Eric Medecke- Director of IT/OT Solutions and Cybersecurity, E Tech Group
Casey Noonan, VP of Business Development, E Tech Group
Artificial intelligence is quickly moving from industry buzzword to practical manufacturing tool. From predictive maintenance and quality analytics to operator decision support, manufacturers are exploring ways to use AI to improve performance, reduce downtime, and increase visibility into operations.
But before implementing AI, there is a more important question to answer:
Is your plant infrastructure ready to support it?
Many facilities have invested heavily in automation over the years. Production lines may include advanced PLCs, robotics, automated packaging equipment, vision systems, and sophisticated control systems. However, automation alone does not guarantee AI readiness.
The reality is that many manufacturers still struggle with disconnected systems, inconsistent data, aging infrastructure, and limited visibility across operations. Without a strong foundation, even the most promising AI initiative can fail to deliver meaningful results.
Start with the Business Problem, Not the Technology
One of the most common mistakes organizations make is pursuing AI before defining the problem they are trying to solve.
Successful projects start by identifying a specific operational challenge, such as:
- Unplanned equipment failures
- Chronic micro stops
- Product quality variability
- Excessive waste or scrap
- Limited operator visibility into process conditions
Once the business objective is clear, manufacturers can determine what systems, data, and infrastructure are required to support the solution.
This approach keeps technology investments aligned with measurable business outcomes rather than implementing AI simply because it is available.
Visibility Comes Before Intelligence
Many manufacturing environments evolve over decades rather than through a single digital transformation initiative.
Equipment from multiple OEMs, expansions completed years apart, and numerous network modifications often create an environment where systems operate independently with limited data sharing.
These “islands of automation” may perform their intended functions effectively, yet they often make it difficult to generate the connected, contextualized data required for advanced analytics and AI applications.
Before moving forward with AI, organizations need a clear understanding of:
- Control systems and automation assets
- Industrial networks and communication pathways
- Current data collection capabilities
- System dependencies
- Lifecycle and obsolescence risks
- Cybersecurity vulnerabilities
An IT/OT assessment provides the visibility needed to identify gaps and create a roadmap for modernization.
Why Cybersecurity Matters to AI Readiness
As manufacturers increase connectivity and begin sharing operational data across systems, cybersecurity becomes increasingly important.
Many plants still operate on flat networks with limited segmentation. Others contain legacy remote-access solutions that were installed for maintenance or commissioning and may not align with current security standards.
Expanding data accessibility for analytics and AI without addressing these vulnerabilities can increase operational risk.
A secure foundation should include:
- Network segmentation
- Controlled remote access
- Asset visibility
- Risk-based remediation planning
- Alignment between IT and OT teams
AI initiatives depend on trusted data moving securely throughout the organization. That cannot happen without a well-designed IT/OT architecture.
Prioritize Risk Instead of Equipment Age
Not every legacy asset needs to be replaced.
In many cases, older equipment continues to perform reliably and support production requirements. The more important question is whether the asset creates operational, cybersecurity, or data-access challenges.
For example, a critical production asset that relies on unsupported hardware may present a significantly greater risk than multiple aging systems performing noncritical functions.
Similarly, an older machine may not require replacement if the necessary production data can be extracted through:
- Additional instrumentation
- Protocol conversion
- Edge devices
- Software integrations
Modernization strategies should focus on reducing business risk and improving visibility rather than replacing equipment solely because of age.
Good AI Requires Good Data
One of the biggest misconceptions surrounding AI is that success simply depends on collecting more information.
In reality, AI needs the right data.
Data must be accessible, reliable, and contextualized.
For example, predictive maintenance requires more than sensor values alone. Understanding asset health often depends on combining:
- Process conditions
- Equipment operating states
- Maintenance records
- Production data
- Historical performance trends
Without context, AI systems struggle to distinguish normal process variation from conditions that truly require attention.
The same challenge exists when addressing issues such as micro stops, throughput losses, or recurring quality concerns. Manufacturers need the ability to connect events to the operating conditions that caused them if they want actionable insights.
Build AI Readiness Through Focused Use Cases
Organizations do not need to modernize an entire facility before exploring AI.
A more effective approach is to:
- Identify a business challenge.
- Assess existing IT/OT infrastructure.
- Address the highest-priority risks.
- Ensure required data is available.
- Execute a targeted pilot project.
- Use lessons learned to guide future initiatives.
Starting with a focused use case allows manufacturers to demonstrate value while building the foundation needed for larger transformations over time.
AI Readiness Begins with the Foundation
The most successful AI initiatives are built on a strong operational foundation. Secure networks, reliable infrastructure, accessible data, and clear visibility into plant operations are what ultimately enable advanced technologies to deliver value.
For manufacturers looking to explore predictive maintenance, quality analytics, operator decision support, or other AI-driven applications, the first step is often not implementing AI itself. It is understanding the current state of the plant and developing a modernization roadmap that supports future growth.
Want to dive deeper?
Read the original article, “Getting the Plant Ready for AI,” by Eric Medecke and Casey Noonan for additional insights into evaluating AI readiness across manufacturing operations.