AI Strategy 2027: Planning Guide for Regulated Enterprises

Pillar 1: AI Governance

• AI governance board: Cross-functional committee (legal, compliance, engineering, business) meeting monthly to review AI initiatives
• AI risk classification: Framework for classifying AI systems by risk level — aligned with EU AI Act Annex III categories
• Model registry: Central inventory of all AI/ML models in production — owner, purpose, risk classification, last audit date
• Responsible AI principles: Documented principles (fairness, transparency, accountability, privacy, safety) with practical implementation guidelines
• AI policy library: Acceptable use policy, model development standards, deployment approval process, monitoring requirements

Pillar 2: Infrastructure Investment

• GPU infrastructure: Build vs rent decision. For sustained inference workloads, on-premise or reserved instances. For experimentation, cloud spot instances.
• MLOps platform: Automated ML lifecycle — experiment tracking, model training, deployment, monitoring, retraining
• Data platform: Data governance, data quality, feature stores, data catalogues. AI is only as good as the data.
• LLM infrastructure: Self-hosted models for sensitive use cases (data sovereignty), API-based for non-sensitive applications
• Vector databases: RAG infrastructure for enterprise knowledge retrieval — Weaviate, Pinecone, Qdrant, Milvus

Pillar 3: Talent & Organisation

• AI engineering team: Distinct from data science. Focused on production ML systems, not notebooks. Skills: Kubernetes, MLOps, distributed systems.
• AI literacy programme: Every business leader should understand AI capabilities, limitations, and risks — not just the AI team
• Upskilling: Existing engineers trained on AI/ML fundamentals, prompt engineering, RAG patterns
• AI ethics role: Dedicated responsible AI lead (or shared with compliance) to review high-risk AI systems

• Q3-Q4 2026: AI inventory + risk classification. Establish governance board. EU AI Act compliance for high-risk systems.
• Q1 2027: MLOps platform v1. Model registry operational. First production AI workloads on governed platform.
• Q2 2027: Data platform maturity — feature store, data quality monitoring. LLM infrastructure for internal use cases.
• Q3 2027: Scale to 5-10 AI use cases in production. Automated compliance monitoring. First AI literacy cohort completed.
• Q4 2027: Full governance automation. AI contributing measurable business value. Roadmap for 2028.

AI investment breakdown for a mid-size regulated enterprise:

1. Infrastructure (40%): Compute (GPU/CPU), storage, networking, cloud services
2. People (35%): AI engineering team, MLOps, data engineering, AI governance
3. Tools & platforms (15%): MLOps platforms, data tools, monitoring, security
4. Training & enablement (10%): AI literacy, upskilling, external training, certifications

ROI expectation: Most regulated enterprises see AI ROI within 12-18 months for well-scoped use cases. The key: start with high-value, low-risk use cases (internal efficiency) before moving to customer-facing AI.