Industrial AI Readiness: Why System Integrators Matter
When a customer says they want to use artificial intelligence in their operations, the first question should not be, “Which AI tool should we choose?” A better question is: What does your industrial environment look like today?
Before AI can predict equipment failures, identify process variability, optimize energy use, or recommend an operator’s next action, it needs reliable information about what is happening across the operation. That requires collecting data from programmable logic controllers (PLCs), supervisory control and data acquisition (SCADA) systems, historians, manufacturing execution systems (MES), maintenance platforms, databases, and other sources.
Collecting data is only the beginning. Industrial organizations must also determine whether their data is accurate, whether equipment and processes are represented consistently, and whether information from different systems can be connected and understood together.
For many manufacturers, this foundation is still under development. Research cited by Deloitte found that nearly 70% of manufacturers identified data-related issues—including data quality, contextualization, and validation—as major barriers to AI implementation. Deloitte’s 2025 Smart Manufacturing and Operations Survey also found that only 29% of respondents were using AI or machine learning at the facility or network level, while another 23% were still piloting the technology.
This creates a clear gap between exploring industrial AI and having an operation that is ready to support it. For system integrators, that gap presents an important opportunity: helping customers turn their existing industrial systems, data, equipment, and process knowledge into an AI-ready foundation.
Industrial AI Starts with the Right Operational Problem
When customers begin discussing AI, the conversation can quickly shift toward models, copilots, predictive analytics, or the latest capability they have seen demonstrated. On the plant floor, however, several more fundamental questions should come firstWhen customers begin discussing AI, the conversation can quickly shift toward models, copilots, predictive analytics, or the latest capability they have seen demonstrated. On the plant floor, however, several more fundamental questions should come first:
- What operational problem are we trying to solve?
- What information is required to solve it?
- Where does that information currently live?
- How accurate and reliable is it?
- Does it contain enough context to explain what was happening when an event occurred?
Consider a manufacturer trying to use AI to understand why product quality changes from one run to another. The answer may depend on hundreds of variables across equipment, raw materials, process conditions, operator actions, environmental conditions, and previous production steps. Some of this information may already be available, but having the data does not mean it is ready to be analyzed together.
Before an AI model can identify meaningful relationships, someone must ensure that the right information is being collected, systems can communicate, and the data accurately represents the process. System integrators already understand these challenges. The work may not include “AI” in the project name, but it can determine whether an AI initiative ultimately produces useful results.
Years of Industrial Data Do Not Automatically Make It AI-Ready
Most industrial organizations are not starting from zero. Many have years, or even decades, of operational information spread across historians, SCADA applications, PLCs, MES platforms, maintenance systems, spreadsheets, databases, and other applications installed throughout the life of a facility.
The problem is that these systems were not necessarily designed to work together. One site may use one name for an asset while another follows an entirely different naming convention. Two production lines running nearly identical processes may have been engineered years apart and structured differently. Critical process information may be stored in a historian, while the maintenance history needed to explain it resides in a separate system.
Experienced employees may know how to navigate those differences, but an AI model does not understand them automatically. Deloitte’s 2025 survey found that 57% of manufacturers were already using data analytics at the facility or network level, compared with 29% using AI or machine learning at the same scale. This gap illustrates an important point: collecting and analyzing industrial data does not automatically make an operation ready for advanced AI applications.
System integrators can help customers extract more value from the infrastructure and information they already have. Standardizing applications, improving tag structures, connecting systems, strengthening data quality, and building consistent asset models may sound less exciting than deploying AI, but these steps often make industrial intelligence possible.
Operational Context Is One of the Most Valuable Foundations an SI Can Build
Imagine a pump begins drawing more current than usual. Knowing that the value changed is one thing, but understanding why requires much more information. Was the pump running faster? Did the flow rate change? Was a valve repositioned? Did inlet pressure change? Was maintenance recently completed? Is the increase unusual for these operating conditions, or is it normal?
These relationships turn isolated data points into operational context. That context becomes essential when AI is expected to do more than flag an anomaly. If a customer wants AI to explain what is happening or recommend what to do next, the system needs a clear and consistent understanding of the assets, conditions, and processes behind the numbers.
This is where industrial system design becomes even more important. AVEVA System Platform, for example, can organize equipment, processes, alarms, events, and historical information into structured operational models. Instead of treating thousands of tags as unrelated data points, organizations can associate that data with the equipment and processes it represents.
Reusable object models have traditionally helped system integrators improve engineering consistency and make applications easier to deploy, maintain, and scale. As customers adopt advanced analytics and AI, those same models can provide a consistent layer of operational context for new technologies to use.
Industrial AI Is Expanding the Integration Challenge
Integration projects once focused primarily on delivering the right information to plant-floor operators. Today, operational data serves a much broader audience. Maintenance, engineering, continuous improvement, corporate operations, sustainability teams, and business systems may all require information that originated on the plant floor. Analytics and AI applications are now joining that list.
As a result, the architecture conversation must go beyond sending every piece of plant-floor data to another system or the cloud. Industrial organizations need to decide:
- Which information should remain close to the process?
- Which data needs to be shared, and how quickly?
- How much operational context must travel with it?
- Who—or what—should be permitted to access it?
Platforms such as AVEVA CONNECT can make trusted industrial information available beyond individual sites and applications to support enterprise visualization, analytics, collaboration, and AI use cases. DataOps technologies such as Crosser address another part of the challenge by connecting, processing, filtering, and contextualizing information across edge, on-premises, and cloud environments.
For a system integrator, the value lies not only in knowing how to connect these technologies, but also in understanding how the architecture should support the operation. A high-frequency process variable may need to remain at the edge, while an abnormal event may require immediate local processing and rapid sharing with central operations. A calculated production key performance indicator (KPI) may need to be available across several sites.
These decisions depend on the customer’s processes, infrastructure, cybersecurity requirements, and business goals. That is why there is no single AI-ready architecture that works for every industrial organization.
AI Makes Industrial Knowledge More Valuable, Not Less
Much of the conversation around AI focuses on what the technology may eventually automate. However, industrial operations are not generic environments. A recommendation that works for one production line may be inappropriate for another. Even identical equipment can behave differently depending on the product, process, operating conditions, or surrounding assets.
System integrators understand control systems, equipment, networks, software, and often the customer’s production process itself. They know why a five-second delay may be irrelevant in one application but unacceptable in another. They also understand why connecting a system to the cloud is not, by itself, an architecture strategy—and why an upstream change can create consequences elsewhere in the operation.
That experience becomes even more important when AI begins influencing operational decisions. Deloitte found that 78% of manufacturers surveyed allocate more than 20% of their overall improvement budgets to smart manufacturing initiatives, with data analytics and AI among their technology investment priorities. As investment moves from pilots toward real operational use cases, customers will need experts who can connect AI capabilities to the realities of their facilities.
The System Integrator Opportunity Extends Beyond a Single AI Project
System integrators do not necessarily need to become AI companies. Their opportunity is to become the trusted partners who help customers assess whether their operations are ready for AI—and identify what must change when they are not.
That conversation should begin with the business or operational outcome the customer wants to improve and then work backward:
- Which systems are already in place?
- Where does the required data live?
- Can that data be trusted?
- Is enough historical information available?
- Does the data include the necessary operational context?
- Can information move securely between the systems that need it?
Answering these questions provides a more practical starting point than selecting an AI technology first and then trying to make the operation fit around it.
The answers may point to a historian modernization project, a SCADA upgrade, stronger asset models, improved system connectivity, a DataOps strategy, better data governance, or a new edge-to-enterprise architecture. In other words, the path to industrial AI may begin with many of the services system integrators already provide. What changes is how that work connects to the customer’s long-term goals and expected outcomes.
Building the Foundation for AI-Ready Industrial Operations
Rather than focusing only on deploying the next system, system integrators can help customers understand how their existing systems, infrastructure, and industrial data can support where they want to go next.
AI may start the conversation, but connected systems, trustworthy data, operational context, and industrial expertise are what make it useful. By building that foundation, system integrators can play an even greater role in the future of industrial automation.
Help Your Customers Get Ready for What’s Next
AI is creating new conversations with industrial customers, and system integrators have an opportunity to lead them. Cimsoft works alongside SIs with the technology, expertise, training, and support needed to strengthen industrial architectures, solve data challenges, and help customers prepare for what comes next.