F091: AI Foundations for Smarter Manufacturing: Building the Data and AI Readiness Layer

While everyone is talking about AI-driven insights, predictive maintenance, autonomous workflows, and smarter decision-making, few organizations are truly prepared to implement them. The reality is simple: AI is only as strong as the data behind it.

This session demystifies the data readiness journey and offers practical steps for building a foundation that delivers ROI on AI initiatives. We’ll break down what “AI‑ready data” really looks like, how manufacturers can assess where they stand, and the processes and governance needed to support AI adoption.

Learning Objectives:

1. Understand the essential role of data in AI applications and the impact it has on operational efficiency.
2. Learn strategies to bridge data silos across operations, supply chains, and production systems.
3. Explore real-world examples of data-ready AI implementations in manufacturing.
4. Develop a practical roadmap for data readiness, including techniques for managing and maintaining data quality over time, strategies for data architecture modernization, and establishing data governance.

Determining where AI can deliver immediate value versus where additional work is needed can be a struggle. With every vendor promising ROI and every workflow a candidate for modernization, the risk of investing in tools that don’t address the biggest bottlenecks is high. This session cuts through the noise and shows how to focus on high‑impact, high‑feasibility AI opportunities that actually move the needle.

Learning Objectives:

By the end of this session, attendees will be able to:

1. Identify and prioritize AI use cases across the manufacturing ecosystem using criteria like business impact, feasibility, data availability, and time‑to‑value.
2. Assess data and process readiness to minimize production impacts and maximize efficiency gains
3. Build a right‑sized AI roadmap tailored to your company’s operational goals, workforce capabilities, and technology systems.
4. Differentiate AI hype from high‑ROI opportunities by exploring successful AI uses cases in manufacturing.