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Executive Summits

ALIGN AI Executive Summits—the exclusive forum for enterprise data + AI leaders

Featured 2026 Speakers

Data + AI decision-makers from the world's top enterprises

Shashank Kadetotad

Sr. Director of Enterprise Data Science and AI

Anqi Zou

SVP, Head of Fair & Responsible Banking Analytics

Ram Kumar

Sr. Director - Networks, Automation and Analytics

Julie Elmore

SVP of Technology

Srini Masanam

Global Head of Data Quality & Governance

Kelly O'Brien

VP of Product Strategy, Commercial Card

Nasim Khoshkhou

SVP, Analytics and Data

Felipe Archilla

Director, Digital Workplace Analytics

Jyoti Mohapatra

General Manager - Data & AI Architecture

Abdullah Bokhari

Director, Enterprise Data Engineering

Rachna Narem

Senior Managing Director, AI Innovation Lead

Joshua Ainsley

Senior Director, Head of Data Science

Sergey Fogelson

VP, Head of Data Science

Faizan Javed

Sr. Director - Data Science & Engineering

Sreeja Kaimal

Senior Director Enterprise Data and Machine Learning

Nathan Ghadirifard

VP, Business Data & Architecture

Claire Dulaney Willet

Global Executive Director of Data & Analytics

Srijani Dey

Senior Director - Data Strategic Programs

Ravikanth Gadicherla

Director Analytics

Isaac Wagner

Vice President, Analytics and Insights

2026 Events Theme

AI for Enterprise:
From Pilots to Production

Enterprise AI is entering a new phase. In 2026, it’s no longer about access to models or experimentation—it’s about building systems that are scalable, governed, secure, and tied to real business outcomes. As organizations move from pilots to production, success depends on operationalizing AI with confidence.

Our events explore what it truly takes to make AI work inside the enterprise—from systems and governance to data foundations and economics. From context engineering and agentic workflows to platform strategy and measurable ROI, we focus on the realities leaders must address now.

Our point of view is simple: enterprise AI is no longer a model conversation. It is a systems, governance, and value conversation.

This Year's Content Pillars

Pillar 1: AI as a System

Enterprise AI is no longer about deploying a model. It is about operating a system.

This pillar explores the shift from isolated AI capabilities to full production systems that require evaluation, observability, context management, release discipline, and resilience over time. It includes the move from traditional RAG toward broader context engineering approaches that incorporate retrieval, orchestration, tool use, and emerging protocols such as MCP within a larger enterprise architecture. The focus is not just intelligence, but reliability.

Key discussion points:
— Context engineering beyond classic RAG
— Evaluation, observability, and AI system reliability
— LLMOps and GenAIOps for production environments
— Agents as systems, not just features
— Enterprise knowledge architecture and grounded AI

Pillar 2: Governance That Runs

In 2026, governance must move from policy decks to runtime enforcement.

As regulation tightens and enterprise exposure grows, governance can no longer live only in committees, documentation, or static controls. This pillar focuses on how organizations make governance operational through technical controls, monitoring, auditability, security engineering, and cross-functional accountability. It reflects the growing reality that enterprise AI must be not only innovative, but provable, defensible, and trustworthy.

Key discussion points:
— Runtime governance and policy enforcement
— AI risk, security, and model accountability
— Privacy, sovereignty, and data rights
— Audit readiness and evidence generation
— Governing agents, autonomy, and human oversight

Pillar 3: The Economics of AI

AI is now a portfolio decision, not a side experiment.

As organizations scale usage, leaders are under pressure to demonstrate impact while managing cost. This pillar examines the economics of enterprise AI: how to prioritize use cases, measure value, understand cost-to-serve, and create discipline around infrastructure, tooling, vendor spend, and human oversight. The goal is to move beyond vague transformation language toward credible ROI and durable operating models.

Key discussion points:
— Measuring AI ROI in real business terms
— FinOps for AI and cost governance
— Use case prioritization and portfolio strategy
— Productivity, performance, and value realization
— Sustainable scaling and long-term economics

Pillar 4: The Enterprise Foundation

AI maturity is still constrained by data maturity, platform choices, and organizational design.

This pillar focuses on the underlying foundation that makes enterprise AI possible. That includes AI-ready data, metadata, lineage, semantics, interoperability, platform strategy, and the operating models that determine who owns AI and how it scales. It also addresses the organizational realities behind enterprise adoption: workforce readiness, cross-functional alignment, and the tension between centralized control and distributed innovation.

Key discussion points:
— AI-ready data foundations and semantic consistency
— Data governance and AI governance convergence
— Platform strategy, interoperability, and lock-in
— Operating models for data and AI leadership
— AI literacy, adoption, and organizational readiness

Topics to Expect

AI Systems & Application Layer

— Agentic AI Platforms
— Enterprise AI Applications
— Copilots & Workflow Automation
— Context Engineering & Orchestration
— Model Context Protocol (MCP) & Tooling

AI Engineering, LLMOps & Evaluation

— LLMOps / GenAIOps Platforms
— AI Evaluation & Testing
— Prompt & Context Management
— AI Observability
— AI System Monitoring & Debugging

Data Foundations & Engineering

— Data Platforms (Lakehouse, Warehouse, Hybrid)
— Data Engineering & Pipelines
— Data Quality & Data Observability
— Metadata, Lineage & Cataloging
— Unstructured Data & Content Operations

Knowledge & Retrieval Systems

— Retrieval Systems & Vector Databases
— Enterprise Search & Knowledge Management
— Knowledge Graphs & Semantic Layers
— Context & Retrieval Optimization

AI Infrastructure & Performance

— AI Infrastructure & Compute (GPU, Inference)
— Model Serving & Inference Optimization
— AI Platform Engineering
— Scalability, Latency & Performance Optimization

Governance, Risk & Security

— AI Governance Platforms
— Model Risk Management
— AI Security (Prompt Injection, Data Leakage, etc.)
— Privacy Engineering & Data Protection
— Compliance & Regulatory Technology

AI Economics & Optimization

— FinOps for AI
— Cost Optimization & Usage Monitoring
— AI ROI Measurement & Value Tracking
— Vendor & Model Cost Management

Platform Strategy & Integration

— Interoperability & Data Sharing
— API & Integration Platforms
— Data/AI Ecosystem Strategy
— Vendor Consolidation vs Best-of-Breed

Enterprise Adoption & Operating Model

— AI Operating Models (Centralized vs Federated)
— AI Product & Platform Teams
— Workforce AI Enablement & Literacy
— Change Management & Adoption

Why They Keep Coming Back

Thank you all for your hard work on the event! We've heard great feedback for it from the team, with one member saying it was the best event he'd ever been to at Plotly!

I heard from my team that COLLIDE was an amazing event! Congrats and well done!

The topics were well selected and the mix of panels and roundtables provided opportunities to engage in problem solving with fellow leaders.

Thank you for hosting such a fantastic event yesterday. I truly enjoyed it and gained valuable insights into the latest trends and challenges in AI from industry leaders.

Outstanding. Many diverse perspectives and interesting insights.

We had a great experience this year. The location of the event was fantastic, and the attendees were great this year. We're looking forward to following up with several of them and hope to turn one or two into a new customer.