Webinars

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.

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.

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.

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.

Agentic AI Demos: Snowflake Cortex Code and Precisely's MCP Server

Snowflake demos spec-driven development with Cortex Code, and Precisely shows an AI assistant pulling location risk data through MCP.

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.

AI Demo Day: What Mistral AI, SAS, Fivetran and Precisely Showed

Four enterprise AI demos: Mistral document and voice AI, SAS forgery detection, Fivetran's one-copy data lake and Precisely location data.

How AI Is Replacing Traditional BI, and Why Context Is the New Job

PromptQL's Adam Malone explains why text-to-SQL and semantic layers stall, and why analysts' future work is teaching AI how the business works.

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.

Agentic-Ready Data: Why AI Agents Need Metadata, Not Just Clean Data

Snowflake and Precisely experts explain why data built for dashboards fails AI agents, and how metadata, semantic layers and lineage make it agent-ready.

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.

Self-Service AI Analytics for KYC and AML: Inside Actian AI Analyst

Actian's Phil Wall demos how an AI analyst grounded in governed business context speeds AML and KYC investigations without report requests.

AI Workflows in Claude: Supermetrics and Precisely Demo Read-Write AI

Supermetrics and Precisely demo Claude workflows that analyze ad performance, launch campaigns and fix SAP master data through an MCP server.

Agentic AI Demos: Lumi AI, Precisely, SAS Viya and Domino Data Lab

Agentic AI in Action Q2 recap: Lumi AI, Precisely, SAS Viya and Domino Data Lab demo proactive analytics, MCP data enrichment and auditable agents.

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.

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.

AI Demo Day Q2: Mistral AI, SAP, Rhino, Precisely, Skyflow and SecuPi

Six 10-minute enterprise AI demos: Mistral AI agents, SAP Joule agents, federated AI, location data via MCP, PII tokenization and AI access control.

Why AI Pilots Fail to Scale, and How Enterprises Get to ROI

Snowflake, Revefi and Coca-Cola AI leaders explain why AI pilots stall, how to choose what to scale, and the 70-20-10 rule.

How to Build AI Agents With MCP Tools: Teradata and Coder Live Demos

Teradata builds an agent that reasons over policies and tables via governed MCP tools; Coder shares agent harness, sub-agent and context lessons.

Building Enterprise AI Agents With Mistral: Right-Sized, Custom Models

Mistral AI's Aaron Delp demos no-code agents, OCR with MCP search and an FDA submission workflow, and explains model right-sizing and Forge.

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.

Agent-Ready Data Architecture: Streaming Real-Time Data to AI Agents

Striim and Google Cloud explain why batch warehouses fall short for AI agents and how CDC, event streaming and MCP deliver fresh context on Google Cloud.

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 Action: Five Live Demos of AI Agents on Enterprise Data

Informatica, PromptQL, Teradata, Precisely and Lumi AI showed AI agents finding, cleaning and analyzing enterprise data in 10-minute live demos.

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.

Four Signs Your Data Is Ready for AI: Real-Time, Governed, Event-Driven

Striim and Google Cloud explain four signs of AI-ready data: current state, derived not accessed, preserved business meaning and event-driven design.

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.

AI Demo Day Q1: Mistral, Google Cloud, Aerospike, Striim and More

Mistral AI, Google Cloud, Aerospike, Precisely, Striim and PromptQL showed agents, churn analysis, real-time nudges, location context and data masking.

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.

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.

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.

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.

Agentic AI Showcase: Supermetrics, Striim and Lumi AI Demo Data for Agents

Supermetrics, Striim and Lumi AI demo how AI agents get trusted data: marketing data via MCP, real-time CDC with masking, and an AI data analyst.

Choosing AI Use Cases That Pay Off: A Data Executive's Guide to ROI

Leaders from AtScale, Snowflake, Denodo, Fivetran and Revefi on telling AI hype from value, build vs. buy, board pressure and use cases with proven ROI.

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.

GenAI Demo Day Q3: Snowflake, Denodo, Zenlytic, PromptQL and More

Snowflake, Denodo, Zenlytic, PromptQL, Fivetran, Google Cloud and Precisely demo AI agents, semantic context, A2A and MCP servers in 10 minutes each.

One Real-Time Data Layer for Predictive, Generative and Agentic AI

Aerospike's Sesh Seshadri explains multi-step inference, why recommendations, search and RAG share one pipeline, and the data agentic AI needs.

Multimodal AI Search: How Tensors Make Video Retrieval Work at Scale

Vespa.ai's Bonnie Chase and Zohar Nissare-Houssen explain why vectors aren't enough for multimodal AI and demo hybrid video search with tensors.

People, Culture & Leadership · November 6, 2026

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.

GenAI Demo Day Q2: Snowflake, Google, Precisely, Zenlytic and More

Seven GenAI demos: Snowflake Cortex Agents, Gemini 2.5 on Vertex AI, Precisely Data Graph, Zenlytic, Fivetran, Dataiku and Zerve's data science agent.

What Is an AI Factory? NVIDIA and HPE on Scaling Enterprise AI

NVIDIA and HPE leaders explain AI factories: compute, networking and software as a relay team, private cloud AI options, liquid cooling and AI strategy.

How AI Augments HPC Simulation: Physics Meets Probability

HPE's Steve Heibein explains how AI narrows the search space for physics-based HPC simulation, from COVID research to NASA parts, and why cooling matters.

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.

GenAI Demo Day: Snowflake Cortex Agents, TiDB GraphRAG and Zenlytic

Snowflake, TiDB and Zenlytic demo AI agents over enterprise data: Cortex Agents, GraphRAG in a single database and open-ended AI analytics.

People, Culture & Leadership · November 6, 2026

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.

Production Python Data Apps: How Broadband Insights Scaled With Dash

Broadband Insights CEO Patrick Hutto shows how Python data apps on Plotly Dash Enterprise helped small ISPs plan networks and cut install times.

The Hidden Costs of AI: Scaling ML From Notebook to Production

Tecton, Mosaic Health and JPMorgan Chase leaders explain the people, pipeline and premature-optimization costs of scaling ML to production.

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.

ColPali for Multimodal RAG: Retrieving Charts, Tables and PDFs Visually

Vespa.ai CEO Jon Bratseth and RavenPack's Aakarsh Ramchandani explain how ColPali retrieves PDF pages visually, its limits and how to scale it.

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.

Large Quantitative Models: How Physics-Based AI Speeds Materials R&D

SandboxAQ's Ang Xiao explains how large quantitative models pair AI with physics simulations to speed battery, chemistry and materials discovery.

From Prototype to Production AI: Cutting Tech Debt and Train-Serve Skew

Tecton, Emory Healthcare and BBDO leaders on moving AI to production: tech debt, standardization, train-serve skew, automation and governance.

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.

Security & Fraud · November 6, 2026

Fraud Detection Build vs. Buy: How North Built Real-Time ML In-House

North's Ben Orkin explains why the payment processor replaced a third-party fraud tool with its own real-time ML system and feature store.

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.

Relational Deep Learning: Graph Neural Networks on Snowflake Data

Kumo AI's Hema Raghavan explains relational deep learning: turning Snowflake tables into graphs so neural networks replace manual feature engineering.