Literature Review on Prior Studies
Discuss about the Adoption of artificial Intelligence within business organization.
The artificial intelligence has been changing the way the businesses are conducted. This has been also determining the daily of customers. Further, it has promised to boost various profits and different digital transformations. In the following study various prior literatures is studied to analyze the adoption of artificial intelligence at businesses.
It has been seen that AI has been the game-of-few-players. Here various digital frontiers such as Facebook, Microsoft, Microsoft, Amazon and Google have been incorporating technology of AI to their various business processes. This has been mainly because of human-level performances and the capability in predicting and automating high quantity of data. AI-based algorithms in long-run have been served as the new general purpose as the process of invention. It has been reshaping innovation processes as per as Steels and Brooks (2018).
The proactive AI-adopter has been seeing notably higher margins of profits. This has been as compared to various non-adopters. Further, they have been also positive regarding future and have been predicting grow and taking advantages even more as AI has been maturing.
Cohen & Feigenbaum (2014) highlighted that adoption of AI has been rising. As per the current article from Forbes, 80% of the businesses has been inventing at AI. Mark Hurd, the CEO of Oracle has agreed and has been betting the future of Oracle on the fact that most of the enterprise data has been autonomously within 2020. Hurd stated that the future has been autonomous. However, AI has not been mature enough and it is not needed at the niche category. Further, there have been various practitioners and experts as it is the time to fetch how the innovation has been affecting the business. Moreover, there has been disrupting of business giving up on the competition.
The statement of Hurd is interpreted by Moro, Cortez and Rita (2015). They state that the Oracle should be utilizing the machine learning for making the data integration and apps, analytics and identity and system management autonomous. Further, Trcatia, which is a popular market intelligence firm, has been focussing on human interactions with the technology has been releasing researches to value of AI for the future days. Their report has shown that the quantitative analysis has been providing market opportunities for segmenting, sizing and forecasting numerous AI use cases. Further, Partanen, Jajaee and Cavén (2017) has pointed out that the rise in tide of AIU adoptions around various industries has been driving notable growth for the following decade with various AI software potential revenues to rise from $3 billion at 2016 to $90 billion within 2025. The forecast has been a notable upgrade of the previous projection of Tractica, for the market revenue of AI. This was been published at the second quarter of 2017 because of developed outlook for various specific use cases around various businesses. Further, Walczak (2018) mentioned that as compared to last few years, the AI market has begun to solidify across various real-life applications under the speed of change that has been faster than before.
Rise in AI Adoption around Every Industry
This is because the technology providers and start-ups have been rushing to develop targeted niche solutions and platforms to solve particular problems of enterprise. Here, AI has been the key to the way how consumer internet agencies have been operating at present as argued by Valter, Lindgren and Prasad (2018). This has been to roll out various hyper-personalized services as followed by the strategy of AI-first. Secondly, the rest of the market at government and enterprise sectors has been catching up in adopting AI. However, it has been needed to understand the value that includes depth and breadth of various use cases along with various technology choices across AI and its implementation methods.
Boyd and Holton (2017) analyzes that the enterprises has not comprised of any specific choice. However, in order to deploy AI, it has been not a longer strategy for the business. The business has been acting now and applying various natural applications that have been having closer interaction with customers. However, it has been remaining in control of the businesses’ futures. As per as survey done in the article of Hengstler, Enkel and Duelli (2016) 40% of the organizations has been using AI till 2016. However, this would be rising to 60% within 2018. It has been driving customer engagements and collecting information for business for better understanding the customers that needs to predict the needs. The businesses have been using AO regarding conversational marketing for driving customer engagements and create loyalty for brads. This impacts the bottom line positively. Further, people have been turning to be comfortable with various virtual assistants.
Moreover, the human civilization requires making sense how to use this innovation. This is helpful to languages, terminologies and what they actually needs.
The tech stack of all companies has been distinct in managing various interactions of the various technologies. It has been rousingly significant. According to Wu, Chen and Olson (2014), any business providing software strengthens various relationships with customers through the mobile messaging platform of the company. The organizations needed to stay relevant has been needed to adopt the technology in some for or the other as they never need to get disrupted. Further, this tool requires utilizing in wise manner. However, the reality is that the AI has been just a single component of larger solutions. Besides, AI has been unable to create real values till they get totally integrated. The organizations requires to decide the most effective use-cases for AI that has been possessing largest effect.
Application of AI in Driving Customer Engagements
Lu et al. (2018) highlighted that AI at its present form has been the ability to compensate the notable deficiencies within enterprise, especially across skill shortages and delivering top experiences of customers. Further, AI has been addressing various questions of various skill shortages and AI powered that has been rousingly vital. However, this has been not in terms of quality of service organizations can supply. However, this has been in the context of rising skill shortages at the industry.
As per the current IFS Digital Change Survey of 150 decision makers in service industry, retaining, training, recruiting various skilled technicians are rated to be the highest inhibitor for growing service revenues. This has been over 30% of the business that has been claiming to feel totally or slightly unprepared for dealing with various deficits in skills. Moreover, AI has easing the challenges of various uncomplicated queries. This has been driving notable potential for business to connect to voice assistants that is powered by AI. This has been beyond the scenario of enterprise software having the abilities like scheduling optimization engines, self-service diagnostics for appointing various slots automatically. It has been making business lighten and effective for the burden for stretched contract centre agent workforce.
Further, the final though is derived from the article of Iafrate (2018). They stated the CEO and founder of eXalt solutions revealed the speed if current business many times quicker than the current businesses that has been a decade before. They have further analyzed that the highest challenge at present has been to help customers know how to utilize AI and then digitally transform the business smartly. The customers are needed to be stopped from doing businesses. It has been vital that the organizations have been taking various actions in leveraging the AT for developing various frictionless experiences for customers and then agile back those office procedures.
Getting familiar with AI:
The business must be taking some time period to understand what the current AI can be able to do. Here, the accelerators have been offering start-ups with various arrays through the partnerships with various businesses at the AI domain. Further, one must be taking benefits of resources of different online data that has been available to familiarize the primary ideas of AI. Some of the distant workshops and various online courses provided like Udacity has been an easy way to start AI and raise the knowledge of various areas like predictive assessment under the business.
Rise of AI Integrations
Determining the issues to be solved by AI:
Once the basics are fastened, the following step for any business has been to start exploring various ideas. They must be thinking about how AI can incorporate AI abilities to the current services and products. Here, more significantly, the organizations have possessed various aims in mind about particular use cases. This can be solved by AI for various business problems and then provide demonstrable values. As one works with any company, they start the overview of the primary technology problems and programs. Copeland (2015) has shown hoe processing of natural languages, identification of images has been fitting to products commonly under workshops of any kind of management of business. Here, the specifications have been varying across industries. For instance as any company performs video surveillance, they are able to capture numerous values through adding ML to the process.
Prioritizing concrete values:
Then the potential business and economic values of various probable AI implementations must be determined. Fan et al. (2018) states that in order to prioritize, the various dimensions of feasibilities and potential is needed to put into 2*2 matrix. Moreover, this has been helpful to know and near-term visibilities for the financial values of the business. To perform the stage, business requires recognition and ownership from various managers and different level of executives.
Acknowledging the internal gap of capability:
There has been difference between what has been needed to be accomplished and what organizational ability is needed to have under the given system. Russell, Dewey & Tegmark (2015) states that the business must be knowing what has been capable of and what this has been from business and technological process perspective proper launching the full-brown AI implementation. However, it has been taking a long time to do so and there is the scope with AI in changing the strategy and innovation has a strategic element of the equation. However, they have not possessed any well-established procedures already. In order to address the processes, the internal capability gaps has been meaning the identifications of what has been needed to acquire and various processes that is needed to evolved internally prior they are achieved.
Bringing experts and setting up pilot projects:
As the business is ready from technological and organizational standpoint, it is the time to create and integrate. Huang and Rust (2018) revealed that the most vital element of here has been to begin with small and have various goals in mind. This has been most significantly to be aware of what is known and what one can know about AI. It is the place where bringing of external experts and various AI consultants has been invaluable.
Application of AI in Skill Shortages
The above discussion has showed that expectations regarding AI or artificial intelligence has been higher and determined what the current businesses has been actually doing. Here, the aim of the report has been to provide a realistic basis that must be helpful for companies in comparing the AI efforts and ambitions. Here, the study has shown that gap between execution and ambition has been high for most of the businesses. The implications of AI regarding organizational and management practices has been effective. Though there have been various models to organize for AI has been the organizational flexibility. That has been the flexibility that has been the centrepiece across around them. Thus for the huge companies the change of culture has needed to deploy AI that can be daunting, as per various articles that are demonstrated before
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Cohen, P. R., & Feigenbaum, E. A. (Eds.). (2014). The handbook of artificial intelligence (Vol. 3). Butterworth-Heinemann.
Copeland, J. (2015). Artificial intelligence: A philosophical introduction. John Wiley & Sons.
Dirican, C. (2015). The impacts of robotics, artificial intelligence on business and economics. Procedia-Social and Behavioral Sciences, 195, 564-573.
Fan, W., Liu, J., Zhu, S., & Pardalos, P. M. (2018). Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS). Annals of Operations Research, 1-26.
Hengstler, M., Enkel, E., & Duelli, S. (2016). Applied artificial intelligence and trust—The case of autonomous vehicles and medical assistance devices. Technological Forecasting and Social Change, 105, 105-120.
Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155-172.
Iafrate, F. (2018). Artificial Intelligence and Big Data: The Birth of a New Intelligence. John Wiley & Sons.
Lu, H., Li, Y., Chen, M., Kim, H., & Serikawa, S. (2018). Brain intelligence: go beyond artificial intelligence. Mobile Networks and Applications, 23(2), 368-375.
Moro, S., Cortez, P., & Rita, P. (2015). Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation. Expert Systems with Applications, 42(3), 1314-1324.
Partanen, J., Jajaee, S. M., & Cavén, O. (2017). Business Intelligence Within the Customer Relationship Management Sphere. In Real-time Strategy and Business Intelligence (pp. 123-147). Palgrave Macmillan, Cham.
Russell, S., Dewey, D., & Tegmark, M. (2015). Research priorities for robust and beneficial artificial intelligence. Ai Magazine, 36(4), 105-114.
Steels, L., & Brooks, R. (Eds.). (2018). The artificial life route to artificial intelligence: Building embodied, situated agents. Routledge.
Valter, P., Lindgren, P., & Prasad, R. (2018). Advanced Business Model Innovation Supported by Artificial Intelligence and Deep Learning. Wireless Personal Communications, 100(1), 97-111.
Walczak, S. (2018). Artificial Neural Networks and other AI Applications for Business Management Decision Support. In Intelligent Systems: Concepts, Methodologies, Tools, and Applications (pp. 2047-2071). IGI Global.
Wu, D. D., Chen, S. H., & Olson, D. L. (2014). Business intelligence in risk management: Some recent progresses. Information Sciences, 256, 1-7
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