New: Give Your AI Analyst the Context to Explain Performance and Help You Take Action

You open your performance dashboard and notice that your sales pipeline has dropped 15% since last week. You can see the decline, but the numbers alone don’t explain what caused it. Maybe several deals were pushed into next quarter, your team lost a major opportunity, or a campaign generated fewer qualified leads than expected. The answers are likely somewhere in your CRM, project management tool, or conversations your team has been having.

Why B2B Software Is Being Judged by B2C Standards

The bar for B2B software is being set by B2C experiences. In this clip, Kim Faura, former Managing Director at Gumtree, joins ThoughtSpot's Jane Smith and Mark Hill-Smith to talk about what this expectation shift means for product teams, and what it takes to close the gap between what B2B tools deliver today and what users now expect.

Governed End to End: How Spotter Analysts Control Data Access

Before any data leader deploys an AI analyst, they ask the same question: what can it touch? In this video, see how Spotter Analysts are governed end to end. What you’ll see: → How admins control which certified models a Spotter Analyst can read→ How data access travels with a shared analyst→ How “show work” traces every answer back to its source→ What governed end to end means in a production agentic analytics environment.

Ep 96 | Leaving the Sandbox: AI In the Real World

The AI worked in the sandbox. Then it met the real world. David Hardoon learned that lesson during an ATM optimization project designed to improve cash-delivery routes. Once deployed, the system collided with traffic and unpredictable schedules, which are the realities that face the people doing the work. Recorded at *EVOLVE26* Singapore, this episode of The AI Forecast brings Paul Muller together with David Hardoon, Head of Advanced AI at Accenture, to explore why the human side of AI transformation can determine whether promising technology ever delivers value in production.

Build an AI Analyst Your Team, or Your Customers, Can Trust

You can get an AI Analyst from most of the players, or you can try to build one yourself. Fewer let you decide what it knows, what it can touch, and who can use it. And even fewer still let you define what it won't do. You need all three before you hand one to a team or put one inside your product. In most of the product experiences, your AI analyst's context had two homes: one expert's head, or org-wide instructions.

Is Your AI Running Wild? #podcast #news #techshort #cloudsecuritypodcast

Is hands-off AI adoption turning your business into the "Wild West?" Handing out credit cards for unlimited tokens without proper guard rails is a recipe for failure. Discover why establishing best practices and security rails is essential as AI agents transform the enterprise.

How Snowflake's Paid Media Team Cut Reporting From a Week to Hours | AI Blueprint for Go-To-Market

Mariam el Baghdady (Manager, Digital Marketing, Snowflake) shows how AI changed two of her most time-consuming tasks: weekly performance reporting and quarterly forecasting across 20+ countries. The old reality: spending hours formatting data just so she could talk about it. Now a weekly HTML report in CoCo flags what needs attention across channels and regions in minutes, and a forecast that used to take nearly a full week turns around in a fraction of the time. She also shares how she got there with no technical background whatsoever.

Best Graph Databases for AI Agents

AI agents that forget their context are just expensive chatbots. The difference between intelligent agents and simple assistants comes down to memory architecture, specifically how well your system tracks relationships, maintains temporal context, and reasons over evolving information. Graph databases have moved from niche infrastructure to essential AI components, with GraphRAG implementations showing 80% improvements in agent truthfulness and significant accuracy gains over vector-only approaches.