AI readiness doesn't begin with AI tools. It begins with how an organization collects, manages, connects, and governs its data.
This toolkit, as seen in AMEC AI Days 2026, brings together five resources to help communications and measurement professionals understand their existing data infrastructure, identify opportunities for improvement, and explore a more scalable approach to intelligence.
AI Readiness Audit
Assess your infrastructure across the data engineering lifecycle to identify gaps in data ownership, management, governance, and usability that may limit AI readiness.
Source Inventory
Identify where your data originates, who owns and manages it, how it moves through your organization, and what intelligence it supports.
Example Use Cases
Explore three practical use cases illustrating how a unified enterprise data environment differs from traditional, fragmented data infrastructure.
Common Data Model Guide
A companion to the Example Use Cases, providing a practical overview of how to apply the data engineering lifecycle and establish a common data model.
Infrastructure Cost Review
A companion to the Source Inventory, helping you identify the teams, vendors, processes, and costs involved in managing your data and establish a baseline for potential efficiencies.
