| name | data-ai-tech-strategy |
| description | Use when creating Data/AI strategy, principles, roadmaps, MLOps plans, or executive docs. |
| allowed-tools | Read,Write,Edit,Bash,Glob,Grep,WebSearch,WebFetch |
Data and AI Technical Strategy
Use to turn Data, AI, and ML work into clear strategy: business alignment, architecture direction, principles, roadmap sequencing, MLOps maturity, and leadership communication.
Workflow
- Clarify the artifact and audience.
- Tie recommendations to measurable value.
- Separate current state, target state, and transition path.
- State principles as decision rules, not slogans.
- Sequence roadmap work by dependency, value, feasibility, risk, and capacity.
- End with metrics, owners, governance, and review cadence.
Strategy Pillars
Use five pillars: business alignment, technical vision, organization design, delivery excellence, and governance/ethics.
Leadership Standard
Translate technical work into executive terms. Prefer numbers, tradeoffs, commitments, and known unknowns over generic platform language.
References
Load references/strategy-document-template.md, references/engineering-principles.md, references/mlops-principles.md, or references/roadmap-planning.md only when needed.
