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Most organizations today recognize the potential of AI to improve decision-making. Many have experimented with large language models (LLMs) and chatbot-style interfaces in the hope of unlocking value from their internal knowledge. Yet despite significant investment, the results have often fallen short. … Read More

Over the past three posts, we explored a pattern that many enterprise leaders recognize. AI pilots are widespread and experimentation is accelerating, yet measurable ROI remains inconsistent. Current approaches struggle as deployments expand and traditional knowledge graph projects, while powerful, … Read More

If current approaches to enterprise AI do not scale, the natural question becomes what does. As organizations push AI beyond isolated pilots, they quickly discover that document retrieval and similarity search are not enough. To scale AI across the enterprise, … Read More

In theory, enterprise AI should improve as it is exposed to more information. In practice, many organizations experience the opposite. As AI moves beyond small pilots and into broader deployment, performance becomes less predictable, answers lose consistency and confidence begins … Read More

Over the past two years, enterprises have invested billions of dollars in artificial intelligence. Large language models are impressive, copilots are everywhere and pilot programs have launched across nearly every department. Boards are demanding AI strategies and executives are under pressure to demonstrate results. … Read More

Many organizations are experimenting with simple ways to use AI on top of their existing document repositories. A common approach is to connect a large language model directly to a SharePoint corpus using basic search or retrieval augmented generation. While this can appear attractive at first, it quickly runs into fundamental limitations.  … Read More