5.3 Preliminary PPt

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5.4AssignmentPaperonAI.docx

Running head: 5.4 Assignment 1

5.4 Assignment 11

5.4 Assignment

Indiana Wesleyan University

Mandar Sathe

Date: 08/02/2022

Introduction

The paper is based on Conversational AI, an innovative technology that seeks to establish effective communication in a business organization by engaging different stakeholders. In this stage, detailed problem analysis is conducted by developing a comprehensive problem statement to demonstrate the problems associated with the implementation of the project and the proposed solutions. There is one primary problem and two secondary problems where the immediate problem focuses on the business problem that is to be addressed through the project implementation. The two secondary problems are the potential problems that are likely to be experienced in the performance of the Conversational AI. The two secondary problems are technology-related problems and ethics-related problems.

This artificial intelligence provides a platform for customers to post questions and receive personalized responses to improve customer service. Because of this project, customers can get answers to their questions about the company quickly and easily. This change will directly result in a reduction in customer service time and an improvement in customer satisfaction. Standard chatbots are limited in their abilities compared to conversational AI chatbots, which can provide answers to common questions and even engage in light conversation. On the other hand, conversational AI can be accessed and used via audio and video, as well as any combination of these three media, as opposed to static chatbots, which are typically located on a company's website and only provide textual interactions. As well as improving customer service through conversational AI, artificial intelligence has the potential to reduce various risks across multiple industries. Artificial intelligence can help businesses in a variety of ways. Artificial intelligence will profoundly impact our lives, both positively and negatively, even though we are still in the early stages of the revolution. This is the case regardless of how far along we are in the process. An estimated $15.7 trillion will be reaped by AI's financial impact on the global economy by 2030, according to the most recent estimates. Artificial intelligence is predicted to lose 40 percent of current jobs (Fotheringham & Wiles, 2022).

Problem statement

Business-related problem

Neglecting customers' needs and providing poor customer service results in customers defecting to the competition. It could be the worst mistake a company can make regarding customer service. According to Microsoft's 2018 State of Global Customer Service survey results, sixty-one percent of consumers have switched brands. Poor customer service is to blame for this. As a result, poor customer service results in a decrease in revenue and profits. According to the Serial Switchers report, businesses lose $75 billion annually because of subpar customer service. Companies that fail to provide excellent customer service risk losing current and future customers. A company's online reputation can suffer significantly if customers hear only bad things about it, leading them to shop elsewhere. According to the same NewVoiceMedia report, 20% of those polled would leave an online review after just one bad service experience. 20% of those polled on social media and 8% of those surveyed said they would tell their friends and coworkers not to use the company they were unhappy with. All areas of a company are affected by poor customer service. It can also lead to the departure of front-line workers. There is a high risk of burnout due to dealing with dissatisfied customers daily. There are additional costs for recruiting and training new employees because of the high turnover rate (Eitel-Porter, 2021).

Several different characteristics and practices can identify poor customer service, as listed below. One of the features is the ability to ignore service calls. One of the most common blunders in providing excellent customer service is ignoring service requests. 62% of companies did not respond to a customer service request, based on the Customer Service Benchmark Report. As a result, 90% of the respondents did not acknowledge or inform the customer that their email had been delivered. It's the second trait that doesn't resolve an issue. Another central pain point for consumers is not being able to resolve a service issue during the first contact or being unable to do so at all. 60% of those polled said they were forced into making more than one attempt at resolving a recent client query. One in ten customers reported that their issue had not been fixed in the Northridge Group State of Customer Service Experience report. Long service or wait times are the third characteristics. It's frustrating for customers to be put on hold and forced to wait for long periods when they need help. According to a recent Genesys State of Customer Experience study, nearly half of consumers are willing to wait on hold for between one and three minutes. In the three to five minutes range, 30% of people are willing to wait. Only 10% of people are willing to wait longer than five minutes. Developing AI to improve customer service aims to enhance employee knowledge as the fourth characteristic of poor service. Angry customers are fed up with incompetent customer service representatives. According to Microsoft, the most frustrating aspect of customer service is a representative's lack of knowledge.

An additional example is when customers are required to repeat themselves. Lack of manners and etiquette was identified as the fifth characteristic that needs to be addressed. Any interaction with a service representative who exhibits poor behavior, bad behavior, and an unfriendly attitude leads to churn and an unpleasant customer experience. In a New Voice Media report, 42 percent of consumers stop supporting brands because they are put off by rude or unhelpful staff, which is another worst customer service error (Fotheringham & Wiles, 2022).

Technology-related problems

The organization must understand its strengths and weaknesses as AI advances and technologies become more widely used in the business world. The lack of technical know-how prevents most organizations from adopting this niche domain. Only 6% of businesses are having a smooth time implementing AI technologies. A specialist must find the stumbling blocks in the enterprise's deployment process. AI/ML adoption tracking can be made more accessible with the help of well-trained human resources (Mahalakshmi et al., 2022).

The acquisition and storage of data are one of the most challenging aspects of AI. Sensor data is critical to the success of business AI systems. Sensor data is gathered in abundance to validate AI. Irrelevant and noisy datasets are difficult to store and analyze, making them an obstructive factor. AI is most effective when it has access to a large amount of high-quality data. Data increases the algorithm's strength and efficiency. The AI system performs poorly when it doesn't receive enough high-quality data.

There is a pressing need for Artificial Intelligence (AI) to be more stable and accurate because even small changes in data quality can enormously impact results and predictions. There may be a lack of data in some industries, such as those in the industrial sector, which may limit the use of AI.

There is a lot of responsibility that comes with the use of AI. A single person must bear hardware malfunctions at all times. A few years ago, it was relatively simple to determine whether a user, developer, or manufacturer was to blame for an incident. Now, it's much more difficult. High-end processors are required for AI, machine learning, and deep learning solutions, which need high computation speed. Most developers are put off by the amount of power these algorithms consume. Artificial intelligence's first stepping stones are machine learning and deep learning, which necessitate an ever-increasing number of cores and GPUs to function effectively. Deep learning frameworks can be implemented in a variety of fields, including asteroid tracking, healthcare delivery, tracking of celestial bodies, and many others. A supercomputer is needed to perform these calculations, and supercomputers are not cheap. Although Cloud Computing and parallel processing systems have made it easier for developers to work on AI systems, they have a cost. Data inflows and algorithm complexity are increasing rapidly, and everyone can't keep up. The cost and infrastructure requirements of these processors have stifled the widespread adoption of AI. In this case, a cloud computing environment and multiple processors running in parallel can provide a viable solution. Computation speed requirements will increase as the data available for processing grows exponentially. Next-generation computational infrastructure solutions must be developed (Mahalakshmi et al., 2022).

Ethics-related problems

Ethics and morality are major AI issues that have yet to be resolved. Because of the developers' efforts to perfect the AI bots' ability to mimic human speech, it's getting harder and harder to tell the difference between a machine and an actual customer service representative. For an AI algorithm to make predictions, it needs to be trained. The algorithm will use the data it has been trained on to make assumptions about what it sees. Because of this, it will ignore the accuracy of data, such as data that reflects racism or sexism, and the algorithm's predictions will reflect this rather than correct it. The deliberate use of artificial intelligence to cause harm to others has also been documented. In addition to deep fakes and facial recognition cameras, which are used to produce fake news and hoax stories, there are other examples. The use of an invisible universal noise filter, where the addition of a sticker next to a picture drastically alters the result, can fool machine learning models, on the other hand. You can fool machine learning models using this method. Although academics are rapidly producing powerful AI, there are still many challenges when AI is put into the real world. As artificial intelligence (AI) advances and our understanding of AI's fundamental workings decreases, this situation will become increasingly problematic.

Although data can help solutions succeed, it is the ability to recognize and appreciate the human factors that play a role in problem-solving that sets outstanding solutions apart. Artificial intelligence has a long way to go before it can solve its most serious flaw. People frequently point to our inability to overcome obstacles like fear and a lack of willpower as sources of our downfall. But when human-centered design principles are applied, these elements can become the key to a better understanding of people and the foundational insights required to develop exceptional solutions for very human problems—human-centered design principles. Unlike human brains, AI is not designed to operate in a very different way. Even so, they have not yet figured out how to provide their customers with the right mix of pleasure and frustration. So you will be able to defeat the future rulers of artificial intelligence.

Potential and proposed solutions

A potential solution to the business-related problem

Hiring customer support workers may be prohibitively expensive if the company deals with requests outside regular business hours. Using conversational interfaces for customer service by small and medium-sized businesses could save them money on compensation and planning. Every day, seven days a week, chatbots and virtual assistants are available to assist customers. A virtual assistant can respond to a customer's question more quickly than a human assistant—efforts by the government agency. Conversational artificial intelligence (AI) can now handle some customer service issues without human intervention. Primary responsibilities may fall into this category, such as finding a company's store or reviewing the account's balance sheet. When artificial intelligence takes over, agents will have more time to deal with more complicated situations (Adam et al., 2021).

A potential solution to technology-related problems

Problems related to the development of conversational AI are addressed in this paper. When a user requests voice-activated help, the information sent must be appropriately organized and stored in a secure location. An organization's attention and strict security guidelines for voice assistants and chatbots are necessary for clients to believe that these channels are secure and safe. A conversational AI application must be designed with security in mind when conducting sensitive individual data analytics to ensure that all personal information is protected or censored depending on the channel used. This is especially critical when the application is responding to user queries. It is vital to protect the data that AI relies on for its predictions and decisions because it relies on data. Hackers can use the AI model to launch denial-of-service attacks, for example. The company should also use techniques like k-anonymity to protect sensitive information while maintaining the accuracy of the models if the data is stolen or manipulated. Private/Permissioned Blockchains can be used to solve the data security problem. An additional way to ensure the privacy of sensitive information is to use AI to anonymize the business records before using them to make machine learning predictions. Non-technical people have a hard time comprehending AI systems. It is difficult to pinpoint exactly why these systems do what they do because they learn from their experiences and apply that knowledge to their decisions. There is no easy way of predicting why a particular image classification model made a specific prediction. For example, image classification models can be taught to recognize specific patterns in images like faces, objects, and traffic signs. By utilizing techniques like Regularization and Bayesian Optimization, the company can resolve the "black box" problem by making the models more interpretable and accountable for the data they use and the decisions they make based on that data (Suominen et al., 2022).

Potential solutions to the ethics-related problem

There is also a discussion of the ethical implications of conversational AI. Very few people in the world can speak English, so it is difficult for a voice assistant to interact with people who do not speak English. How many people a voice assistant can persuade and how well it establishes trust in its capabilities is directly related to how well you can communicate with it in your native language. There are numerous linguistic and cultural differences worldwide, so it's essential to keep this in mind. To increase the number of people willing to test out more conversational AI applications, it is possible to make it easier and more standard for the general public to use. More people would be able to experiment with a broader range of applications. Very few people in the world can speak English, so it is difficult for a voice assistant to interact with people who do not speak English. How many people a voice assistant can persuade and how well it establishes trust in its capabilities is directly related to how well you can communicate with it in your native language. There are numerous linguistic and cultural differences worldwide, so it's essential to keep this in mind. To increase the number of people willing to test out more conversational AI applications, it is possible to make it easier and more standard for the general public to use. More people would be able to experiment with a broader range of applications. Teaching people who aren't familiar with new technology about its advantages may become more comfortable using it (Libai et al., 2020).

Conclusion

With the help of artificial intelligence (AI), a customer's experience can be tailored to feel like it was made just for them. Predicting what customers might be interested in or when it's time to reorder can be done by analyzing previous purchases and behaviors, such as how often they shop. Currently, 99.9 percent of Fortune 1000 companies report actively investing in Big Data, and a further 91.9 percent say that the pace of investment is increasing. Thanks to AI and machine learning, the insights can now become much more data-driven. It is possible to use AI to help you discover trends, quickly identify problems, or uncover insights that can help you make improvements to the website or app interface. Keeping your customers is becoming easier as AI progresses at a rapid pace. Using machine learning algorithms, companies can now more effectively identify and prevent problems that could lead to churn. Artificial intelligence (AI) makes it possible to pinpoint and prioritize tasks based on actual data rather than hunches. For example, uninstall tracking features turn lost users into valuable insights that can help solve customer retention issues and craft effective strategies to regain them.

Reference

Adam, M., Wessel, M., & Benlian, A. (2021). AI-based chatbots in customer service and their effects on user compliance. Electronic Markets31(2), 427-445.

Eitel-Porter, R. (2021). Beyond the promise: implementing ethical AI. AI and Ethics1(1), 73-80.

Fotheringham, D., & Wiles, M. A. (2022). The effect of implementing chatbot customer service on stock returns: an event study analysis. Journal of the Academy of Marketing Science, 1-21.

Libai, B., Bart, Y., Gensler, S., Hofacker, C. F., Kaplan, A., Kötterheinrich, K., & Kroll, E. B. (2020). Brave new world? On AI and the management of customer relationships. Journal of Interactive Marketing51, 44-56.

Mahalakshmi, V., Kulkarni, N., Kumar, K. P., Kumar, K. S., Sree, D. N., & Durga, S. (2022). Implementing Artificial Intelligence and Machine Learning Technologies to create Competitive Intelligence in the financial services industry. Materials Today: Proceedings56, 2252-2255.

Suominen, O., Inkinen, J., & Lehtinen, M. (2022). Annie and Finto AI: developing and implementing automated subject indexing. Annie and Finto AI: Developing and Implementing Automated Subject Indexing, 265-282.