Building Agentic and Generative AI: What Actually Works

Most companies are experimenting with AI. Fewer are seeing consistent results. This report explains why and what to do differently.

Generative and agentic AI are widely adopted, but results are uneven. While many teams are building assistants and testing new use cases, only about 40% report consistent, measurable value.

The gap is not about models. It comes down to data, context, and how AI is applied inside real workflows.

This TDWI research report looks at what separates early experimentation from systems that work in production.

What you’ll learn:

• Why AI projects stall after initial pilots
• How organizations move from copilots to agents and multi-agent systems
• What’s required to scale AI safely across the enterprise

The research shows a clear pattern: organizations that see value treat enterprise data as foundational, not optional. AI doesn’t fail because of models. It fails because the foundation isn’t ready

The report outlines a practical framework for building AI systems that can move beyond experimentation with a trusted data foundation.

Short on time? Read a quick overview of the findings.