Systems | Development | Analytics | API | Testing

Use of AI in Software Development

Jul 28, 2026 By SmartBear In SmartBear
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.
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appian

Building Enterprise-Grade AI Agents: From Prototype to Production

Jul 28, 2026 By Wei Smith In Appian
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?
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Why Trusted Data Is the New AI Moat (w+ Rick Kranz from the AI Marketing AUtomation Lab)

Jul 28, 2026 By Databox: AI-powered analytics that answers back In Databox
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.
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yellowfin

Conversational Analytics in 2026: Where Natural Language Search Helps

Jul 28, 2026 By Hagop Panosian In Yellowfin
In 2026, asking a BI tool, “What happened to revenue last quarter?” feels almost effortless. That ease is the appeal of conversational analytics. It turns plain language into charts, metrics, and short explanations. It sits inside the broader world of augmented analytics, where AI helps people ask better questions and move faster. But speed can hide problems. If the metric is vague, the calendar is wrong, or access rules are loose, a fast answer can still be the wrong answer.
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yellowfin

From Dashboards to Decision Flows: Embedded Analytics That Trigger Action

Jul 28, 2026 By Hagop Panosian In Yellowfin
Teams have more dashboards than ever. They also have more dashboard fatigue. Metrics are easy to display. Decisions are harder. A chart can show a drop in revenue, but it rarely tells a product leader what to do next. That gap is why many teams stay stuck in review mode instead of action mode. This is where decision-centric analytics changes the pattern. The goal is not more visibility. The goal is faster recognition, better understanding, and clear follow-through when something changes.
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zymr

Building a Custom ML Pipeline: The 2026 Reference Architecture, Open-Source Building Blocks, and Decision Framework

Jul 28, 2026 By Yogesh Karachiwala In Zymr
Enterprise AI is moving beyond experimentation. Today, the real challenge is not building machine learning models but operationalizing them at scale through reliable training, deployment, monitoring, governance, and continuous improvement. This shift is accelerating rapidly. Gartner reports that organizations with high AI maturity are more than twice as likely to keep AI initiatives operational for three years or more, underscoring the growing importance of robust MLOps practices.
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smartbear

How agentic QA cuts the test maintenance tax

Jul 28, 2026 By Maggie Bean In SmartBear
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.
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qlik

Beyond the Budget: The AI Decisions That Only Humans Can Make

Jul 28, 2026 By James Fisher In Qlik
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.
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