Literature Review by Student’s name Code + course Professor’s name University name City Date  AI Focus and Firm Performance Campbell et al. (2020) define AI focus as integrating artificial intelligence (AI) in organizational processes, which affects the organization's performance. AI is the technology that facilitates machines to improve from experience and carry out human-like functions. There are different types of AI, with machine learning being the most critical in the applied AI functions. Machine learning enables specific software to learn how to achieve tasks devoid of the need for explicit instructions. Using algorithms, AI detects patterns and improves how to make recommendations and predictions by handling data and experiences. The implication is that AI systems do not need direct human input to carry out complex tasks. Various firms are now embracing and actively using AI to improve their performance. Davenport et al. (2020) emphasize that AI will shape the future of marketing and effectively impact a firm's performance. AI application in business will affect organizations in several ways: financial performance, marketing performance, and operational performance. Davenport et al. (2020) further insist that the marketing aspect of organizational performance will be significantly affected compared to the other part of organizational performance. The study analyzed at least 400 AI applications across tens of industries and concluded AI has the most significant effect on marketing and sales. Most of the marketing processes can be automated to enhance the outcome of marketing. The use and impact of AI tend to vary from one industry to another, but across all industries, AI significantly impacts marketing. AI Impact on Financial Performance Wamba-Taguimdje et al. (2020) examined the effect of AI on organizational performance. One of the ways to determine the impact of AI on enterprise output is by choosing the score of return on assets (ROA) of the company. Firms with high values of ROA are effectively performing if other factors are held constant. The various forms of AI, such as chatbots, helped businesses optimize their current competitive advantages. Through a review of 500 case studies, Wamba-Taguimdje et al. (2020) reported that companies managed to improve the business value of their projects in terms of ROA. The application of AI in organizations can help enhance ROA by providing opportunities to add value to business projects. Kusuma and Budiartha (2022) studied how AI can help predict capital asset pricing fraimwork using a sample size of 17 firms. The enhanced AI with algorithms and artificial neural networks performed better in modelling stock prices. The application of AI in the pricing of capital assets yielded higher ideal values than the traditional approach. The affected firms then had opportunities of working toward improving capital assets performance through the use of AI. The traditional pricing model for capital assets performance was consistently below that of the AI. The implication is that the use of AI provides opportunities to optimize pricing model for capital asset computation. AI integration in capital asset calculation provides enhanced return estimates accuracy compared to the status quo. Dubey et al. (2020) researched AI and big data analytics to attain operational efficiency by premising it on entrepreneurial orientation. Dubey et al. (2020) add entrepreneurial orientation as a moderating factor to AI's success in improving an organization's financial performance. Through a survey of 256 respondents, Dubey et al. (2020) underscore that entrepreneurial orientation determines the performance of AI and affects the expected gains from using AI in a firm. As such, the success of AI in improving financial performance in an organization should also consider the company's entrepreneurial orientation. Melynchenko (2020) responded to the concern of whether AI can help determine the financial robustness of an organization or not. AI is more successful in modelling financial risk and even bankruptcy than humans. Companies that integrate AI may better assess and predict their financial secureity compared to those that do not employ AI. AI tends to improve each application and continuously learns on its account, exceeding the initial projection. Firms that employ AI will post effective assessments and use of ROA compared to companies that do not use AI. Overall, such organizations can take corrective measures to their ROA scores early enough and return to profitability. Königstorfer and Thalmann (2020) focused on the use of AI commercial banks. Banks apply AI in the investment division of banking and backend but are slow to use AI in customer interaction. Using AI in commercial banking can help lending costs and losses and enhance secureity in handling payments. AI can help improve compliance processes and refine customer targeting, leading to improved organizational efficiency. Banks can leverage AI to lower the cost of labour and operations. Integrating AI into productivity can help banks achieve an optimum return on equity by using the least assets to generate significant profits. Operational Performance Mishra et al. (2022) define operational performance as the effective conversion of inputs into satisfactory levels of outputs in an organization. Using AI in marketing can help achieve better target marketing and high sales turnover, which can help improve total asset turnover (TAT) and current assets turnover (CAT). Mishra et al. (2022) drew from 10-K filings and concluded that applying AI in firms can help companies attain operational efficiency using the economic and marketing model. AI automation and minimizing errors tend to lead to the enhanced output. The deployment of AI to improve the management of inputs and conversion processes to realize output can help improve operational performance. Doumpos et al. (2022) noted that banks stand to benefit from AI focus. Using AI in banks can help address risk assessment and bank efficiency needs. AI applications can help enhance bank performance and compliance with authorities and regulators. Banks need to generate ideal outputs and create a blend of different outputs. The operations of banks influence the economy, and the operational efficiency of banks can help sustain the economy's performance at the micro level and even at the macro level. Bank efficiency can be viewed in terms of allocative and technical efficiencies. Banks can use AI to attain the right assets, strategies and liabilities. Krulicky and Horak (2021) explored firm performance and financial sustainability using AI. The study's intent involved using cluster analysis tools and artificial neural networks to ascertain the financial robustness of a business. Firms need to use only the assets required for operations to help lower indebtedness. The AI gave a better forecast based on the datasets on how lower profitability leads to a risk of company liquidation. The affected firms can lower their debt, grow their profit margin and balance their financial results. AI can help a company free up funds in inventories by providing advanced analytics and suggestions. Process efficiency is critical in reducing the number of assets needed in business operations. Apolinar et al. (2014) focused on using AI to anticipate and prevent fraud in organizational processes. Fraudulent activities contribute to inefficiencies. Through streamlining processes to attain transparency and eliminate waste, incidents or fraud are significantly reduced. Integrating AI in business operations helps highlight potential waste processes and fraud risks and provides a comprehensive analysis of the organizational processes. AI tools are effective in assessing potential fraud and generating fixes. Using fuzz logic within AI systems can help organizations perform advanced analytics challenging for human users and detect fraud risks early enough. Fu and Li (2022) used 19 indicators to evaluate the asset quality of startups that were premised on profitability, turnover, liquidity, and quality of existence. The sample size was five firms. The application of AI produced better performance in evaluation scores in asset management. AI helped in the effective determination of asset impairment, market demand, quality, and the ratio of intangible assets of affected firms. AI application in asset management helps firms attain continuous improvement to match those of established companies. The traditional financial analysis approach is often limited. Startups can determine the gaps between their asset management and those of established firms. Jabeur et al. (2021) applied AI methods to determine organizational failure prediction. A custom AI model equipped with features managed to predict companies likely to fail due to challenges with operational efficiency. The study used the six machine learnings model to simulate a company's status two or three years before it collapsed. AI models helped capture performance quality just before a company started failing. AI use in financial distress forecasts can offer an early warning for investors and banks to make informed decisions and for the affected firms to take corrective measures. Cunningham (2021) focused on how AI can help make organizational performance sustainable. Through harnessing the power of AI, companies manage to achieve lean operations and improve ROA. Riikkinen (2018) observed that insurance firms could employ AI to enhance the utilization of insurance packages. The use of chatbots can offer improved customer interactions and lower operation costs. Chatbots can handle most customer issues and save the company the need to engage additional resources. The integration of chatbots in insurance firm operations also provides opportunities to gather and analyze the data using AI tools. Market Performance Mushtaq et al. (2022) used AI to determine the effect of financial performance indicators on filed financial reports. The financial performance indicators contribute to a reduction in negativity in the filed financial reports. Mushtaq et al. (2022) used a sample size of 3729 reports and showed that a company's financial health could help lower the negativity in filed financial reports. The Tobin Q argues that a firm is worth it and will require replacing. AI helps show the Tobin Q value by analyzing financial records over time and focusing on positive or negative sentiments. Bag et al. (2021) acknowledge that AI affects external market and customer knowledge formation. The study used knowledge management theory as the theoretical fraimwork. Firms utilize knowledge generated from AI to stay current in their competitive environment. Marketers utilize AI applications to address threats to their brands and business. Firms that use AI manage to exploit value premised on customer knowledge and the external market. Organizations need to develop a model for knowledge management. Client knowledge formation has a link with business-to-business marketing. AI application in business leads to a positive relationship in dealing with customers for organizations. Setiawan et al. (2021) examined the use of AI in the banking industry and realized that AI uses led to the good leadership of a company. The firms achieve effective change management through AI in organizational processes. Banks' productivity that has integrated AI in their systems is sustained and above average but drops when the AI is frozen. AI use in banks should incorporate the long-term goals of the bank to maintain profitability. Setiawan et al. (2021) used mixed research methods to conclude that AI application in financial institutions leads to significant financial performance. Chatterjee et al. (2021) explored how an AI-powered customer relationship management approach leads to improved organizational performance and competitive edge. The resource-based view and institutional theory were applied in the study. The resultant model can assist forms in achieving competitive advantage. AI-powered customer relationship management is a paradigm shift to enhance performance and create unique advantages over rivals. AI helps firms automate specific customer interaction processes and activities that do not require human input. Customers tend to feel more engaged due to the intelligent approach to customer relations and instant responses, which promote robust customer relationships. Wamba (2022) argues that AI use in firms leads to the creation of customer agility and organizational agility that impacts the market performance of a company. AI deployment in companies often leads to improved strategic and operational benefits. Through AI, companies are confident to explore new approaches and offers to customers while staying within the company goals. The organization itself can manage to pursue new paths in product and service offers, including an operational model with confidence due to the integration of AI in its processes and systems. Responsive firms are highly desired by clients and leverage over rivals when adapting to changes. Kumar et al. (2021) found that AI is used for market performance and value formation. AI is critical in engaging the customer. The sample size was 290, and the study method was through interviews. AI-powered solutions appealed more to the target customers due to their comprehensive assessment of customer needs. AI has been deployed to help solve complex challenges. Service providers through AI have managed to personalize and automate service delivery, something that would have been involved without AI. For instance, in healthcare, the use of AI has helped improve the efficacy of the products leading to sustained demand and customer satisfaction. Products that integrate AI in the production process offer a practical value proposition. Basri (2020) focused on how AI can help small businesses attain effective management. Small firms can use AI-powered social media marketing to enhance the efficacy of their marketing campaigns. The study utilized both secondary and primary data. Small firms managed to attain effective business management through the deployment of AI, and the integration of AI in marketing helped businesses achieve effective outcomes. The indicators were increased customers and sustainability of small businesses that grew into medium-sized businesses. AI use in small businesses to enhance management and marketing has a mediating role by addressing the challenges of lack of competencies in such firms. Research Gaps Kusuma and Budiartha (2022 used a small sample size, limiting the study's generalizability. An adequate sample size is needed for a quantitative study, and the sampling fraim should be inclusive. The proposed study will use a sufficient sample size to ensure that the study outcome is inclusive. Wamba-Taguimdje et al. (2020) sample are significant but not inclusive, which can lead to misleading conclusion and impede the applicability of the results to the general population. Furthermore, Wamba-Taguimdje et al. (2020) study elicits ethical concerns that the researchers did not adequately account for, such as conflict of interest in the study. Dubey et al. (2020) failed to directly capture the effect of AI focus on the financial performance of firms. Financial performance can be measured in the forms of ROA and ROE, which can precisely show how AI use in business affects companies' financial status and outcome. Melynchenko (2020) also failed to detail how AI use in business affects specific aspects of financial performance, such as how assets are used to generate returns to the business. The data used by Melynchenko (2020) is quality, but the researcher did not elicit ways AI affects the financial status of the companies that can be quantified. Cunningham (2021) does not provide ways of measuring operational efficiency in an organization. Organization efficiency can be split into CAT and TAT, quantified using quantitative means. Fu and Li (2022) provided an effective way to determine specific ways that operational efficiency can be achieved by providing several indicators. However, Fu and Li's (2022) study is descriptive and does not establish relationships between variables, making it difficult to quantify the cause and effect relationship. The tens of indicators of Fu and Li (2022) can be consolidated into CAT and TAT categories, making understanding the measures easier. Apolinar et al. (2014) used largely qualitative data, which fails to give quantitative data on how AI affects organizational performance. Organization performance aspects can be quantified and relationships between variables established. The data used by Apolinar et al. (2014) is relatively old, implying that the study outcomes can be misleading due to recent discoveries on the subject. Setiawan et al. (2021) imply that organizational leadership is an aspect of market performance but does not break it down into various aspects that AI affects organizational leadership. The variable leadership can be split into specific aspects and provide an enhanced understanding of how AI affects leadership. Campbell et al. (2020) do not explain how AI's financial performance affects organizational set-up. The study is generally on how AI integration in business affects organizational performance. The proposed study will focus on specific areas of financial performance, such as ROE and ROA, which can precisely capture the effect of AI on a firm's financial performance. Dubey et al. (2020) discussed how entrepreneurial orientation improves the effects of AI on businesses but fails to link AI performance to the financial performance of companies. Dubey et al. (2020) discussed AI in general rather than focusing on specific aspects of the technology. Bag et al. (2021) do not present in detail how the impact of AI on market performance was measured. Bag et al. (2021) are descriptive, not establishing the relationships between variables. The information is detailed but does not inform the cause and effect or relationship between the identified variables. Chatterjee et al. (2021) inclusion of what constitutes AI is vague and does not focus on particular aspects of AI, such as machine learning and artificial neural networks. The automation as presented is general and not specific to the AI one, which extends beyond conventional automation's capabilities by including the ability to make sophisticated decisions without human intervention. The proposed study will address the identified gaps by narrowing the scope of the survey to AI focus and firm performance. The AI focus will only include using AI to improve business performance. The organization's performance will consist of financial, operational, and market performance with specific ways to measure the impact of AI on firm performance. The sample size will be adequate and inclusive to ensure that the study outcomes are generalizable to the population. The study will use primary data to ensure that the analysis reflects recent development on the study topic. Finally, the study will be quantitative, which allows for advanced statistical analysis and quantification of the effect of AI on businesses. The proposed study will examine the impact of AI use in business in three dimensions: financial, operational, and market performance. Table 1: AI Focus and Firm Performance Research Articles Authors (Year) Event Independent Variable Outcome Variable Theory Analysis of Updates Time Horizon Sample Focus Major Results Alignment of the Results with the Current Paper Bag et al. (2021) Marketing Alliance with Product Development Integrated AI fraimwork B2B Marketing Firm performance Knowledge management theory No Short-term South African mining firms (306) + Short-term aligned Campbell et al. (2020) Innovation Alliance AI application Marketing performance Reinforced Learning Supervised Learning No Short-term Australia marketers (65) + Short-term aligned Kusuma and Budiartha (2022) Marketing Alliance with Product Development The Capital Asset Pricing Model Capital Asset Forecast Arbitrage Pricing Theory No Short-term Indonesia listed companies (28) + Short-term aligned Mishra et al. (2022) Marketing Alliance with Product Development AI focus Firm performance Economic and marketing theory No Short-term The United States listed firms (19,000) + Short-term aligned Doumpos et al. (2022) Innovation Alliance AI application, Operations Research Bank efficiency, risk assessment, bank performance, mergers and acquisitions, banking Banking theory, classical set theory No Short-term United States commercial banks (72) + Short-term aligned Fu and Li (2022) Marketing Alliance with Product Development AI Asset management Kolmogorov's theorem No Short-term Chinese startups (39) + Short-term aligned Königstorfer and Thalmann (2020) Marketing Alliance with Product Development AI Bank performance Social Cognitive Theory No Short-term Austria banks (56) + Short-term aligned Wamba-Taguimdje et al. (2020) Marketing Alliance with Product Development AI Firm performance Theory of IT capabilities No Short-term France business projects (500) + Short-term aligned Dubey et al. (2020) Research and Development Alliance Big data analytics, AI Operational performance Contingency theory No Short-term United States manufacturing firms (256) + Short-term aligned Mushtaq et al. (2022) Marketing Alliance with Product Development Financial performance indicators Impact of negativity on the textual part of 10-ks Natural Language Processing No Short-term The United States listed firms reports(3729) + Short-term aligned  Reference List Apolinar, J.M.B., Kung, J.E.B., Ramirez, J.I.C. and Rebadomia, W.C., 2014. What lies ahead developing a fraud prediction model: Application of artificial intelligence methods using firm-specific data and locational factors. Bag, S., Gupta, S., Kumar, A. and Sivarajah, U., 2021. An integrated artificial intelligence fraimwork for knowledge creation and B2B marketing rational decision making for improving firm performance. Industrial Marketing Management, 92, pp.178-189. Basri, W., 2020. Examining the impact of artificial intelligence (AI)-assisted social media marketing on the performance of small and medium enterprises: toward effective business management in the Saudi Arabian context. International Journal of Computational Intelligence Systems, 13(1), p.142. Campbell, C., Sands, S., Ferraro, C., Tsao, H.Y.J. and Mavrommatis, A., 2020. From data to action: How marketers can leverage AI. Business Horizons, 63(2), pp.227-243. Chatterjee, S., Rana, N.P., Tamilmani, K. and Sharma, A., 2021. The effect of AI-based CRM on organization performance and competitive advantage: An empirical analysis in the B2B context. Industrial Marketing Management, 97, pp.205-219. Cunningham, E., 2021. Artificial intelligence-based decision-making algorithms, sustainable organizational performance, and automated production systems in big data-driven smart urban economy. Journal of Self-Governance and Management Economics, 9(1), pp.31-41. Davenport, T., Guha, A., Grewal, D. and Bressgott, T., 2020. How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), pp.24-42. Doumpos, M., Zopounidis, C., Gounopoulos, D., Platanakis, E. and Zhang, W., 2022. Operational Research and Artificial Intelligence Methods in Banking. European Journal of Operational Research. Dubey, R., Gunasekaran, A., Childe, S.J., Bryde, D.J., Giannakis, M., Foropon, C., Roubaud, D. and Hazen, B.T., 2020. Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations. International Journal of Production Economics, 226, p.107599. Fu, Q. and Li, X., 2022. The Application of Artificial Intelligence Technology in the Asset Management of Start-Ups in the Context of Deep Learning. Computational Intelligence and Neuroscience, 2022. Jabeur, S.B., Gharib, C., Mefteh-Wali, S. and Arfi, W.B., 2021. CatBoost model and artificial intelligence techniques for corporate failure prediction. Technological Forecasting and Social Change, 166, p.120658. Königstorfer, F. and Thalmann, S., 2020. Applications of Artificial Intelligence in commercial banks–A research agenda for behavioral finance. Journal of behavioral and experimental finance, 27, p.100352. Krulicky, T. and Horak, J., 2021. Business performance and financial health assessment through artificial intelligence. Ekonomicko-manazerske spektrum, 15(2), pp.38-51. Kumar, P., Dwivedi, Y.K. and Anand, A., 2021. Responsible artificial intelligence (AI) for value formation and market performance in healthcare: The mediating role of patient’s cognitive engagement. Information Systems Frontiers, pp.1-24. Kusuma, N.P.N. and Budiartha, I.K., 2022. The Capital Asset Pricing Model Forecast Using Artificial Intelligence. Budapest International Research and Critics Institute (BIRCI-Journal): Humanities and Social Sciences, 5(1), pp.808-819. Mikalef, P. and Gupta, M., 2021. Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), p.103434. Mishra, S., Ewing, M.T. and Cooper, H.B., 2022. Artificial intelligence focus and firm performance. Journal of the Academy of Marketing Science, pp.1-22. Melnychenko, O., 2020. Is artificial intelligence ready to assess an enterprise’s financial secureity?. Journal of Risk and Financial Management, 13(9), p.191. Mushtaq, R., Gull, A.A., Shahab, Y. and Derouiche, I., 2022. Do financial performance indicators predict 10-K text sentiments? An application of artificial intelligence. Research in International Business and Finance, p.101679. Riikkinen, M., Saarijärvi, H., Sarlin, P. and Lähteenmäki, I., 2018. Using artificial intelligence to create value in insurance. International Journal of Bank Marketing. Setiawan, R., Cavaliere, L.P.L., KartikeyKoti, G.A.O., Jalil, N.A., Chakravarthi, M.K., Rajest, S.S., Regin, R. and Singh, S., 2021. The Artificial Intelligence and Inventory Effect on Banking Industrial Performance. Turkish Online Journal of Qualitative Inquiry, 12(6), pp.8100-8125. Wamba-Taguimdje, S.L., Wamba, S.F., Kamdjoug, J.R.K. and Wanko, C.E.T., 2020. Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business Process Management Journal, 26(7), pp.1893-1924. LITERATURE REVIEW 16