AI Governance & LLMOps: EU-Ready, Production-First

Executive Summary

AI delivers value only when it runs reliably in production with compliance, observability, and ownership. This blueprint shows how to align AI initiatives with EU AI Act readiness, SAP/ERP integration, and secure-by-design principles.

What Enterprises Want

• Reliable outcomes: measurable cycle time and quality improvements.
• Auditability: logs, approvals, and data lineage for compliance.
• Safety: guardrails, PII protection, human-in-loop.
• Integration: API-first with ERP/SAP and existing data contracts.

Key Controls (EU AI Act Aware)

• Classification & risk assessment for each use case.
• Guardrails: input/output filters, PII scrubbing, policy-enforced prompts.
• Observability: traces, metrics, feedback loops, drift checks.
• Human-in-loop for medium/high-risk flows.
• Evidence: config snapshots, test cases, release notes, approval trails.

LLMOps & Delivery Discipline

1. Design: Define contracts, data scopes, and rollback paths.

2. Build: Implement gateway, prompt/policy store, validation layers.

3. Test: Scenario tests, red-team cases, latency/SLO targets.

4. Release: Staging/PRD parity, feature flags, approvals.

5. Operate: Monitoring, incident runbooks, continual retraining loops.

Integration with SAP/ERP

• Use API/EDI contracts; avoid brittle screen scraping.
• Map roles/authorizations to AI scopes; propagate audits.
• Keep transports/releases aligned with AI gateway changes.

Security & Privacy Essentials

• IAM with MFA/Conditional Access; least privilege.
• PII filtering/masking; secrets management; encrypted stores.
• Backup/Restore tested; logging central and immutable.

Time-to-Value & Phasing

• Discovery: 1–2 weeks — use-case scan, risk/ROI, architecture sketch.
• Pilot: 4–8 weeks — one workflow with guardrails, KPIs, observability.
• Scale: ongoing — rollout, SLOs, training, governance cadence.

Outcomes to Aim For

• 35–50% faster cycle times in targeted workflows.
• 25–40% fewer manual steps.
• 99.9% uptime targets with defined rollback and incident playbooks.

Next Steps

• Start with a Discovery + AI Risk & ROI assessment.
• Prioritize one medium-risk, high-ROI workflow for pilot.
• Establish observability and evidence collection before rollout.