Connectivity alone does not make a plant AI-ready. Two machines may use different tags, naming conventions or signals to describe similar operating conditions. An experienced operator may understand that relationship intuitively, but an AI application requires enough context to understand what the data represents and how it relates to the production process. 

For many food and beverage manufacturers, particularly those operating brownfield facilities, the plant’s IT/OT and data foundation has developed over decades rather than according to a single digital strategy. Production lines are often assembled from equipment supplied by numerous OEMs, installed at different times, and connected through a combination of hardwired signals, industrial networks and vendor-specific technologies. A processing or packaging line may involve up to 15 vendors whose equipment performs its intended function but does not necessarily share information in a consistent or accessible way. These “islands of automation” may perform their individual functions well without consistently sharing the information needed for broader digital initiatives. Understanding those gaps is the starting point for a phased modernization approach to AI readiness.

The First AI Question Is a Business Question

The most useful starting point for an AI initiative is not the technology itself, but the operational problem the manufacturer needs to solve. That might be a piece of equipment that repeatedly fails, micro-stops reducing throughput, a quality issue that is difficult to correlate with production conditions, or the need to give operators better information about what is happening in the process. Defining the problem first allows the engineering team to work backward through the controls, networking and data requirements needed to address it.

Explore More Automation

This business-first approach also keeps modernization tied to business value. Food and beverage manufacturers often operate within tight margins, and major infrastructure investments have to compete with immediate production needs. Improving uptime, reducing waste or identifying a recurring source of downtime creates a clearer basis for investment than pursuing AI simply because the technology is available. 

Establish What Is Actually on the Plant Floor

Once the objective is defined, manufacturers need complete visibility into the environment they already have. Many plants operate successfully for years without a fully documented understanding of how production assets, control systems, networks and data flows interact. That visibility becomes increasingly important when evaluating readiness for AI and other digital initiatives. Food and beverage plants frequently contain legacy equipment, undocumented control systems and networks that have changed over time without corresponding updates to drawings or documentation. Temporary break-fix solutions may have become permanent, and systems installed as part of separate projects may now depend on one another in ways that are not immediately obvious.

An IT/OT assessment creates a documented baseline of the plant’s current automation and network environment. A structured IT/OT assessment goes beyond identifying equipment to document system relationships, network architecture, data pathways, lifecycle risks and cybersecurity considerations. It should reveal where reliability or cybersecurity risks are concentrated and where infrastructure improvements may be needed. For AI readiness, the assessment should also identify whether the required operational data is accessible and whether the infrastructure can move that data securely to the systems that need it. 

Cybersecurity has to be part of the IT/OT assessment because increased data access also means increased connectivity. Flat or undocumented networks can make it difficult to establish appropriate boundaries between systems. Also, OEM equipment may include remote-access devices originally installed for commissioning or service. If those connections remain outside established IT controls or are not properly maintained, they can introduce pathways into the production environment that the manufacturer may not even realize are present. 

Building toward AI readiness therefore requires not just connectivity, but secure network architecture, appropriate segmentation and controlled access based on a clear understanding of how the plant operates. Remediation must also account for production requirements. An OT asset cannot always be patched, replaced or disconnected in the same way as a conventional IT system, so remediation decisions need to consider both the technical exposure and the asset’s operational role. In most food and beverage facilities, modernization efforts must fit within production schedules rather than disrupt them. Prioritizing improvements based on risk and operational impact helps manufacturers strengthen infrastructure while minimizing effects on day-to-day operations.

Prioritize Risk, Not Simply Age

An IT/OT assessment is likely to identify more issues than a manufacturer can address at once, which makes prioritization critical. Equipment age and lifecycle status are important, particularly when components are obsolete or unsupported, but those factors should be considered alongside operational criticality, redundancy and the consequences of failure.

Consider a filler running on an obsolete PLC. If that filler is a single point of failure and losing it stops the entire line, the controller may represent a high-priority modernization need. Two case packers with similarly old PLCs could present a different level of risk if one can continue operating while the other is unavailable, allowing production to continue at a reduced rate. Looking at age alone treats the equipment similarly; looking at operational impact shows where limited capital can reduce the greatest risk.

The same principle applies when evaluating legacy equipment for future data applications. An older asset does not automatically require replacement simply because its communication technology is dated. If the machine remains reliable but cannot expose the information a new application requires, a protocol converter, interface, additional sensor or software integration may provide a more practical path forward. 

Modernization decisions should be driven by reliability, security, business objectives and data requirements rather than equipment age by itself. The result should be a roadmap that distinguishes what needs attention now, what can be addressed incrementally and what can safely wait, giving manufacturers a basis for directing limited capital toward the greatest operational and business risk. 

AI Needs the Right Data, Not Just More Data

After the network and operational risks are understood, the next question is whether the plant can produce the information required for the chosen use case. Predictive maintenance for a motor that repeatedly fails, for example, might require amperage and voltage data already available from the control system, along with vibration data from additional monitoring. Those signals then need to move securely into a database or other platform where they can be contextualized before an AI application can use them meaningfully.

Sensor values are only part of what an advanced application may need. For predictive maintenance, equipment-health data becomes more meaningful when associated with the asset, process conditions, maintenance history and known good or bad operating conditions. That context helps analytics and machine-learning applications distinguish normal variation from potential degradation or emerging failure. A plant may generate enormous volumes of operational data while still lacking the visibility and context required to transform that information into actionable insights.

Micro-stops provide a good food and beverage example. Frequent stop-and-go events create waste and reduce efficiency, but identifying where they originate requires more than a record that the line stopped. Manufacturers need enough operational context to understand which asset was involved, the equipment state, what was being produced and the conditions surrounding the event. Connecting current and historical information in that way makes it possible to attribute losses more accurately, establish a performance baseline and determine where analytics or AI could add value. 

Build Readiness One Use Case at a Time

AI readiness does not require connecting every asset or modernizing an entire facility at once. Manufacturers can define a business problem, assess the current environment, address the most significant risks, identify the required data and prove the approach on a focused use case. A successful pilot should also be viewed as proof of the use case, not proof that the entire plant is ready to scale it. Expanding an application to additional equipment, lines or facilities can introduce different operating conditions, data structures and infrastructure constraints. Lessons from the initial use case can then guide the next stage of the modernization roadmap. 

A phased modernization approach can support predictive maintenance, quality improvement, operator decision support and other advanced applications, but the foundational work also produces value before AI enters the equation. Better asset visibility supports lifecycle planning, secure network architecture reduces cybersecurity exposure, and more accessible operational data improves troubleshooting and performance analysis. For food and beverage manufacturers, AI readiness begins with gaining visibility into the plant that exists today, identifying the risks and constraints that matter most, and establishing a practical roadmap for modernization. Organizations that take this measured approach are better positioned to support future AI initiatives while also improving reliability, security and operational performance along the way.