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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.

Agentic AI in Banking: How Autonomous AI Is Changing Financial Services

Quick answer: Agentic AI in banking refers to AI systems that don’t just generate text or answer questions – they take a goal, break it into steps, use tools and data sources, make decisions, and complete multi-step workflows (like investigating a fraud case or processing a KYC file) with minimal human intervention, looping in a person only for genuine judgment calls. Walk into any banking technology conference in 2026, and you’ll notice the conversation has quietly shifted.

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.

[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.

From IoT Data to AI-Ready: The Edge Solution

Is your data actually ready for AI? While companies rush to deploy machine learning models, 83% of executives realize that high-value, real-time data is trapped at the physical edge—on factory floors, inside hospitals, and at retail terminals. With billions of connected IoT devices, managing this data creates massive hidden headaches like security risks and pipeline blind spots. True AI readiness starts at the edge. Bridge the gap between your edge devices and your AI goals today.

[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.

[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.

AI in Claims Processing: What's Actually Working in 2026

‍AI claims processing applies predictive models, machine learning, computer vision, and generative AI across claims workflows. These technologies analyse documents, images, policy terms, historical records, and structured claim data. Insurers use AI for document extraction, claim triage, fraud detection, damage assessment, and adjuster assistance. Predictive models classify claims, estimate severity, and identify cases requiring specialist review.

Raw WebSockets for AI streaming: when patching stops paying off

Raw WebSockets drop connections, lose track of canceled responses, and don't natively reach a second device. If your AI streaming feature has been in production for a while, you've probably already built a fix for at least one of these and found another one waiting. Reconnection, cancellation, multi-device delivery, and crash detection are the four problems raw WebSockets leave for you to solve, and each is its own piece of infrastructure to build. Solve one, and the other three remain unsolved.

Using MCP Tools for Declarative Pipelines - Creating the Data Pipeline - Video 2

This is Part 2 of a two-part video series exploring how Qlik MCP tools can be used with declarative pipelines directly within VS Code. In this video, Mike Tarallo, demonstrates how to now create the data pipeline using declarative YAML in VS Code. You will see the creation process, learn some tips and tricks and see the final result.