How to access mainframe data stored in Db2 on z/OS
Automated data integration enables AI and analytics based on mission-critical operational data stored in IBM Z.
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Modern Software Development For Today's Systems & Applications
Qlik Answers and the Automate Agent - Hello World - Part 3
In Part 3, we keep things simple with a “Hello World” example that shows how to create a basic Qlik Automate workflow and trigger it directly from Qlik Answers. You’ll see how an insight can quickly become an action—all within Qlik, without a complicated setup. Check out Parts 1 and 2 for the full series.
Unlocking the Power of Trusted Data Intelligence: Amazon Quick Meets Qlik MCP Server
When was the last time you made a major business decision and were completely certain the data behind it was accurate, complete, and trusted? For most organizations, that certainty is less common than it should be. Qlik and Amazon Quick together solve one of the biggest obstacles to AI adoption: knowing whether you can trust the output.
Why AI Sovereignty Is an Operational Problem
AI sovereignty has become one of those phrases that sounds precise until someone asks what it actually means. For one federal agency, sovereignty means keeping sensitive data inside accredited boundaries. For another, it means running open-weight models in a FedRAMP-authorized private cloud. In defense and intelligence settings, it may mean operating inside an air-gapped environment at IL5 or IL6.
How can AI agents reduce operational costs throughout the organization?
Every CFO has looked at the headcount report and asked the same question. Why does it take twelve people and four tools to do something that feels, on paper, like it should take three? The honest answer is usually not laziness or bloat. It is a coordination tax. Someone has to read an email, decide what it means, open three other systems to check context, type a response, update a tracker, and notify two other teams. None of that is hard work.
Beyond the Budget: The AI Decisions That Only Humans Can Make
Earlier this month I spent time with a group of senior executives discussing the economics of AI: what it actually costs, where the value is and is not materialising, and what the organisations that are getting returns are doing differently from the ones that are not. That conversation encapsulates why this series is called Beyond the Budget. Not because cost does not matter. It does. But because the budget is where the consequences show up.
How agentic QA cuts the test maintenance tax
Every QA budget has a line item for building test coverage, but 30–50% of that automation budget ends up spent on maintenance instead of new tests. That disparity stays invisible until a release goes out, the application shifts underneath the tests, and the QA team spends the next three days rewriting broken scripts instead of finding new bugs.
Why Trusted Data Is the New AI Moat (w+ Rick Kranz from the AI Marketing AUtomation Lab)
Rick Kranz has built over 100 AI automations for his community and clients — he has no reason to defend Databox. But when he tried to run his AI analysis without the Databox MCP, it just stopped working. In this episode, Rick and Pete break down exactly why: the semantic layer, the metric definitions, and the standardized math that make an AI's answer trustworthy instead of a guess. If you've ever wondered why connecting five random MCP servers to Claude doesn't give you the same results as a purpose-built data layer, this is the episode.
Building Enterprise-Grade AI Agents: From Prototype to Production
Everyone can build an AI agent today. The hard part isn't getting an agent to answer a question or complete a demo. It's deploying one that employees trust, security teams approve, and operations teams can manage at scale. That's where many AI projects stall. As organizations move beyond experimentation, the conversation shifts from prompt engineering to production readiness. Can the agent safely access business data? Can you evaluate changes before deployment? Can you understand why it made a decision?
Use of AI in Software Development
Quick application integrity check: can your quality strategy survive the tsunami of code coming its way? AI is accelerating development, increasing code abstraction, and multiplying the volume of software teams need to validate. But existing QA approaches weren't built for this level of speed and scale. Application integrity closes the growing gap between what teams build and what they can verify, providing continuous assurance that software works as intended.