The Enterprise AI Postmortem Playbook: Diagnosing Failures at the Data Layer

When an enterprise agent gives an incorrect or illogical output, most people assume it’s a problem with the model, or the prompt wasn’t clear enough. In some cases, they might just blame the data platform vendor. Too technical. Too complicated. However, what if the model did exactly what it was told and there was nothing…

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ThoughtSpot Pushes Upstream With Agentic Data Preparation

Agentic analytics platform ThoughtSpot launched its next generation of Analyst Studio, which it claims offers a “new suite of capabilities to revolutionize how data teams deliver AI-ready data with speed, flexibility and control.” The analytics market has spent the last decade obsessing over usability. They wanted faster dashboards and queries, while keeping the interface easy…

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Context Engineering Will Decide Enterprise AI Success, Says Cognizant CIO Neal Ramasamy

Artificial intelligence dominated discussions at Davos this year, but inside enterprises the conversation has shifted from excitement to execution. Organizations are moving quickly to deploy agents and automation. However, many are discovering that technology alone does not guarantee operational value.  Against that backdrop, Neal Ramasamy, Chief Information Officer at Cognizant, shared his perspective with BigDATAwire…

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What DOE’s 26 AI Challenges Reveal About Building a National Science Engine

At BigDATAwire we outlined the key data challenges that will define the Genesis Mission. There is a growing acknowledgment that scientific AI often breaks down at the data layer. Fragmented datasets and uneven metadata introduce friction that no model alone can overcome. Federated access rules and mismatched computing environments add to the challenge. While the…

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Databricks Doubles Down on AI With Lakebase, Genie, and a Surging Valuation

The hidden costs of traditional databases often force developers to create custom infrastructure for managing workflows. This is a critical resource conflict. Instead of focusing on building new products or advancing AI initiatives, the developers spend more time solving operational problems. As enterprises move deeper into AI, this issue is pushing data teams outside their…

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Are Your AI Agents Actually Delivering ROI?

Agentic AI has dominated industry conversations since 2025. Adoption across enterprises is on the rise, and investment plans remain aggressive. Use cases are expanding rapidly, from simple employee assistants to advanced agentic workflows for insurance renewals or support ticket triage, to name just a few. The shift has been undeniably fast. The momentum notwithstanding, a…

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The Connectivity Paradox Holding Back Enterprise Agentic AI

AI agents have become the primary driver of enterprise productivity. According to a recent report by Salesforce, organizations currently use an average of 12 agents, with that number expected to climb 67% within the next two years.  Currently, 83% of organizations report that most or all teams and functions have adopted AI agents. However, business…

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Why Real-Time Became the Default — and How Data Teams Are Actually Using It

Until recently, most enterprises treated real-time data as something you reached for only when absolutely necessary. It sat at the edges of enterprise architecture. However, that has changed. If you have been following our coverage at BigDATAwire, you would have seen that real-time data and analytics has been a core theme for enterprise modernization efforts…

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Personalized Flights to Intelligent Skies: How Agentic AI will Reshape the Future of Air Travel

The aviation industry is already implementing AI solutions, but it is barely scratching the surface of the value AI can bring, according to Ali Pourshahid, Chief Engineering Officer and Alam Khan, Principal Architect, Solace. The industry is weighed down by diverse and siloed processes, preventing airlines from harnessing the full power of AI, in particular…

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OpenScholar Shows Why Grounded AI Matters for Scientific Research

Researchers from the Allen Institute for AI (Ai2) and the University of Washington have developed a new open-source AI model named OpenScholar that they claim can synthesize scientific literature and verifiable citations at a level comparable to a human expert. With millions of scientific papers published every year, it’s challenging to keep up with the…

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