7 Factors for Successful AI implementation in Manufacturing

7 Key Factors for a Successful AI Implementation in Manufacturing

We are at the peak of the AI hype curve, and the manufacturing industry is breaking from its usual 10-year delay in adopting cutting-edge technologies. The potential benefits are too appealing to ignore, as AI promises to solve long-standing challenges and address gaps that traditional approaches have struggled to fill. In fact, Deloitte’s 2024 Future of the Digital Customer Experience survey found that “55% of surveyed industrial product manufacturers are already leveraging [generative] AI tools in their operations, and over 40% plan to increase investment in AI and machine learning over the next three years.”¹

Despite the buzz, AI remains largely undefined and success stories are rare. While AI is being touted as a game-changer in nearly every industry, tangible examples of its impact are still limited. Much of the excitement is built on potential rather than proven results, leaving many organizations uncertain about how to move beyond isolated successes to achieve scalable, measurable outcomes.

So, where do you begin? With all of the hype, AI can often seem like an elusive, almost magical solution to all problems. The truth, however, is far more grounded—it’s a powerful tool that relies on clear strategies, defined goals, and robust data to deliver meaningful results. The intent of this paper is not to dive deeply into the technical intricacies of AI; instead, it aims to demystify the topic by providing a straightforward, high-level overview of AI and its challenges. By understanding the fundamentals of how AI works and the common obstacles you may encounter, you’ll be better prepared to evaluate its practical applications and determine how it can add value to your organization.

Challenge 1: Understanding AI

The AI widely available today doesn’t possess the cognitive abilities of humans to create or innovate, but it does mimic certain aspects of how we learn. For instance, humans learn the alphabet and can recognize letters even if they appear in a different font. Our subconscious mind does this by determining the closest matching pattern. When we see the character Þ, we compare it to the letters we have previously learned and narrow down that the pattern best fits the letter P. In reality, AI simply relies on algorithms and math to process textual, audio, image, and numerical data and recognize patterns much faster than humans. It is important to understand that AI’s capabilities are entirely dependent on the data it is trained on. The quality and scope of this data determine what AI can and cannot do.

Significant effort is being dedicated to developing AI technologies that attempt to mimic human reasoning, including Agentic AI, Expert Systems, Symbolic AI, Fuzzy Logic, Knowledge Representation and Reasoning (KRR), Reinforcement Learning (RL), Computational Creativity, and Artificial General Intelligence (AGI). Among the many approaches being explored, two stand out as the most widely used today: Machine Learning (ML) and Large Language Models (LLMs). These technologies form the backbone of many AI applications, enabling systems to recognize patterns, make predictions, and respond intelligently to data.

Machine Learning

Machine learning (ML) is a branch of artificial intelligence that uses methods like linear and polynomial regression, among other mathematical techniques, to identify patterns and relationships in past data. These patterns and relationships are then used to make predictions on new data without the need for explicit programming. Because ML relies on mathematical calculations, text data must be converted into numeric representations for effective processing.

ML is used to predict outcomes or the probability of certain outcomes. In manufacturing, a key application is predictive maintenance, where equipment data is analyzed to anticipate failures and reduce downtime. Other practical applications include defect forecasting to enhance quality control and supply chain optimization to improve efficiency.

ML depends on large datasets for training, as it uses this data to uncover patterns and relationships for predictions. However, the quality of the data is critical—models trained on inaccurate, biased, or incomplete data often produce unreliable results. Additionally, scalability can be a significant challenge, as creating ML systems that perform consistently across varying environments and scales can be complex.

Large Language Model

Large Language Models (LLMs) are statistical models that use Natural Language Processing (NLP) and deep learning architectures to analyze text and other content. They typically learn from massive amounts of data, enabling them to converse on a wide range of topics and scenarios. These models analyze patterns, relationships, and structures in language, enabling them to perform tasks such as summarization, translation, and content generation. Unlike traditional AI models focused on numerical data, LLMs cannot perform calculations and are specifically tailored to understand and generate coherent text.

LLMs are widely used to facilitate human-computer interactions. They power applications like chatbots, virtual assistants, and automated customer service tools by interpreting user queries and generating meaningful responses. Their ability to handle unstructured data makes them valuable in contexts like document analysis and knowledge extraction, where they can sift through large volumes of text to provide insights.

LLMs require massive training datasets to interpret context, tone, and subtle language nuances. However, their performance depends heavily on data quality. If the training data contains biases, outdated information, or inaccuracies, the model’s outputs will reflect those flaws. Scalability is another challenge, as deploying LLMs often requires significant computational resources, making it costly for smaller organizations to implement them effectively.

Despite these limitations, LLMs hold significant promise for improving efficiency and accessibility in various industries. Their ability to process natural language makes them particularly useful for automating repetitive tasks, aiding in decision-making, and enhancing communication tools. As these models continue to evolve, they are expected to play an even greater role in enabling organizations to manage and utilize text-based information.

Balancing AI Expectations with Real-World Applications

Realistic expectations are key when it comes to Machine Learning and Large Language Models. Both are based on past data to identify patterns and relationships, but the future doesn’t always align with the past. Their accuracy depends entirely on the quality and relevance of the data they are trained on, meaning they often fall short of perfection. No one has been able to achieve 100% accuracy—if they had, AI would already be dominating the stock market.

Understanding that AI is not 100% accurate, there are still huge opportunities to improve production efficiencies by predicting (anticipating) issues before they negatively impact the achievement of production targets.

Challenge 2: Knowing What to Solve

To begin, you need clear and specific goals for what you want to achieve with AI. Broad objectives like improving efficiency or increasing quality are too vague and require a more detailed definition. Unfortunately, it’s not as straightforward as asking ChatGPT how to enhance the quality of your specific products. Unlike ChatGPT, which is trained on general knowledge, no LLM has been trained on production results from manufacturers worldwide due to the industry’s reluctance to share data. While this may change in the future, for now, AI must be trained in-house, and efforts should remain focused on solving specific, well-defined problems to avoid becoming overwhelmed.

To optimize your production schedules, start by breaking them down into equipment categories or even specific pieces of production equipment. Since AI relies on training, collecting the right data is essential—and this starts with identifying the factors that influence production schedules. For instance, if production efficiencies vary by raw material vendors, leading to delays or schedule adjustments, you’ll need to collect data on vendors and associate it with specific production runs. Machine learning also requires a clear target to train against, such as production losses, OEE, or another metric that accurately represents production efficiency.

The same approach applies to reducing quality issues. Training AI requires collecting detailed defect data, including any relevant factors that might influence quality. This could include production conditions, raw materials, processes, or any other variables that affect the final product. By providing AI with this comprehensive dataset, it can identify patterns and relationships between these factors and product outcomes.

Challenge 3: Cultural Factors

Adopting AI in manufacturing isn’t just a technical challenge—it’s a cultural one. Success requires not only skilled personnel and robust infrastructure but also the willingness of teams to embrace change and navigate uncertainty. From talent shortages to resistance on the shop floor, addressing these cultural factors is critical for ensuring a smooth and successful AI implementation.

Skilled Engineers

Implementing AI in manufacturing requires expertise in data science, machine learning, and AI. Since this technology is still relatively new to the manufacturing industry, most organizations don’t have staff with the necessary technical knowledge to build, deploy, and maintain AI systems. To bridge this gap, companies may need to hire specialized talent, upskill existing teams, or partner with external experts.

Resistance to Change

While upper management may be eager to embrace AI’s potential and initiate new projects, IT and engineering teams are often left uncertain about where to begin. These initiatives frequently push technical teams beyond their typical responsibilities, requiring them to tackle unfamiliar challenges with limited resources or expertise. At the same time, operations teams may resist AI adoption due to skepticism about its effectiveness or concerns about job displacement. Overcoming this resistance requires clear communication, transparency about AI’s goals and limitations, and strong leadership to foster trust, build alignment, and guide teams through the transition.

Unknown ROI

Determining the return on investment (ROI) for AI initiatives is challenging because it is a new technology—the benefits may be long-term and not immediately visible. AI requires substantial resources for implementation, including data collection, model development, training, and testing. These upfront costs, coupled with the uncertainty of measurable results in the short term, can make it difficult to justify the investment. To address this, companies should focus on setting clear, achievable goals for their AI projects and identifying key metrics to track progress, helping build confidence in the potential value AI can deliver over time.

Challenge 4: Data Collection

Data collection is the backbone of any AI initiative, but in manufacturing, it comes with unique challenges. From ensuring device connectivity to overcoming OEM restrictions and legacy system limitations, gathering the necessary data can be a complex process. Beyond simply collecting data, manufacturers must address issues like security, accuracy, and proper storage to ensure that the data is both usable and reliable for AI and machine learning applications. This section explores the critical factors to consider when building a robust data collection strategy.

Connectivity

There are plenty of technologies available to communicate with devices on the plant floor. While many devices are already integrated with higher-level systems, gaps in technology, infrastructure, and access can create significant obstacles.

- - Older Controllers: Existing controllers may not support communications with modern information systems. Replacing the controller on a perfectly running machine solely to collect data can be costly and feel unnecessary.
  - Network Infrastructure Gaps: The networking infrastructure required for seamless connectivity may not exist, and installing it can be expensive. This often involves upgrading or replacing outdated systems to ensure devices can communicate effectively within the network.
  - OEM Restrictions: Machines are commonly purchased from original equipment manufacturers (OEMs). OEMs often restrict access to the internal systems of their machines, including controllers and the data they generate, creating barriers for integration into broader data infrastructures. They may refuse to share data points or protocols and prohibit modifications, warning that doing so could void warranties.
  - Legacy and Proprietary Systems: Accessing data from legacy or proprietary information systems may require custom software development for an interface, duplicate data entry, or replacement with more modern, open systems to ensure compatibility.

Data Security and Privacy

Unlike publicly available datasets, manufacturing data is proprietary and considered a competitive advantage. Sharing such information could have serious consequences, making manufacturers highly protective of their data. While AI implementations would benefit from access to industry-wide production data, the current reality is that companies must rely exclusively on their own datasets. This creates an additional burden to secure and manage data properly, ensuring it is protected from breaches while remaining accessible for internal use.

Data Accuracy

Data accuracy is one of the most significant challenges in AI training—the effectiveness of any AI system depends entirely on the quality of the data it is fed. Inaccurate data can lead to biased models, unreliable predictions, and incorrect conclusions, undermining the very purpose of implementing AI. Even small errors in collected values, inconsistencies in formatting, or outdated information can significantly skew results. For example, a failing pressure sensor or a density gauge measuring air instead of product can produce inaccurate, irrelevant values. Ultimately, maintaining high data accuracy requires ongoing effort, from careful data collection and preparation to rigorous validation processes throughout the AI lifecycle.

MES and ERP data is typically more accurate because it is closely tied to real-time production processes and equipment on the shop floor, allowing for immediate detection and resolution of discrepancies. Errors in MES data often lead to noticeable disruptions in production, such as halted workflows or incorrect product specifications, which require immediate correction to avoid costly downtime. However, human error can still introduce inconsistencies. For example, if machine controls are not interlocked with MES systems, operators might start a process without first selecting or activating the appropriate production order in the ERP or MES system, leading to mismatched or incomplete data.

Storage

Collected data must not only be stored securely but also in a way that ensures it remains accessible, organized, and actionable for AI and analytics tasks. Various storage approaches, including data lakes, data warehouses, and hybrid solutions, influence how easily data can be prepared and utilized for AI model training and system-wide analysis. The right choice depends on factors such as AI performance requirements, storage costs, and the level of data preparation needed. By making strategic storage decisions early, you can avoid costly reprocessing, inefficient data workflows, and scalability challenges down the line. Two of the most common storage methods are:

Data Lakes: A data lake is a centralized repository that stores raw data in its native format, including structured, semi-structured, and unstructured data. It is designed for flexibility, allowing organizations to collect and store vast amounts of diverse data types such as logs, documents, images, and videos. While data lakes are capable of housing the varied datasets needed for machine learning (ML) and big data analytics, their unstructured nature can make them less practical for ML applications. The lack of consistent organization often makes it difficult to define the features (input variables) and labels (target variables ML is trying to predict) required for training ML models, increasing the complexity of data preparation.

Without proper governance and organization, data lakes can degrade into “data swamps”. The raw nature of the data often requires significant preparation and cleaning before it can be used for analysis or ML tasks. While data lakes are powerful for storing and exploring diverse datasets, their usability depends heavily on robust management practices.

Data Warehouses: A data warehouse is a highly structured repository that organizes data into a consistent format, often aggregated from multiple sources, to support analytics and reporting. Designed for efficiency and speed, data warehouses are optimized for querying and analyzing structured data, making them a strong fit for machine learning tasks that require well-organized data. Their time-based architecture provides superior performance for time-series data analysis.

Despite their strengths, data warehouses are less suited for handling unstructured or semi-structured data, as their rigid schema can limit flexibility. They also are not typically used for real-time processing or iterative training of ML models, as they prioritize batch processing and historical analysis. Additionally, data warehouses tend to be more expensive to scale compared to data lakes, making them less ideal for massive datasets or exploratory analyses. While they excel in delivering high-quality, consistent data for business intelligence and ML preparation, their design limits versatility.

Data Formats

AI and machine learning rely on well-prepared and consistently formatted data, but the diversity of formats makes standardization difficult. Selecting appropriate data formats is essential for ensuring compatibility with AI workflows and avoiding costly delays during data preparation.

Before diving into development, ask yourself: What format is your AI model designed to process? Does it match the format of your machine data? Is the data formatted consistently across various machines and sources? Addressing these questions upfront can prevent major roadblocks later in your project, such as:

- - Processing failures – The AI model rejects or misinterprets incoming data due to inconsistencies in structure and format.
  - Inaccurate predictions – Data inconsistencies introduce errors that degrade model performance, leading to unreliable AI-driven decisions.
  - Data corruption – Key information is truncated or mis formatted (e.g., timestamps in CSV vs. JSON), affecting analytical accuracy.
  - Manual reformatting bottlenecks – Engineers must spend time writing scripts to restructure data instead of focusing on AI development, delaying the project.
  - Escalating costs – Additional engineering resources are required to clean, restructure, and standardize data, driving up project expenses.

Choosing the Right Data Format

Selecting the right data format is essential to ensure compatibility with your AI model’s requirements and the scale of your data. While numerous data formats exist for AI and machine learning, many are not commonly used on the factory floor or in industrial data exchanges. The following are some of the most widely used formats in industrial and AI applications:

Structured Data

- - JSON (JavaScript Object Notation) – An open-source, lightweight, human-readable data format used for representing hierarchical structured data. While primarily used for exchanging data between a server and a client in web applications, JSON has become a widely accepted data format across various programming languages due to its simplicity and flexibility.
  - XML (Extensible Markup Language) – Originally developed for exchanging data between business systems and web applications, XML is widely used for structured documents and industry-specific data formats.
  - CSV (Comma-Separated Values) – A lightweight, human-readable format that organizes data into a fixed structure of columns and rows. CSV is commonly used for structured datasets in machine learning and data exchange between different systems.
  - Parquet – An open-source, non-human-readable storage format optimized for efficient storage and processing of structured data. It is particularly well-suited for analytics workloads where performance and storage efficiency are critical, especially when handling large-scale datasets commonly used in AI and machine learning tasks.

Unstructured Data

- - Text (TXT, DOCX, PDF, etc.) – Commonly used in natural language processing (NLP) models.
  - Image (JPEG, PNG, BMP, TIFF, etc.) – Used in computer vision applications for object detection, image classification, and other AI-driven analyses.
  - Audio (WAV, MP3, FLAC, etc.) – Utilized in speech recognition and audio processing tasks.
  - Video (MP4, AVI, MOV, etc.) – Applied in video recognition, AI-driven media analysis, and automated content generation.

Challenge 5: Transforming Data

Turning raw data into actionable insights requires more than just collecting and storing it—it demands thoughtful preparation and transformation. From adding context to aligning timeframes and standardizing formats, these steps ensure data is both usable and reliable for AI and machine learning systems. The process of organizing the data to be useful is commonly called cleaning the data. This section explores key concepts like data contextualization, text vectorization, time shifting, and unified data structures to help create a solid foundation for advanced analytics and AI applications.

Data Contextualization

Data contextualization is not the easiest concept to explain, but is one of the most important factors in training data for AI. Many data collection efforts today focus on just gathering data from the shop floor—via sensors, HMI, SCADA, MES, and other systems—and storing it in a data lake or warehouse, with little consideration for how it will be used later. This often results in time-series data with no context, where time-series data values are simply recorded on a value change or at fixed time intervals.

For example, the temperature of a mixing tank might be recorded every 10 seconds, 24 hours a day. This results in 8,640 values logged daily, regardless of whether the tank contains product or is actively in production. When data scientists begin preparing these temperature values for AI learning, they often lack critical context—such as which values correspond to periods when the tank contains product, the acceptable temperature range for the product being produced, or other relevant production details. Without this context, the data is incomplete and fails to provide a reliable foundation for training AI or machine learning systems to identify patterns and make accurate predictions.

AI can ingest various types of content, such as PDFs, images, and documents stored in data lakes, but this data must be linked to specific production events to provide the necessary context for analysis. The lack of context creates significant challenges for data scientists, who often spend over 80% of their time cleaning and preparing data for AI training. By the time the raw data reaches them, critical manufacturing knowledge is often lost or difficult to recover. This highlights the importance of involving plant-floor staff, whose expertise in manufacturing systems and processes can help contextualize and prepare data at the source. Context-rich, well-prepared data ensures AI systems have a solid foundation.

Text Vectorization

At its core, machine learning relies on numerical computations—it does not process words or phrases directly. In manufacturing, where textual data like production loss reasons, equipment identifiers, or material names play a critical role, inconsistency in handling this data can lead to significant errors. For instance, if numeric values assigned to words or phrases during training are later mismatched, the model’s predictions can become misleading, potentially driving poor decisions and inefficiencies. To avoid this, text vectorization is required to consistently assign numeric values to words and phrases so they can be accurately processed by machine learning algorithms.

There are multiple methods to perform text vectorization, but consistency is key. For example, if the model was trained with “Raw Material A” assigned a numeric value of 34,100, that same value must be used when retraining or making predictions. Otherwise, 34,100 could be misinterpreted as “Raw Material Z,” compromising the reliability of predictions.

Time Shifting

Not all processes involved in producing a serialized item or batch of product happen simultaneously. For example, Lot ABC might be produced on Machine 1 at 10:00 AM and then consumed on Machine 2 at 1:00 PM. Patterns in the data from Machine 1 could influence the performance of Machine 2. Without aligning the timeframes of these datasets, the cause-and-effect relationships between processes can become obscured. This misalignment complicates root cause analysis, process optimization, and predictive maintenance, potentially leading to inefficiencies and missed opportunities for improvement. To identify these patterns and correlations, the data collected from Machine 1 at 10:00 AM must be shifted to align with the data collected from Machine 2 at 1:00 PM, ensuring a cohesive and actionable analysis.

Integrating AI with a Unified Namespace

AUnified Namespace (UNS) provides a structured approach to real-time industrial data, centralizing information for seamless access and interoperability. Ideally, a UNS should provide full data context by connecting related manufacturing data. However, in practice, most UNS implementations focus only on organizing data into structured categories and metadata without linking information across systems. As a result, manufacturers often rely on extra tools, manual work, or custom solutions to connect data points. In some cases, these connections are never made.

For example, a UNS may store a machine’s temperature reading under Factory/Site1/Line3/Machine7/Temperature, but it doesn’t inherently link that reading to the product being produced, the operator on shift, or whether it contributed to a quality issue. Without this relational context, AI and analytics tools must work harder to extract meaningful insights, often requiring manual intervention or additional processing.

A well-designed UNS must go beyond organizing data into structured hierarchies—it needs to establish meaningful connections across systems. This means linking machine data to work orders, production batches, operator logs, and maintenance records so that insights aren’t limited to isolated data points. When these relationships are built into the data model, AI and analytics tools can more easily trace the impact of process conditions, identify root causes of inefficiencies, and support faster, data-driven decision-making without requiring extensive manual data reconciliation.

Challenge 6: Post-Production Updates

Post-production updates are a common reality in manufacturing data, where errors, missing entries, or adjustments often need to be made to reflect accurate and up-to-date information. AI tools must be capable of handling these updates, enabling you to modify or remove existing entries and insert missing data as needed. When updates occur, adjacent data within the same time period must also be adjusted. For instance, if a downtime reason is reclassified from “Unplanned” to “Planned”, the associated durations for both categories must also be updated. Similarly, if quality control lab results become available a day later, they need to be entered into the system and aligned with the correct batch after the batch has been run. Without this flexibility, erroneous or missing data will be used to train AI models, leading to inaccurate patterns recognition and predictions.

Challenge 7: Real-Time Results and Predictions

Many companies focus their efforts on analyzing production data after the fact, using AI to guide strategic planning and long-term decisions. This approach often prioritizes high-level metrics and trends, which are useful for management and executives in setting goals and making informed decisions about the future. While this post-production analysis can be valuable, it leaves a gap in addressing real-time challenges where the data is most urgently needed—on the factory floor, right now.

Without real-time results, production staff is forced to rely on manual monitoring or delayed reports, which limits their ability to act quickly. Issues such as equipment malfunctions, production bottlenecks, or deviations in quality may go unnoticed until it’s too late to correct them efficiently. Providing real-time information and predictions directly to the factory floor empowers teams to proactively address these challenges, prevent losses, and optimize processes as they happen.

Conclusion

When it comes to manufacturing AI, if something sounds too good to be true, it probably is. The industry often promotes the idea that manufacturers simply need to collect data into a data lake to get started. What’s frequently left out of this messaging is the critical context—how the data was used to train AI, what specific insights were gained, and what tangible benefits or return on investment were achieved. When evaluating these solutions, it’s essential to ask detailed, pointed questions to ensure the approach aligns with your company’s unique needs and goals.

Implementing AI in manufacturing is not without its challenges. Success requires more than just adopting new technology—it demands investment in the right infrastructure, access to skilled talent, a clear strategic vision, and an organizational culture that embraces innovation and change. By addressing these factors, manufacturers can navigate the complexities of AI adoption and achieve meaningful, sustainable results.

SepaIQ AI and Analytics

SepaIQ by Sepasoft was purposefully built to mitigate many of the challenges described in this post, helping manufacturers transform raw data into structured, contextualized insights for AI and analytics. Whether you’re looking to enhance predictive maintenance, optimize production schedules, or improve real-time decision-making, SepaIQ provides a scalable, manufacturing-focused solution to support your AI initiatives. Learn more about how SepaIQ can strengthen your AI strategy or request a demo today.

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Sources:

^{¹ Deloitte. (2024). 2024 Manufacturing Industry Outlook.}