Agenda

Session Catalog

Highlights + Experiences

A curated experience designed for senior data and AI leaders—where strategic insight meets meaningful connection.

Fortune 500 Executive Panels

Candid discussions with senior data and AI leaders on what’s working, what’s not, and what’s next.

Keynotes from Industry Leaders

Hear directly from the executives shaping the future of AI, with strategic insights and real-world lessons you can apply immediately.

Executive Roundtables

Closed-door, peer-level discussions designed for depth, collaboration, and high-trust problem-solving.

Exclusive Executive Dinners

Private, small-group dining experiences that foster deeper relationships and meaningful conversation.

AI Unfiltered: Anonymous Peer Exchange

A signature ALIGN experience where executives workshop real challenges anonymously—creating a candid, constructive, and judgment-free environment.

Leading-Edge AI Presentations

Explore cutting-edge technologies, use cases, and implementation strategies driving real business impact.

Identify AI Partners That Deliver ROI

Connect with credible, experienced partners who have a proven track record of delivering real business outcomes—so you can move from exploration to execution with confidence.

Stunning Venues + Culinary Experience

Hosted in stunning, carefully selected venues—paired with elevated dining and hosted drinks to enhance connection and conversation.

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