From prompt to purpose - Unlocking business value with agentic AI

Most enterprises see early promise from generative AI but struggle to move beyond pilots into consistent, production impact. Fragmented data, slow time-to-value, unclear ROI, and governance risk hold teams back. This paper explores how agentic AI can help close that gap and what’s required to deploy it responsibly at enterprise scale, drawing on joint perspectives from Snowflake and Capgemini.

In the paper, you’ll learn:

• Why gen-AI initiatives stall when moving from proof of concept to production
  
• What “agentic AI” means in practice for enterprise workflows and decisioning
  
• How trusted, governed data foundations enable predictable AI delivery
  
• Where organisations lose time and value in AI programmes — and how to reduce friction
  
• Practical considerations for deploying AI in regulated industries like financial services and life sciences

It also outlines how enterprises can approach:

• Automating and optimising high-value processes
  
• Accelerating time-to-value without increasing delivery risk
  
• Embedding governance, security, and compliance into AI programmes from day one

This paper provides a clear, grounded view of how to think about agentic AI and the operating model needed to make it work in real organisations.