Week 3 discussion response- managerial finance | Management homework help

Week 3 Discussion Response- Managerial Finance Lakenya86 Colleague 1 Katlyn Bone Generative AI is quickly becoming one of the most influential tools in managerial finance—not simply because it can process information faster, but because it can help leaders make more ethical, transparent, and socially conscious decisions. McKinsey (2024) explains that generative AI tools like ChatGPT and other advanced models can produce everything from text to simulations by drawing on massive datasets. In a financial management context, this ability goes far beyond convenience; it creates opportunities to strengthen integrity and accuracy in ways that protect both the organization and its stakeholders. One-way generative AI supports ethical financial practices through enhanced oversight. AI models can scan thousands of financial transactions in seconds, flagging unusual patterns that might suggest fraud, misuse of funds, or inconsistencies in reporting. This minimizes the risk of ethical breaches and gives managers confidence that internal controls are functioning as intended. AI can also support transparency in forecasting. Rather than relying solely on human judgment—which may be influenced by optimism, pressure, or incomplete information—generative AI can run scenario analyses that clearly show how different economic conditions would impact a project. These unbiased projections help prevent manipulative reporting and encourage disciplined, honest decision-making. Generative AI also creates new pathways for promoting positive social change. As a financial manager, I could use AI-powered models to evaluate the long-term impact of socially responsible initiatives, such as energy-efficient upgrades or community health investments. AI can quantify both the financial benefits and the social value, making it easier to advocate for projects that might traditionally be viewed as “nice to have” rather than strategically essential. AI can even help prioritize which social programs generate the greatest return for the organization and the community, ensuring resources are used thoughtfully and equitably. Of course, using AI responsibly means recognizing its limitations. McKinsey (2024) notes that generative AI can produce outputs that are inaccurate, biased, or based on flawed assumptions if not carefully monitored. Because these models learn from real-world data—which often reflects societal inequities, managers must be vigilant about reviewing AI-generated insights rather than accepting them at face value. There are also privacy concerns, reputational risks, and the broader danger of over-relying on systems that may feel authoritative but still behave unpredictably. Maintaining a human-in-the-loop is not just good practice; it is essential to ensure that AI strengthens, rather than compromises, ethical judgment. Ultimately, generative AI has the potential to elevate financial management by making it more transparent, fair, and socially responsible. When paired with human oversight and a commitment to integrity, AI becomes more than a productivity tool—it becomes a catalyst for meaningful, positive change within organizations and the communities they serve. Reference McKinsey & Company. (2024, April 2). What is generative AI? https://www.mckinsey.comLinks to an external site. Colleague 2 Tameika Coats The Use of Generative Artificial Intelligence (AI) Generative AI has the potential to significantly enhance ethical integrity in managerial finance, especially in healthcare- and nursing-oriented organizations where financial decisions have immediate and profound effects on patient care and the overall health of the community. One way that Generative AI supports ethical financial practices is by enabling transparent financial forecasting through data-driven synthesis of multiple datasets. By synthesizing large volumes of data, AI can produce multiple Budget Modeling Scenarios (BMS) that account for the ethical ramifications of funding allocation. For instance, an AI can accurately forecast the impact on long-term patient care from a reduced staffing budget or a delayed equipment upgrade (Faiyazuddin et al., 2025). In such cases, Generative AI helps managers determine the appropriate course of action to maintain patient safety and professionalism, as required in nursing. Similarly, AI can assist managers with fraud detection, compliance monitoring, and related services by notifying them of abnormal transactions, billing errors, high costs, etc., thereby contributing to both organizational integrity and compliance with regulatory guidelines such as Medicare and Medicaid. Generative AI is useful for financial managers in healthcare organizations, as it helps promote social good by selecting and funding projects with strong social responsibility and positive social impact. Cost-benefit analyses (CBA), supported through AI tools, identify the most beneficial initiatives to execute humanitarian work, providing better culturally competent care, and developing additional nurse-led preventative services (El Arab, 2025). Economic projections developed with these tools include long-term forecasts of savings from reduced readmissions or improved chronic disease management. This information helps justify the allocation of funds to social justice projects by providing evidence. Additionally, AI can streamline the process of applying for funding from outside sources by developing draft narratives and summaries of community impact to support faster acquisition of funding needed to execute programs promoting social responsibility. However, there are many issues and concerns related to the responsible use of generative AI. One of the primary concerns is algorithmic bias, which can reinforce inequities by emphasizing the financial efficiency of medical decisions to the detriment of the underserved. The quality of the data used to generate AI output can lead to incorrect conclusions about current inequities if the data are based on historical patterns of inequity or fail to reflect current ethical standards. Another concern is the limitations of AI systems’ moral reasoning. Although AI systems can identify patterns, they still cannot make moral choices or conduct a deep assessment of the impact of their actions on humans, which is what nursing leaders need. Besides, there are significant privacy and cybersecurity risks associated with generative AI, especially when financial data and sensitive health information are disclosed. Hence, the use of generative AI by managers should be visible, under human supervision, and in line with the institution's principles of fairness, responsibility, and patient-centeredness.

References:

El Arab, R. A., & Al Moosa, O. A. (2025). Systematic review of cost-effectiveness and budget impact of artificial intelligence in healthcare. NPJ digital medicine, 8(1), 548. https://doi.org/10.1038/s41746-025-01722-yLinks to an external site. Faiyazuddin, M., Rahman, S. J. Q., Anand, G., Siddiqui, R. K., Mehta, R., Khatib, M. N., Gaidhane, S., Zahiruddin, Q. S., Hussain, A., & Sah, R. (2025). The impact of artificial intelligence on healthcare: A comprehensive review of advancements in diagnostics, treatment, and operational efficiency. Health science reports, 8(1), e70312. https://doi.org/10.1002/hsr2.70312Links to an external site.

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