Read more about author Arvind Rao. The rise of generative and agentic AI is ushering in a new era of transformation for enterprises. In just one year, enterprise?use of generative AI (GenAI) jumped from 55% to 75%,?and by 2026,?over 80% of organizations are expected to have integrated generative AI?into production environments. As businesses embed these technologies to drive automation, efficiency, and innovation, the importance of a strong data foundation has…

Businesses are intensively exploring generative AI (GenAI) in their search for game-changing use cases. In this context, LLM-based AI engineering is outgrowing simple prompt engineering, leading to the rise of what has been dubbed PromptOps.? PromptOps is a systematic methodology for optimizing the way large language models (LLMs) are prompted. This approach to managing AI prompts at scale...

Generative AI is?evolving at a rapid pace –?from?automating document?extracts to?reviews to powering customer interactions. But with speed comes greater risks, including privacy leakage,?intellectual property leakage, bias in outputs, regulatory frameworks, and unclear accountability. Unlike traditional IT systems, generative AI adoption is often bottom-up?with?employees’?vibe coding with models,?making it hard to control technically?while?foundation models evolve unpredictably...

The global market for artificial intelligence is evolving under two very different legal paradigms. On one side, the European Union has enacted the AI Act, the first comprehensive and enforceable regulatory regime for AI, applicable across all member states and with far-reaching extraterritorial scope. On the other, the United States continues to advance AI oversight primarily at the state level, resulting in a patchwork of rules that vary in focus, definitions, and enforcement...