Enterprise-Scale AI Integration — POV by Dr. Dodi Mossafer

Summary

Enterprise AI is credible, only when scaled beyond labs into ERP, finance, and frontline systems. This POV outlines the framework for platform readiness, adoption waves, and value realization at scale. Success is measured not by number of models, but by the P&L, resilience, and decision quality they improve.

1) The Framework

Platform Readiness

Cloud-native, API-first architecture.
Data fabric & lineage guardrails.
Security, privacy, and model risk policies.

Adoption Waves

Pilot → departmental → cross-functional → enterprise scale.
Structured change & training programs.
Use-case library with ROI, risk, adoption metrics.

Value Realization

Benefits register tied to P&L and cash.
Lead/lag signal loops by domain.
Audit-ready evidence of adoption and impact.

2) Working Principles

Business first: AI must serve decisions, not novelty.
Guardrails embedded: privacy, bias, explainability enforced by design.
Value tracked: no adoption claim without a financial or operational delta.
Enterprise cadence: quarterly kill/continue reviews across domains.

3) Use Cases

Finance

Forecasting, variance analysis, capital allocation.

AI-assisted forecast accuracy checks.
Variance copilot with explainability trails.
Capital ROI sensitivity simulations.

Supply Chain

Demand planning, inventory, logistics routing.

Predictive demand with bias detection.
AI-driven inventory optimization.
Routing & load-balancing copilots.

Workforce

Scheduling, retention, skills management.

Attrition prediction with explainable drivers.
AI scheduling with fairness guardrails.
Skill-gap analysis and training pathways.

4) Possible Metrics to Track (Enterprise Scale)

Finance

Forecast error / bias.
Capital ROI vs. plan.
Close cycle time; variance actions closed.

Supply Chain

Inventory turns; service-level adherence.
Routing cost vs. baseline.
Lead time variability.

Workforce

Attrition rate delta.
Schedule adherence & fairness scores.
Skill coverage index.

5) Scaling Cadence & Signal Loops

Scaling Cadence

Pilot (1–2 domains).
Wave rollout (3–6 months, cross-domain).
Enterprise adoption with governance embedded.

Signal Loops

Lead/lag KPIs refreshed by cycle (weekly/monthly).
Evidence packs generated for quarterly reviews.
Drift alerts for adoption or model performance.

6) Common Failure Modes

Pilot trap: dozens of PoCs, no enterprise rollout.
No guardrails: models bypass privacy, lineage, explainability.
Slideware adoption: no evidence of usage in the flow of work.
Disconnected metrics: benefits untraceable to P&L or KPIs.

7) Practical Artifacts

Enterprise AI adoption roadmap (waves, domains, metrics).
Benefits register tied to financial & operational KPIs.
AI governance handbook (risk, bias, explainability).
Adoption playbook with role-based training modules.
Evidence pack template for quarterly reviews.

8) About the Author

Dr. Dodi Mossafer is a corporate strategy and transformation advisor. Experience spans large-scale ERP, finance, and AI integration programs across industries. Academic work covers decision sciences, finance digitalization, and AI readiness.

9) Use & Citation

Cite as: “Dr. Dodi Mossafer, DBA — Enterprise-Scale AI Integration (Advisory POV), 2025.” Independent perspective; suitable for academic and industry reference with attribution.