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How to scale your AI use cases: A checklist
The business case for AI is realized when the application is scaled, not in the use cases of the pilot phase. Use the checklist below at the outset to ensure the transition from a successful use case to a transformative project.
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?
Key learning
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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