According to the 2024 AI in Manufacturing & Energy study, 64% of manufacturers with AI in production environments are already seeing positive ROI, and nearly one in three expect between a $2 to $5 return for every $1 invested.  

Among all industrial respondents, 88% say AI is helping drive measurable business outcomes, and 83% report productivity improvements from AI-powered initiatives.  

The message is clear: AI is no longer experimental in manufacturing. It’s delivering bottom-line value, increasing uptime, and unlocking smarter decision-making—from quality labs to the plant floor.  

What’s often missing in these discussions is this: You don’t need to be a global enterprise to benefit. INS3 works with mid-size to small manufacturers every day, delivering the same types of results by focusing on scalable, practical applications of AI and Industrial IoT.   

Through this blog post, we have discussed how manufacturing process optimization  using AI is delivering real ROI.

What Enterprise Leaders Are Doing—You Can Do Too

The study highlights that the most successful manufacturers using AI are focusing on use cases like:

Predictive Maintenance 

AI-powered predictive maintenance has a big involvement in the current landscape of the manufacturing industry. Both large and advanced mid-sized manufacturing companies are using predictive maintenance to predict equipment failure and prepare before disruptions occur.

AI algorithms analyze sensor data from equipment and predict potential failures. It enables them to proactively maintain and reduce costly production downtimes.

Quality Control 

From production process optimization to improving overall product quality, AI-powered quality control assistants are also essential parts of manufacturing process optimization. With AI systems, it’s easier to identify subtle defects and improve inspection time.

With AI-powered quality control processes implemented, it’s easier to reduce waste and control the product quality.

Generative Design 

With AI algorithms at our fingertips, it’s easier to generate new designs and optimize existing processes. Generative design is an iterative engineering process that uses AI and the power of cloud computing to create different design iterations at scale.

This is the transformative power of AI to provide different design ideas to designers, helping them with more options to choose the perfect combinations.

Supply Chain Optimization 

AI is bringing transformative changes in the supply chain and logistics management part of the manufacturing industry. With AI, it’s easier to enhance efficiency, smart factory optimization, reduce the costs of production, and improve decision-making at an organizational level across the supply chain process.

This includes different areas such as demand forecasting, inventory management, predictive maintenance, and logistics. AI is bringing a new level of agility and responsiveness to manufacturing businesses, improving manufacturing process optimization at scale.

AI Chatbots

Manufacturing workers can use AI chatbots to improve physically demanding and repetitive tasks. It can improve and automate communication gaps, handle repetitive tasks, and offer real-time insight into the production process.

Ultimately, AI chatbots boost productivity and reduce overall production costs. Furthermore, it also adds a new layer of personalization to the day-to-day working process that manufacturing workers are used to, giving them far better experience at handling complex tasks.  

Digital Twin Manufacturing

With AI-powered digital twins, it’s easier to simulate the manufacturing process for virtual optimization and risk management. The AI-powered digital twins are revolutionizing smart manufacturing by creating virtual replicas of different physical systems, enabling real-time monitoring, analysis, and optimization.

AI algorithms add new capabilities such as predictive analytics and automated decision-making to the digital twins, making them more efficient. Furthermore, the use of anomaly detection reduces downtime and improves overall manufacturing performance.

These aren’t limited to companies with in-house data science teams. The techniques behind them—good data architecture, process integration, and real-time analytics—can be applied at any scale.

INS3 helps smaller manufacturers apply these exact strategies by simplifying what’s needed and starting with what you already have—your existing PLCs, SCADA systems, and operators.

Discover how AI is transforming production efficiency, reducing costs, and driving innovation—read our full guide on the Role of AI in Manufacturing now!

A Process-First Approach That Works

One of the biggest missteps in AI adoption is starting with tools instead of starting with the process. Dashboards are great—but they’re useless if the underlying data is inconsistent, delayed, or unstructured.

INS3’s approach starts with a plant walkthrough and data readiness evaluation. We don’t lead with software—we lead with understanding your operations. From there, we:  

Assess and Map Existing Processes

Map out how data flows through machines, sensors, operators, and systems. This involves checking out production lines, cross-checking the quality control process, checking maintenance schedules, and supply chain operations.

Identify Pain Points and Opportunities

Identify areas where Industrial IoT analytics and automation can improve efficiency or reduce errors. As we’ve discussed before, there’s no point in having a comprehensive dashboard if the data remains unstructured.

At INS3, we take an objective-oriented approach to AI implementations. The approach sometimes involves prioritizing resource allocation or enhancing decision-making.

Introducing AI-Powered Models

Introduce AI-powered models—for things like predictive quality, maintenance alerts, or yield optimization—only when the process is ready for them. This step requires a thorough analysis of different AI tools and solutions available and utilizing the right fit for the right task.

Build lightweight, scalable solutions that work alongside your current systems and people.

This method ensures high ROI and fast adoption, without overwhelming your team or disrupting operations.

Discover how generative AI is transforming—not replacing—manufacturing. Read the full article here.

Results That Match the Study—At a Smaller Scale

While the study’s respondents skew toward enterprise-level manufacturers, the outcomes are repeatable. Here’s what we’ve seen from mid-size to small manufacturers working with INS3:

  • A regional food processor reduced downtime by 30% using process-stage anomaly detection and alert logic tied to existing SCADA tags.  
  • A discrete manufacturer improved throughput by 12% by identifying bottlenecks and automating work cell scheduling  
  • A packaging facility reduced scrap by over 15% through predictive quality models built from a mix of sensor data, lab results, and machine logs.

In each case, we used existing infrastructure and focused on solving one high-impact problem before expanding the solution.

Why This Matters More Now Than Ever?

With labor shortages, rising costs, and increased demand for traceability and efficiency, AI is no longer optional—it’s becoming the new baseline for competitive operations. The Dataiku study showed that:  

1st Reason 

54% of manufacturers are already using AI for predictive maintenance. AI can analyze sensor data, effectively detect equipment failures, and suggest a proactive maintenance process. It helps identify bottlenecks and inefficiencies in manufacturing, improving the overall manufacturing process.  

2nd Reason 

49% of manufacturers are applying AI to optimize product quality. Optimum process monitoring and real-time data assistance provided by AI help in not only decreasing production downtime but also boosting production quality. 

3rd Reason 

58% of manufacturing companies are using AI models for production optimization and performance visibility. A visibility over the production process in real time helps optimize workflow and maintain the manufacturing process and performance.  

These numbers are growing rapidly. As enterprise manufacturers push ahead, mid-size and smaller operations need to follow—but with solutions that fit their size, pace, and people. That’s exactly what INS3 delivers.

Case Studies of Large Manufacturers Using AI for Manufacturing Process Optimization

From mid to large-sized manufacturing companies, everyone is using AI to its fullest potential today. With AI-powered solutions, it’s easier to reduce costs and enhance product quality. The following are some examples of large and mid-sized manufacturers using AI for manufacturing process automation: 

Turbulent Hydro (Renewable Energy) 

This SME uses AI to optimize the performance of different micro-hydro turbines. The AI-powered process helps optimize the overall water flow level. And automated turbine adjustments according to changing environmental conditions. This implementation resulted in a 15% increase in energy output and cost reduction for Turbulent hydro. This is a perfect example of AI-powered predictive maintenance.  

Philips 

According to DigitalDefynd, Philips uses AI-powered quality control systems for doing a detailed inspection of their manufacturing process. It helps them ensure compliance with consumer and medical standards, showcasing optimal use of AI for quality control in manufacturing process optimization. 

Siemens 

With more than 75% automation, Siemens uses AI systems for predicting machine failures, dynamically optimizing production workflows, and analyzing data for improving efficiency. This is a proper example of production optimization with the use of AI. Such implementation of AI has resulted in Siemens achieving a 99.99% in product quality as per Classic Informatics.

How Does INS3 Help with Manufacturing Process Optimization

INS3 specializes in bringing that clarity to mid-size to small manufacturing teams, with no-pressure discovery, practical guidance, and rapid, scalable execution.

We offer a complimentary plant walkthrough and data readiness study, where we:   

  • Evaluate your current process visibility and data infrastructure  
  • Identify your highest-ROI use cases based on your existing systems  
  • Build a roadmap to integrate Industrial IoT analytics, process automation, and AI-driven optimization—at your scale

Your Size Doesn't Limit Your Potential—Your Strategy Does

AI in manufacturing is no longer theoretical—it’s working. But successful outcomes depend on more than the tools you buy. They depend on your strategy, process clarity, and ability to act on the data you already have.  

If you’re ready to make your plant smarter—not just more digital—INS3 is ready to guide the way. AI isn’t just for the big guys. It’s for manufacturers ready to take the next step, on their terms.