The comparison of AI and SWOT is differentiated on the basis of data collection and interaction with others. AI emphasizes positive experiences and interactions, and seeks to uncover strengths and successes through interviews, focus groups, and other forms of qualitative data gathering (Sacco‐Bene, 2022). This approach requires a high level of engagement and participation from stakeholders, and is focused on building relationships and fostering a positive organizational culture. In contrast, SWOT analysis is a more quantitative approach that focuses on identifying strengths, weaknesses, opportunities, and threats through surveys, metrics, and other objective data sources (Phadermrod et al., 2019).
Another key difference between AI and SWOT analysis is the way in which they interact with others. AI emphasizes collaboration and dialogue, and seeks to create a shared vision and sense of purpose among stakeholders. This approach can be particularly useful for building buy-in and support for change initiatives. SWOT analysis, on the other hand, is more focused on individual perspectives and opinions, and may be less effective at building consensus and shared vision (Aziz, 2019).
It is important to recognize that both AI and SWOT approaches have their own assumptions and biases. According to Dent (2019) AI assumes that positive experiences and interactions are more valuable than negative ones, while SWOT assumes that objective data sources are more accurate than subjective opinions (Hollingsworth & Reynolds, 2020). It is important to be aware of these assumptions and biases when selecting an approach to analysis, and to consider how they may impact the results of the analysis. By acknowledging and addressing these assumptions and biases, we can ensure that our analysis is more accurate, effective, and meaningful.
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