Enterprise‑Wide Generative AI Roll‑out
A pragmatic, 24‑month roadmap that ties strategy, data, technology, people, and risk together.
1️⃣ Vision & Governance (Month 0‑3)
| Action | Who | Output | KPI |
|---|---|---|---|
| Executive Sponsorship Charter | C‑suite + Chief AI Officer (CAIO) | Signed AI charter, budget envelope, success metrics | Charter signed, budget approved |
| AI Ethics & Governance Board | Legal, Risk, Data, Diversity, Security leads | Governance framework, policy repo (model use, bias, data privacy) | Board formed, policies published |
| Use‑Case Portfolio | Business unit leads + AI PMO | Ranked list of 8‑12 pilot use‑cases (quick‑win + strategic) | 5‑8 pilots identified, ROI estimate ≥ 2× |
| Funding Model | Finance + CAIO | CAPEX vs OPEX split, cost‑recovery model (charge‑back, ROI targets) | Funding model approved, cost‑center allocation |
Best Practices
- Keep the charter action‑oriented (e.g., “Reduce claim‑processing time by 30 %”).
- Include risk appetite statements (e.g., acceptable false‑positive rate).
Pitfalls
- Vague “AI‑first” slogans without measurable goals → drift.
- Skipping legal early → retro‑fit compliance later.
2️⃣ Data & Infrastructure Foundations (Month 3‑6)
| Action | Who | Output | KPI |
|---|---|---|---|
| Data Inventory & Classification | Data Engineers + DPO | Catalog of all structured/unstructured data, sensitivity tags | 100 % of data assets classified |
| Data Quality & Enrichment Pipeline | Data Ops | Automated cleansing, deduplication, labeling (where needed) | > 95 % data quality score |
| Data Governance Platform (e.g., Collibra, Alation) | Data Governance Lead | Data lineage, access control, audit logs | 100 % lineage captured for AI data sets |
| Compute & Storage Baseline | Cloud/IT Ops | Reserved GPU/TPU pool (on‑prem or hybrid), object storage with lifecycle policies | 80 % of pilot compute needs met in‑house |
Best Practices
- Adopt a “data mesh” approach for domain‑owned data products.
- Store raw, curated, and model‑ready layers separately (Bronze‑Silver‑Gold).
Pitfalls
- Relying on a single “big data lake” → bottlenecks & security blind spots.
- Ignoring PII tagging → compliance violations.
3️⃣ Model Selection & Development (Month 6‑12)
| Action | Who | Output | KPI |
|---|---|---|---|
| Model Landscape Scan | AI Architecture Team | Matrix of LLMs, diffusion models, domain‑specific models (open‑source vs vendor) | Decision matrix completed |
| Fit‑Gap Analysis | Solution Architects + Business SMEs | Mapping of each pilot to model families (e.g., GPT‑4‑Turbo for chat, Stable Diffusion for design) | 80 % of pilots have a clear model fit |
| Proof‑of‑Concept (PoC) Kit | AI Engineers | Containerised PoC repo (Docker, Helm) + evaluation scripts | PoC repo ready for all pilots |
| Evaluation Framework | Data Science + Risk | Metrics: accuracy, hallucination rate, latency, cost‑per‑token, fairness | Baseline scores captured, targets set |
| Model Procurement/Training | Procurement + AI Ops | Contracts for API access (Azure OpenAI, Anthropic) or internal fine‑tuning pipelines | 3‑5 models licensed or trained |
Best Practices
- Start with foundation models via API (low CAPEX) then fine‑tune only when ROI justifies.
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