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.

What's new in our latest release: Spreadsheets UI on Live Data lets you build formulas and save reusable answers directly on live, governed data using a familiar spreadsheet interface. Spotter Memory, now General Access, helps Spotter deliver smarter, more personalized answers as it learns from your dashboards and conversations. Take embedded analytics further by embedding entire Collections pages directly into your apps for instant access to curated insights.

How long does it take your marketing team to answer a follow-up question that comes up in a meeting? For most teams, the loop is familiar: take the question back to the analyst, wait for an answer, ask the follow-up, wait again. By the time the answer arrives, the meeting is a memory. In this clip from the Thought Tank: Marketing in the Age of Agents, ThoughtSpot CMO Micheline Nijmeh and SVP Marketing EMEA Katie Marcham show what the alternative looks like.

Diego Lomanto, Chief Marketing Officer at Writer, joins Snowflake CMO Denise Persson to discuss the current investment frenzy in AI and how to navigate the hype. Learn how understanding the technology cycle can help marketers identify real value and drive wealth creation. Discover Diego's unique career path from developer to CMO and how his background in technology and engineering shapes his approach to marketing. Gain insights into the societal impact of technology and the importance of reshaping and reforming to drive a better standard of living for humanity.

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.