Industrial AI

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 various 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.

Overcoming AI Adoption Challenges

The Future of Industrial Automation AI

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 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.