Systems | Development | Analytics | API | Testing

Retail merchandising teams spend countess manual hours evaluating new product submissions—crunching revenue models, analyzing sales cannibalization, and writing supplier rejection emails. Powered by *Cloudera AI agents* , this video demonstrates how retailers can transform that tedious, bottlenecked onboarding workflow into a seamless, automated process. As soon as a new product is submitted, autonomous background agents map it against existing inventory, evaluate growth risks, assess market saturation, and highlight specific low-performing SKUs to replace.

An AI project can deliver a great prediction and still fail the business. Rajesh Kelvalkar has seen it happen firsthand. In one mining project, the model worked, but the team discovered that the downstream business process wasn’t configured to act consistently on its recommendations. The technology had done its job, but the transformation hadn’t. Recorded at EVOLVE26 Singapore, this episode of The AI Forecast brings Paul Muller together with Rajesh Kelvalkar, Senior Advisor at Tech Data APJ, to explore what more than 20 years of transformation work has taught him about putting AI into practice.

After 23 years in machine learning and data science, Cao Hong has seen plenty of AI projects come and go. His measure of success is simple: did it create value for the business? Recorded at *EVOLVE26* Singapore, this episode of The AI Forecast brings Paul Muller together with Cao Hong, Principal Director of Data Apps at NCS, to explore how organizations can turn AI experimentation into measurable business impact.

Every new AI chat is another first day on the job. Rob Collie describes today’s general-purpose AI as a brilliant new hire who wakes up on their first day every time you start a new conversation. It may understand your industry, but your workflows and business context have to be explained all over again. In this episode of The AI Forecast, Paul Muller speaks with Rob Collie, founder and CEO of P3 Adaptive, former Microsoft product leader for Excel and Power BI, and author of “Fair Game: Bringing AI Into Reach for All Business” to explore why business context can make or break enterprise AI.

Copying enterprise data to run AI is officially dead. Most platforms force you into a risky "lift and shift" trap—shuffling petabytes of protected data into the public cloud, exposing sensitive PII, and burning budgets on massive egress fees. As we’re on the ground with ServiceNow in Chicago this week, we’re taking a closer look at how the Cloudera Workflow Data Fabric Zero Copy Connector for @ServiceNow helps AI agents query governed data directly where it resides — enabling organizations to automate workflows while reducing the cost, complexity and risk of data movement.

Most AI pilots never make it past the demo phase. AWS Machine Learning Lead Praveen Jayakumar has seen plenty of promising AI projects get stuck between a successful demo and production. Teams often define what success looks like without deciding what failure looks like, leaving underperforming projects alive long after they should have been shut down. As Praveen puts it, they become “zombie” AI projects.

Human biology is extraordinarily complex, and researchers often have only fragments of information to work with. Brian Martin compares it to looking at a skyscraper through a keyhole: you can see something clearly, but only a tiny piece of the whole. Recorded at EVOLVE26 Singapore, this episode of The AI Forecast brings Paul Muller together with Brian Martin, CTO of Applied AI at Cloudera and co-founder of Rare Hopes NFP, to explore what one of the world’s most data-intensive industries can teach us about AI and decision-making.

Enterprise AI loves a shiny object. Sol Rashidi would rather talk about procurement. After years of leading AI deployments, Sol has learned that some of the biggest wins come from the decidedly unglamorous parts of the business. Her favorite function to transform? Procurement.

Small files are a common problem in Lakehouse tables such as Apache Iceberg, particularly with streaming workloads that continuously commit data in small batches. In this episode, Dipankar explores how Apache Iceberg compaction helps address the small file problem.

80% cheaper AI model. Nearly 2X the data warehouse bill. Switching to a cheaper AI model can cut your token costs by 80%, but it might accidentally double your public cloud data warehouse bill. In this video, we break down the "Agentic Cost Shift"—how autonomous AI agents generate hidden, high-compute SQL queries that drive up data lakehouse costs—and how to fix it using Cloudera. See where the real cost of AI is shifting.