The three dimensions are cost–benefit ratio, technological feasibility, and existing adoption constraints.

According to the chapter, AI use cases should be assessed by AI experts and strategists on three dimensions. The cost–benefit ratio compares the value an AI system delivers against its total costs, including financial and non-financial effects plus all adoption costs. Technological feasibility checks whether the required data, resources, performance levels, and provider capabilities are realistically available. Existing adoption constraints cover conflicts with AI governance policies, strategic technology dependencies, and objections from stakeholders such as employees, suppliers, customers, or partners.

Key points

The three evaluation dimensions are cost–benefit ratio, technological feasibility, and adoption constraints.
Cost–benefit analysis should consider both financial metrics and non-financial gains, while accounting for all AI-related costs.
Technological feasibility examines resource access, data availability, time limits, and required algorithmic performance.
Adoption constraints include ethical, legal, social, governance, dependency, and stakeholder concerns.
Use cases that do not clearly outperform simpler alternatives may not require an AI solution at all.

What are the three dimensions for evaluating AI use cases according to the chapter?

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