Executive Summary An in-depth analysis of probable challenges of Artificial intelligence is critical to the successful application of the system. Organizations should carefully evaluate replacement decisions and address any technology gap that may affect the consumer experience. The system heavily relies on data, which is useful in determining customer satisfaction and preference. The accuracy of data collected influences the outcome of the expected results. Organizations should improve the methods of data collection to increase the chances of obtaining accurate predictions. Wearable devices enable motor insurance firms to acquire information that can be used to provide tailored services and products and to enhance the pricing strategies based on customer requirements. Before the implementation of AI systems, organizations should also consider critical success factors which have the potential to derail the implementation process. This research uses a troubled, though potentially successful, insurance company as an example to show on practice how artificial intelligence can be implemented by similar companies in the industry and beyond. Successful implementation of artificial intelligence (AI) in any insurance company requires an in-depth evaluation of critical challenges the organization confronts while executing its obligations. These issues often influence the profitability of the system (Mehr, Ash and Fellow 2017). AI systems affect the most profit-influencing spheres of activities. The replacement of systems to pave the way for new AI programs may face a myriad of challenges. However, a proper understanding of the likely outcomes enables the management to identify the problems that need to be solved and the mechanisms to address the challenges. Therefore, it is paramount for the organization to consider the effect of AI on stakeholders' perceptions. AI implementation often faces the resistance force of the system. It is essential in the firm's profitability. Consumer experience is one of the most competitive drivers of growth when successful. However, it may be the most significant source of risk when handled inappropriately. Therefore, the implementation process starts with the collection of enough and appropriate information to understand the difficulties the company faces when addressing customer products and insurance claims. AI allows companies to identify the needs of customers and establish the impact of any changes on consumer loyalty. These abilities address the current unstructured data and improve the organization's predictive analysis in identifying and managing risk promptly. Consumers expect a unified experience and may get disappointed when the company fails to deliver as expected. Therefore, customer journey analytics and AI are the essential components required to join all the organization's departments. The application of AI in customer experience requires the company's capabilities in data unification and real-time insights product delivery. The client's dissatisfaction leads to losses. Additionally, adopting AI systems by the company is critical in making effective decisions regarding suited for each customer's needs. Thus, the system enhances product delivery, saves time and costs, and promotes customer satisfaction. The most significant impact of AI involves the transformation of customer service delivery to make it fast and reduce unnecessary inconveniences. Description and Justification of Information Architecture The AI system aids decision making, which is critical in service delivery and profitability of the organization. Problems. Current database system, mainly unstructured, required replacement; Current storage of records makes the predictive analysis rather complicated; The system cannot adequately determine customer preferences; The system cannot deal with required marketing initiatives, and product pricing strategies; The current system requires modification to promote consumer satisfaction and predict future implications based on previous trends; The information provided as of now is complex, which makes it difficult to breakdown the available data and to subsequently determine significant implications; Inaccurate or wrong information leads to wrong interpretation and subsequently increases the risk of getting incorrect predictions. . AI systems streamline flow and filter relevant information to determine any change in customer needs. An offered decision AI can analyse large amounts of data to detect unfavourable patterns. This system does evaluate data more accurately and in real-time. It shall enable the company to monitor different applications, such as shifts in consumer demand, pricing and marketing development, unsettled claims, and fraud (Castro and New, 2016). Moreover, AI can extract valuable information, which can be used to make decisions through simulation. This process is efficient in uncovering abstract patterns that the current system does not detect. Processing and conveying the required information enhances communication, which improves customer loyalty. Thus, the firm shall benefit from improved predictive outcomes and customer satisfaction. Outcome. Technology Architecture AI changes the way the sector is run through telematics and wearable sensors, which collect information about the insured products or items. For instance, installing such detectors in a car gathers the required information on how the customer drives. This information helps the company to rate its customers. Based on statistics from different clients, the insurance products and services can be tailored to suit individuals. The devices help the company to make accurate predictions based on the client’s record. Therefore, AI utilizes real-time and current information to enhance the decision-making process. Additionally, it provides precise information and saves money, which may be incurred from regular assessments and audits. The insurance company in question needs to develop devices that can provide the required information compared to the current technology in use. Thus, wearables play a central role in data collection, analysis, and interpretation. The architecture of wearables involves the use of several components: data, physical energy, social properties, etc. geographical locations, social interactions, videos, photos, biomedical data. The physical aspects of the wearables cover elements related to and . These elements shall enable the company to enhance direct communication between wearable devices. In addition, the devices require power supply based on the consumption of each element, which often varies depending on the functioning rate and the frequency with which the device is used. This information enables the management to device effective marketing and pricing strategies. hardware software applications Social interaction is a critical element of the wearable components. The devices are manufactured considering the functionality of the wearable based on customer requirements and expectations. The manufacturing of the element also examines the market demand since the organization's financial performance is fundamental to its sustainability. Description and justification of the Governance and Methodologies Applied in the Project The ICT department should manage multiple AI activities within the organization, and here is why: The department, under its current constitution, is sufficiently staffed to handle AI issues. The department’s value chain, which comprises planning, building, delivering, and running are crucial streams that produce the desired results. The ICT team drives critical decisions that are essential in providing appropriate products within the established resource constraints. The team drives the AI implementation schedule, defines business requirements, the budget, and the project’s application functionality. The ICT department drives core decisions, which require integration across all departments of the organization. Since the introduction of AI includes increasing the company’s productivity, the department should determine significant tradeoffs between cost, schedule, and deliverable. While discharging the required duties, the ICT team should prepare a detailed work plan and a timeline, which integrates all activities. Also, the team should identify mitigation measures, quality assurance plans, develop a comprehensive system design document, and status reports. The team should also manage the transition from the current system to avoid inconveniences or gaps that may compromise customer delivery and prevent losses. Furthermore, the systematically-defined AI frameworks extend beyond knowledge inquiry and organization. Structured subdivisions promote understanding, dissemination, and measurable implementation by providing information and arguments in manageable proportions (Succar, 2009). It is essential to develop a framework that positions AI as an integrated product and modeling process as opposed to disparate technologies. Work Breakdown and Work Package Decomposition The work breakdown structure (WBS) of AI is divided into packages that can be easily handled by different sections of the ICT department. The breakdown. The WBS constitutes essential stages, which eliminate errors and risks during the implementation process. Moreover, an appropriate WBS makes the process easier to monitor and manage. Step-by-step approach. A successful AI implementation system should be integrated into the organization's culture. Therefore, culture becomes a significant driver of the designed AI program. Corporate culture integration. It also serves as a guide that relates project activities to specific objectives. For instance, the insurance’s primary goals are to reduce costs to improve the product delivery process and increase the level of customer satisfaction. WBS represents all the activities linking objectives (Iranmanesh and Madadi, 2009). Also, it shows tasks given to each section of the ICT department to save time and avoid overlapping activities. Activities are, therefore, attached to different departments or phases. Determination of the project’s total scope. Moreover, the AI WBS structure assists the management in estimating project costs, developing the schedule, obtaining necessary resource information, which is essential in planning and controlling framework. Proven management assistance. Identification of Critical success Factors (CSF) The popularity of wearable devices continues to expand globally. However, the success of these gadgets in motor insurance depends on the ability of organizations to eliminate or minimize identified barriers and obstacles (Nah and Siau, 2018). Therefore, it is critical to address fundamental factors, which influence the adoption of wearable devices in the motor insurance industry. Additionally, factors inhibiting the application of these devices affect the success of their application in the industry. The implementation process of the wearable gadgets involves the analysis of numerous critical : success factors the perceived case of use and usefulness affects the application of wearable gadgets; the degree of effectiveness of devices as perceived by prospective consumers is critical to the success of the AI system; the success of AI systems is based on technological functions, the attractiveness of the design (Nah and Siau, 2018); The potential : risks are the perceived usefulness includes the customer’s subjective probability regarding the effect of the device on his life; ICT staff may also feel threatened by the device since it is expected to perform the tasks of many workers. Thus, the fear of losing jobs may endanger the success of the AI systems; those clients with focus on technology find it harder to embrace and apply devices compared to those with fashion perceptive. The former focus on the functional benefits while the later prioritize social benefits and risks associated with infringing other peoples’ privacy. These factors affect the adoption of wearable devices by both ICT employees and customers. In addition, the price of the wearable device is critical to the success in the success of AI systems. Implementing the technology requires massive investment, which hinders the adoption of the wearable devices. Impact of the Implementation on the Organisation The adoption of AI shall fundamentally shift the organization's ICT department. The system will dramatically enhance the performance of communications, content, applications, e-commerce. These changes may trigger the organization to create and merge different ICT sections to avoid overlap of tasks and to accommodate additional responsibilities. Additionally, the changes associated with AI may require the company to adopt new business models, which impact the ICT department directly or indirectly. For instance, the organization may change the advertising and marketing strategies which may be driven by the ICT department. It implies that the ICT department will be required to perform tasks previously done by other departments (Makridakis, 2017). The department will also be tasked with regular changing and creation of user interfaces to accommodate emerging trends. Furthermore, the evolution of ICT and digital technologies will transform other industries. AI requires the use of technology in every department compared to the current system. It implies that the entire ICT department should be restructured to accommodate these changes. For instance, the marketing and advertising department shall require an ICT manager to oversee the technical aspects compared to the current system, which does not need such an employee. Consequently, the company may need to employ more ICT staff and offer further training to enhance their ability to understand the components and other new aspects of their work (Frank et al., 2019). The organization is likely to increase the pay of the ICT employees who may be compelled to undergo further training. An increase in the share of job roles in the ICT requires high levels of formal education at the expense of those in other departments (Royal Society and British Academy, 2018). Recommendations The introduction of AI requires massive resource allocation, which may affect the general performance of the organization. As a result, the organization should take appropriate precautionary measures to prevent or mitigate probable risks. The company should conduct a survey before the implementation of the program to obtain first-hand information on the consumers expectations. The ICT department should attend training programs and seminars. It will prepare them and enhance their understanding of the implemented system. Due to rapid changes in technology, regular training is necessary to keep employees updated on the new developments. Wearable devices should be tailored to suit different tastes and fashion to avoid negative perceptions which may hinder the success of the project. Conclusion AI implementation requires the evaluation of numerous aspects to determine the viability of the project. The application of wearable devices in the insurance industry helps organizations to gather critical information, which assists management in establishing new market and pricing strategies. The collected data can be applied to offer tailor-made products and services based on consumers’ requirements. The entire AI process should be undertaken carefully to avoid gaps which may compromise customer satisfaction. Moreover, factors affecting the adoption of AI may vary depending on different perceptions exhibited by individuals. Social influence is critical in the adoption of AI systems. Information privacy is one of the highest privacy concerns linked to social factors. Ultimately, AI can effectively increase the organization's productivity by reducing wastage and increasing sales. References Balasubramanian, R., Libarikian, A. and McElhaney, D., 2018. Insurance 2030—The impact of AI on the future of insurance. McKinsey & Company, New York, NY, USA, Apr. Castro, D. and New, J., 2016. The promise of artificial intelligence. Center for Data Innovation, pp. 1-44. Davenport, T.H., 2018. The AI Advantage: How to Put the Artificial Intelligence Revolution to Work. MIT Press. Frank, M.R., Autor, D., Bessen, J.E., Brynjolfsson, E., Cebrian, M., Deming, D.J., Feldman, M., Groh, M., Lobo, J., Moro, E. and Wang, D., 2019. Toward understanding the impact of artificial intelligence on labor. Proceedings of the National Academy of Sciences, p.201900949. Iranmanesh, H. and Madadi, M., 2009. Framework of intelligent systems to support project scope. In Recent Advances in Technologies. IntechOpen Lamberton, C., Brigo, D. and Hoy, D., 2017. Impact of Robotics, RPA and AI on the insurance industry: challenges and opportunities. Journal of Financial Perspectives, 4(1), pp. 8-20. Makridakis, S., 2017. The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, pp.46-60. Mehr, H., Ash, H. and Fellow, D., 2017. Artificial intelligence for citizen services and government. Ash Cent. Democr. Gov. Innov. Harvard Kennedy Sch., no. August, pp.1-12. Nah, F.F.H., Hall, R.H., Siau, K., 2018. Factors influencing the adoption of smart wearable devices. International Journal of Human–Computer Interaction, 34(5), pp.399-409. Royal Society and British Academy, 2018. The impact of artificial intelligence on work. pp. 1-69. Succar, B., 2009. Building information modelling framework: A research and delivery foundation for industry stakeholders. Automation in construction, 18(3), pp.357-375. doi:10.1016/j.autcon.2008.10.003.

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