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34 · Business · AI Strategy

AI Strategy and Governance

Frame model build-versus-buy, AI economics, governance questions and staffing.

kistrategiechief-ai-officer-advisor

Chief AI Officer Advisor · Original SKILL.md

---
name: "chief-ai-officer-advisor"
description: "Chief AI Officer advisory for startups: model build-vs-buy decisions (API vs fine-tune vs in-house), AI risk classification under EU AI Act + US state patchwork, AI cost economics (API-to-self-hosted breakeven), and AI team org evolution. Use when deciding whether to call an API or fine-tune, classifying AI use cases for regulatory risk, calculating when self-hosting pays off, sequencing AI hires, or when user mentions CAIO, AI strategy, model selection, foundation model, fine-tuning, EU AI Act, NIST AI RMF, AI governance, model risk, or AI economics. Strategic only — does not duplicate engineering AI/ML skills."
license: MIT
metadata:
  version: 1.0.0
  author: Alireza Rezvani
  category: c-level
  domain: chief-ai-officer-leadership
  updated: 2026-05-12
  python-tools: model_buildvsbuy_calculator.py, ai_risk_classifier.py, ai_cost_economics.py
  frameworks: model-buildvsbuy, ai-risk-governance, ai-economics, ai-team-org
---

# Chief AI Officer Advisor

Strategic AI leadership for startup CAIOs and founders without one. **Four decisions, no AI hype:**

1. **Should we use an API, fine-tune, or build our own?** — model build-vs-buy with 3-year TCO
2. **Is this AI use case high-risk under regulation, and how do we govern it?** — EU AI Act + NIST AI RMF + US state patchwork
3. **When do we switch from API to self-hosted, and at what cost?** — token economics with breakeven analysis
4. **What AI role do we hire next?** — stage-to-role map (AI engineer ≠ ML engineer ≠ research scientist)

This skill does **not** cover tactical AI/ML engineering. For RAG implementation, agent design, prompt engineering, eval infrastructure, model deployment, or cost optimization, see `engineering/rag-architect/`, `engineering/agent-designer/`, `engineering/prompt-governance/`, `engineering/self-eval/`, `engineering/llm-cost-optimizer/`.

## Keywords

CAIO, chief AI officer, AI strategy, model selection, foundation model, fine-tuning, RLHF, DPO, LoRA, QLoRA, build vs buy, AI build-vs-buy, model risk tier, EU AI Act, AI Act Article 6, Article 9, Article 10, Annex III, prohibited AI, high-risk AI, NIST AI RMF, AI risk management framework, NYC Local Law 144, Colorado SB 21-169, Illinois HB 53, model card, eval set, eval harness, hallucination rate, jailbreak risk, prompt injection, AI red team, AI safety, alignment, model lifecycle, model registry, API-to-self-hosted breakeven, GPU economics, A100, H100, inference cost, fine-tuning cost, AI team, AI engineer, ML engineer, research scientist, MLOps, AI platform

## Quick Start

```bash
# Decision A: API vs fine-tune vs build
python scripts/model_buildvsbuy_calculator.py                          # embedded customer-support sample
python scripts/model_buildvsbuy_calculator.py path/to/use_case.json

# Decision B: Risk classification under EU AI Act + US state laws
python scripts/ai_risk_classifier.py                                   # embedded hiring-AI sample
python scripts/ai_risk_classifier.py path/to/use_case.json

# Decision C: API vs self-hosted economics
python scripts/ai_cost_economics.py                                    # embedded 5M tokens/day sample
python scripts/ai_cost_economics.py path/to/workload.json
```

## Key Questions (ask these first)

- **What does this AI need to be good at, and how would you measure it?** (If no eval set, no ship.)
- **What's the SLO on hallucination / error rate?** (Without one, "AI quality" is a vibe.)
- **What happens when the model is wrong?** (Fallback behavior, human-in-the-loop, blast radius.)
- **What's the risk tier under EU AI Act, and is conformity assessment required?** (Determines product launch timeline.)
- **At what monthly token volume does self-hosting beat API?** (Almost never below 100M tokens/month at frontier quality.)
- **Are we hiring an AI engineer or an ML research scientist?** (Different jobs; founders confuse them.)

## Core Responsibilities

### 1. Model Build-vs-Buy

The decision is not "use AI or not" — it's **API vs fine-tune vs in-house** for each use case. Each path has a different TCO curve, latency profile, and capability ceiling.

**Default path: API (frontier model)**
- Use when: well-served by frontier (Claude, GPT, Gemini), QPS < 100, latency budget > 1s, cost < $50K/month
- Why: frontier APIs are 10-100x more capable than what most teams can fine-tune in-house
- Failure mode: API rate limits at scale, vendor lock-in, capability drift between model versions

**Fine-tune a smaller model**
- Use when: domain-specific behavior the API can't be prompted into (medical coding, legal redlining), high volume reducing API cost, latency budget < 500ms, specific style/format consistency required
- Approaches: full fine-tune (rare), LoRA/QLoRA (common), RLHF/DPO (when alignment matters)
- Failure mode: fine-tuned model lags frontier capability within 6-12 months; ongoing retraining cost

**Build from scratch / pre-train**
- Use when: almost never. You're a foundation-model company, OR you have a unique data corpus, $50M+ funding, and 18+ month patience.
- Failure mode: by the time you ship, frontier models have caught up and your sunk cost is unrecoverable

**Run** `model_buildvsbuy_calculator.py` for a use-case-specific recommendation with 3-year TCO. See `references/model_buildvsbuy_strategy.md` for full decision tree.

### 2. AI Risk Classification & Governance

The 2026 question every founder is facing: **does this AI use case trigger high-risk regulatory obligations?**

**EU AI Act (in force 2026) tiers:**

| Tier | Examples | Obligations |
|---|---|---|
| **Prohibited** | Social scoring, real-time biometric surveillance, manipulative AI | Cannot deploy in EU |
| **High-risk** | Employment screening, credit scoring, education access, critical infrastructure, law enforcement, biometric ID | Conformity assessment, registration, post-market monitoring, transparency, human oversight |
| **Limited-risk** | Chatbots, deepfakes, emotion recognition | Transparency: user must know they're interacting with AI |
| **Minimal-risk** | Recommendation systems, spam filters, most B2B SaaS internals | No specific obligations |

**Run** `ai_risk_classifier.py` to classify a use case and get the required-controls list.

**US state patchwork (non-exhaustive):**

- NYC LL 144 — Automated Employment Decision Tools (AEDTs) require annual bias audit + candidate notice
- Colorado AI Act / SB 21-169 — AI in consumer decisions (credit, insurance, employment, housing)
- Illinois HB 53 — AI in interview/hiring
- California SB 1001 — Bot disclosure
- Texas TCPA — Biometric identifier capture
- Federal NIST AI RMF — voluntary; increasingly referenced in contracts

**Industry-specific overlays:**

- Healthcare: FDA AI/ML guidance (2023), MDR (EU) for medical-device AI, 510(k) pathway for AI/ML-enabled medical devices
- Financial: NYDFS Reg 23, FTC Section 5, ECOA for credit decisions
- Insurance: NAIC model bulletin, state insurance commissioner rules

See `references/ai_risk_governance.md` for the full regulatory landscape + governance program checklist.

### 3. AI Cost Economics

**The breakeven question:** at what monthly token volume does self-hosted inference beat API costs?

**Key components:**

- **API cost** — variable, per-token. Frontier models 2026: Claude Sonnet 4.6 ~$3/$15 per M tokens (input/output), GPT-4o ~$2.50/$10, Gemini 2.5 ~$1.25/$5
- **Self-hosted cost** — fixed (GPU commitment) + variable (electricity). H100 spot ~$2-5/hour, A100 spot ~$1-3/hour. Llama 3.1 70B / Qwen 2.5 72B: ~$0.50-2.00 per million output tokens at 70% utilization
- **Hidden costs of self-hosting** — ops on-call, monitoring, model updates, scaling overhead, idle time penalty
- **Hidden costs of API** — rate limits requiring multi-vendor failover, vendor lock-in, capability drift between versions, data residency

**Typical breakeven (frontier-quality):** 100M–500M tokens/month, depending on model size and acceptable quality tradeoff. Below this, API wins. Above this, run the calculator.

**Run** `ai_cost_economics.py` with workload characteristics for a breakeven point + sensitivity to GPU rates and model size.

See `references/ai_cost_economics.md` for the full economics model and operational considerations.

### 4. AI Team Org Evolution

**The wrong question:** "Should we hire an ML engineer or a research scientist?"
**The right question:** "What's the next AI capability we need to ship, and what role unblocks that?"

Stage-to-role map:

| Stage | First AI hire | Then | Then |
|---|---|---|---|
| Pre-PMF | Founder + 1 ML-curious engineer playing with prompts | — | — |
| Series A | **AI engineer** (applied, full-stack; owns prompts/evals/deployment) | Second AI engineer for evals/quality | — |
| Series B | AI/ML platform engineer (inference, evals, observability) | Third AI engineer for production reliability | Data scientist if model is core IP |
| Series C | Manager of AI | ML research scientist (only if model IS the product) | AI safety / red team (if customer-facing AI) |
| Late-stage | Head of AI → CAIO | Multiple research scientists, platform team, safety/red team | Federated AI leads per business unit |

**Critical distinctions:**

- **AI engineer** ≠ **ML engineer** ≠ **research scientist**
  - AI engineer: full-stack + prompts + evals + deployment. Most startups need this, not the others.
  - ML engineer: production deployment, monitoring, retraining infrastructure. Hire after data engineer.
  - Research scientist: model invention, novel architectures. Only at Series C+ if model is core IP.

**Centralize-vs-embed for AI:** AI starts centralized (one team) and stays there longer than data team, because the surface area is smaller. Embed only when AI is being deployed in 4+ product surfaces.

See `references/ai_team_org_evolution.md`.

## Workflows

### Workflow 1: Model Selection Decision (1 hour)
**Goal:** Decide whether a specific use case should use API, fine-tune, or build.

```bash
# 1. Define use_case.json (volume, latency, accuracy, team size, budget)
python scripts/model_buildvsbuy_calculator.py use_case.json
# 2. Review 3-year TCO + breakeven
# 3. Cross-check with cs-cfo-advisor on budget commitment
# 4. Cross-check with cs-cto-advisor on engineering capacity (esp. for fine-tune)
# 5. Log via /cs:decide; consider /cs:freeze 60 on multi-year vendor commitment
```

### Workflow 2: AI Risk Classification (2-4 hours)
**Goal:** Classify a use case under EU AI Act + US state laws, identify required controls.

```bash
# 1. Define use_case.json (decisions affected, users, geography, sector)
python scripts/ai_risk_classifier.py use_case.json
# 2. For HIGH-RISK: budget conformity assessment + registration
# 3. For LIMITED-RISK: implement transparency requirements
# 4. Cross-check with cs-general-counsel-advisor on contractual implications
# 5. Cross-check with cs-ciso-advisor on technical safeguards
# 6. Log via /cs:decide
```

### Workflow 3: API-to-Self-Hosted Breakeven (1 day)
**Goal:** Decide when (and whether) to migrate from API to self-hosted inference.

```bash
# 1. Build workload.json (tokens/day, model size, latency, quality tolerance)
python scripts/ai_cost_economics.py workload.json
# 2. Run sensitivity scenarios (low/mid/high GPU rates)
# 3. Estimate migration cost (engineering time + risk)
# 4. Cross-check with cs-cfo-advisor on capex commitment
# 5. Cross-check with cs-cto-advisor on platform readiness
# 6. Log via /cs:decide; pair with /cs:freeze if signing GPU commitment
```

### Workflow 4: AI Team Roadmap (1 week)
**Goal:** Sequence next 18 months of AI hires aligned to capabilities to ship.

1. List top 5 AI capabilities the product needs in 12 months
2. Map each capability to the role that ships it (see `ai_team_org_evolution.md`)
3. Sequence hires (one role at a time, ramp before next)
4. Cross-check with cs-chro-advisor on comp + leveling
5. Identify the centralize-vs-embed trigger

## Output Standards

```
**Bottom Line:** [one sentence — decision and rationale]
**The Decision:** [one of: model selection | risk classification | economics | next hire]
**The Evidence:** [numbers from the tool, not adjectives]
**How to Act:** [3 concrete next steps]
**Your Decision:** [the call only the founder can make]
```

## Adjacent Skills

- `c-level-advisor/skills/chief-data-officer-advisor/` — Training data rights, data product strategy (chains directly to model decisions)
- `c-level-advisor/skills/cto-advisor/` — Architecture capacity, scaling cliffs (esp. for self-hosted inference)
- `c-level-advisor/skills/ciso-advisor/` — Threat modeling for AI (prompt injection, jailbreak, training data poisoning)
- `c-level-advisor/skills/general-counsel-advisor/` — AI contracts (vendor liability, output ownership, training-data licensing)
- `c-level-advisor/skills/cfo-advisor/` — Build-vs-buy TCO math, multi-year vendor commitments
- `c-level-advisor/skills/chro-advisor/` — AI team hiring + comp
- `engineering/skills/rag-architect/` — Tactical RAG implementation
- `engineering/skills/agent-designer/` — Tactical agent architecture
- `engineering/prompt-governance/` — Tactical prompt management
- `engineering/skills/self-eval/` — Tactical eval infrastructure
- `engineering/llm-cost-optimizer/` — Tactical inference cost optimization

## References

- [model_buildvsbuy_strategy.md](references/model_buildvsbuy_strategy.md) — Full decision tree + 3-year TCO components + when each path fails
- [ai_risk_governance.md](references/ai_risk_governance.md) — EU AI Act + NIST AI RMF + US state patchwork + industry overlays + governance program
- [ai_cost_economics.md](references/ai_cost_economics.md) — API pricing 2026 + GPU rental economics + utilization realities + migration cost
- [ai_team_org_evolution.md](references/ai_team_org_evolution.md) — Stage-to-role map + role definitions (AI engineer ≠ ML engineer ≠ scientist) + anti-patterns

---

**Version:** 1.0.0
**Status:** Production Ready
**Disclaimer:** AI regulation is evolving rapidly. This skill surfaces decisions and tradeoffs as of 2026 but cannot replace qualified AI counsel for binding compliance decisions, especially under EU AI Act conformity assessments.

When to use

Frame model build-versus-buy, AI economics, governance questions and staffing.

What you get

The complete original guidance with attributed source and declared limitations.

How it works

Clarify the task and evidence, then apply relevant source instructions within authorized scope.

Requirements

Use case, measured requirements, data rights, costs and jurisdictions.

Delivery and review notes

Brief source danger screening: full-text pattern triage and review of flagged passages, not a comprehensive audit or functional test. Cost tables, regulatory risk classifications and legal dates are not verified current guidance. Use actual evals, current provider costs and qualified legal review before decisions. Referenced scripts, templates, integrations and sibling skills are not supplied or installed unless explicitly listed as included. External calls, account changes, messages and file writes require the actual user task and authorization.

Starting prompt

Use AI Strategy and Governance for [TASK]. Ask for missing inputs: Use case, measured requirements, data rights, costs and jurisdictions. Identify assumptions and missing tools before execution; do not claim results or approvals without evidence.

Not for

Claiming runtime installation, certified safety, verified numerical outputs or permission for external actions merely from loading this source.

What M11 added

German routing, task scope and source limitations. Original author attribution: Alireza Rezvani. Brief source danger screening: full-text pattern triage and review of flagged passages, not a comprehensive audit or functional test. Cost tables, regulatory risk classifications and legal dates are not verified current guidance. Use actual evals, current provider costs and qualified legal review before decisions. Referenced scripts, templates, integrations and sibling skills are not supplied or installed unless explicitly listed as included. External calls, account changes, messages and file writes require the actual user task and authorization.

Original authorship remains with alirezarezvani/claude-skills · Original source ↗

Original license & copyright

MIT License

Copyright (c) 2025 Alireza Rezvani

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

Source SHA-256: f578a33ea16493c2c981ead3215d9428f76d7770e3fb779518ca510c4f524c26
Snapshot checked: 2026-09-28T15:50:31.645Z

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