Omron Automation
Webinar Date: June 18, 2026
Key Takeaways
- Learn how to collect and use machine data without disrupting operations.
- Understand how edge technology enables scalable AI initiatives.
- Discover ways to eliminate manual data collection and silos.
- Gain practical strategies for improving visibility and decision-making.
- See how to unlock value from existing equipment and infrastructure.
Frequently Asked Questions
How should leadership balance speed vs. governance when rolling out AI in operations?
- Move intentionally during prototyping, keep pilots limited in scope, with reasonable complexity, and off networks where possible. As you scale, ensure IT governance, leadership expectations, and replacing pilots with permanence remain in lockstep. This can be achieved with clear deliverables and realistic milestones.
What does a “good baseline” look like in practice, what data must be included vs. optional?
- A good baseline captures what is actually happening in the process, displays it in a way teams can use, and focuses on something the business cares about. At first, collect broadly because you may not know which data is critical; over time, the key leading indicator signals will become clear. This is especially impactful in processes that generate revenue and have a lot of tribal knowledge.
How do you identify the right process to baseline first?
- Start with a process that is stable and repeatable, but not “too perfect.” It should have real business value, some variability, untapped tribal knowledge, and enough automation complexity that better visibility would matter. Avoid both extremes: total chaos and processes nobody cares about.
How do you prevent pilots from becoming isolated proof-of-concepts that never scale?
- Design the pilot to answer a specific question, then sunset the pilot and to free resources move the lessons learned into standard architectures. The goal is not to keep a special one-off setup alive; it is to learn what data matters, how it should be displayed, and how to integrate it into the next PLC, edge, or automation architecture.
What’s the right balance between collecting more data vs. acting on the data you already have?
- Start by collecting the data you already have. You have enough data when a trend, spike, or alarm points people toward a clear action. If the data only says “something is wrong” but no one knows where to go next, you need more data.
What are the biggest cybersecurity concerns when connecting legacy equipment?
- The biggest risk is false confidence. Legacy machines may have no vulnerabilities simply because they are very manual. Once connected, risks at the network level and end point level now exist. Start with a security audit, document vulnerabilities, and build the connectivity rules jointly with IT and OT. If this means keeping the pilot off the network, then do so.
How do protocols like IO-Link or MQTT practically fit into a modern architecture?
- MQTT is useful for efficiently sharing data because it sends updates when values change instead of constantly flooding the network, which helps older networks with limited capacity. IO-Link adds sensor-level granularity beyond simple on/off signals and can often be enabled without ripping and replacing everything, making it a practical path for scalable data collection.
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