AI Governance &amp; Trust

AI Agent Sprawl: Why Enterprises Need an Agentic Control Plane

IBM's Ann Leach explains how an agentic control plane governs AI agents' access, behavior and cost across clouds and vendors at enterprise scale.

From Shadow AI to Sovereign Agents: Why AI Governance Must Run at Runtime

Kindo's Shan Zhou explains why AI pilots fail, why policies must be enforced at runtime, and how to test, audit and cost-manage AI agents.

AI Agents in the Enterprise: Context, Control and Getting to Production

Leaders from Honeywell, Capital One, Synchrony, TelevisaUnivision and EXP Realty share how to give AI agents context, access and oversight.

The Agentic Data Contract: Six Data Rules for AI Agent Decision Quality

Informatica's Peter Manta explains why data quality alone isn't enough for AI agents and the six pillars of an agentic data contract.

Mission-Critical AI Agents: The Architecture That Gets Them to Production

LangChain's Christine Manegan explains why production AI agents need an owned harness, evals, observability, middleware and governance.

Garbage In, Liability Out: Data Quality and Governance for Trusted AI

Leaders from Bank of America, Synchrony, Alteryx and Truist explain how data quality, clear ownership and modern governance make AI trustworthy.

Why AI Fails Without Data Products and Data Contracts

Actian CTO Emma McGrattan explains how data products and data contracts help enterprises move AI from pilot to production.

Data and AI Control Plane: Why AI Agents Need One Identity and Boundaries

NexusOne CEO Anu Jain on why AI agents need one identity, complete context and hard boundaries, and why enterprises should own their ontology.

Single Source of Truth for Agentic AI: From Databases to Meaning

SAP, Turgon, Skyflow and AtScale leaders on how agentic AI redefines the single source of truth: semantics, runtime controls and evals on your own data.

Crum & Forster's 5 Lessons for Scaling Enterprise AI in Insurance

Crum & Forster leaders share how 91% employee AI adoption, unified data and embedded governance turned AI pilots into results.

Why the Enterprise AI Stack Needs a Control Plane for Agents

Xnode's Hemakumar Velugula explains why AI agents need data-layer access controls, unified policy and cost limits to reach production.

From Automation to Autonomy: Trusted Agentic AI Controls for Banks

Midships' Ajit Gupta explains why banks are stuck in AI automation and how purpose, intent and action checks let agents act safely.

AI-Ready Data: Why Enterprises Are Bringing AI to Their Data

Everpure's Kaycee Lai explains what AI-ready data means, why unstructured data blocks production AI, and how to run AI where data lives.

Sovereign AI Architecture: 7 Layers for Production-Ready AI Agents

Fulcrum Digital founder Rajesh Sinha explains a seven-layer sovereign AI architecture and why inference routing makes or breaks production AI.

AI Agent Overreach: Why Enterprises Need Context-Based Controls

Rubrik's Cal Al-Dhubaib explains how AI agents overreach, why static security controls fall short, and how context-based controls help.

Governing AI Agents at Scale: Testing, Feedback and a Control Plane

IBM's Sabtain Khan explains why building AI agents is the easy part, and how testing, feedback loops and a control plane get them adopted.

AI Governance as Growth Strategy: SAS on Closing the AI Trust Gap

SAS's Reggie Townsend shares survey data on why 97% of people override AI and the five governance behaviors that set AI leaders apart.

AI Cost per Outcome: How Enterprises Prove Value as AI Spending Grows

Leaders from New Balance, Mastercard, Omnicom, Earnest and Crum & Forster on measuring AI value, cost per outcome, token usage and AI platforms.

AI Observability: How to Build Trustworthy AI in Production

SAP, Dynatrace and RaceTrac leaders explain AI observability: evaluations, drift detection, token costs, rollbacks and audit trails.

AI-Ready Data Governance: Why Business Context Is the New Foundation

Actian, Fiserv and Slalom leaders explain how living business context, automated stewardship and lineage make data governance AI-ready.

Agentic AI Governance: Where Humans Belong in the Loop at Enterprise Scale

AT&T, SAP, Posit, Revefi and Rubrik leaders on governing AI agents: policy layers outside the model, risk-tiered approvals, tracing and least privilege.

Agentic AI With Mistral AI Studio: Why Enterprises Should Own Their AI

Mistral AI's Aaron Delp explains how to pick a first agentic AI use case, run a 3-4 week pilot and build governed workflows in Mistral AI Studio.

The Context Layer for Enterprise AI: Metadata, Semantics and Memory

Collate, Graphwise, Precisely and AtScale debate the AI context layer: metadata graphs, semantic layers, knowledge graphs, RAG, memory and orchestration.

Semantic Intelligence: Why AI Agents Need Always-On Data Governance

Analyst Mike Ferguson and Collate explain why fragmented metadata stalls agentic AI and how unified, agent-assisted governance and knowledge graphs help.

Agentic AI in the Enterprise: From Human in the Loop to Agent Ops

Revefi, Precisely, AtScale, Google Cloud and Rhino leaders explain what makes AI agentic, why human in the loop won't scale, and how to govern agents.

AI Prototype to Production: What Makes Enterprise AI Apps Production-Ready

DataHub, Precisely, Snowflake and Fivetran leaders explain why AI prototypes stall, what enterprise-grade means, and why governance must start on day one.

Data Observability vs. AI Observability: Why They Must Converge to Scale AI

Revefi, Monte Carlo, Dollar General and Verizon Business leaders explain data vs. AI observability, inference tables, data downtime and model drift.

Agentic Data Management: How AI Agents Let Analysts Self-Serve Data

Matillion's team shows how the Maia agent lets analysts and data scientists build governed pipelines and fix failures without waiting on data teams.

The Hidden Costs of Poor Data Quality in AI, From Bias to Context

Revefi, Snowplow, Informatica and Fivetran experts explain how poor data quality, missing data and weak context undermine AI accuracy and ROI.

AI-Ready Data: Why AI Fails on Foundations, Not Algorithms

Fivetran, Snowflake and Accenture leaders explain what AI-ready data means and how quality, access and governance decide whether AI projects succeed.

AI Change Management: Building Trust and Upskilling the Workforce

Leaders from Coca-Cola, UnitedHealth Group, Clorox and Randstad Enterprise share how they build trust in AI, ease job fears and upskill employees.

AI Data Governance at Speed: Governance by Design, Not as a Blocker

Leaders from Quest Software, ADP, WellStar and Carter's explain how to scale AI data governance with trust scores, risk reviews and hub-and-spoke teams.

Custom MCP Servers and the Composable, Agentic Enterprise Platform

SnapLogic's Dylan Vetter explains how integration platforms prepare data for AI, democratize integration with governance and build custom MCP servers.

The AI Production Gap: Why Bad Data, Not Models, Blocks AI at Scale

Syncari's Jack Smith explains why integration sprawl, data lakes and legacy MDM stall AI pilots, and how a centralized agentic MDM layer helps them scale.

How to Measure AI Quality: Custom Benchmarks Beat Leaderboards

HumanSignal's Micaela Kaplan explains AI evals vs. benchmarks, why leaderboards fall short, and how to build rubric-based custom benchmarks.

Synthetic Data for AI Governance: Protecting PII Without Losing Signal

SAS's Sophia Rowland and Sundaresh Sankaran explain how governed synthetic data protects PII, builds robust test sets and balances privacy with accuracy.

Agent Mesh: How to Build an Enterprise Platform for AI Agent Collaboration

Grid Dynamics' Stas Stolpovskiy explains the agent mesh: what to control centrally, top-down vs bottom-up platforms, and why A2A beats MCP there.

Five Questions Enterprises Must Answer to Scale Agentic AI, per IBM

IBM's Ryan Zombo explains how agents, trusted data and centralized governance take enterprise AI to production, and shares IBM client zero results.

AI-Ready Data for AI Agents: Ownership, Curation and Governance

Leaders from TK Elevator, Baptist Health and Novelis explain how to govern agents, curate messy data and scale AI past pilots, at COLLIDE 2025.

Trusted AI at Scale: Walmart's Super Agents, AT&T's AI Board and More

Leaders from Walmart, AT&T, Kellanova and TelevisaUnivision discuss building trust in AI agents, governance boards, model upgrades and agent sprawl.

How to Detect Shadow AI and Replace It With Tools Employees Want

Airia's Dave Horton and Rahul Parwani explain why AI bans fail, how to detect shadow AI in layers, and how AI champions drive sanctioned adoption.

Scaling Enterprise AI: Why Trust and Monitoring Decide What Ships

AtScale, Monte Carlo and Revefi leaders on why AI pilots stall, how MCP makes queries deterministic, which KPIs prove value and how to govern fast.

Why Data Democratization Fails, and How Enterprises Close the Gap

AtScale, T-Mobile and Mars data leaders on why self-service data efforts stall, hub-and-spoke governance, data literacy and AI's pressure on readiness.

Data Lakehouse Architecture for AI: Governance, Cost and Migration

Fivetran, General Mills and Webster Bank leaders explain how data lakehouses, Apache Iceberg and open catalogs support governed, cost-effective AI.

Sovereign AI for Government: Data, Operations and Air-Gapped AI

NVIDIA and HPE leaders define sovereign AI, the three pillars of digital sovereignty and the challenges of deploying AI in air-gapped environments.

Agentic AI Planning, Reasoning and Memory: How to Build Enterprise Agents

Zenlytic, Nexla, Alation and Dataiku leaders define AI agents, explain why memory is unsolved and share how to make agents reliable and scalable.

Enterprise GenAI Applications: Security, Accuracy and Evaluation at Scale

AtScale, Nexla, Informatica, Vespa and Skyflow leaders explain how to secure data, reduce hallucinations and evaluate enterprise GenAI apps at scale.

Data + AI Observability: The Four Root Causes That Block AI Readiness

Monte Carlo's Shane Murray and Michael Segner explain how data, system, code and model failures break AI and how AI agents help monitor data quality.

Low-Code and No-Code AI: Democratizing AI Without Losing Governance

Leaders from Informatica, ADP, LTK and Baptist Health South Florida explain how low-code AI speeds experimentation and what guardrails it needs.

AI Data Governance: How ADP Protects Sensitive Data From LLMs

ADP's Amber Eshelman and Orlando Insight Group's Henry Clavo on AI risk points, two-way scanning, data stewards and building to the strictest regulations.

Scalable RAG Systems: Cutting Cost and Securing Enterprise Retrieval

Tecton, Nexla and Novartis experts on scaling RAG: batch vs. real-time serving, smaller models, index sync, chunk-level access control and evaluation.

Future-Proof AI Infrastructure: Self-Hosted LLMs, Governance and Lock-In

SandboxAQ, Alteryx and Plotly engineers on self-hosted vs. cloud LLMs, governed AI access, data quality testing and avoiding vendor lock-in.

AI Risk and Reward Across Industries: Lessons From NORC, IHG and AT&T

NORC, IHG and AT&T leaders on AI risk in regulated industries, showing business value, governance sandboxes, fairness tools and scaling AI with quality.

Responsible AI: How TD Bank, TelevisaUnivision and Vertex Build Trust

TD Bank, TelevisaUnivision, Vertex and Truist leaders on LLM privacy risks, AI ethics boards, explainability, generative AI security and data minimization.

Future of Enterprise AI: Nike, ADP and Yum! Brands on Human Oversight

Nike, ADP and Yum! Brands leaders on the future of enterprise AI: human in the loop, data strategy, governance as enablement and AI-first culture.

AI Transformation From Hype to Reality: Norfolk Southern, Equifax, Baptist

Elder Research, Norfolk Southern, Equifax and Baptist Health on AI hype vs. reality, model drift, AI governance councils, talent and use cases that work.

Data Democratization With PII Controls: Piedmont and Home Depot

Piedmont, The Home Depot, Alteryx and BBDO on democratizing data: certified data sets, business-owned access approvals, break-the-glass PHI logging.

GenAI Demo Day: Rafay, AnswerRocket, Crowdbotics and DataRobot Demos

Four generative AI demos: Rafay self-service GPUs, AnswerRocket's Max analytics assistant, Crowdbotics AI requirements and DataRobot apps with guardrails.

ALIGN AI Executive Summits · June 5, 2026

Building AI Agents That Survive Contact With Enterprise Reality

At ALIGN AI Executive Summit ATL, leaders from BlackRock, Dollar General and ManTech explain what it takes to run AI agents in production.