Will the scaled use case contribute significantly to our strategic objectives (e.g., customer experience, operational excellence, employee experience, business model, etc.)?
Is the use case solving a repeatable business issue (i.e., it’s not a one-off) that merits a long-term AI solution?
Do we have the resources and budget to scale (e.g., financial planning, vendor and partner management, etc.)?
Do we have a scalable technical foundation? What underlying standards across the use case portfolio should be applied (e.g., common data and technology layer)?
Do we have high-quality and sustainable data sources for the long term (e.g., data governance and quality controls, flexible data partnerships, etc.)?
Can we manage security, compliance, and ethics at scale (e.g., regulations, data privacy, ethical guardrails, etc.)?
Do we have the skills and organizational readiness to deploy at scale (e.g., cross-functional collaboration, talent, and training plans)?
Will there be clear budgetary responsibility and a business owner who will be accountable for embedding the AI solution into the business process landscape?
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By treating scalability early as a core objective rather than an afterthought, AI use cases can transition from exploration to production and pave the way for AI deployment that will deliver meaningful business value over the long run.
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