Classification
AI Strategy and Risk Management
Overview
A business use case in the context of AI refers to a clearly defined scenario where artificial intelligence is applied to address a specific business problem or opportunity. It articulates the rationale for using AI, including the expected benefits, costs, and alignment with organizational objectives. Business use cases help stakeholders understand why an AI solution is appropriate, what value it brings, and how it supports the company's mission. They typically include an analysis of alternatives, potential risks, and success metrics. However, a limitation is that use cases can sometimes be overly optimistic, failing to account for data quality, integration challenges, or the risk that AI may not outperform traditional methods. Properly defined use cases are foundational for prioritizing AI investments, ensuring accountability, and enabling effective governance.
Governance Context
In AI governance frameworks such as NIST AI RMF and ISO/IEC 42001, organizations are required to formally document the intended use and business justification for AI systems. Obligations include conducting impact assessments to evaluate whether the AI use case aligns with organizational values and legal requirements, and implementing approval processes for new AI deployments. Additional controls often include establishing ongoing monitoring procedures and maintaining records of decision-making rationale. For example, NIST AI RMF emphasizes mapping business context and risk, while the EU AI Act mandates that high-risk AI systems undergo conformity assessments, including justification of their intended use. These controls help ensure that AI is deployed responsibly, with clear accountability for outcomes and alignment with corporate strategy.
Ethical & Societal Implications
Defining business use cases for AI raises ethical considerations such as fairness, transparency, and societal impact. Poorly conceived use cases can lead to unintended harms, such as reinforcing biases or misaligning with stakeholder values. Ensuring that AI applications are appropriate and justified helps mitigate risks like discrimination, privacy violations, or erosion of trust. It is critical to consider not only business benefits but also broader societal consequences when developing and governing AI use cases. Additionally, transparent communication with stakeholders and regular review of use cases can help address emerging ethical challenges.
Key Takeaways
Business use cases articulate the rationale and expected value of AI applications.; Clear use cases support alignment with organizational goals and regulatory requirements.; Frameworks like NIST AI RMF and EU AI Act require formal documentation of AI use cases.; Poorly defined use cases can lead to project failure or ethical risks.; Ongoing evaluation is needed to ensure use cases remain relevant and effective.; Impact assessments and approval processes are key governance controls for AI use cases.