Using Generative Artificial Intelligence in Budgeting and Planning 

As generative AI models become more adaptable and accessible to organizations of all sizes, their uses in many managerial functions will increase. Since the models provide the ability to easily analyze large datasets and to capture and replicate patterns and structures found in the data, their use in operational planning, forecasting, budgeting, and reporting seems to be a natural fit.

In this Discussion, you will consider the subjects of the past 2 weeks, strategic planning, forecasting, budgeting, and balanced score cards, and how generative AI can be utilized in these managerial accounting functions.

To prepare for this Discussion:

• Review this week’s Learning Resources.
• Conduct a search of the Walden Library for more information on generative artificial intelligence.
• Consider the subjects from the past 2 weeks and the methods used to produce artifacts needed for each.

Post an analysis of the role of generative AI in strategic planning, budgeting, forecasting and balanced scorecards in an organization, to include the following:

• Provide a detailed example of how generative AI might be used in the strategic planning, budgeting, and forecasting processes or in the development and reporting of a balanced scorecard.
• Describe an advantage and disadvantage an organization might experience when using generative AI for either of these functions.

SOLUTION

Discussion Post: Using Generative Artificial Intelligence in Budgeting and Planning

Generative artificial intelligence (AI) is emerging as a powerful tool in managerial accounting functions such as strategic planning, budgeting, forecasting, and balanced scorecards. By rapidly analyzing large datasets, identifying trends, and simulating possible outcomes, generative AI can support decision-makers in crafting more accurate and adaptive strategies. For example, in the budgeting and forecasting process, an organization might use generative AI to analyze historical sales, economic conditions, and customer behavior to generate predictive revenue models under different scenarios. This allows managers not only to prepare a flexible budget but also to run “what if” analyses that account for uncertainty. Similarly, in developing a balanced scorecard, generative AI could be used to analyze both financial and non-financial performance indicators—such as customer satisfaction scores, employee engagement data, and supply chain efficiency—and then generate narrative reports that help leaders interpret performance in a more holistic manner (Kaplan & Norton, 2001). This use of AI makes the planning and reporting process more dynamic and evidence-based.

One major advantage of using generative AI in these functions is efficiency and precision. Managers can reduce the time required to prepare budgets or forecasts while gaining insights that might otherwise be missed through manual analysis. AI can also uncover hidden correlations that improve the accuracy of long-term planning. However, a disadvantage is the risk of over-reliance on the technology without adequate human oversight. Generative AI models are only as strong as the data they are trained on; if the data contain biases or do not capture future market disruptions, the forecasts or strategy recommendations may mislead decision-makers. For this reason, organizations must balance AI-driven insights with professional judgment and ethical considerations. In sum, generative AI has the potential to enhance the effectiveness of managerial accounting tools, but it should be viewed as a supplement rather than a replacement for human expertise.

Reference
Kaplan, R. S., & Norton, D. P. (2001). The strategy-focused organization: How balanced scorecard companies thrive in the new business environment. Harvard Business School Press.

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