The Rising Cost and Strategy of Grounding AI Agents in Reality

By Ted Schadler, Forrester | May 28, 2025

Grounding AI agents in reality—ensuring they avoid hallucinations and operate based on accurate, proprietary knowledge—is becoming a critical yet costly endeavor for corporations. Unlike the natural act of human grounding (or "earthing"), which involves connecting with the earth, grounding AI requires significant investment in knowledge infrastructure. This infrastructure includes structured databases, content stores, vector databases, and graph databases to ensure AI agents function reliably.

The Battle for Knowledge Infrastructure

Every major tech vendor—from hyperscalers like AWS, Google, and Microsoft to software giants like Salesforce and SAP—is vying to host corporate knowledge assets. These assets are essential for grounding AI agents in proprietary data, creating a competitive landscape. Companies must decide where to host their AI agents and knowledge assets, balancing performance, latency, and cost.

Key Questions for Corporate Strategy

1. Where to host AI agents? Proximity to data and systems is critical. For example, B2B sales agents should reside in AI-ready CRM platforms like Salesforce.
2. Where to place knowledge assets? Latency-sensitive applications require close integration, while cost-sensitive ones may use shared infrastructure.
3. How to optimize knowledge infrastructure? Companies must invest in storage governance, security, and synchronization to ensure AI agents perform efficiently.

The Future of AI Grounding

As AI agents become more prevalent, the battle for hosting and managing knowledge assets will intensify. Companies that strategically invest in their knowledge infrastructure will gain a competitive edge, ensuring their AI agents operate reliably and cost-effectively.

The original article is here.

Image credit: iStockphoto/Kirillm