Observability &amp; Data Quality

Context Over Compute: How a Semantic Layer Improves AI ROI

AtScale's Mark Palmer explains why context, not bigger models, drives AI ROI, and how semantic layers and accuracy testing cut errors.

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.

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.

AI Trust Gates and Sandboxed Coding Agents: Precisely and Coder Demos

Precisely demoed an MCP trust gate that blocks agents from acting on low-quality data. Coder showed coding agents running in governed remote workspaces.

The AI Enterprise Conference · November 6, 2026

The Self-Funding AI Strategy: How Verisk Lets AI Pay for AI

Revefi's Girish Bhat and Verisk CDO Louis Dimodugno explain how cutting data and AI waste can fund new AI work, plus a 4-step playbook.

The AI Enterprise Conference · November 6, 2026

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 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.

Scaling AI Architecture: How to Build AI Systems That Grow Reliably

SAP, Skyflow and Fivetran leaders explain the isolation, modularity, evaluation and governance patterns that take AI from proof of concept to scale.

AI Agent Observability: Six Early Warning Signals for Production Agents

Monte Carlo and Snowflake on AI agent observability: why agents degrade after launch, a four-layer trust framework and six early warning signals to track.

AI Infrastructure at Scale: Semantic Layers, GPUs and Inference Costs

AtScale and Rhino Federated Computing leaders explain the infrastructure layers, governance and inference cost controls production AI and agents need.

How AI Agents and Bots Break Web Analytics, and How to Fix the Data

Snowplow's Nick Stanchenko explains how bots skew metrics, why AI agent traffic is invisible to analytics, and how to handle agentic browser sessions.

Why AI Projects Fail: 4 Practical Fixes From Data Fluency to Trusted Data

Elder Research practitioners share four fixes for stalled AI projects: leadership data fluency, value-based prioritization, trusted data and data SLAs.

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.

GenAI Demo Day Q4: Snowflake, PromptQL, AtScale, Monte Carlo and More

Snowflake, PromptQL, Google, AtScale, Dataiku, OneTrust, SAS and Monte Carlo demoed AI agents, semantic layers, AI governance and agent observability.

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 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.

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.

Why AI ROI Stalls: Eight Analytics Gaps Leaders Can Diagnose

Elder Research's Jennifer Schaff explains eight gaps that erode AI ROI, from strategy misalignment to data literacy, with client stories and questions.

Human-Centered GenAI: Why Trust, Not Models, Decides Pilot to Production

Snorkel AI's Dennis Panos explains why GenAI pilots fail on trust, and how expert-built evaluations and data development move systems into production.

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.

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.

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.

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.

Real-Time AI Architecture: Fresh Features, Low Latency, Lower Cost

Tecton, Sam's Club and AT&T panelists explain when AI needs real-time data, how to build fresh low-latency features, monitor drift and control costs.

AI Strategy Starts With Data Strategy: Southern Company, Clorox, Tecton

Southern Company, Clorox, Morgan Stanley and Tecton on aligning data and AI strategy: data quality, features as code, scaling pilots and real-time data.