Systems | Development | Analytics | API | Testing

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.

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.

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.

Paul McDonough-Smith estimates that many business leaders would struggle to define their organization’s problem clearly in fewer than 25 words. With AI, that lack of clarity can quickly turn into fragmented solutions and misplaced expectations. In this episode of The AI Forecast, Paul Muller sits down with Paul McDonough-Smith, a Visiting Senior Lecturer at MIT Sloan School of Management and a Senior Advisor to NASA's Goddard Space Flight Center, to explore how organizations can approach AI with greater clarity and purpose.

AI changed the process, but not what matters. In the latest episode of The Data & AI Chief, three authors explore what it takes to lead through the AI era, from scaling innovation to building AI-ready data foundations to keeping humans at the center. This episode features: Linda Hill, Harvard Business School Professor and author of Genius at Scale, on scaling innovation and leading through uncertainty.

In this video, learn how to set up AgentSpot's self serve free tier from scratch. You'll connect the apps you already work with (Google Workspace, Slack, and more) and authenticate them in a couple of clicks. Plus, you'll build your first agent, one that catches you up on Slack threads so you don't have to. Tip: the more tools you connect, the more context your agent has, and the better it works for you.

How does our sales and revenue operation teams use Spotter, our agentic analyst, to instantly access and analyze their data using natural language? In this video, you'll see how Spotter makes data interrogation easy and reliable. Learn how to: Explore Data Sets: Quickly understand what data you have access to, where it is pulling from, and get immediate examples of what you can ask. Get Instant Answers: Type a natural language question (like "What's the booked ACV this quarter by region?") and instantly receive easy-to-read charts and deep analyses.

As enterprise AI scales, traditional data architectures are reaching their limit. Workloads are becoming more distributed, data movement is accelerating, and tech leaders face growing pressure to justify AI spend while delivering real business impact. Chief Technology Officer Sergio Gago breaks down key findings from Cloudera’s latest global survey of enterprise architects, data architects, and cloud leaders.