data-ai-tech-strategy

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

1. Clarify the artifact and audience.
2. Tie recommendations to measurable value.
3. Separate current state, target state, and transition path.
4. State principles as decision rules, not slogans.
5. Sequence roadmap work by dependency, value, feasibility, risk, and capacity.
6. 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.