Artificial intelligence in industry

Unlike general artificial intelligence which is a frontier research discipline to build computerized systems that perform tasks requiring human intelligence, industrial AI is more concerned with the application of such technologies to address industrial pain-points for customer value creation, productivity improvement, cost reduction, site optimization, predictive analysis[1] and insight discovery.

Data is recorded using sensors and processed on computational entities and, if desired, actions and decisions are translated back into the physical world via actuators or by human operators.

These challenges are attributable to the encounter of process, data and model characteristics: The production domain's high reliability requirements, high risk and loss potential, the multitude of heterogeneous data sources and the non-transparency of ML model functionality impede a faster adoption of ML in real-world production processes.

Due to the lack of transparency and the stochasticity of these models, no deterministic proof of functional correctness can be achieved complicating the certification of production equipment.

Given their inherent unrestricted prediction behavior, ML models are vulnerable against erroneous or manipulated data further risking the reliability of the production system because of lacking robustness and safety.

This degree explores advanced topics such as AI-driven decision-making, big data analysis, and the ethical implications of AI in business.

The program is designed for professionals seeking to leverage AI technologies to transform business practices and improve efficiency.

Core subjects often cover topics such as machine learning, data science, business strategy, and financial management.

The programs aim to give students a broad understanding of AI applications within a business environment, while also allowing them to specialize in areas such as supply chain management, marketing analytics, and AI-driven innovation.

In addition to technical courses, many programs include practical training, such as internships, real-world AI projects, and industry case studies.

This helps students gain practical experience in applying AI tools and techniques to solve business challenges.

Many universities offering these degrees hold accreditation from recognized educational bodies, ensuring that their programs meet rigorous academic and industry standards.

For example, ESADE and IE University are both accredited by institutions such as EQUIS and AACSB, which evaluate the quality of business education programs.