Changes apply to all in-flight assessments. Historical completed assessments keep their original question text via version snapshots.
Every question is tagged at the database level to exactly one construct. This is the chain a consultant or client can show the stakeholder asking “where did this question come from?”
1 · Item
Our organization has a documented AI strategy with a named executive sponsor and approved budget.
لدى منظمتنا استراتيجية موثقة للذكاء الاصطناعي مع راعٍ تنفيذي مسمى وميزانية معتمدة.
2 · Pillar
3 · Score map
| Option | Score (1–5) |
|---|---|
| 1 - Not at all | 1 |
| 2 - Early exploration | 2 |
| 3 - In progress | 3 |
| 4 - Mostly in place | 4 |
| 5 - Comprehensive | 5 |
Not yet surfaced to clients
This is an AI-suggested anchor. LLM citations can subtly hallucinate paper-level details. Verify the citations are accurate before clicking Accept; only then will they appear in the consultant report appendix.
AI strategic planning and governance foundation
Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
Rationale: The item directly measures the foundational strategy elements that Davenport & Ronanki identify as prerequisites for successful AI implementation in organizations.
Iansiti, M., & Lakhani, K. R. (2020). Competing in the age of AI. Harvard Business Review Press.
Rationale: The item aligns with Iansiti & Lakhani's framework for strategic AI planning that requires formal documentation, leadership commitment, and resource allocation.
For multiple choice / yes-no, enter options and score_map as JSON.