# Interview Question Bank

Source: the AIDE Matrix and Opportunities Audit Workbook (Chapter 21 of *No One Works Here*, (c) Cheek LLC). Every question the interview phases draw from lives here. Ask in small batches (3 to 5 per turn), never as a form dump. Reflect answers back before moving on.

## Research-first protocol

Once the company is known, research before asking, using web search, web fetch, and any connected data sources available in the session. Roughly by section: A (basics) is largely researchable for established companies; B's regulatory regimes are researchable from the industry; C and D's public evidence (posts, talks, earnings-call mentions, job postings, announced deployments) is researchable; E (culture) and the internal halves of C/D are not, ask those directly. For each researched answer, present the finding with its source and ask the executive to confirm or revise. For each question research could not answer, tell them it could not be found online and ask them to supply it. Their confirmation always wins over the research.

## A. Company basics (Company Profile sheet)

1. Company name
2. Industry / sector
3. Company stage: Startup / Growth / Scale / Enterprise
4. Annual revenue
5. Team size (FTEs)
6. Revenue per FTE (derive it yourself: revenue / FTEs; do not ask)
7. Primary competitor(s). Competitors get the research-first treatment with a hard gate: before asking, research the competitive landscape and propose the likely competitive set, with a line on each (who they are, positioning, scale, any recent moves relevant to AI). Present the list for the executive to verify or revise, and do not move forward until they have confirmed it. A wrong competitive set poisons the risk asymmetry math and the enrichment downstream. If research finds no clear competitors (a niche or young market), say so and ask the executive to name them.
8. Board size
9. Executive team size
10. Website
11. Who is completing this assessment, and their role

Derive and state revenue per FTE immediately. It becomes the anchor metric for the risk asymmetry discussion later.

## B. Risk and regulatory determination

The workbook expects you to make a determination, not just collect answers. Ask:

1. What regulatory regimes govern your business? Probe by industry: healthcare (HIPAA), public company (SOX), EU customers (GDPR, EU AI Act), financial services (FINRA, OCC, SEC), education (FERPA), government contracts (FedRAMP), consumer data at scale (CCPA).
2. What is the most sensitive category of data your teams touch daily? (PII, PHI, payment data, trade secrets, client-privileged material)
3. Where does your work face the customer directly, and what happens if an automated output is wrong there?
4. Has your organization already had a data, security, or compliance incident that shapes how leadership thinks about new technology?

Then state a risk posture determination: **Low / Moderate / High**, with a one-paragraph rationale tied to their answers. This posture calibrates every Risk score in the funnel. Say that out loud so the executive understands why you asked.

## C. AI literacy: board and senior team (AIDE Matrix Assessment, X-axis)

Surveys overreport AI literacy. Do not ask "how AI-literate is your board." Ask for observable evidence instead. Score each dimension 1 to 5 using the anchors, weight 0.25 each; the weighted sum is the Leadership Score (X-axis).

1. **Board AI Literacy.** Has any board member personally used AI tools in their own work this quarter? Can any of them articulate your AI strategy without slides?
   - 1 = board views AI as IT's responsibility · 3 = some members have used AI tools personally · 5 = board regularly uses AI in decision-making and can articulate AI strategy fluently
2. **Board AI Advocacy.** Does any board member post about AI, speak at AI events, or reference AI in governance communications?
   - 1 = no board member mentions AI publicly · 3 = 1 to 2 members occasionally reference it · 5 = multiple members actively advocate with published perspectives
3. **Top Management Team AI Literacy.** Do executives demonstrate hands-on understanding, not just awareness? Who on the exec team used an AI tool themselves this week?
   - 1 = executives view AI as IT's responsibility · 3 = some executives have used AI tools personally · 5 = the executive team regularly uses AI in decision-making and can articulate AI strategy fluently
4. **Top Management Team AI Advocacy.** Do executives publicly champion adoption? The workbook calls this the single most powerful predictor of organizational AI maturity.
   - 1 = no executive mentions AI externally · 3 = CEO references AI in earnings calls · 5 = multiple C-suite members actively publish, speak, and advocate

## D. Operational integration (AIDE Matrix Assessment, Y-axis)

Weighted: Orientation 0.4, Implementation 0.6. The weighted sum is the Company Score (Y-axis).

1. **AI Orientation.** Is AI referenced in job postings, earnings calls, corporate communications, strategic planning? Orientation is the gateway to implementation.
   - 1 = no AI in job postings or communications · 3 = AI in strategy docs, some AI-related roles posted · 5 = AI competencies in most postings, AI central to communications and earnings narratives
2. **AI Implementation.** Are there tangible deployments: patents, AI features in products, AI infrastructure in operations, measurable AI-driven workflows?
   - 1 = no AI in production · 3 = 1 to 3 AI tools deployed, early automation pilots · 5 = AI embedded across multiple functions, patents filed, AI-driven products in market

### The four quadrants (X = Leadership Score, Y = Company Score; boundary at 3.0)

| Quadrant | Position | Meaning |
| --- | --- | --- |
| **Laggard** | Low X, Low Y | No strategic mandate, no operational reality. In a market where a team of 12 can outperform a team of 1,200, standing still is an active bet against survival. |
| **AI Visionary** | High X, Low Y | The Magic Wand trap. Leadership is vocal, but employees still schedule meetings manually. The gap between strategy and execution is a structural failure, not a temporary lag. |
| **Stealth Adopter** | Low X, High Y | Operational teams integrate AI without boardroom direction. Without governance these wins are fragile, legally exposed, and impossible to scale. |
| **Trailblazer** | High X, High Y | The AI-Driven Enterprise. The only quadrant where revenue growth is decoupled from headcount growth. |

## E. Innovation Pre-Requisite (8 dimensions, score 1 to 5 each)

The AI muscle and the innovation muscle are the same muscle. Ask for evidence, not self-assessment:

1. **Experimentation culture.** Does the organization tolerate and fund experiments that may fail, or are new ideas killed by consensus-seeking?
2. **Speed of decision-making.** Can a team go from idea to pilot in weeks, or does every initiative pass through months of approval layers?
3. **Cross-functional collaboration.** Do product, engineering, and sales collaborate on new ideas, or do silos prevent it?
4. **Leadership appetite for risk.** Do leaders publicly champion bold bets, or default to incremental improvement?
5. **Learning rate.** How quickly does the organization absorb and act on new information? Is administrative latency baked into every decision?
6. **Resource allocation for innovation.** Is there dedicated budget and time (tinker time, R&D sprints), or is everyone 100% allocated to deliverables?
7. **Track record of scaling.** In the past 3 years, how many internal experiments scaled into production workflows or revenue?
8. **Tolerance for ambiguity.** Can teams operate with incomplete information, or does culture demand certainty before action?

Average = Innovation Readiness Score. Below 3.0, name it plainly: AI adoption will stall on culture before it stalls on technology.

## F. Risk Asymmetry inputs

1. Your 5-year revenue growth target (%)
2. A competitor benchmark for revenue per FTE. Research this first: for the confirmed competitive set, pull public revenue and employee counts and compute revenue per FTE where possible, presenting each figure with its source for verification. If no competitor figures are public, say so and use the workbook's framing: what if a competitor reaches $1M+ ARR per FTE while you stay at your current ratio?

Then compute and present the Cost of Inertia: projected 5-year revenue at target growth, FTEs needed at the current revenue-per-FTE ratio, the FTE gap versus a Trailblazer operating model, and the estimated annual cost of that gap at $100K per FTE. Deliver the New Audit Question verbatim: instead of asking what the risk is if an AI initiative fails, the board must ask what the systemic risk is to the 10-year valuation if competitors achieve $1M+ ARR per FTE while you remain where you are.

## G. Operational Deep Dive (12 functional areas)

Use during funnel enrichment (Phase 2, step 2). For areas the executive flags as relevant, optionally score Relevance (1-5), Pain Level (1-5), Data Availability (1-5).

| Functional area | High-impact use cases |
| --- | --- |
| Strategy & Planning | Scenario simulation, demand forecasting, board-grade narrative drafting |
| Market & Competitive Intelligence | Autonomous desk research, sentiment tracking, pricing surveillance |
| Revenue: Sales | Deal scoring, next-step email drafting, dynamic forecasting |
| Revenue: Marketing | Asset generation (copy, creatives), intent-based segmentation, SEO brief writing |
| Product & R&D | Requirements co-authoring, concept testing with synthetic users, design QA |
| Engineering & DevOps | Code suggestion, test-case authoring, incident root-cause chats |
| Customer Support & Success | Self-service chat, proactive risk alerts, ticket summarization |
| Operations & Supply Chain | Demand sensing, dynamic routing, doc extraction for logistics |
| Finance & Accounting | Invoice coding, close checklist automation, fraud anomaly alerts |
| HR & Talent | Role-fit assessment chats, internal mobility matching, policy Q&A |
| Legal & Compliance | Contract risk summarization, clause comparison, control testing |
| Fundraising / Investor Relations | Investor-match ranking, deck tailoring, FAQ agent |

Functional-area prioritization bands (Relevance + Pain + Data): 12-15 Pilot Now (launch an 8-week pilot) · 9-11 Build Data Readiness · 6-8 Low-Stakes Experiment · 3-5 Defer.

## H. Board discussion questions (bring-home material)

Give these to the executive at the end of Phase 1 as questions to raise with their board:

1. Are we currently AI Visionaries who talk about AI strategy in every meeting while our teams still manually route data? If so, what specific action is missing to move us from talk to execution?
2. Do we obsess over the loud risks of AI execution failure while ignoring the silent killer of market inertia? What is the systemic risk to our 10-year valuation if competitors decouple revenue from headcount while we don't?
3. Does our innovation culture support a high learning rate, or are we bolting AI onto a legacy system that historically stifles new ideas? How do we shift governance from minimizing failure to maximizing adaptation velocity?

## I. Quadrant prescriptions (Vector of Change)

Use the executive's quadrant to frame the funnel and the closing 90-day framing:

| Quadrant | Prescription | 90-day move |
| --- | --- | --- |
| Laggard | You need both leadership commitment AND operational capability. Start with board and top-management AI education plus a single sandbox pilot to build momentum. | Board AI literacy program + appoint an AI champion + a single 8-week pilot |
| AI Visionary | You don't need more strategy. You need a Sandbox Policy and a Chief Node Officer to start the implementation phase. | Appoint an operational AI lead + fund 2 to 3 sandbox pilots + define success KPIs within 90 days |
| Stealth Adopter | You don't need better tools. You need to solve the boardroom literacy crisis. Without governance, operational wins are fragile and legally exposed. | Board AI education sprint + governance framework + catalog existing AI deployments for scaling |
| Trailblazer | Run the AIDE maturity cycle iteratively. Benchmark against your peer group. Focus on decoupling revenue from headcount. | Set ARR/FTE targets + expand AI to remaining functions + publish AI strategy externally to attract talent |

## J. Tooling landscape reference

When the executive asks "with what tools," orient (not endorse) with the workbook's layer map: foundation and chat models (Claude, GPT, Gemini) · vertical SaaS copilots (GitHub Copilot, Einstein GPT, Gong) · automation/RPA (UiPath, Zapier Agents, n8n) · retrieval and orchestration (LangChain, LlamaIndex, vector stores) · observability (Monte Carlo, Evidently). Remind them the matrix is tool-agnostic on purpose: opportunities first, vendors later.