---
type: CP
asset_id: CP-EL-HIOrgReadinessScore-QuestionSet-v01
version: v01
status: Draft — build-ready
owner: Victor Heredia
sherpa_owner: Jay
intellibank: IB-EL-EmpowerLabs
subbank: PB-EL-Project-Bank/PB-HiOrg-HyperintelligentOrg
fecha_creacion: 2026-06-13
proposito: Build-ready question bank, answer anchors, scoring formula, tier mapping and report logic for the free HIOrg Readiness Score (L0). Companion to CP-EL-HIOrgReadinessScore-Spec-v01.
deriva_de: "[[CP-EL-HIOrgReadinessScore-Spec-v01]] (spec) · [[ANA-EL-OrgSingularity-ExO3-v01]] (frameworks) · Organizational-Singularity-v24-FULL (AI-Pilled Ladder, Four Pillars)"
tags: [CP, HIOrg, ReadinessScore, questionnaire, scoring, build-ready, L0]
---

# HIOrg Readiness Score — Question Set, Scoring & Report Logic
## Build-ready · 7 dimensions · 20 questions · ~6 minutes

> Hand this to a developer (web quiz) or wrap as an MPI Claude Skill. All scoring is deterministic. Answer anchors are written so a respondent picks the statement that best fits; each maps to a 1–7 value.

---

## 0. Intake (not scored — used for benchmarking & the drag teaser)

- **I1. Company size:** `<50` · `50–200` · `200–1,000` · `1,000–5,000` · `5,000+`
- **I2. Sector:** Manufacturing/Physical · Financial/Regulated · Retail/Consumer · Services/Professional · Tech/Software · Public/Gov · Other
- **I3. Your role:** CEO/Owner · CIO/CTO/Tech leader · COO/Operations · Other C-suite · Manager · Other
- **I4. Work email** (lead capture; report unlock).

> I1+I2+I3 feed the **indicative drag-cost range** (teaser) and route logic. Role gates the B2O fast-path.

---

## 1. Question bank — 7 dimensions, 20 questions

> Each question is single-select. The value (1/3/5/7) is hidden from the respondent. Intermediate options interpolate. Dimension score = mean of its questions, rounded to one decimal.

### D1 · Organizational Drag  *(weight 1.5×)*

**Q1.1 — To get a non-trivial change approved (e.g., launch a new feature or process), how many sign-offs does it pass?**
- 5+ separate approvals, takes months *(1)*
- 3–4 approvals, takes weeks *(3)*
- 1–2 approvals, takes days *(5)*
- A person or an agent decides in hours *(7)*

**Q1.2 — When a competitor moves, how fast can you evaluate and decide a response?**
- Months — it needs committees and cycles *(1)*
- Weeks *(3)*
- Days *(5)*
- Hours — we sense, interpret and decide near-continuously *(7)*

**Q1.3 — How much of your managers' time goes to *coordination* (gathering, repackaging, reporting) vs. judgment/problem-solving?**
- Mostly coordination (>70%) *(1)*
- More coordination than judgment *(3)*
- Balanced *(5)*
- Mostly judgment; agents handle coordination *(7)*

### D2 · AI as a First-Class Citizen  *(weight 1.0×)*

**Q2.1 — What best describes AI's status in your company?**
- Not used / shadow use by individuals *(1)*
- A few tools, injected by IT, no strategy *(3)*
- Funded initiatives with an owner *(5)*
- A Chief AI Officer (or equivalent) building AI-native capability *(7)*

**Q2.2 — Is anyone's compensation or role explicitly tied to AI-native workflow adoption?**
- No *(1)*
- Informally encouraged *(3)*
- Some roles/KPIs include it *(5)*
- Yes — it's built into comp and role design *(7)*

### D3 · Data Architecture & Access  *(weight 1.0×)*

**Q3.1 — How accessible is your operational data to build on?**
- Locked inside ERP/SaaS; hard to extract *(1)*
- Partly accessible with effort *(3)*
- Mostly accessible; some integration *(5)*
- A data lake with object-level permissions; agent-ready *(7)*

**Q3.2 — Could you stand up a new app/workflow on your data without a multi-month IT project?**
- No — everything routes through legacy systems *(1)*
- Rarely, with heavy lift *(3)*
- Sometimes *(5)*
- Yes — custom-built quickly on owned data *(7)*

### D4 · Governance & Trust — Four Pillars  *(weight 1.5×)*

> Sub-scored 1–5 each in the report; here each maps to 1–7 for the index.

**Q4.1 — Trusted evaluation: do you have a way to test/score agent behavior before and after deployment?**
- None *(1)* · Ad-hoc *(3)* · Standard for some agents *(5)* · Formal eval architecture *(7)*

**Q4.2 — Searchable log: is every agent action logged and auditable?**
- No *(1)* · Partial *(3)* · Most *(5)* · Every agent, fully searchable *(7)*

**Q4.3 — Granular rollback: can you revert one agent class (prompt/model/policy) without taking the rest down?**
- No *(1)* · Risky/manual *(3)* · For some *(5)* · Versioned, diffable, recoverable *(7)*

**Q4.4 — Human review queue: do money/legal/customer-of-record actions route to a named human with an SLA?**
- No *(1)* · Informally *(3)* · For some *(5)* · Yes — humans-above-the-loop by design *(7)*

### D5 · Workflow Readiness  *(weight 1.0×)*

**Q5.1 — Are your highest-value workflows documented and prescriptive?**
- Mostly tacit, in people's heads *(1)*
- Some documented *(3)*
- Most documented *(5)*
- Mapped, prescriptive, scoreable task-by-task *(7)*

**Q5.2 — If a key person left tomorrow, how much undocumented "how we actually do it" would you lose?**
- A lot — critical tacit knowledge *(1)*
- Significant *(3)*
- Some *(5)*
- Little — it's captured *(7)*

**Q5.3 — Have you scored any workflow's tasks for automation potential (which tasks an agent could own)?**
- Never *(1)* · Talked about it *(3)* · Done informally *(5)* · Yes — task-decomposition with 1–5 scores *(7)*

### D6 · Talent & Culture  *(weight 1.0×)*

**Q6.1 — How does the organization react to AI that changes how people work?**
- Active resistance / quiet sabotage *(1)*
- Skeptical, slow *(3)*
- Curious, mixed *(5)*
- Pull-driven; super-users compounding *(7)*

**Q6.2 — Is there a plan to re-skill people displaced by automation (apprenticeship/bridge)?**
- No plan *(1)* · Discussed *(3)* · Starting *(5)* · Active bridge/apprenticeship program *(7)*

### D7 · Leadership & Mandate  *(weight 1.5×)*

**Q7.1 — Does the CEO/board actively sponsor an AI-native transformation (not just "use AI")?**
- No *(1)* · Verbal interest *(3)* · Funded mandate *(5)* · CEO-sponsored, board-backed, edge-twin under way *(7)*

**Q7.2 — Is your purpose/strategy clear enough to guide autonomous decisions (an MTP that could tell an agent what NOT to do)?**
- No clear purpose *(1)* · A poster on the wall *(3)* · Clear and used *(5)* · Encoded as decision boundaries/protocol *(7)*

**Q7.3 — Are you willing to build a new AI-native unit at the edge rather than only retrofit the core?**
- No — fix the existing org only *(1)*
- Unsure *(3)*
- Open to it *(5)*
- Yes — edge-first is the plan *(7)*

---

## 2. Scoring

```
dimension_score(D) = mean(values of D's questions)        # 1–7, one decimal
weights = {D1:1.5, D2:1.0, D3:1.0, D4:1.5, D5:1.0, D6:1.0, D7:1.5}
Readiness_Index = Σ(dimension_score × weight) / Σ(weights)   # 1.0–7.0
```

**Tier mapping (AI-Pilled Ladder):**

| Index | Tier | Label |
|---|---|---|
| ≤ 2.0 | **L0** | Unaware |
| 2.1–3.0 | **L1** | Dabbling |
| 3.1–4.0 | **L2** | Assisting |
| 4.1–5.0 | **L3** | Compounding starts |
| 5.1–6.0 | **L4** | Compounding OS |
| 6.1–7.0 | **L5** | AI-native firm |

**Biggest gap** = the dimension with the lowest `score × weight`. Drives the headline recommendation.

**Four Pillars gate flag:** if any of Q4.1–Q4.4 < 5 → flag *"not ready to deploy a new agent class (need ≥3/5 on all four pillars)."*

**Indicative drag-cost range (teaser):** `f(D1_score, I1 size band, I2 sector)` → a *range* from the anonymized EmpowerScan benchmark. Always shown with the disclaimer: *"Indicative only — EmpowerScan measures your exact figure and the ROI of fixing it."* Never a single number.

---

## 3. Report logic (what renders)

1. **Hero:** "You're at **L{n} — {label}**." If tier < L3, add the line: *"Below L3, the architecture hasn't started compounding for you."*
2. **Radar** of D1–D7 (1–7), house dark palette (min #777 text on #0a0a0a).
3. **Four Pillars mini-bar** (Q4.1–Q4.4) with the gate flag.
4. **Your biggest gap:** {dimension} + 1 paragraph "what good looks like."
5. **Indicative drag range** + the EmpowerScan bridge line.
6. **Next step (routed, per spec §5):**
   - `tier < L3 AND (D1 low OR D7 high)` → **EmpowerScan** ("measure the exact cost + the ROI of acting").
   - `tier ≥ L3` OR role ∈ {CEO/Owner, CIO} with high D7 → **LABX/WORX conversation** (book a call).
   - `D7 low / not ready` → **nurture** (Paper, MPI L0/L1) + invite to re-score in 90 days.
7. **Board-forward PDF** (1 page) — "send this to your CEO/board."

---

## 4. Routing matrix (summary)

| Condition | Tier | Primary CTA |
|---|---|---|
| Low mandate (D7 ≤ 3) | any | Nurture → re-score in 90d |
| Below L3 + measurable drag (D1 ≤ 4) | L0–L2 | **EmpowerScan** (quantify + ROI) |
| L3+ or warm B2O (CIO/CEO, D7 ≥ 5) | L3–L5 | **LABX/WORX call** (EmpowerScan optional as board proof) |

---

## 5. Notes for build

- 20 scored questions + 4 intake ≈ **5–6 min**. Keep one question per screen on mobile; progress bar.
- Store raw answers (not just scores) — schema compatible with **SumaX/EmpowerScan** so a lead can carry context into the paid diagnostic.
- A/B test the teaser drag-range copy (range vs. "what this typically costs companies your size").
- MPI Claude Skill version: expose the same 20 questions as a conversational flow; output the same JSON (dimension scores, tier, gap, route) so both surfaces share scoring.
- **Do not** present the readiness output as an EmpowerScan result — it is a self-report that *motivates* EmpowerScan.

## CHANGELOG

| Date | Change | By |
|---|---|---|
| 2026-06-13 | Creation. 7 dimensions, 20 scored questions with 1–7 anchors, weighted scoring, L0–L5 tier map, Four Pillars gate, routing matrix, report logic. Build-ready (web quiz + MPI skill). | Jay |