this lightning talk delivers the single most important finding first: misconfiguration not missing features or exotic adversarial attacks is the dominant security failure mode in ML infrastructure. In 10 minutes I'll walk the six-category threat taxonomy against ML systems, show why production teams can't afford the 15–30% overhead most adversarial defenses demand, and give you a defense-in-depth cheat sheet across access control, runtime, infrastructure, and operations. You'll leave knowing the three misconfigurations to hunt for in your own stack today.

• Sonam Kanungo - Staff Data Engineer at Replit, Ex-Crusoe, Ex-Netflix

  **Sonam Kanungo** is a data engineering leader specializing in building scalable data platforms that power AI, analytics, and business decision-making. **Most recently, she served as the Head of Data Engineering at Crusoe**, where she was the founding data engineering leader responsible for establishing the company's data engineering function and modern data platform from the ground up. Her work spanned data warehousing, analytics engineering, real-time data pipelines, and self-service analytics, enabling teams across product, finance, sales, operations, and AI cloud infrastructure to make data-driven decisions. Prior to Crusoe, Sonam spent nearly five years at **Netflix**, where she built and scaled data systems supporting the company's global payments platform. Her work contributed to improving payment reliability, detecting fraud patterns, and optimizing payment costs, including reporting initiatives that delivered millions of dollars in annual savings. Earlier in her career, she developed large-scale data platforms at **Mercedes-Benz Research & Development**, **Verizon**, and **Accenture**, working on connected vehicle analytics, telecommunications data processing, and enterprise analytics. With more than a decade of experience, Sonam has deep expertise in modern data engineering, including distributed data processing, cloud-native architectures, analytics engineering, and AI data infrastructure. She is passionate about building reliable, high-performance data platforms that transform complex datasets into actionable business insights. In addition to her engineering leadership, Sonam has contributed to the broader technology community by serving as a judge and reviewer for technology innovation awards and hackathons. She is passionate about advancing the field of data engineering, mentoring engineers, fostering data-driven cultures, and building the data foundations that enable the next generation of AI-powered products.

• Priyanka Kukreja - Staff Product Manager at CodeRabbit, Inc.

  Trust is the bottleneck in AI agent adoption, not its capability anymore. When an agent can write production code, send emails, and make irreversible decisions on your behalf, "just check the output" is not a viable review strategy. Yet most teams treat explainability as an afterthought: a log dump, a reasoning panel, or a "show your work" toggle bolted on after the real product ships. This session introduces a practitioner's framework for building explainability that earns trust rather than just performing it. It's built from hands-on experience shipping AI code review at scale. You'll learn to identify the three conditions that make explainability non-negotiable. You'll also discover how to map your user's needs to the three distinct jobs they need to do — verification, debugging, and auditing, each requiring a fundamentally different product response. Finally, you'll walk away equipped to apply a 3-step framework (Audience → Layer → Workflow Moment) to design explainability products at scale.

• ANKITA SANJAY CHATRATH - VP Corp Finance AI Enablement at State Street Bank and Trust Company

  Enterprise AI stalls when the data layer loses business meaning. Ownership blurs. Access drifts from intent. Quality rules live in too many places. Governance fragments across systems, teams, and tribal knowledge. Under that kind of pressure, AI products inherit confusion before they produce value. This lightning talk draws from the experience of building an AI-powered data marketplace inside large regulated enterprises, where data discovery alone solves very little. In this environment, data has to earn trust. People need to know who owns it, how it is governed, how it should be interpreted, and whether it belongs in high-stakes workflows at all. The session moves past the familiar idea of a data catalog and gets into what it takes to build a marketplace that AI teams, business users, and governance partners can actually rely on. It gets into the parts that usually stay buried: how to capture business-owned metadata with operational value, how to distinguish certified, draft, deprecated, and restricted assets, how to surface quality issues before someone consumes the data, how to tie access controls to business intent, and how to make ownership visible enough for accountability to hold as data moves into downstream AI use. It also looks at the shift from passive documentation to active infrastructure. In most enterprise settings, people still piece together data context through old reports, tickets, shared drives, relationships, and institutional memory. AI raises the cost of that fragmentation. When a person cannot tell which metric has approval, which source carries authority, or which dataset is safe to use, an AI system will only move faster in the wrong direction. It will retrieve, summarize, and automate on top of ambiguity. Using real implementation work, this session shows how a governed data marketplace can serve as the connective layer between data producers, business consumers, policy owners, and AI builders. It focuses on the metadata that matters most: ownership, certification state, lineage, quality thresholds, access context, usage constraints, semantic definitions, policy tags, and consumption patterns. Those elements give downstream search, analytics, agents, and AI workflows the context they need to operate with precision and accountability. The talk closes on the broader point: when AI-powered analytics runs on top of a trusted marketplace, the marketplace becomes far more than a place to find data. It becomes part of the control plane for enterprise AI readiness. The infrastructure choices organizations make now will shape whether AI adoption scales with trust and discipline, or whether every new use case reopens the same unresolved questions around meaning, ownership, and control.

• Twisha Shah Brandenburg - Senior Design Leader at Target

  Most AI rollouts target the wrong layer of the organization. Leaders automate the easy, repetitive work first because it's the path of least resistance, but that work is the substrate on which judgment is built. Entry-level reps, low-stakes decisions, pattern exposure over time, these aren't inefficiencies waiting to be optimized away. Strip them out, and you remove the conditions under which junior people become senior ones. This talk introduces Decision Architecture: a framework for deciding where AI should take on a task and where humans need reps. Drawing on 20+ years building capability inside Target, Instacart, PayPal, and BMW, I'll cover the Two-Clock Problem (performance metrics move in 12 to 24 months, judgment takes 5 to 7 years, and most orgs optimize for the wrong one) and a five-lens framework for evaluating automation decisions before they hollow out your bench strength. This isn’t an anti-AI talk. AlphaFold is the model: AI absorbing a hard constraint while expanding what experts can do, not degrading it. The problem isn't automation. It's automating without architecture.

• MinJee Hahm - Lead Product Designer at Meta / Instagram

  As AI becomes embedded in product development, the biggest opportunity isn't generating designs faster—it's reducing the organizational friction that slows teams down. Designers today spend countless hours waiting for design critiques, design reviews, accessibility feedback, and design system approvals before they can move work forward. What if AI agents could act as your first line of reviewers? This session explores how specialized AI agents can provide instant feedback on usability heuristics, design system consistency, accessibility, implementation feasibility, and product strategy, enabling individual contributors to make informed decisions earlier in the design process. Rather than replacing human collaboration, AI elevates it by handling predictable feedback, allowing teams to spend more time discussing product vision, customer problems, and strategic trade-offs. Attendees will learn how AI is reshaping design workflows, increasing designer autonomy, and changing the role of design reviews in modern product organizations.

• Saurabh Pitkar - Director Product Management, Agentic Commerce & Agentic AI at Dell Technologies

  Saurabh Pitkar is the Director of Product Management for Agentic Commerce and Agentic AI at Dell Technologies where he leads product strategy for commerce across all Dell transactions worth $115B+ annually. He is building agentic commerce for Dell integrating with LLM apps like Gemini, Copilot, ChatGPT; marketplaces like BestBuy.com and is designing native AI agents buying on dell.com. He is enabling consumer, commercial and enterprise buyers to do commerce autonomously with Dell no matter where the customers are. With 12+ years spanning stints across Sequoia-funded logistics unicorn (Porter), supply chain and Fortune 50 enterprise, Saurabh builds at the intersection of agentic AI systems and large-scale digital commerce. Earlier at Dell, he led product strategy for unified and AI-ready commerce platform processing $115B+ in annual transactions. He holds an MBA from Duke University (Fuqua) and a B.Tech from IIT BHU.

• Angela Byers - Sr. Director GTM, Microsoft Security at Microsoft

  Managers today are overwhelmed by an endless cycle of meetings, feedback conversations, talent development, and performance demands—while also being asked to drive AI transformation. AI presents an unprecedented opportunity not just to increase productivity, but to fundamentally reimagine the role of the manager from task dispatcher to growth catalyst. In this session, we will showcase top use cases for using AI to augment leadership capabilities, accelerate team performance, and drive adoption across the organization. You’ll see how AI can be used to surface unseen coaching opportunities, detect employee sentiment, and generate personalized feedback prompts. You’ll experience how AI can role-play difficult conversations—helping managers prepare for high-stakes discussions with greater empathy and confidence. With experience in scaling Microsoft AI businesses, leading marketing teams, and working as a career coach, the speaker will share tools and frameworks for using AI in coaching, performance management, and culture-building. Attendees will receive resources to help their teams implement AI with consistency and confidence.

• Sumithra Rajendra - Head of Product, Knowledge Operations at World Bank Group

  As organizations race to deploy AI, many discover that technical success does not guarantee adoption. Even the most capable AI products struggle when users question accuracy, relevance, transparency, or governance. Drawing from lessons learned introducing AI-powered experiences within a complex global organization, this session explores the factors that build or break trust in enterprise AI. From hallucinations and knowledge quality to stakeholder alignment and user expectations, attendees will gain practical insights for designing AI products that earn credibility and drive lasting adoption.

• Aiswarya Venkitesh - Principal Cloud Solution Architect at Microsoft

  Many organizations have moved beyond asking "What is AI?" and are now asking "How do we generate measurable business outcomes?" Yet many AI initiatives remain stuck in proof-of-concept mode. In this session, I will share real-world lessons from helping enterprises design, deploy, and adopt AI-powered solutions using Azure AI, Microsoft Fabric, modern data platforms, and Agentic AI architectures. Attendees will learn what worked, what failed, and the critical decisions that moved projects from experimentation to production. The session covers governance, security, data readiness, adoption challenges, ROI measurement, and practical strategies for scaling AI across the enterprise. Through customer success stories and implementation patterns, attendees will gain actionable insights for turning AI investments into sustainable business value.

• Mrunal Gangrade - Vice President of Engineerin at JPMorgan Chase

  As organizations rush to adopt AI, security teams face a new challenge: protecting systems that are fundamentally different from traditional software. This session examines the intersection of cybersecurity and machine learning, covering how AI models introduce new attack surfaces, from data poisoning to adversarial inputs, and how the same machine learning techniques can be turned into powerful defensive tools. Drawing on experience securing systems in regulated financial environments, the talk walks through practical strategies for threat detection, risk scoring, and building AI systems that are resilient by design. Attendees will leave with a clearer framework for treating AI security as a core engineering discipline, not an afterthought.

• Charu Maheshwari - Product Finance & Strategy at Anthropic

  AI pricing doesn't behave like SaaS pricing because the underlying economics are different: marginal cost came back. This talk gives builders and buyers a mental model for reading an AI price sheet and understanding what it's actually telling you. We'll cover why per-token pricing exists, what drives the spread between model tiers, how capability and cost trade off, and how to reason about unit economics when your product depends on a model API. The content is drawn from the work of pricing frontier models at Anthropic, but the economics apply across the industry and nothing in the talk is company-specific.

• Vidya Bansal - Principal CXO Architect at Adobe

  Putting AI into customer decisioning sounds easy—until you have to answer practical questions like: How confident is the recommendation? What happens when the AI is wrong? Who approves high-impact decisions? In this session, we'll share how we built a production AI decisioning architecture on top of a customer data platform. The system generates lifecycle classifications, next-best actions, and channel recommendations for individual customers, then uses confidence scores to determine whether decisions are automatically activated or routed for human review. We'll cover how we ground AI with customer context, design human-in-the-loop workflows that scale, and govern decisions without slowing down the business. We'll also show how the same architecture powers a marketer-facing campaign co-pilot, turning AI from a point solution into a reusable enterprise capability. This is a real-world architecture story focused on production tradeoffs, governance, and lessons learned.

• Rishab Jolly - Senior Product Manager Azure at Microsoft

  Most teams ship AI agents to production with the same observability they'd give a static API, which is to say almost none. Then the agent silently loops, burns its token budget, or returns a confident, well-formatted, completely wrong answer while every dashboard stays green. Traditional APM assumes deterministic code: same input, same output, errors that throw. Agents break all three. This talk covers how to instrument agents for the failures that matter, not "did it run?" but "what did it decide, why, what did it cost, and was the answer any good?" I'll cover the signals worth alerting on, tracing a multi-step run as a decision graph, and where the open OpenTelemetry GenAI conventions give you a vendor-neutral foundation today, plus where they stop, so you know what you still have to build. Candid about what bit teams in practice and what changed after.

• Shirsha Ray Chaudhuri - Senior Director, Applied Research, AI at Thomson Reuters

  Business processes, IT contracts, SLAs and protocols are all getting rewired in this age of AI driven transformation. But in this journey there are ample opportunities to go down an alley that is horror filled with insufficient guard rails, incomplete harnesses and context leak to cause mayhem and instability that the existing IT world is not familiar with. I would like to share about one such real failure of Agentic workflow transformation gone wrong, and how it culminated into best practices, templates and sdlc that builds the right kind of Agentic workflows.