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Looking to connect with peers who are shaping the future of enterprise AI?
DSC’s ALIGN AI Executive Summits bring together 200+ senior data and AI leaders for focused conversations, dynamic planning sessions, and real exchange on what’s working — and what still needs work — when it comes to operationalizing AI at scale.
Join us in NYC, Atlanta, San Francisco, or Boston. These are invite-only gatherings designed for leaders navigating the complexity of AI strategy, governance, and impact.
At DSC, we pride ourselves on hosting leading executives who are real-world changemakers: the senior data leaders from Fortune 500 companies who are at the forefront of innovation.
| AGENDA | |
| 9:00 AM | Registration & Breakfast |
| 9:45 AM | Opening Remarks – The AI Quotient: Are You Ready for the Next Era of Enterprise AI? Presenter: Brian Mink, Co-Founder & President @ Data Science Connect The gap between AI ambition and AI reality is widening. In this opening session, we’ll introduce the “AI Quotient”—a practical framework for measuring enterprise readiness across data, infrastructure, talent, and governance. It’s not about hype or potential. It’s about execution. Are you truly positioned to deploy AI at scale, or just piloting forever? |
| 10:05 AM | Panel Discussion – AI-Ready or Risk-Heavy? How to Prepare Your Org for What’s Next in AI Panelists: - Gil Gerstl | VP, Product Development, Chief Data Office @ ADP -Sabrina Chen | Global Head of AML Customer Risk @ Citi - Satyam Singh | AVP Data Management @ Barclays Bank - Graham Wilkinson | Chief Innovation Officer @ Kinesso AI is no longer optional—but scaling it successfully requires more than just ambition. This panel will explore what it takes to build an organization truly ready for AI, from data and infrastructure to governance, talent, and executive alignment. Hear how leading teams are navigating risk, driving adoption, and preparing for what’s next. |
| 10:50 AM | Networking Break |
| 11:20 AM | Executive Roundtable (Invite-Only) – Agentic AI in Action: A New Organizational Paradigm, brought to you by Deloitte Facilitators: - Ambo Bose, Managing Director @ Deloitte Consulting - Aprameya Bhat, Head of AI Products and Services @ Deloitte Consulting Navigating the hype with a critical view of its nuances and intricacies, while uncovering the immense possibilities and opportunities. |
| 11:20 AM | Presentation – Building AI That Works: Real Lessons from Enterprise Applications at Scale Presenter: Bonnie Chase, Director Product Marketing @ Vespa.ai From personalized search to real-time recommendations, today’s enterprise AI applications demand performance at scale. In this session, we’ll explore how leading organizations are using Vespa.ai to power intelligent, data-driven experiences across domains like e-commerce, finance, and media. You’ll see real-world use cases in action—then go behind the scenes to understand how the Vespa platform enables low-latency, large-scale retrieval, ranking, and inference in production. Whether you're building next-gen AI search or looking to simplify your stack, this talk will show how Vespa helps enterprises turn their data into differentiated value. |
| 11:45 AM | Presentation – The Secret Weapon Behind Reliable AI? A Semantic Layer Your Engineers Don’t Have to Build Presenter: Rajoshi Ghosh, Cofounder / Chief Ecosystem Officer @ Hasura Everyone’s scrambling to bolt GenAI onto their SaaS stack—but most hit the same wall: AI that doesn’t understand your business. The reason? Your data is messy, siloed, constantly changing—and LLMs can’t reason over it without context. That context is the semantic layer. And no, it’s not your warehouse schema. In this talk, the speaker will share: • Why traditional approaches (data modeling, knowledge graphs, manual prompt tuning) are too brittle to scale. • How PromptQL builds an agentic semantic layer (autonomous, self-improving) across APIs, SaaS tools, and internal data—no centralization needed. • Real-world examples of how this powers agents that can handle thousands of edge cases without blowing up in production. If you’re responsible for operationalizing AI at scale, this is the architecture shift that will give you reliability and determine whether your AI systems get adoption—or stay stuck in POC mode. |
| 12:05 PM | Networking Lunch + Open Bar Begins |
| 1:15 PM | Executive Roundtable (Invite-Only) – AI’s New Era in Healthcare & Life Sciences: Crossing the Chasm from Pilot to Production and the Role of Search Facilitators: - Harini Gopalakrishnan, GTM & Strategy - Health & Lifesciences @ Vespa.ai - Bonnie Chase, Director of Product Marketing @ Vespa.ai This roundtable discussion will explore how artificial intelligence is moving from experimental applications to truly transformative solutions in healthcare and life sciences. We'll examine business impact, the investment strategy, adoption success, technical approaches, implementation challenges, regulatory considerations, and future visions that center around moving to business value with AI and hear from peers on their journeys. Open forum for brainstorming discussion on building the next generation RAG. And finally we will close with understanding as to why building a RAG is actually building a real Search engine. |
| 1:15 PM | Panel Discussion – Next-Gen AI Enablement: What Happens When Everyone Can Build? Panelists: - Debu Saha | SVP, Architecture & Engineering @ Citi - Sai Teja Akula | Sr. Director of Data Science @ Broadridge - Mark Zabezhinsky | Director, Portfolio Marketing @ Alteryx - Ravikanth Gadicherla | Director, Supply Chain Analytics @ Mondelez Digital Services As AI tools become more accessible, everyone—from marketers to product managers—can now build. This expert panel explores how democratized AI is reshaping roles, governance, and innovation across organizations, and what it means for agility, accountability, and the future of intelligent creation. |
| 2:00 PM | Networking Break |
| 2:35 PM | Panel Discussion – AI ROI in Practice: What Leading Enterprises Get Right Panelists: - Don Spaulding | Director of Technology Innovation @ Verizon Wireless - Claire Willett | Global Executive Director of Analytics @ Condé Nast - Mihir Sanghavi | Senior Director @ LTX, A Broadridge Company - Deepak Agarwal | Director of ORx IT Operations @ Optum Everyone’s talking about AI value—but few can measure it beyond anecdote or aspiration. In this closing panel, we’ll hear from leaders who’ve built AI programs that deliver real, quantifiable returns. Learn what they track, how they manage trade-offs, and what they’d do differently if they were starting again today. |
| 3:20 PM | Closing Remarks Presenter: Amelia Mink, Co-Founder & CEO @ Data Science Connect |
| 3:30 PM | Networking Reception |
Agentic AI systems act with autonomy, pursuing goals, making decisions, and adjusting behavior based on feedback. These systems combine reasoning, memory, and planning, enabling more complex workflows. They’re key to scaling AI from task-specific tools to intelligent agents handling end-to-end processes.
RAG enhances generative AI by combining it with a retrieval mechanism that pulls relevant data from external sources. Instead of relying only on training data, RAG injects up-to-date, domain-specific information into responses, improving accuracy, reducing hallucinations, and enabling grounded, real-time outputs.
Enterprise-grade AI demands robust architecture, scalable infrastructure, strong security, governance, and integration with existing systems. Successful builds prioritize user adoption, compliance, and performance—balancing technical complexity with business value and operational readiness across teams.
Effective AI starts with good data. Data governance ensures policies, access control, and compliance, while data management focuses on collection, quality, lineage, and storage. Together, they ensure data is trusted, secure, and usable—laying the foundation for ethical, high-impact AI initiatives.
A data strategy defines how an organization collects, manages, and uses data to drive business goals. It aligns data architecture, governance, and use cases across departments. A strong data strategy ensures readiness for AI, improves decision-making, and turns data into a competitive asset.
Observability helps teams monitor, debug, and optimize data and AI systems in real time. For data, it ensures quality and pipeline health. For AI, it tracks performance, drift, and anomalies. This transparency is critical for reliability, compliance, and improving models post-deployment.
Low-code/no-code platforms enable non-technical users to build apps and workflows, lowering the barrier to data and AI adoption. They foster experimentation, speed up innovation, and empower more people to solve problems with AI—while raising the need for governance and best practices.
Scaling AI requires powerful compute, storage, networking, and orchestration. From model training to deployment, organizations need infrastructure that supports reliability, cost efficiency, and integration. This includes GPUs, MLOps pipelines, and cloud/on-prem hybrid setups to handle enterprise needs.
Measuring AI ROI involves linking models to business outcomes. It includes tracking cost savings, efficiency gains, revenue growth, and user satisfaction. Clear KPIs, baseline metrics, and post-deployment analytics are essential for justifying investments, refining use cases, and driving AI adoption.
Our sponsors represent the leading edge of data and AI. From generative AI to vector databases, graph, feature engineering, data management, and more, you’ll find the solutions you need to take your organization to the next level.
