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Artificial Intelligence (AI) can enhance the efficient use of green energy and increase the scalability of renewable energy. The global shift towards green energy has to managed and optimized which is a major challenge. Deep learning can play a vital role in predictive maintenance and storage. The data available from renewable energy systems can be analyzed by the artificial intelligence (AI) in real time to predict and manage energy generation and demand response which drastically reduces wastage of energy and distribution of energy can be more efficient. In this context, the integration of AI with renewable energy systems represents a paradigm shift in addressing the global energy crisis. Currently, an increased importance is gained by green technologies in order to obtain a sustainable economic wealth.

The future in the services industry belongs to Artificial Intelligence (AI) driven machines, which is a major source of worry for the job market in India. Over 50% of India’s GDP constitutes services, and it is a major source of employment for the skilled manpower of India. The research measures the impact of AI on service jobs in India based on qualitative parameters such as logical, natural, physical, and compassion; and finds which aspect serves the jobs better between machines and humans. The jobs taken over by AI are primarily at the task level more than the job level and for the basic tasks predominantly. The replacement starts with the basic tasks involved in providing service and then it grows to perform all the tasks involved in services. The research finds out that the logical aspects of the service will slowly reduce in the coming 5–10 years as AI will perform all the logic-related tasks leaving more emotional tasks such as compassion for humans. Finally, even these emotional-related tasks will be taken over by the AI which provides us with a very interesting combination of man and machine in the Indian scenario still threatening human employment.

ABSTRACT: South Asia has seen a digital revolution in recent years. The number of persons who use the internet has risen drastically. They use it for shopping, social media and online sales. However, there exists a literature gap as far as the effect of outbound digital marketing in B2B markets is concerned. The research builds a model based on brand and consumer interactions in Indian B2B markets using a vector autoregressive model to systemically analyze the cost and outcome of digital marketing efforts by the start-ups operating in South Asia. The multivariate time series analyzed in identifying simultaneous and consistent impacts by the start-ups. We use Vector autoregressive model as it allows us to analyse the relationship among the factors as it changes over time. The research finds evidence for the conceptual framework in South Asian markets. The results prove that sales are greatly influenced by digital media, and outbound marketing efforts, predominantly word of mouth, has a huge impact in building a brand image as it spread over in the social media platforms. It is observed that the digital marketing strategies and consumer interaction are the same across South Asia, but its effect varies from country to country within South Asia thus suggesting a need of developing a new strategy in digital marketing for B2B markets.

Artificial Intelligence has been under researched. Machines with deep learning abilities can take digital marketing to new heights with their Artificial Intelligence making all the difference. This research aims to identify the outcomes from the study of Indian customer’s responses across varying demographics to machines and their abilities to sell, which will well be the future of digital marketing. We find that software developers need to build the architecture is partnership with digital marketers who use machines with deep learning by taking attitude of the customers, behavior and choices into consideration. This will unlock huge benefits to the companies as accurate information about customers will be easily available to the marketers in future. How the machines are going to perform under various conditions are explained using a causal model using regression models. SPSS version 24 and R software were used for analysing the data and data regarding the customer’s behaviors, their choices and emotions are collected and based on fuzzy-set qualitative comparative analysis (fsQCA) approach how they can be influenced to use the services of the machine, fsQCA is used to compare case oriented and variable oriented quantitative analysis.

The paper is based on information which is a combination of store advertisement and consumers’ path inside the store along with product information. With this information, the authors find how advertisement affects the behavior of consumers when making the decision. The findings suggest that advertisement has a small impact on customers inside the stores. Null effect is determined, and one standard deviation in advertising has an impact on store traffic by 1.2%. But the impact at a lower end of the model is observed. One standard deviation in advertisement has impacted the store sales by 8.4%. Based on further data mining, the research has found that there is no significant improvement in the number of customers, but the increase in sales is because of the higher quantity of purchases by the existing consumers. However, the effect of advertisement on products placed in the same or nearby shelf is not found, the impact on product varieties in the same segment is also not found. Based on these research findings, the authors find the right approach towards advertisement.The research is limited to consumers of retail industry in a Tier 3 Indian city of a developing geographic segment only.