TLDR: This research paper explores how Generative AI (GAI) can automate strategic plan development for large government organizations. It proposes a modular model and evaluates topic modeling techniques (BERTopic and NMF) for generating “Vision Elements” from public reports. The study found that both techniques can identify relevant themes, with BERTopic performing best, achieving strong correlations with existing strategic plan elements, proving GAI’s viability in this domain.
Strategic planning is a critical process for any organization, defining its future vision, goals, and objectives. For large government bodies, it’s not just about growth; it’s also about transparency, accountability, and responsible use of public funds. This complex and often lengthy process, typically led by consulting firms, can take months or even years and cost millions of dollars, making it a multi-billion dollar industry globally.
Recent advancements in Generative Artificial Intelligence (GAI) and Large Language Models (LLMs) are opening new avenues for automating tasks once thought impossible. This research explores how GAI can be leveraged to streamline the development of strategic plans, potentially reducing both the time and cost involved significantly.
The paper introduces a modular framework that breaks down strategic plan development into several key components: Vision Elements, Goals, Measurements, Strategies, and Activities. Each component involves specific cognitive tasks. The study hypothesizes that by addressing these tasks with machine learning, a system can be designed to automate strategic plan generation with minimal human intervention. It’s important to note that this isn’t about a single AI model doing everything; rather, it envisions an orchestration of different models, each tackling a specific task.
For this initial exploration, the research focused on one crucial aspect: generating “Vision Elements.” These elements represent an organization’s desired future state and typically involve summarizing large amounts of information into key themes. The study specifically evaluated the effectiveness of topic modeling techniques for this “Summary Task.”
To test this, the researchers used a large volume of reports from the Government Accountability Office (GAO) as their dataset, representing external perspectives on challenges and trends. After collecting and preprocessing these reports (including splitting them into individual pages to increase data points), two leading topic modeling techniques were applied: BERTopic and Non-negative Matrix Factorization (NMF).
BERTopic uses advanced language models like BERT to create word embeddings and then clusters them to identify topics. NMF, on the other hand, is an algebraic technique that analyzes the importance of words within documents to derive topics. The goal was to see how well the topics generated by these AI models aligned with the Vision Elements of a real-world strategic plan, specifically the Department of Energy Office of Legacy Management’s 2020-2025 Strategic Plan.
The evaluation involved comparing the machine-generated topics against the published Vision Elements and assigning a correlation strength: “weak,” “medium,” or “strong.” The results were promising. Both BERTopic and NMF were capable of generating themes that correlated with 100% of the Vision Elements being evaluated, indicating the general viability of using GAI for this purpose.
However, BERTopic emerged as the superior performer in this application. While NMF showed a higher percentage of “weak” correlations and zero “strong” correlations, BERTopic demonstrated a more balanced distribution, with more than half of its correlated topics achieving a “medium” or “strong” correlation. This suggests that BERTopic is better at identifying more relevant and distinct themes for strategic visioning.
In conclusion, this study provides strong evidence that generative AI, particularly through topic modeling techniques like BERTopic, can significantly aid in developing strategic plans by automating the generation of Vision Elements. While further work is needed to optimize these models and explore other modules of the proposed framework, this research marks a significant step towards a scalable, GAI-enabled solution for strategic planning, potentially saving considerable time and cost for large government organizations and ensuring compliance with regulatory requirements.
Also Read:
- AI Models Master Community Resource Allocation Through Participatory Budgeting
- Retrieval-Augmented Generation: A Comprehensive Review of Its Landscape
For a deeper dive into the methodology and detailed findings, you can read the full research paper: Generative AI for Strategic Plan Development.
