Agentic AI Runs On Integration, Not Data Lakes
Your AI agent strategy is already failing. Here's why: most enterprises are building on data lakes, not integration.
Why it matters
As enterprises move agentic AI from pilots to production, the critical bottleneck isn't model capability—it's systems integration. Organizations optimizing for data centralization are missing the architectural requirement for agents to actually *act* across disparate enterprise systems.
The key facts
10 to knowEnterprises shifting from AI experimentation to production deployments
Agentic AI requires integration architecture, not data lake strategy
AI agents without integration limited to response generation, not action
Organizations repeating familiar mistakes in AI infrastructure planning
Published by Forrester Research (industry authority perspective)
Shift from AI experimentation to production deployments underway
AI agents positioned as action-layer tools, not just answer engines
Integration architecture identified as competitive constraint for agentic deployments
Organizations repeating data-lake-centric mistakes in agentic era
Source: Forrester analyst commentary (Jul 2026)
Go to the source
Forrester Blogforrester.com
Publisher excerpt: Agentic AI is moving fast. Enterprises are shifting from experimentation to real deployments — using AI agents to act, not just answer. This should put integration at the forefront. After all, an AI agent without integration is merely an answer engine, it cannot take action. Yet many organizations…