It is an exciting time for artificial intelligence (AI). As organizations move from exploring generative AI to deploying it at scale, a new phase of innovation is already emerging: the rise of agentic ...

Effective governance has become a key component of modern data and AI strategies. Without clear policies, accountability, and standards, organizations face business, legal, and ethical risks from ...

Retrieval-ugmented generation (RAG) frameworks provide real-time data access for generative AI systems. RAG works by retrieving data from external sources (often vectorized databases) and combining ...

Innovation with data, analytics, and AI is redefining how enterprises create value, compete, and operate. Enterprises that succeed use their data, analytics, and AI to drive differentiating ...

Organizations today face a challenge with AI that looks deceptively simple. AI has moved beyond speculation and is now within reach of almost every enterprise. Leadership teams generally understand ...

Every organization today faces a similar contradiction: AI adoption is both urgent and perceived as risky. This tension creates a dangerous pattern where organizations either move too slowly and fall ...

Many organizations today are looking to move from traditional BI to AI-powered, talk-to-your-data experiences. Yet most organizations still depend on established BI and dashboarding environments. The ...

Responsible data and analytics is a framework that ensures the ethical, legal, societal, and environmental implications of data and analytics are carefully considered in enterprise decision-making. It ...