- 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
Call #1 - Identify the relationship between current initiatives and capabilities.
Call #2 - Create initiative profiles.
After each Info-Tech experience, we ask our members to quantify the real time savings, monetary impact, and project improvements our research helped them achieve. See our top member experiences for this Blueprint, and what our clients have to say.