Sitecore Version 10.4 - Refreshed
Contact Us
 
  • English
  • Japanese
  • Chinese

Industrial AI: Intelligent Automation for Operational Excellence & Process Optimization

Transforming Industries with Industrial AI Systems

In asset-intensive industries today, artificial intelligence (AI) has emerged as a critical driver of innovation, change and industrial digitalization. In the years ahead, industry executives are expected to invest more than 25% of their total budgets on AI implementation, recognizing its potential to reshape market leadership across various sectors for priorities such as value chain optimization, production optimization, reducing emissions and maximizing margins.

However, the adoption of AI in complex industrial operations has been met with hesitation due to concerns about accuracy, data quality and safety. This is where Industrial AI steps in, offering a solution that combines AI with domain expertise to provide robust, trustworthy results with the guardrails of engineering fundamentals and industry knowledge. 

Industrial artificial intelligence can also incorporate market information to optimize the value chain based on evolving prices of inputs and outputs. This is especially important in the petroleum industry, where AI in oil and gas helps firms stay nimble when prices change dramatically. Prior to the implementation of Industrial AI, the decision to scale back production would require agreement between many levels of management. Now, a smart enterprise will let the algorithm coordinate these changes. The machine learning software can keep information from many levels of an organization at hand, letting market and sales data inform maintenance scheduling, for instance.

 

 

Industrial AI platforms have played a pivotal role in digital transformation. Currently, 79% of companies have an Industrial AI project live or in pilot stage. AI promises to drive new business value in areas of cost, productivity and efficiency, while also providing a competitive advantage.

 

Key Features of Industrial AI Software

Guardrails and Robustness. Industrial AI is built on real-world datasets and simulation models that represent a wide range of experiences, operating conditions and vertical contexts. This approach allows AI applications to predict performance and behavior in new situations, not just under normal operating conditions.

Transparency and Trust. One of the most crucial aspects of Industrial AI is its transparency. It is developed and delivered in a way that allows human operators and decision-makers to understand the recommendations provided by the technology. This ensures that the human operator remains in control, fostering trust in the AI system.

Purpose-Built Solutions. Industrial AI employs a broad spectrum of machine learning types, technologies and tools, selecting the most suitable method for each problem and use case. This "purpose-built AI" approach minimizes data requirements and enhances interpretability for simpler problems while enabling complex nonlinear use cases with state-of-the-art technology.

 

Creating Value Through AI for Industry

Process Industrial AI creates value in three critical areas: agility, guidance and automation.

  • Agility. In a world of rapidly changing macroeconomics, global supply chain shifts and regulatory uncertainties, Industrial AI provides the agility needed to adapt quickly. It enables rapid optioneering for developing innovative technologies, faster modeling of design and operations through data utilization and insight creation amidst complexity. For example, Dow Chemical is using Industrial AI-based hybrid models to improve the performance of new and complex chemical reactions, providing a competitive advantage.
  • Guidance. As the workforce evolves, bringing in more digital-native workers with less experience, Industrial AI becomes crucial in providing guidance for accurate decision-making. It helps navigate increasingly complex processes and optimize operations that are challenging even for experts. Maestro for Aspen Mtell®, for instance, enables Sardeolica to deploy advanced analytics across their new wind farms, providing guidance on future equipment degradation and enabling better maintenance decisions.
  • Automation. With the explosion of data and increasingly complicated work processes, Industrial AI automates repeatable steps in turning data into insights and accelerating business decision-making. This is particularly valuable in areas like advanced control and planning, where expertise and experience are at a premium. RH PetroGas in Indonesia has employed Industrial AI to automate the iterative steps needed in assembling and interpreting seismic and well data, which has transformed a multi-week interpretation project into a task that can be completed in just a few hours.

 

Unlocking the Full Potential of Industrial AI

According to findings from Ernst & Young LLP, 95% of senior executives from the US say their organizations are already investing in AI, and those investments are poised to increase significantly.

The consensus is clear: “Industrial AI is key.” A significant majority of IT and Operations decision-makers recognize its critical role in digital transformation, promising substantial benefits in cost reduction, productivity boosts, and enhanced efficiency. This technological shift is not just an advantage; it's essential for maintaining a competitive edge.

However, the path to fully leveraging Industrial AI is not without its hurdles. A striking statistic reveals that “over 98% of industrial companies face challenges with data quality.” Nearly every surveyed organization reports significant issues with data management, directly inhibiting the true potential of their Industrial AI initiatives. Compounding this, a staggering one-third of industrial data is simply not visible. AspenTech's research highlights that a substantial portion of an organization's industrial data remains undiscovered, severely limiting the insights and capabilities that AI solutions could otherwise provide.

These data-centric challenges underscore a critical bottleneck. To truly harness the power of Industrial AI, organizations must prioritize robust data strategies that ensure data quality, accessibility, and visibility.

Looking to the future, the vision of the self-optimizing plant powered by Industrial AI offers a compelling solution to many of these challenges. Value chain optimization technology, underpinned with Industrial AI, will enable close integration between multiple self-optimizing plants and the value chain, and will drive a number of key breakthroughs. This innovative concept aims to dramatically improve safety, sustainability, and profitability within industrial operations. AspenTech, a leader in this space, envisions a future where self-optimizing plants utilize Industrial AI to maximize asset performance, significantly reduce emissions, and accurately predict equipment failures.

The journey towards a self-optimizing future hinges on addressing current data deficiencies. By investing in solutions that clean, manage, and uncover hidden data, industries can pave the way for Industrial AI to unlock unprecedented levels of operational excellence.

 

Overcoming AI Adoption Challenges

Industrial AI addresses common concerns about AI adoption in industrial settings:

  1. Trust: By incorporating decades of industry experience and operational data, as well as advanced models based on first principles of engineering (physics, chemistry, mathematics) Industrial AI ensures accurate and reliable results.
  2. Cybersecurity: Industrial AI solutions are designed with robust security measures to protect intellectual property and maintain the integrity of operations.
  3. Explainability: By keeping people in charge and providing transparent recommendations, Industrial AI maintains the critical role of human expertise in decision-making processes.

 

The Future of Industrial Automation AI

"For three decades, we've integrated AI capabilities into our products to enhance accuracy and predictability. The first principles of engineering—the chemistry, physics and mathematics—serve as guardrails for Industrial AI. When operating complex assets, customers demand solutions that are trustworthy, robust and include these essential safeguards." — Antonio Pietri, former AspenTech President and CEO, on CNBC

 

 


As Industrial AI continues to evolve, it promises to enable assets to become an increasingly self-learning, self-adapting and self-sustaining part of the smart enterprise. These systems will be able to predict future market and operational conditions, provide actionable insights and automate execution securely in a closed feedback loop.

For an enterprise that would prefer to keep a human in the loop, the Industrial AI software can produce a list of variables and adjustments, or simply articulate a relationship or connection that had previously escaped human attention. If a company uses process simulator software, the changes can be tested in a simulation before being deployed on the ground.

The future of Industrial AI will be powered by pervasive real-time access to data, rich domain knowledge capture and automation and a combination of first principles modeling and AI. This powerful combination will drive safer operations, improved sustainability, higher margins and enhanced reliability across asset-intensive industries.

Industrial AI represents a significant leap forward in the application of artificial intelligence to complex industrial environments. By combining the power of AI with deep domain expertise and engineering fundamentals, it offers a trusted, robust and effective solution for driving innovation and operational excellence in the industrial sector.

 

 

FAQs

What is Industrial AI?

Industrial AI combines artificial intelligence with domain expertise to enhance asset-intensive industries. It ensures robust, trustworthy results through real-world data and engineering fundamentals, providing agility, guidance and automation. This approach addresses concerns about accuracy, data quality and safety, driving innovation and operational excellence in complex industrial environments.

How can machine learning help the manufacturing industry?

Machine learning is transforming the manufacturing industry by boosting agility, providing intelligent guidance and automating complex processes. Its ability to quickly adapt to evolving conditions, offer valuable decision-making support and automate intricate tasks is revolutionizing the way manufacturers operate. By integrating AI in manufacturing with industry-specific knowledge, Industrial AI ensures reliable and trustworthy outcomes.

What is industrial grade AI?

Industrial-grade AI refers to artificial intelligence solutions specifically designed for industrial applications. It combines data science, machine learning and domain expertise to deliver purpose-built AI applications that drive sustainable business value in industrial settings. These solutions help optimize processes, improve efficiency, enhance safety and enable intelligent automation across various industrial sectors.

What is the difference between AI and Industrial AI?

Industrial AI is specifically designed for process industries, combining domain expertise with AI algorithms. Unlike generic AI, Industrial AI:

  • Incorporates basic principles of physics and chemistry
  • Scales across enterprise operations
  • Accelerates time to value
  • Addresses industry-specific challenges like sustainability
  • Requires less expertise to implement and maintain

What are the benefits of Industrial AI?

Industrial AI offers significant advantages, including:

  • Increased productivity and efficiency
  • Cost reduction and optimization
  • Enhanced safety and reliability
  • Improved decision-making through data-driven insights
  • Accelerated innovation and product development
  • Better asset performance and maintenance
  • Sustainability improvements and emissions reduction
  • Overcoming workforce challenges and skill gaps
  • Competitive advantage through optimized operations

What are the benefits of AI in industrial automation?

AI in industrial automation offers several key benefits:

  • Increased productivity and efficiency
  • Enhanced AI for process optimization
  • Improved safety and reliability
  • Better decision-making through data-driven insights
  • Reduced operational costs
  • Sustainability improvements, including reduced emissions and waste
  • Automated knowledge sharing to address workforce shifts
  • Faster adaptation to changing market conditions

These advantages help industrial organizations achieve operational excellence and maintain competitiveness.

How is AI used in process engineering?

AI is revolutionizing process engineering through technologies like Aspen Hybrid Models, which combine first principles, process data and machine learning to create more accurate simulations. It enables engineers to build complex models faster, optimize operations, develop virtual sensors and enhance advanced process control. AI also improves predictive maintenance, reducing downtime and increasing safety.

What industries use AI the most?

Capital-intensive industries, particularly those in the process sector, are leading AI adoption. The top users include oil & gas, chemicals and petrochemicals, mining and metals, and power generation. These industries leverage AI for predictive maintenance, quality assurance and process optimization to enhance efficiency, safety and sustainability.

How do you optimize energy usage with AI technology?

One helpful application is AI for energy management. AI optimizes energy usage by:

  • Creating accurate hybrid models of industrial processes
  • Predicting equipment failures to reduce unplanned downtime
  • Analyzing real-time data to identify efficiency improvements
  • Optimizing multi-unit operations in closed-loop systems
  • Balancing complex energy grids with renewable sources
  • Suggesting energy-saving projects through intelligent analysis