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AI Implementation Roadmap
Build your AI capability in the right sequence
Why Sequence Matters
AI initiatives have dependencies. Data quality improvements unlock certain agent types. Process definition is prerequisite to process automation. Building in the wrong sequence creates expensive rework and organizational frustration. The roadmap ensures each phase builds on the one before it.
The Six Phases of Operating AI™ Implementation
Our standard implementation follows six phases: Assess (AI Readiness Assessment and opportunity identification), Architect (Operating AI™ design and integration mapping), Build (agent development and integration), Adopt (rollout with training and change management), Operate (performance monitoring and optimization), and Optimize (continuous improvement and expansion).
Resource Planning for AI Implementation
AI implementation requires four types of resources: technology (platform and integration infrastructure), external expertise (AI design, development, and change management), internal capacity (operations, IT, and functional leaders for requirements and testing), and ongoing management (monitoring, governance, and optimization). The roadmap template helps you plan all four.
Measuring AI Implementation Progress
AI implementation success should be measured against business outcomes — not deployment milestones. The roadmap template includes a metrics framework: leading indicators (adoption, usage, data quality) and lagging indicators (cost savings, productivity improvements, error reduction) for each initiative.
FAQ
Frequently asked questions
Most middle market AI roadmaps cover 12-18 months. Longer horizons lose specificity; shorter ones miss the build-up phases that make advanced deployments possible.
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