ALIGN AI Executive Summit ATL

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

Who Attends

Connect with data + AI executives from leading enterprises

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.

Venue

City Winery Atlanta at Ponce City Market

Located in the Ponce City Market development, Atlanta’s premiere live-work-play destination, City Winery Atlanta offers a distinctive venue that blends urban sophistication with creative energy—ideal for executive-level gatherings and intimate summits.

Designed to host high-impact events, the space features flexible indoor layouts, state-of-the-art sound and lighting, and a refined atmosphere that encourages meaningful conversation and connection.

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