In addition to AI, robotics are used in the field of medicine, and the combination of robotics in medical situations has the potential to improve the efficiency of patient outcomes. This makes robotics a treasured asset in modern healthcare (Jin et al. 2020; Haick et al. 2021).

The incorporation of AI with machine learning (ML) and robotics in agriculture creates powerful tools and insights to boost the productivity of farms (Jin et al. 2020).

The use of robotics in farming automates labor thorough tasks such as irrigation with dispersal of seeds, pest control and harvesting gives farmers’ more time to concentrate on more fruitful activities. The mixture of AI and robotics in agriculture brings about the power to drive positive change, leading to the complete development of the global agricultural landscape (Khogali et al. 2023; Kuru et al. 2024).

In storage and warehouse settings, the use of AI has significantly improved safety by helping machines understand their surroundings better, especially with the help of thermal and haptic sensors. These sensors act as the “eyes and touch” for robots, guiding their decisions and allowing them to operate smoothly and safely. Automated guided vehicles (AGVs) and carts (AGCs) are commonly used to move stock around, enabling 24/7 operations while keeping costs consistent.

Automation robotics plays a dynamic part in the automotive industry. The rewards of robotics in motorcars are diverse including true vision for positioning and situating required particulars, enabling tasks such as fitting door buffers, panels and other factors (Lee et al. 2019; Lutz et al. 2019).

AI supported by all robotic and automation units can be seen in Figure 16.2.

Some algorithms used for robotics are:

• reinforcement learning;
  
• supervised learning;
  
• computer vision;
  
• simultaneous localization and mapping (SLAM);
  
• evolutionary algorithms;
  
• deep learning (DL).

Each of the several algorithms used in AI for robotics has advantages and disadvantages of its own. SLAM is essential for mapping and navigation, supervised learning is useful for tasks involving labeled data, DL is strong for perception and control, reinforcement learning is excellent for adaptive decision-making and computer vision offers visual perception capabilities (Miller et al. 2020). Task requirements such as data availability with computational resources and desired degree of robot autonomy all influence which method is best.

Chopra et al. (2023) give an overview on the exciting connection of AI and robotics, reshaping both economic and organizational dynamics. Together, AI and robotics have unlocked many new opportunities in areas of human–robot interaction, social robotics and perceptive robotics. Recent highlights of AI in robotics are innovative research trends with some technical tactics, and real-world applications which also raises some ethical, social and economic questions in regard to this rapidly growing field. AI in robotics sheds light on the prospects of basic tasks connected with this technology.

As employers adopt automation technologies developed by AI, workers may fear job displacement, creating challenges for both parties (Naha et al. 2022). The study “Automation Fears: Drivers and Solutions” surveyed 502 people in Bulgaria, revealing that individuals prioritize their own solutions over profitable or social ones due to rising automation concerns. This basic survey found that fears of job automation are influenced by personal beliefs and demographics (Nadikattu 2021). Key factors driving these fears include peer pressure, the likelihood of a job being automated, concerns about technology dehumanizing work and an individual’s self-perception of professionalism.

The main objective of this chapter is to explore the long-term societal impacts of current progress in AI technology and computerization. The findings are shaped by various subtopics addressed throughout the research (Nam et al. 2021). Each section begins with an explanation of the chapter’s structure. The study expresses common features and establishes a theoretical framework. It also examines the drivers of automation and AI, as well as how society perceives and accepts these technologies in general (Leary et al. 2013).

Hisham et al. (2023) explored social-impact assessment theory, as described by Dietz et al. (2024). It involves identifying, evaluating and measuring how an event affects society.

Similarly, the societal effects of AI should be carefully analyzed, just as the impact of scientific research is assessed. To track AI’s influence amid rapid technological growth, a hybrid approach can be used. This approach combines narrative evaluation, which identifies relevant studies on a specific issue, and thematic pattern analysis, which detects recurring themes, trends and concepts within texts such as transcripts.