Industries offer multiple products and services along with their core product offering. The required combination of products and services varies with customers due to data modeling, computation methods and algorithms. The incorporation of an artificial intelligence-based architectural framework for computations and methods within the operating system or platform enables generalized and greedy models. Industry-adapted dynamic platforms apply data learning to make real-time contextual suggestions that eventually benefit both customers and businesses. The prerequisite to study this chapter would require strong analytical skills and the ability to understand the complex algorithms and the basic knowledge of statistical modeling. This chapter senses robot swarms, distributed computing on the Internet of Things, distributed computing for the next generation, membrane computing models, digital twin methods in manufacturing services, healthcare IoT applications, deep learning models for breast cancer, convolutional neural network models for visually impaired people and chest X-ray classification, cloud computing, Q-learning and LSTM models and information security protocols for tertiary industries.