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AI and Hardware Integration in Business Work Processes
AI chips, also referred to as AI hardware or AI accelerators, are specially designed devices that improve and support the functionality of artificial neural networks, or ANN (Dilmegani, 2021). In a commercial sense, the ANN and AI chips provide companies with tools that have deep-learning applications. The ANN is a subfield of AI hardware which focuses on a machine-learning process which works somewhat like a human brain. Its first focus is a creation of analysis of thousands of labeled data which is used to identify patterns. Secondly, the ANN becomes able to make predictions based on acquired and assessed knowledge. This is an incredibly useful tool for a business that manages a significant amount of information and eliminates the necessity for a human employee to make these predictions themselves which may be prone to error.
Currently, AI-driven hardware within businesses has little competition as it is the leading tool for time-saving, cost-reduced, and efficient method processes. For example, unlike other banks, insurance companies, and investment companies, Ant Financial has a digital workspace as their primary model for conducting their work and providing services (Iansiti & Lakhani, 2020). This is successful for multiple reasons, which include the lack of workers in the operation of critical path activities, no necessity for manager-approved loans, or no employees or representatives authorizing medical expenses for consumers. Without these traditional operation constraints, Ant Financial can observe significant growth while also allowing for impact and entry into a variety of industries. This cohesive process is the result of AI integration within the companys workspace and their interface presented to their clients. The breaking of these typical limitations allows companies like Ant Financial to function in a completely separate manner from other companies, which in turn does not translate into the competition but more into progression. Many companies are currently investing in these moves towards AI-oriented processes which is when the competition will become more visible.
References
Dilmegani, C. (2021). AI chips in 2021: Guide to cost-efficient AI training & inference. AI Multiple. Web.
Iansiti, M., & Lakhani, K., R. (2020). Competing in the age of AI. Harvard Business Review. Web.
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