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Ever wonder why so many enterprise AI projects never make it past the pilot stage? It’s not the AI—it’s the foundation. In this video, we break down why rushing into complex models without fixing inconsistent data, fragile pipelines, and afterthought governance is a recipe for failure. Fix the basics first!

AI products today are being let down not by the models — but by the delivery layer between the agent and the user. In this session, Fiona Corden, Technical Product Manager, at Ably, breaks down why AI UX is eroding consumer trust, how to spot the delivery-layer problems hiding in your product data, and what the companies getting it right are doing differently. You'll come away knowing how to diagnose whether your AI product has a session layer problem, what durable sessions are, and why they're becoming the standard solution for resilient AI UX at scale.

Get a recap of innovations and product updates from Appian World 2026. Catch up on key announcements and trends, identify use cases, and learn how to apply new features to your processes and applications.

And get Fivetran’s latest news at.

And get Fivetran’s latest news at.

And get Fivetran’s latest news at.

Is your organization actually driving value with AI, or just scratching the surface? USC's Founder of the AI Trust Lab, Shub Agarwal, joins to share his definitive 9-step playbook to take AI from concept to production without burning your budget.

We've been getting a lot of questions about how Rovo works with Zephyr, so Matt Bonner gives a quick look at a few of the capabilities teams are finding most valuable: Test case generation Test coverage insights Release Risk Analyzer.

Are you actually building an open data platform, or are you just using open source file formats inside a new type of vendor lock-in? Many organizations assume that migrating to Apache Iceberg or Parquet automatically makes their data architecture open. However, true architectural freedom requires a strategy that spans across your entire data estate—not just the storage layer.

Shub Agarwal (Founder of the AI Trust Lab at USC) flips the script. Stop over-investing in massive data overhauls. Instead, reverse your approach: start with a brutal business problem, pull only the specific data needed to solve it, and build incrementally. Chief Data & AI Strategy Officer Cindi Howson agrees that true value comes from scaling immediate business impact, not waiting for a flawless architecture that will never arrive.