Systems | Development | Analytics | API | Testing

Enterprise interest in generative and agentic AI has accelerated dramatically over the past two years. Organizations across industries are exploring how AI agents, intelligent assistants, and automation can improve productivity, streamline operations, and unlock insights from growing volumes of enterprise data. Yet as enthusiasm grows, so do questions around cost, security, and operational complexity.

In 2025, Model Context Protocol (MCP) captured the imagination of the AI engineering community. 2026 will be the year that everyone realises their prototypes need to run in production.

You deploy a web app. Users open it. Something breaks. It could be a button that doesn't respond on Safari. A form that submits twice on slow connections. A page that loads fine for 10 users but crashes for 500. These aren't rare edge cases. They're what happens when testing gets skipped, rushed, or treated as a final step before launch. It's not one activity. It's a system of checks that runs across the entire development lifecycle, from the first commit to post-deployment monitoring.

Modern SAP customers often face competing demands. While navigating the routine complexities of an SAP system, they must also prepare for faster releases and looming S/4HANA deadlines, juggling the day-to-day with long-term innovation. Intelligent quality assurance helps SAP users balance these priorities.

The EMEA region is undergoing a massive transformation, driven by companies demanding instant, actionable insights embedded directly into their applications and workflows. This fundamental shift away from legacy BI has translated into record-breaking momentum for ThoughtSpot, positioning EMEA as our fastest-growing region globally. The Agentic Analytics revolution is here, and ThoughtSpot is delivering on the promise to make the world more fact-driven.

When executives talk about improving logistics performance, the conversation often circles around the same three goals: speed, cost efficiency, and reliability. Yet the reality on the ground tells a different story. Traffic congestion, rising fuel costs, driver shortages, changing customer expectations, and unpredictable disruptions continue to make route planning one of the most complex operational challenges in logistics. Now add one more pressure point: customer expectations have fundamentally changed.

The most effective way to adopt streaming machine learning (ML) is not by rebuilding your entire platform but by adding a single, high-value inference step to your existing data flow. This incremental approach allows you to transition from batch-based processing to real-time decision-making without the risk of a "big bang" migration, ensuring that your microservices architecture remains agile and responsive. What Is Streaming ML? ML in streaming is the practice of.

Most AI pilots fail to reach production not because the models don’t work, but because enterprises struggle with data governance. While pilot-phase AI projects demonstrate impressive results in controlled environments, they hit governance walls when moving to enterprise-scale deployments. This post examines why AI initiatives stall before production and provides a governance-focused approach for breaking the cycle.

When it comes to QA, AI-powered automated testing tools promise more speed, better coverage, and lower maintenance. But they don’t all solve the same problems, and their approach to solving problems can be fundamentally different. Some platforms lean heavily into autonomy. Others focus primarily on speed or aggressive self-healing. A smaller group applies AI in specific parts of the workflow while preserving test execution reliability and human control.