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.

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