We’re starting to see AI transition from simple request-response interactions to a more dynamic and action-oriented paradigm. This new generation of agentic systems will soon operate with a rich contextual understanding, enabling conversational, context-aware interactions. These systems won’t merely process inputs; they will analyze the situation, draw insights from comprehensive contextual graphs, and autonomously take…

As we close out 2024, it’s clear this was the year when enterprises got serious about AI, but not without missteps. The excitement around generative AI was hard to miss. However, many of us quickly realized that jumping straight to Gen AI applications without a solid data foundation and a clear AI strategy didn’t end…

Knowledge graphs are quickly evolving how we use large language models (LLMs). Traditional retrieval-augmented generation (RAG) helps by connecting models to external data sources so they can pull in relevant information. But there’s a catch: traditional RAG isn’t perfect. It can pull outdated, inconsistent, or irrelevant data, which leads to problems like hallucinations and inaccurate…

Retrieval-augmented generation (RAG) has proven to be a powerful method for connecting large language models (LLMs) to external data sources. By dynamically retrieving information from documents, databases, or APIs, RAG can enhance the relevance of a model’s output. However, traditional RAG isn’t without its limitations; it can expose systems to risks like hallucinations or outdated…

Building a solid enterprise AI strategy shouldn’t be about picking high-impact use cases but rather laying the right foundation so AI actually fits the way the business runs. Linda Tucci’s recent article offers a valuable perspective on enterprise AI’s transformative potential, and it underscores the need for more than just technical ambition. For AI to…

It’s fascinating how quickly perspectives shift in AI. Just two weeks ago, an article celebrated small language models (SLMs) as the future for enterprise AI, citing their efficiency, cost savings, and value for business. Now, a new piece argues that advances in large language models (LLMs) have made SLMs nearly obsolete.

Implementing AI without a robust framework can turn potential benefits into long-term liabilities. While having a strategy is crucial, an implementation framework is what really sets the course for success. Without this foundation, the organization may be at the mercy of its vendor’s architecture and limitations, as we often see in tech decisions where business…

A semantic layer is an essential part of modern data architecture—a layer that translates raw data into familiar business terms and metrics like “revenue,” “customer,” or “retention rate.” By creating this shared layer of definitions, organizations enable everyone to work with consistent data, regardless of which tools they’re using or where the data comes from.…

As Artificial Intelligence (AI) continues to transform industries, it’s essential for businesses to understand how to effectively harness its different types. Bill Schmarzo’s recent exploration of AI identifies four key types—Generative, Analytical, Causal, and Autonomous—each paired with a distinct archetype to simplify their unique capabilities and applications. This framework helps organizations better deploy AI in…

When companies rush to put AI in front of their customers, hoping for the best, things can quickly go wrong. From mismanaged drive-thru orders to false accusations, these AI horror stories all have one thing in common—a lack of proper human oversight. The real power of AI comes from using it to augment your workforce…

The concept of data fabrics is rapidly becoming essential as enterprises explore AI to enhance their operations. A data fabric is an integrated architecture that unifies, automates, and manages data across diverse environments, including cloud, on-premise, and edge systems. This framework is crucial in today’s business landscape, where data is scattered across numerous applications, making…