Shape the Future of
Enterprise AI

September 1, 2026 | NYC

Built for Fortune 500 Trailblazers

The AI Enterprise Conference is where Fortune 500 decision-makers and leading-edge solution and service providers convene to build the foundations of the AI-driven era.

Connect with enterprise data + AI leaders

Scale and govern AI responsibly at scale

Benchmark with peers at roundtables and dinners

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Featured 2026 Speakers

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

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Joshua Ainsley

Senior Director, Head of Data Science

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Srini Masanam

Global Head of Data Quality & Governance

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Lu Ai

VP Data Governance & Innovation leader

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Sai Teja Akula

Senior Director - AI, ML and Data Science

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Mukundan Rengaswamy

Sr. Managing Director. Head of Data Soln Arch Engineering & Innovation

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Vikram Murukutla

Director, Head of Digital Acquisition Analytics

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Sravan Kasarla

Chief Data Officer

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Michael Beal

Head of Enterprise Data Science Specialists, Americas

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More speakers coming soon

Save Your Spot Now

All-Access Pass
(IN-PERSON)

$499 $999

Skip the application process and secure your in-person, all-access pass to the AI Enterprise Conference by registering today with our “Early Bird” discount.

Registration includes in-person access to all conference features including:

– All general and breakout sessions, including presentations and panels

– Breakfasts, lunches, and networking receptions

– Exhibit Hall

– Private networking app

Apply to Attend for Free​

FREE $999

The AI Enterprise Conference free passes—for qualified attendees only—are highly limited and 100% free.

You may qualify if you meet all 3 criteria below:

1. Your company is not a vendor and has no sales interest in end-users of data or AI-related products or services

2. You work at a non-vendor organization with 250+ employees

3. You hold a senior-level executive or technical position

After you complete the registration form, we will be back in touch to let you know if you have been selected.

Media Pass (FREE)

If you’re a journalist, influencer, or media professional actively reporting on AI and data-centric subjects, apply for our complimentary Media Pass! 

To be eligible, you must currently be engaged in journalism, influencing, or media activities, specifically focused on AI or data-oriented topics.

All applications will be reviewed by our team, and acceptance is highly selective.

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Venue

Pier Sixty NYC | Chelsea, Manhattan

Set along the Hudson River in the heart of Chelsea Piers, Pier Sixty offers a premier waterfront venue designed for large-scale, high-impact conferences.

The space features expansive ballrooms, flexible staging options, and advanced audiovisual capabilities—making it ideal for keynote presentations, panel discussions, and immersive enterprise-level programming, all within a sleek, modern setting built to support complex event production.

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.
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This Year's Content Pillars

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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
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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
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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
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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

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

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

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

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

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

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

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

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

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

Why They Keep Coming Back

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