Large-scale control system migrations create a significant volume of repetitive engineering work. A single project may involve thousands of control loops, legacy files, narratives, graphics, code elements, and configuration records. Many of these tasks involve processing large amounts of data through repeatable steps, making them practical use cases for artificial intelligence.

Turning legacy data into control narratives

One recent migration project required more than 1,000 control narratives, each describing in plain language how a control loop was intended to function. The necessary data already existed in the legacy system but had to be extracted, organized, and transferred into a consistent set of Word document templates. This work was previously handled with Excel macros, which are effective but slow to build. In this project, an AI model was given the templates and a table of extracted legacy system parameters, then prompted to write a script that populated the templates automatically.

Using AI reduced the time required to develop the script from an estimated day or two to a matter of minutes. The engineering team reviewed the script, ran it against the project data, and performed a back-check to confirm that the information was transferred correctly. The same approach also made large-scale revisions easier. Adding a field or changing wording across every narrative required a new script instead of hundreds of manual edits. 

This use case was successful because the task had clear inputs, a consistent template, and an output the engineering team could verify. The AI model was not determining how the loops should function; engineers supplied the source information, established the template, specified the desired changes, and checked the final output.

Making legacy information more accessible 

Control system migrations often involve equipment that has been in service for decades, and documentation may be incomplete, outdated, or available only as a scanned copy of a printed manual. In practice, that can mean a binder of technical information turned into a folder of images. An engineer can review the page images individually but cannot use a standard keyword search to quickly locate a specific term. 

An AI model that reads scanned pages can convert them into searchable information, letting engineers locate specific terms or hardware references more quickly. This does not fix every documentation problem, since a legacy manual can still conflict with the installed system, but engineers spend less time hunting for information and more time evaluating what they find.

Working with text-based control files

Many control system platforms allow logic or configuration data to be exported into formats such as XML or HTML that can be parsed and modified with scripts. When a required change is consistent and well defined, AI can help an engineer develop a script that applies it across hundreds of files, using a representative section of exported logic to show the model the existing structure and intended modification.

Using one AI-generated script across many files can also improve standardization on a multi-engineer migration. When work is divided across a team, differences in logic, tags, and documentation can emerge. A common script can reduce that variation and make the finished work easier to review and maintain. 

Using AI becomes less reliable when the task requires the model to interpret a large volume of legacy information rather than apply a defined change. AI output is not consistently accurate. When a team feeds an entire legacy database into a model and asks it to identify the necessary parameters, the model may return unnecessary information, misunderstand how values are used, or omit important context. AI is also not yet reliable enough to take control logic from one platform and automatically convert it into working logic for another without substantial engineering review. 

AI has similar limitations when generating operator graphics. An AI-generated display may contain all the requested information while still being cluttered, difficult to navigate, or inconsistent with operator expectations. Designing an effective operator interface still requires engineering judgment, process knowledge, and an understanding of how operators use the system. 

How to introduce AI into a migration workflow

Teams introducing AI into a migration project should begin with a small, measurable task and a limited sample to avoid spending ten hours automating a five-hour job. Teams should clean and standardize source data before using AI because two pieces of code may perform the same function while using very different structures, which can cause the model to treat them as separate use cases. 

Client-specific information should be removed from project material before using an online AI model. When possible, engineers can also ask the model to generate a script rather than making changes directly to project data, then run that script offline against the actual data. 

AI works best in control system migrations when the assignment is narrow, repetitive, and reviewable. The most useful applications allow AI to handle work at scale while controls engineers remain responsible for the data, validation, and final technical decisions.