3 Use Cases that Show How to Bring Your Industrial AI Strategy to Life

3 Use Cases that Show How to Bring Your Industrial AI Strategy to Life
3 Use Cases that Show How to Bring Your Industrial AI Strategy to Life

AI can best be described as a collection of different technologies brought together to enable a system—a process, asset or machine—to act with intelligence. For businesses, that means helping a system sense, comprehend, perform and learn, most often with the goal of optimizing performance, accuracy and quality.
At the core of enterprise AI is machine learning (ML), and when the best enterprise AI systems are set to the task of learning for themselves, the results can dramatically improve business performance given its ability to learn and improve over time, rapidly identify patterns in very large amounts of data and extract features from structured data (spreadsheets, time series) and unstructured data (text, images, videos).
As such, there has been considerable investment in democratizing access to AI through various AI/ML platforms, frameworks and toolkits, accelerating the enablement of AI-based use cases in business environments. However, this has not necessarily translated to significant business value, especially in the industrial sector and for capital-intensive, process industries. Specifically, Accenture’s AI: Built to Scale study found that nearly 69% of executives in industrial organizations acknowledge knowing how to pilot an AI program, but struggle to scale an Industrial AI strategy across the enterprise.
To overcome this hurdle, there needs to be increased emphasis on democratizing the application of AI to domain-specific industrial challenges with a focus on business outcomes. Industrial AI is a systematic, collaborative and integrative discipline focusing on developing, embedding and deploying various machine learning algorithms as fit-for-purpose, domain-specific industrial applications with sustainable business value.
While exploring and identifying Industrial AI technologies may be intriguing, the starting point of any organizational strategy is never the technology. It begins with identifying the business problems, corporate objectives and strategic goals that Industrial AI can solve.
Organizations looking to drive true business value can take inspiration from these three specific business use cases to unlock the power of Industrial AI, combining data science and AI with software and domain expertise to deliver comprehensive business outcomes for the specific business needs of capital-intensive industries.

1. Predictive maintenance

Predictive maintenance is the single largest use case for Industrial AI, estimated to have made up more than 24% of the total market in 2019, according to the IoT Analytics research report referenced above. Predictive maintenance makes use of advanced analytics and machine learning to determine the condition of a process, an asset or an entire set of assets (a process plant, for example) to predict when issues may arise and when maintenance should be performed.
Predictive maintenance usually combines various sensor readings (sometimes external data sources) and analyzes thousands of logged events to predict equipment failures, detect deviations from normal behavior and prescribe detailed actions to mitigate or solve future problems – all with the goal of optimizing output and reducing downtime.

2. Quality, reliability and assurance

This is the second-largest Industrial AI use case category, claiming 20.5% of the total market, according to the IoT Analytics research report.
One of the key challenges facing industrial enterprise decision-makers is how to maximize the economics of business decisions by going beyond the equipment level and accurately predicting future asset performance of the whole system.
Quality shows how well an object performs its primary function, while reliability shows how well the object maintains its original level of quality over time, through various conditions. Both are significant measurements in an industrial setting and Industrial AI  enables an organization to achieve a specific and accurate understanding of the two—in turn, enabling more cost- and time-effective operations.

3. Process optimization

Process optimization is perhaps the most obvious and compelling use case for Industrial AI, but still one of the most difficult to implement, as it involves multiple AI-based capabilities working across the system: automating repeat human tasks, enabling real-time decisions across various applications, augmenting the asset lifecycle and optimizing the value chain across different business dimensions.
Process optimization leverages advanced ML methods, including reinforcement learning and sophisticated deep learning neural networks, to infer information and intelligence from different data sources, assets and processes. With this, organizations can easily identify and mitigate inefficiencies, which have a direct impact on productivity—the primary economic driver of any industrial enterprise organization.

Where to start?

For most industrial organizations, the trouble isn’t how to get started with AI, but understanding where to start. The key to making AI work in real-world applications is getting the learning right—and more importantly, making it valuable and actionable in an industrial business context. Therefore, the development of Industrial AI-enabled applications needs to be purposefully guided by domain knowledge to derive real business value, with systems purpose-fit for tangible use cases. The use cases outlined above are a concise and clear starting point for any organization building out or redesigning their Industrial AI strategy, and hoping to accelerate time to ROI in turn.

About The Author

Keith is the senior director of product management at AspenTech and leads the product strategy and product management for all edge and on-premise AIoT solutions, including IP.21 data historian and Aspen Connect products.He was previously president and CEO of RtTech, which was acquired by AspenTech in 2017. With more than 25 years of industry experience, Keith’s insight informs product development, ensuring that products integrate the latest technical capabilities and deliver the best results for our customers.

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