There's a lot of emotion around the topic of Artificial Intelligence (AI) ranging from excitement, wonder, and intrigue to worry, anxiety, and apprehension. In this article, we will review what AI is, why it’s different from traditional computer tools, and a few ways manufacturers can use it to be more competitive.

What AI Is and What It’s Not 

When you ask AI what it is, it might say something like…

“Artificial Intelligence (AI) represents methods and systems that perform tasks which, if done by humans, require cognitive abilities — e.g., perception, learning, reasoning, planning, language.”

That makes AI very different from traditional computer programming – a CNC program, for example – where there is a defined algorithm that processes math equations, summarizes data, or executes steps. As we have seen over the past 40 years, traditional computers are a great way to automate a well-defined process. Unlike AI, however, they don’t adapt to changes in our businesses or identify potential issues or improvement opportunities.

Moving from Manual to AI-Supported Processes: Manufacturing Examples

Let’s look at some real-world scenarios to understand where a manufacturer might progress from a traditional, experience- and knowledge-based process (level 1) to an automated process where AI considers many factors that improve or strengthen the process (level 4). 

For each example, consider which statement most closely matches your current state. 

AI for Production Scheduling:

• Level 1 (Experience) – We schedule production orders when it looks like we need more inventory of a specific product.
• Level 2 (Standardized) – We establish a system of standard min/max inventory targets for each product. We manually track the inventory levels, and when one is expected to go below the minimum level, we schedule a production order to refill inventory to the maximum level.
• Level 3 (Automated) – Our ERP system automatically schedules production orders based upon the current or expected customer orders and the current inventory levels.
• Level 4 (AI-based) – We use AI in conjunction with our ERP system, which recommends when to schedule production orders based upon several internal and external factors. External factors might include upcoming weather patterns, world events (like the impact of COVID or Tariffs), or related industry news and trends.

AI for Cost Estimating:

• Level 1 (Experience) – We review historical orders that are similar and adjust the cost estimate based upon experience.
• Level 2 (Standardized) – We use a spreadsheet template to build a cost estimate based upon historical order data and current material and labor costs.
• Level 3 (Automated) – Our quoting tool automatically pulls information from our data sources (ERP, Quickbooks, etc.) through a structured quoting algorithm.
• Level 4 (AI-based) – We provide information (drawings, hand-written notes, voice messages, spreadsheets) to an AI agent that can generate a draft quote for review/approval and provide directions and insights to finish the quote. The AI agent might also recommend quoting alternatives to increase the likelihood of success.

AI for Hiring Process:

• Level 1 (Experience) – We solicit for, review, and rate resumes of applicants based on our experience on how they might fit an open position. We interview the most promising candidates and manually verify their information (references, certifications, degrees, etc.).
• Level 2 (Standardized) – We use an established/standardized process and a tracking spreadsheet for rating resumes, interviewing candidates, and verifying information.
• Level 3 (Automated) – We use an online tool that helps collect and review candidate information and manage the overall process.
• Level 4 (AI-based) – We describe and rank the requirements for the position and any restrictions (work location, travel required, salary cap) to the AI. The AI agent reviews sources for employees on the internet and creates a listing of potential candidates with a rating score and insightful questions to ask each candidate.

AI for Equipment Maintenance:

• Level 1 (Experience) – We manually monitor equipment performance and try to fix equipment before there are serious issues. 
• Level 2 (Standardized) – We established a preventative maintenance schedule to lubricate bearings, sharpen tools, replace filters, and replace worn components on a regular basis.
• Level 3 (Automated) – We instituted a predictive maintenance system with sensors that monitor fluctuations in temperature, vibration, and sound to detect potential problems for us to correct.
• Level 4 (AI-based) – We use an AI agent to review real-time information to identify patterns between sensor data, equipment settings, the environment, materials factors, and finished part characteristics to determine when we should complete targeted maintenance tasks.

AI for Strategy, Marketing, and Sales Planning:

• Level 1 (Experience) – We primarily determine growth strategy, target markets, and sales priorities by leadership experience and informal customer feedback. We make decisions about which markets to pursue or customers to focus on based on intuitional knowledge and past success.
• Level 2 (Standardized) – We use a structured strategic or sales planning process, such as annual planning, pipeline reviews, or market segmentation exercises. We use standard templates and reports to review historical performance and establish targets.
• Level 3 (Automated) – Our CRM, marketing automation, and reporting tools automatically track leads, opportunities, conversion rates, and sales performance. Dashboards give us visibility into pipeline health, campaign effectiveness, and forecast accuracy based on internal data.
• Level 4 (AI-based) – We ask AI to analyze internal data (CRM, ERP, website activity) along with external data such as market trends, competitor activity, and industry signals. The AI provides recommendations on which markets and customers to prioritize, how to position offerings, and where sales and marketing efforts are most likely to succeed.

Is Using AI Right for My Manufacturing Business?

There is both a cost (monetary and time/effort) and a benefit to progressing from level 1 toward level 4. For each example above, make a note of where you are currently and where moving up a level could make a major difference for your business. Document any problems or inconveniences with your current system. 

Then consider the elements of quality, cost, and timeliness to decide if it makes sense to progress toward an automated or AI-supported process. 

• Quality – How “good” is your current system? Are you happy with the results, or is there room for significant improvement?
• Timeliness – How much does timeliness impact your system? Do you have the time to complete required tasks and make decisions, or are you jeopardize sales, productivity or operating costs because of how long the process takes?
• Cost - What are the direct and indirect costs of using your current system? Is it manual and inefficient or streamlined and cost-effective? How much money, time, and resources would it take to move to the next level, and what’s the expected ROI?

Are Manufacturers Using AI?

The consensus across multiple industry studies in 2025 is that about 90% of US manufacturers are using AI at some level. Most firms are still early in their AI maturity curve, but rapidly increasing adoption and investment. Usage tends to be higher in more tech-focused sectors such as electronics, aerospace, and automotive.

For context, high adoption rates mean a broad range of AI use—from simple chatbots and analytics dashboards to integrated machine learning for maintenance, logistics, and quality control. The sector is expected to continue increasing both the depth and sophistication of AI applications over the next several years.

How Does AI Adoption Affect Job Security in Manufacturing?

Roughly 25-50% of people surveyed are concerned about AI-driven job displacement. In many ways, AI adoption is similar to the shift that has occurred in the agricultural and manufacturing industries from the introduction of automation. Automation has made these industries more efficient and cost effective, but at the expense of some job loss. Over time, AI will likely replace some manual and repetitive knowledge-based jobs (much like automation replaced repetitive physical-based jobs). There will certainly be new opportunities to use AI to learn and augment people’s current capabilities so that employees are working in tandem with AI.

Want more on AI? 

MAGNET is hosting a webinar series on AI in manufacturing. Join us for some or all. 

PAST - Webinar: Simple, Practical Ways to Start Using AI (Plus an AI-Readiness Assessment)
Tuesday, March 3, 12:00 PM

AI is already helping small and medium-sized manufacturers cut costs, boost efficiency, and stay competitive. In Simple, Practical Ways to Start Using AI, we'll break down what AI means for your daily operations and spotlight a few high-impact wins:

• Smart inventory forecasting to free up cash
• Quick process tweaks for energy savings
• Predictive maintenance to spot failures before they happen
• AI-powered quality inspection to catch defects faster

We'll offer realistic strategies to avoid messy data, cost concerns, and team resistance and guide you through a quick self-check scorecard to assess your company's readiness. You'll leave with clear, actionable next steps—free trials, local resources, and pilot ideas. Get motivated and equipped to make your first smart move toward AI-powered gains.

PAST - Webinar:  Is Your Data Ready for AI? A Practical Starting Point for Manufacturers
Wednesday, March 11, 12:00 PM

As AI becomes a bigger part of the manufacturing conversation, many organizations are wondering, "Where do we even start?" This session focuses on what AI readiness looks like at a practical level for manufacturers in terms of how data is captured, structured, and shared across everyday operational workflows.

In this beginner-friendly webinar, we’ll:

• Explore how you can improve the way data flows through core processes such as quoting, forecasting, inventory management, production tracking, and quality reporting
• Discuss the importance of data quality, consistency, and visibility
• Detail how you can dramatically improve data efficiency and accuracy to lay the foundation for future AI capabilities

By the end of the session, you'll have a clearer understanding of what “AI readiness” really means in a manufacturing context, where gaps in data and workflows commonly exist, and how improving data capture and visibility today enables more reliable reporting, better decisions, and more advanced automation over time. In Part 2 of the series (date coming soon), we’ll build on these concepts and explore what becomes possible once those fundamentals are in place.

Webinar:  Part 2 of Is Your Data Ready for AI? A Practical Starting Point for Manufacturers
Wednesday, April 29, 12:00 PM

Registration link coming soon.

Event:  AI in Manufacturing
Tuesday, May 19, 8:30 AM - 1:00 PM at MAGNET Headquarters in Cleveland

Registration link coming soon.

Take Steps Toward Implementing AI in Your Manufacturing Facility

MAGNET is strengthening its practice around how AI tools can be effectively applied within manufacturers’ business operations. Our goal is to provide a framework for identifying how AI can help your business, selecting tools that would be the best fit for your organization, and implementing tools so you can get tangible benefits.

If that sounds like something your team needs, let's start a conversation.