How to scale your AI use cases: A checklist 

Checklist

• Strategic objectives

Will the scaled use case contribute significantly to our strategic objectives (e.g., customer experience, operational excellence, employee experience, business model, etc.)?

• Repeatability

Is the use case solving a repeatable business issue (i.e., it’s not a one-off) that merits a long-term AI solution?

• Resources and budget

Do we have the resources and budget to scale (e.g., financial planning, vendor and partner management, etc.)?

• Technical foundation and standards

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)?

• Data sources

Do we have high-quality and sustainable data sources for the long term (e.g., data governance and quality controls, flexible data partnerships, etc.)?

• Security, compliance, and ethics

Can we manage security, compliance, and ethics at scale (e.g., regulations, data privacy, ethical guardrails, etc.)?

• Organizational readiness

Do we have the skills and organizational readiness to deploy at scale (e.g., cross-functional collaboration, talent, and training plans)?

• Budgetary responsibility and ownership

Will there be clear budgetary responsibility and a business owner who will be accountable for embedding the AI solution into the business process landscape?