Myth v Fact | Shipping software with AI
Think you know the rules of shipping software with AI? Myth or fact? Test your instincts and see what it takes to maintain application integrity at the speed of AI.
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Modern Software Development For Today's Systems & Applications
The 3 Things Companies With AI ROI Have in Common
Most companies are investing in AI. Very few are seeing it reflected in their financial results. The gap comes down to three foundational decisions. In this clip from The Data & AI Chief, Raman Tallamraju from Vanguard breaks down what the companies seeing measurable AI benefits have in common.
Centerprise AI: Add New Banking Systems Without Custom Integration Projects
Your next banking initiative shouldn't wait on another custom integration. Keep your core banking system and connect everything around it with Centerprise AI. Describe the pipeline you need, and it generates the connections, mappings, transformations, and data quality checks across APIs, databases, legacy systems, and more.
AI-Powered Claims Automation in 2026: How LLMs and Intelligent Document Processing Transform Insurance Operations
AI claims automation is becoming a major focus for insurers as manual claims processing continues to create cost, speed, and customer-experience challenges. Traditional systems often route documents through multiple human reviewers, creating bottlenecks that consume time and operational resources.
Is Your Data Estate Actually Ready for AI? The 6 Characteristics That Matter
Most organizations are moving fast on AI ambition. Fewer are moving fast on what makes that ambition possible. Before you can reimagine your business with AI at its heart, your data estate needs six things: to be well-defined, trusted, well-connected, contextualized, consumed in a multimodal way, and ready for both humans and machines at scale. Most organizations have two or three. The ones pulling ahead in AI have all six.
From Chatbot to Compound AI System: Infrastructure Patterns for Multi-Model, Tool-Using Applications
Two years ago, GenAI in production usually meant a single LLM serving a single endpoint. In 2026, it usually means much more. The applications shipping in front of users today are compound AI systems: orchestrated pipelines of retrievers, embedders, dialogue models, classifiers, code interpreters, SQL executors, and tools, with a single user request fanning out to several model calls across the stack.
When AI Becomes the Buyer: Payment Testing for Agentic Commerce
Imagine telling an AI assistant: “Find me waterproof trail shoes, size 10, under €140, delivered before Friday. Use my usual card if the total stays within budget.”
QMetry vs. OpenText ALM: Why QMetry is the better choice for regulated QA
Regulated QA teams carry a pressure most testing platforms weren’t built to solve for at the same time. Every release still needs traceability from requirement to test case to defect that holds up under audit. Approvals and evidence still need to be airtight. At the same time, agile releases, DevOps pipelines, and AI-assisted development keep moving, whether or not the testing platform underneath has kept pace.
The evolving role of the CDO in the AI era
The CDO’s job can be boiled down to ensuring data access, building innovative data products, and managing data responsibly.
The EU's New AI Transparency Rules and What They Touch in PropTech
AI adoption across multifamily and PropTech has outpaced the rules meant to govern it, and one of those rules is now in force. On August 2, 2026, the transparency obligations under Article 50 of the EU AI Act began to apply, setting specific requirements for how certain AI systems interact with people and how AI-generated content is marked and disclosed.