COLLIDE AI Conference

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.

Security & Fraud · November 6, 2026

AI Deepfake Social Engineering: How Enterprises Can Defend Against It

TrendAI's Sundari Parekh explains how AI agents speed up deepfake fraud and social engineering, and the defenses security teams need now.

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.

Context, Semantics & RAG · November 6, 2026

What Is Context Engineering? A Field Guide to the AI Context Window

Redis's Jessica Emmons explains context engineering, the four most common context failures, why RAG falls short, and how context engines work.

Observability & Data Quality · November 6, 2026

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.

Agentic Data Engineering: How Matillion's Maia Builds Data Pipelines

At COLLIDE 2026, Matillion's Arawan Gajajiva showed how the Maia AI agent plans, builds and tests data pipelines with a human in the loop.

Context, Semantics & RAG · November 6, 2026

The AI Context Engineer: How the Modern Data Team Is Changing

WisdomAI's Geetesh Iyer explains why AI analytics pilots fail at scale, why semantic layers aren't enough, and the rise of the AI context engineer.

People, Culture & Leadership · November 6, 2026

Why AI Initiatives Get Stuck, and How Leaders Can Move Them Forward

Analytic Vizion's Nelson Davis says stalled AI initiatives are rarely a technology problem, and shares five questions to restore alignment.

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.

From Raw Data to Predictions: How BigQuery AI Speeds Up ML Workflows

Google Cloud's Shan Kulandaivel shows how SQL-based forecasting, prediction and document AI in BigQuery move ML ideas to production faster.

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.

Streamhouse Architecture: Real-Time Data for Smarter AI Agents

IBM's Ann Leach explains why stale data limits AI agents, what a streamhouse is, how it complements the lakehouse, and how Ferrari used real-time data.

Security & Fraud · November 6, 2026

Real-Time AI Fraud Detection: Why Milliseconds Matter

Aerospike's Sesh Seshadri explains how ML, graph analytics and multi-stage inference stop financial fraud within a 50 ms window.

Measuring AI ROI: How to Tie Every Use Case to a Business OKR

Leaders from The Home Depot, Mars Wrigley and Equifax explain how to measure AI ROI, balance speed and cost, and set executive expectations.

People, Culture & Leadership · November 6, 2026

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.

Observability & Data Quality · November 6, 2026

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.

Vibe Analytics for EDA: Turning Weeks of Chart Requests Into Minutes

Broadband Insights CEO Patrick Hutto explains how vibe analytics with Plotly AI Studio speeds exploratory data analysis, mockups and self-service.

What Successful AI Teams Do Differently: Lessons From 120 AI Projects

Nanonets CEO Sarthak Jain shares lessons from 120 AI projects: plan a 6-18 month ramp, design for human intervention, fix data first and go bottom-up.

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.

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.

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.

Observability & Data Quality · November 6, 2026

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.

High-Performance Data Infrastructure for AI: Aerospike's Cost Case

Aerospike CTO Srini Srinivasan explains how hybrid memory cuts AI infrastructure costs and why agentic AI multiplies real-time database load.

Orchestrator Agents and Systems of Intelligence: IBM on Future of Work

IBM's Melissa Hadley explains systems of intelligence, agent sprawl and how orchestrator agents in watsonx Orchestrate route work across HR, sales and IT.

Data Science Agents in BigQuery: Agentic AI for Data Scientists

Google Cloud's Suda Srinivasan demos a data science agent building a bike-share demand forecast in BigQuery and explains what agents can't replace.

Wendy's FreshAi: Lessons From Generative AI in the Drive-Thru

Wendy's Will Croushorn on how FreshAi handles 95% of drive-thru orders, why word choice is data, and how a 'sorry' metric cut friction by 60%.

Aerospike Live Demo: Surviving Node Failure and Catching Fraud by Graph

At COLLIDE 2025, Aerospike's Stan Christ shut down cluster nodes under load, then showed graph-based fraud detection flagging transfers many hops away.

People, Culture & Leadership · November 6, 2026

How AI Enables More Human Leadership: Three Keystone Habits for Leaders

Analytic Vizion CEO Nelson Davis says AI raises the value of empathy, curiosity and judgment, and shares a matrix for when to automate decisions.

The AI Maturity Journey: Machine Learning, GenAI and Agents in One Workflow

KMS Technology CTO Guy Merritt explains how ML, generative AI and agents fit one workflow, and why AI coding gains vary from 3x to a slowdown.

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.

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.

RAG Use Cases: How UPS, TSYS and Shipt Put Retrieval-Augmented AI to Work

UPS, TSYS and Shipt leaders on retrieval-augmented generation: a digital twin operations copilot, internal use cases, RAG risks and prioritizing GenAI.

AI ROI Across the Project Life Cycle: Business Cases, Data and Cloud Costs

Bank of America, Carelon Health, Lockton and PRGX leaders on AI business cases, the three E's of ROI, data bottlenecks and controlling cloud costs.

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.

Generative AI Use Cases Beyond Chatbots: Lessons From Media and Retail

Leaders from Dollar General, Scripps, TelevisaUnivision, Vertex and PRGX on generative AI use cases beyond chatbots, plus security, validation and ROI.

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.

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.