Introduction

Artificial intelligence (AI) is often presented as a universal solution, promising gains in efficiency, predictive capability and decision-making across every sector. In industrial applications, however, the emphasis on safe, reliable and repeatable operation justifiably tempers this enthusiasm and drives a more cautious approach to adoption.

Neither extreme is accurate. Misunderstandings about AI capabilities often distort the discussion, resulting in unrealistic expectations on one end and undue caution or misplaced confidence on the other. In some cases, this imbalance also causes organizations to miss viable opportunities where AI could be applied safely and effectively within well-constrained environments.

The key to effective AI adoption in industrial environments is to confront and correct the most common misconceptions. AI is neither magic nor menace; it is a collection of tools and techniques whose value depends on how well they are understood, applied and governed.

Misconception 1: AI Is a Monolithic Technology

Many discussions in organizations highlight that people conflate generative AI with other forms of AI, like machine learning. AI comprises a range of methods, architectures and applications, including machine learning, deep learning, computer vision, natural language processing and expert systems. Various use cases leverage the benefits of each method, and each method has its own pros and cons.

Misconception 2: AI Works Out of the Box

Industrial contexts vary, and even organizations within the same sector may differ, rendering AI outputs invalid. Consider Waymo’s expansion of its autonomous ride-hailing service into new cities. This requires detailed mapping of the environment, followed by several months of learning using human drivers. The entire process may take over a year before they are ready to launch.

Misconception 3: AI Is Intended to Replace Human Expertise

One consequence of this misconception is that organizations believe they cannot use AI because it would pose an insurmountable risk to safe operation. In truth, most existing AI methods are designed to augment expertise, not replace it.

Expert systems can assist operators by analyzing historical data and summarizing situations, enabling better human decision-making. Machine learning can be used to detect flaws in manufactured products far more quickly and efficiently than humans, allowing them to review these results and handle more complex edge cases.

Misconception 4: AI Provides Deterministic Outputs

Industrial environments require repeatable and reliable operation. Even in non-linear processes or processes that contain stochastic noise, control systems can be designed to operate deterministically. AI, however, is fundamentally designed around probabilities, not certainties. For a given data set, AI can give an output with a measure of uncertainty. Model training, data quality, and verification processes all influence this measure.

This does not mean that AI cannot be used in industrial environments. It simply means that users must understand the limitations of the AI. AI will not tell you a bearing will fail in 20 days, but it can tell you there is an 80% chance that a bearing will fail in 10 to 30 days.

Misconception 5: More Data Means Better AI

Industrial datasets are often messy, inconsistent and incomplete. Many organizations lack the data governance discipline necessary to ensure that AI algorithms operate on reliable, validated and contextually meaningful information. The common assumption that “more data automatically leads to better results” is deeply flawed — in fact, adding more data can often make models less accurate, less explainable and, in the case of generative AI, more prone to hallucination. This is where outputs sound plausible but are factually incorrect or fabricated. These errors frequently occur when the model absorbs contradictory, low-quality or irrelevant information, making it harder to distinguish signal from noise. The result is a model that may generate more confident — but less reliable — responses. In industrial applications, this could mean producing incorrect recommendations by, for instance, mistaking a chemical injection process for a water injection process or vice versa.

That said, carefully curated and representative data can indeed enhance model performance. For example, instead of predicting an 80% chance of bearing failure within 10 to 30 days, a well-trained model might narrow that to a 95% chance of failure within 18 to 22 days.

Ultimately, the goal is not more data, but better data — data that is accurate, contextualized and verified by domain experts. Without rigorous validation and human-in-the-loop review, simply feeding larger datasets into an AI model increases the risk of false confidence and operational missteps.

The Future of Industrial AI

The path forward for AI in industrial applications is defined by clarity, discipline and engineering judgment, just like any other industrial project. For AI, this involves:

• Selecting the right approach from an understanding of both the problem being addressed and the operational environment in which the solution will function.
• Recognizing that a successful AI implementation requires time, domain expertise, data preparation and continuous validation.
• Correctly defining the solution as an augmentation of human expertise, not a replacement for it. AI systems should only accelerate analysis and highlight patterns, but responsibility for decisions — and their consequences — must remain with people.
• Acknowledging the key role that data governance plays in a successful AI implementation. High-quality, contextualized and validated data — reviewed by domain experts — will always outperform large volumes of poorly understood information.

Organizations that succeed with AI will be those that prioritize understanding over hype, governance over novelty and human expertise over superficial or performative uses of technology.

Interested in reading more about the use of AI in critical sectors? The International Society of Automation (ISA)'s new position paper, “Industrial AI and Its Impact on Automation,” is available for download now at www.isa.org/position-papers.