2026 Enterprise AI Sourcebook

Features

Building Trustworthy Data Foundations for AI

A plethora of reports highlight the prevalence and dangers of AI hallucinations and other forms of inaccuracies resulting from AI applications. These errors can be traced to many different root causes, but the solutions boil down to a few methods: restrict the content to a carefully defined and structured set of validated information, protect the application through governance and monitoring, and evaluate the output to provide continuous improvement.

Enhancing AI Reasoning With Knowledge Graphs and GraphRAG

There is a disconnect happening with AI that cannot be ignored. A model can retrieve information, summarize it, or generate an answer, but it cannot determine whether the answer reflects how the business works or if it's truly correct. The limitation is not model intelligence but missing context. Using connected data, semantic relationships, and graph-based retrieval improves AI reasoning, contextual understanding, and decision making.

Governing AI for Security, Compliance, and Trust: Who Gets to Grant AI Access?

Organizations are moving AI from isolated tasks into everyday operations. A tool that started out drafting emails or summarizing documents may soon be asked to search internal files, retrieve customer information, prepare transactions, route requests, or update records.

Improving AI Outcomes Through Context Engineering

Once upon a time, and that time wasn't all that long ago, we heard that content was what mattered. Then AI entered the picture, and enterprises shifted from content to context. Content by itself did not lead to relevancy. AI, lacking context, could all too easily misinterpret what was needed.

Orchestrating Agentic AI Systems and Workflows That Humans Can Trust

Unlike chatbots, agentic systems, at least for now, are not commonly used by consumers.  That means there are few lessons learned to transfer from daily life to work life. And for IT professionals, the non-deterministic nature of AI-based software components introduces new factors into coding and testing that challenge established practice and undermine routines.

Powering AI Agents With RAG and Enterprise Knowledge

It should come as no surprise that AI has already infiltrated many parts of daily life. After all, AI has been around for decades. Still, some people are amazed to discover that things they take for granted, such as spellcheck, autocorrect, real-time traffic maps, spam filters, ride-hailing apps, and personalized music recommendations, are AI-enabled. The more that AI-enabled tools become embedded in daily routines, the less they seem to be advanced technology and more just a normal part of life. It's no longer magic, it's normal.