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What's Next Is Not More AI. It's Better Foundations.

The next real advantage in artificial intelligence will not come from the next AI tool or application. It will come from a stronger data foundation beneath it. At Hitachi Vantara we work every day with customers on the data supporting their systems. That vantage point has led me to a simple conclusion: the leaders who pull ahead will not be the ones with the most advanced AI. The leaders will be those whose data foundations are strong enough so that AI can be trusted to act.

Thought Tank: Marketing in the Age of Agents

Join us for a live broadcast of The Thought Tank: Marketing in the Age of Agents. Our host and CMO Micheline Nijmeh sits down with Katie Marcham, SVP Marketing EMEA at ThoughtSpot, to pull back the curtain on what it actually looks like to run a modern marketing organization on live data in one of the most complex, relationship-driven markets in the world. They'll cover the transformation Katie's led over the past year: leaner teams, smarter tools, and a tighter partnership with EMEA sales, all grounded in what the data is showing in real time.

[Finance Demo] - AgentSpot Use Case - Collections Forecast

Every month, finance teams rebuild their collections forecast by hand, copying and pasting from disconnected files and hoping nothing breaks. In this video, we use AgentSpot to build a Collections Forecast Agent that connects to accounting files, NetSuite, and live bookings data to automate the full monthly rebuild, reconcile actuals against forecast, and output a traceable Excel workbook your whole team can work from.

How to Answer Any Performance Question with an AI Analyst

Ask one question with a time range, a metric, a comparison, and a goal. The AI analyst does the gathering. You keep the judgment. To answer any performance question in minutes, ask Databox’s AI Analyst, Genie, one well-built question that includes: a time range, a metric, a comparison, and a goal. Genie queries the data sources you’ve connected, runs the calculation, and returns the answer with a recommendation attached.

How Real Estate Companies Modernize Legacy Reporting with Custom Analytics Platforms

Two dashboards, same portfolio, different occupancy numbers. This is the moment most reporting modernization projects start, and it is usually read as a dashboard problem. The tool gets blamed, a replacement gets scoped, and the divergence survives the migration intact. It survives because it never lived in the dashboard. When occupancy reads 91% on one screen and 94% on another, both tools are usually working correctly. They are faithfully rendering two different calculations of the same concept.

From CoWork to Action: How to Make Snowflake CoWork Production-Ready

AI agents are moving rapidly from experimentation to execution, and Snowflake CoWork is making it easier for teams to put AI to work across everyday business workflows. But knowing how to use CoWork effectively is only the first step. The bigger question is: how do you make sure the data powering those workflows is complete, current, and reliable enough to trust?

[Product Demo] AgentSpot Use Case - Automate Release Notes

Release notes are the thing that always gets written last, usually by whoever has the least context, usually the morning after ship day. Everything you need is already sitting in GitHub, it just isn't in a form anyone outside engineering can read. In this video, we use AgentSpot to build a Release Notes Workflow that reads what's been merged in GitHub, translates the changes into human-readable notes, posts them to your team's Slack channel, and keeps a running Slack canvas so every release stays in one place.

[Product Demo] AgentSpot Use Case - PM Jira Assistant

Writing tickets is the tax every PM pays. You know exactly what needs to get built, then you spend an hour turning it into properly scoped Jira issues with acceptance criteria, labels, and the right epic. AgentSpot does the writing for you. In this video, we use AgentSpot to build a Product Assistant that turns a rough feature idea into fully drafted Jira tickets, pulls in context from your existing backlog so nothing gets duplicated, and files them to the right epic ready for grooming.