Scale-Up Strategy

A systematic strategy for spreading successful AI use cases company-wide

Stages of AI Scale-Up

    flowchart LR
    A["Stage 1<br/>PoC<br/>Proof of Concept"] --> B["Stage 2<br/>Pilot<br/>Small-Scale Validation"]
    B --> C["Stage 3<br/>Expansion<br/>Team/Department Rollout"]
    C --> D["Stage 4<br/>Enterprise-Wide<br/>Organizational Standardization"]

    style A fill:#EFF6FF,stroke:#2563EB,color:#1E40AF
    style B fill:#2563EB,stroke:#1D4ED8,color:#fff
    style C fill:#7C3AED,stroke:#6D28D9,color:#fff
    style D fill:#16A34A,stroke:#15803D,color:#fff
  

Key Activities at Each Stage

Stage 1: PoC (1-4 weeks)

• Goal: “Is this technically feasible?”
• Team: 1-2 AI developers
• Success Criteria: Confirm technical feasibility
• Deliverables: Demo, technical review report

Stage 2: Pilot (1-3 months)

• Goal: “Does this deliver value to real users?”
• Team: AI developers + domain experts + user group
• Success Criteria: Achieve measurable KPIs
• Deliverables: ROI analysis, user feedback, improvement items

Stage 3: Expansion (3-6 months)

• Goal: “Can this be applied to other teams?”
• Team: AI team + business teams
• Success Criteria: Successful adoption by multiple teams
• Deliverables: Standardized playbook, internal training materials

Stage 4: Enterprise-Wide Standardization (6-12 months)

• Goal: “Has this become part of how the organization works?”
• Team: AI CoE (Center of Excellence)
• Success Criteria: Routine use by all employees
• Deliverables: AI governance policy, sustained operating model

Structuring an AI Center of Excellence (CoE)

An organizational structure for company-wide AI scale-up:

AI CoE
├── AI Strategy (Chief AI Officer)
│   └── AI roadmap, governance policy
├── AI Engineering
│   └── Shared infrastructure, MLOps, security
├── AI Products
│   └── Internal AI tools, platforms
└── AI Capability Building
    └── Training, community, AI champions

Overcoming Resistance to Scale-Up