- Deliver on the AI promise within the organization.
- Prioritize the demand for AI projects and govern the projects to prevent overloading resources.
- Insufficient data management capability.
- Have clear metrics in place to measure progress and for decision making.
AI requires a high level of maturity in all data management capabilities, and the greatest challenge the CIO or CDO faces is to mature these capabilities sufficiently to ensure AI success.
- Build your target state architecture from predefined best-practice building.
- Not all business use cases require AI to increase business capabilities.
- Not all organizations are ready to embark on the AI journey.
- Knowing the AI pattern that you will use will simplify architecture considerations.
Impact and Result
- This blueprint will assist organizations with the assessment, planning, building, and rollout of their AI initiatives.
- Do not embark on an AI project with an immature data management practice. Embark on initiatives to fix problems before they cripple your AI projects.
- Using architecture building blocks will speed up the architecture decision phase.
- The success rate of AI initiatives is tightly coupled with data management capabilities and a sound architecture.
This guided implementation is a five call advisory process.
Guided Implementation #1 - Assess business use cases for AI readiness
Call #1 - Scope requirements, objectives, and your specific challenges.
Guided Implementation #2 - Design your target state
Call #1 - Assess current maturity.
Call #2 - Identify target state capabilities.
Guided Implementation #3 - Define the AI architecture roadmap