---
title: A Practical Starting Point for AI in Manufacturing
description: Learn how Front-End Engineering helps manufacturers reduce scope, cost, and schedule risk before capital projects begin.
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---
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# A Practical Starting Point for AI in Manufacturing
By [Ron Rich, P.E., PMP](https://info.polytron.com/blog/author/ron-rich)
November 4, 2026
4 Minute Read
Manufacturers today are at very different stages when it comes to adopting AI. Some are just starting to explore potential applications while others are incorporating technology into daily operations.
In a [2025 McKinsey survey](https://www.mckinsey.com/capabilities/operations/our-insights/from-pilots-to-performance-how-coos-can-scale-ai-in-manufacturing?utm_source=chatgpt.com) of more than 100 senior operating executives at companies with at least $1 billion in annual revenue, about two-thirds reported that their organizations were still exploring AI or implementing it in targeted areas while just 2 percent said AI was fully embedded across operations. The survey also identified the leading AI investment priorities for manufacturers as predictive maintenance, schedule optimization, and process improvement.
To make matters more complicated for manufacturers, AI capabilities are advancing rapidly. Generative AI tools can now help with tasks such as finding information and preparing documentation, while agentic AI can take this further by coordinating and carrying out steps in a workflow within defined permissions.
As AI capabilities continue to expand, manufacturers need a practical way to determine where the technology can solve an operational problem and what it will take to use it effectively. I recommend starting with a well-defined task, protecting and preparing the data it depends on, and involving the employees and system integrators who can help evaluate the results and guide broader adoption. Let’s explore this more.
## Starting With Work You Understand
Applying AI to a well-defined, repeatable task gives manufacturers a clearer way to evaluate what a tool can and cannot do. It also keeps the focus on a measurable operational outcome, such as improving documentation or reducing the time spent gathering information.
Consider the work involved in preparing for a shift handoff. A supervisor may need to review production notes, downtime records, and outstanding maintenance issues to communicate what the next shift needs to know. An AI tool could draw on approved records to prepare a draft summary for the supervisor to review. By comparing that process with their current approach, the team can assess whether the tool saves time while accurately capturing the information the next shift needs.
Keep in mind, achieving reliable time savings with an AI tool may take some upfront work. The first project may require additional effort to organize information, test the output, and refine the review process before it produces real time savings. Manufacturers should account for that effort when evaluating results and use what they learn to determine whether the approach is ready for broader use.
## Protecting Data
Even a narrowly defined application, such as preparing a shift handoff summary, requires clear boundaries around the information the AI tool can access and use. That’s why before introducing any AI tool, manufacturers should establish what information it can access, who may use it, and how that information will be stored, retained, and used. A private platform alone does not answer these questions. Clear access controls and approved data-handling practices are required to maintain control of sensitive information.
## Preparing Data
Data readiness matters just as much as data protection. AI tools need information that is accessible, consistent, and grounded in the context of the operation. In a [2026 Manufacturers Alliance survey](https://www.manufacturersalliance.org/sites/default/files/2026-05/AI2026-Report-F.pdf), 63 percent of respondents said their data required cleanup and mapping before starting AI pilots.
For manufacturers, part of that work involves connecting information spread across a variety of downtime tracking, quality, and maintenance systems. When those systems aren’t connected, even familiar operational questions, such as whether quality problems increased after a specific maintenance event, can be difficult to answer. Bringing records together allows teams to investigate these types of relationships and gives AI tools the context needed to support their analysis.
But preparing data for AI requires defining what each data point means, how it relates to the production process, and how frequently it needs to be updated. A well-defined [manufacturing execution system](https://polytron.com/services/smart-manufacturing/) (MES) can help put that context into practice by collecting and organizing production data and linking it to equipment, orders, materials, and events. Establishing trusted data sources and a consistent model gives manufacturers a stronger foundation for applying AI across lines and facilities.
Some MES platforms also offer built-in AI capabilities that make this information easier to use. For example, [Parsec’s TrakSYS IQ](https://www.parsec-corp.com/traksys-iq) allows users to ask questions about production performance and downtime in plain language using contextualized MES data. This gives manufacturers another potential starting point: evaluating AI capabilities within their existing systems, with the same attention to data quality, access controls, and human review.
## AI Changes the Way Work Is Done, Not the Need for Expertise
Preparing people to work with AI is just as important as preparing the data. For employees, AI adoption raises reasonable questions such as:
- Will our experience still be valued?
- Will we be expected to trust an answer we cannot verify?
- Will time saved translate into more meaningful work, or simply more work?
Manufacturers need to address these types of concerns directly and involve employees in shaping how AI tools are used. McKinsey’s survey reinforces the importance of employee involvement and preparation: half of respondents identified cultural change as a major barrier to implementation, and nearly as many pointed to reskilling needs.
One way to address these concerns is to involve employees in identifying where AI could reduce routine work and create more time for tasks that require their judgment. When AI reduces time spent gathering information or preparing documentation, engineers can devote more attention to investigating recurring losses and evaluating improvements. Management can explore more complex questions about production tradeoffs, and operations leaders can spend more time coaching employees and following through on improvements that address recurring production problems.
Realizing these benefits requires manufacturers to decide which tasks AI will support, where human review remains essential, and how employees can use the time saved to address more complex operational needs. Employees also need training to evaluate AI output and carry out their responsibilities as new tools are introduced.
Putting those decisions into practice requires a detailed understanding of the plant’s controls, data, and production processes. System integrators can play an essential role in helping manufacturers apply AI within the constraints of their production environment: from preparing data and connecting existing systems to validating performance under real production conditions.
## Building Toward AI Adoption
Strategies for implementing AI in manufacturing are evolving, and no one has every use case figured out yet. To put the approach discussed here into practice, you should:
- Begin with a well-defined task and a measurable outcome
- Protect proprietary information
- Prepare and contextualize data
- Involve employees, provide training, and define human review responsibilities
- Use the results of the initial project to guide the next application
The companies that make meaningful progress will not necessarily be the ones that pursue the biggest vision first. Instead, I believe they will be the ones that work with their manufacturing partners to identify practical opportunities, build confidence through experience, and gradually expand how they use AI.
[Contact us](https://polytron.com/contact-us/) to start the discussion on where smarter, more connected technologies can help create value in your operations.
[](https://info.polytron.com/blog/author/ron-rich)
#### Ron Rich, P.E., PMP
With 30+ years of experience at Polytron, Ron Rich was appointed CEO in 2017. He is focused on connecting with customers and industries, setting vision and strategy for the organization, and strengthening the company’s culture and leadership team. Ron holds a Bachelors of Science degree in Electrical Engineering from Georgia Institute of Technology, a MBA from Georgia State University, and a certificate in Change Leadership from Georgia State. Ron is a licensed professional engineer in the State of Georgia, and a certified Project Management Professional (PMP).
Author
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