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Go beyond AI pilots—learn how leading companies like PayPal, Barclays, Wayfair, and Myntra achieve massive ROI by scaling predictive, generative, and agentic AI. Discover best practices for deploying AI in fraud detection, recommendations, and customer 360, with insights from proven, enterprise-scale success stories.

As Generative AI transforms how organizations create, consume, and act on data, traditional data strategies are being pushed to their limits. In this session, I’ll share how enterprises can rethink their data foundation to support scalable, secure, and trustworthy AI adoption.
Drawing from real-world experience leading supply chain analytics and data product strategy at H-E-B, this talk covers:
• Key pillars of a modern data strategy tailored for Gen AI
• How to bridge the gap between legacy data systems and AI readiness
• Data governance, quality, and privacy frameworks in the Gen AI context
• Organizational alignment: turning data chaos into AI enablement

Healthcare organizations are facing unprecedented challenges in managing exponentially growing patient data, with the average hospital now generating over 50 petabytes of data annually. Traditional monolithic architectures have proven inadequate, with 76% of healthcare CIOs reporting scalability issues and 68% experiencing system downtime that directly impacts patient care.
This presentation explores how distributed systems are revolutionizing healthcare delivery by addressing these critical challenges. With microservices architecture adoption in healthcare growing at 24% annually since 2020, the industry is witnessing a fundamental shift in how digital health platforms are designed and deployed.
Our analysis of 150+ healthcare institutions that transitioned to distributed systems reveals compelling outcomes: 87% reported improved system uptime (from 99.1% to 99.97%), 63% reduced infrastructure costs through targeted scaling, and 92% experienced faster innovation cycles—reducing new feature deployment time from months to days.
We’ll examine how horizontal scaling enables healthcare platforms to accommodate 30-40% annual increases in patient data volume without performance degradation. Case studies will demonstrate how fault tolerance mechanisms have reduced critical system downtime by 94%, ensuring continuous access to patient records even during regional outages or cyberattacks.
Additionally, we’ll explore how load-balancing techniques have improved response times for critical healthcare applications by 72%, with appointment booking systems handling 5x more concurrent users during peak periods without degradation.
For healthcare technology leaders navigating this transition, this session provides a roadmap to implementing distributed architectures that enhance scalability, reliability, and interoperability—ultimately delivering superior patient experiences across the care continuum.

I’ll demonstrate how to an end-to-end AI-powered customer support pipeline that monitors incoming emails, automatically extracts and analyzes content for sentiment and key issues, then intelligently searches your knowledge management system to find relevant solutions. The system doesn’t just analyze—it takes action by drafting contextual responses for human agents to review and customize, ensuring quality while dramatically reducing response times.

Beyond individual case handling, the AI agent continuously feeds categorization data into real-time trending systems, enabling support leaders to identify emerging issues before they become widespread problems. Attendees will learn the technical architecture behind this system, including LLM integration strategies, prompt engineering for consistent outputs, and human-in-the-loop workflows that maintain quality control.

Whether you’re managing a support team of five or five hundred, you’ll leave with actionable insights on implementing AI agents that amplify human capabilities, creating a customer care experience that’s both more efficient and more empathetic.

As generative and agentic AI evolve from buzzwords to real business enablers, the biggest question facing enterprise leaders is: how do we move from experimentation to scaled impact? This panel brings together senior leaders from Google, JPMorgan Chase, RCCL, and the City of Coral Gables to explore what it really takes to deploy and scale AI in complex enterprise environments.

We’ll dig into the infrastructure, governance, and cross-functional collaboration models that separate successful AI initiatives from stalled pilots. Panelists will share candid insights from both public and private sector implementations—ranging from agentic AI for operations to regulatory compliance, customer experience, and data infrastructure modernization. Whether you’re driving innovation at a large enterprise or charting your AI roadmap, this conversation will unpack the real challenges and key enablers of operationalizing AI at scale.

Many companies pursue product or performance improvements without fully understanding how those changes impact user behavior or business value. This talk explores how organizations can apply data science and experimentation to more effectively connect digital experience enhancements—on websites or apps—to top-line outcomes like revenue, engagement, and retention. We’ll discuss how to frame performance changes as testable hypotheses, run targeted experiments, and analyze results to uncover the causal relationships between operational metrics and business impact. A key focus will be on developing and using transfer functions to quantify how improvements in key performance indicators translate into enterprise value. Attendees will learn how to use these techniques not just for one-off experiments, but as a scalable framework for continuous optimization and goal tracking across teams.

As artificial intelligence becomes increasingly embedded in healthcare systems, the challenge is no longer just about building accurate models — it’s about building responsible ones. This talk explores how AI can be applied thoughtfully to improve both clinical decision-making and healthcare operations, without compromising patient trust, privacy, or safety.

Drawing on real-world examples from healthcare and financial services, Mrunal Gangrade will share insights into how machine learning is being used to predict patient outcomes, streamline workflows, and flag anomalies — while navigating the ethical complexities of algorithmic bias, data governance, and regulatory compliance.

Attendees will learn strategies for designing AI systems that are not only intelligent but also transparent, equitable, and resilient. The session will also address the importance of cross-disciplinary collaboration between data scientists, clinicians, and security professionals in ensuring that innovation in healthcare remains human-centered.