AI ambitions at risk: 72% of IT leaders say poor infrastructure is stalling AI growth
Confluent's 2026 Data Streaming Report finds the biggest barrier to AI growth isn't investment, but the infrastructure needed to support it.
Ep 78 | Mastering Enterprise AI: Why Some Projects Succeed While Others Fail
AI may be the most capable intern your organization has ever hired. However, interns still need guidance and clear direction. Enterprise AI is proving no different. In this episode of The AI Forecast, Paul Muller sits down with Michael Gray, CTO of Thrive, to explore the patterns and anti-patterns emerging from real-world enterprise AI deployments. Drawing on his experience helping organizations implement AI at scale, Michael offers a practical framework for evaluating AI maturity, helping leaders understand where adoption breaks down and what it takes to build momentum across the organization.
Reporting Intelligence UI Flyover
See how Reporting Intelligence from insightsoftware transforms the way finance teams build, manage, and distribute ERP-connected reports, without IT involvement, data engineering, or infrastructure overhead. This walkthrough covers the full product experience, from self-serve report building in Excel to cloud-based scheduling and distribution, embedded Lineos AI assistants, and the autonomous Intelligence Layer that monitors your KPIs and surfaces insights around the clock.
How to build your first AI agent
A step-by-step guide to laying the groundwork, choosing the right use case, and getting a practical start with AI.
The internal war over who owns AI.
There is a massive boardroom fight happening right now over who gets to control AI. Should it be IT? A centralized lab? The executives? Boris Rabkin from Ligentia drops a truth bomb: AI belongs wherever value is actually created. If your AI strategy is locked inside an isolated corporate lab instead of in the hands of your product, engineering, and customer teams, it’s going to fail. Full episode out now!
Stop Rebuilding Data Models From Scratch: Meet SpotterModel
Your data engineering team shouldn't be the bottleneck between a business question and a governed answer. SpotterModel turns a natural language prompt into a deployable data model. This release does the heavy lifting on complex calculations, and lets you roll back to any previous model state, anytime, so a bad change never costs you hours of rebuilding. It maps your relationships, dimensions, and measures instantly, and you stay in control of table selection and the build process the whole way.
Build vs Buy Streaming for Real-Time RAG: 2026 Guide
Moving a retrieval-augmented generation (RAG) prototype from a Python notebook into production isn't an API orchestration challenge. It's a distributed systems problem. For engineering managers and data platform leads, the build-versus-buy decision on streaming infrastructure will dictate your artificial intelligence (AI) feature velocity for the next three to five years. This guide assumes you've already prototyped a RAG pipeline.
Build Compliant AI Agents With Stateful Stream Processing
The EU AI Act's general provisions are already in force, and high-risk AI system obligations apply from August 2026. The National Institute of Standards and Technology (NIST) AI Risk Management Framework and its Generative AI Profile set the baseline for what auditors expect, framing governance around four functions: identify, measure, manage, and monitor. Deploying artificial intelligence (AI) agents in regulated environments isn't a sandbox experiment anymore. It's a strict governance challenge.
Building a Data Foundation for AI Is a Rewarding Experience
AI runs on data, and global enterprises are awash with petabytes of data. That might suggest that it’s easy for companies to advance their businesses through the power of AI. Yet enterprise data is often fragmented across departmental and technological silos, and that data is often inconsistent, ungoverned and disconnected from mission-critical systems. As a result, many AI initiatives stall before they can deliver operational value, and the root cause is rarely the model.
Driving Down Ingestion Costs to Unlock More Budget for AI Value
One line from Snowflake Summit 2026 stood out above everything else. Christian Kleinerman, EVP of Product at Snowflake: "We do not want any of you spending money with Snowflake, in any use case, if you are not getting more value in return." It's a refreshing commitment, and it points directly at the cost efficiency conversation we've been having with customers around open lakehouse architectures. Here's the core argument: data movement doesn't directly generate value.