Separate the ERP from the AI.
Unify the data.
A pragmatic IBM watsonx, Fusion lakehouse, and Planning Analytics roadmap — designed around NYX's AS/400 reality, mid-market budget, and zero-tolerance for AI breaking the business.
The window is short, and the AS/400 is non-negotiable.
Andrew said it plainly on April 20: push data out to a lake where AI can ingest it; do not let agents make changes directly in the ERP. This site is built around that principle.
The mid-market squeeze is real
Tier-1 automotive suppliers are caught between OEM price pressure and rising input costs. Every dollar spent on AI has to earn its keep — there is no room for science projects.
Agentic AI is moving faster than guardrails
An incoming AI lead is building on Azure. Without a governed data layer, agents will reach directly into operational systems — exactly the failure mode NYX wants to prevent.
DB2 on IBM i is fast — but it's not the AI substrate
Direct queries work for dashboards. They don't work for governed model training, lineage, multi-source joins with Sterling, Excel, SOWs, and Azure-side data, or write-back review queues.
The owner asked for AI at scale
Not just an LLM chat box. That requires a single, governed copy of NYX's data — ERP, payroll, EDI, engineering, and estimating — with policy, lineage, and security baked in.
What NYX runs today.
An honest inventory. The goal is not to replace what works — it is to give the next wave of AI a safe, unified place to land.
One governed lakehouse. Many engines. Zero direct agent writes to the ERP.
Every layer was chosen against a specific NYX constraint: open formats to avoid lock-in, governance as a first-class citizen, and a hard boundary between AI and the AS/400.
Six concrete bets — sized for a $1B manufacturer.
Each one solves a problem Andrew described. None of them require ripping out the ERP or betting the company on a single model vendor.
Executive KPI dashboard, rebuilt on the lakehouse
The 27-KPI executive board moves off the ASP.NET + SQL Server prep layer onto governed lakehouse views. Same daily / weekly / monthly grain — with lineage back to DB2.
Sales forecasting in IBM Planning Analytics
Connect Planning Analytics to lakehouse-curated order, shipment, and EDI data. Drive S&OP, demand planning, and capacity decisions from a model that learns from history.
SOW & RFQ change detection
Unstructured.io parses 900-page SOWs into clean, chunked sections; watsonx.orchestrate routes the diff to Claude for change detection — versioned, auditable, and tied to engineering review workflows.
Estimating copilot for Excel users
Estimators keep Excel. A copilot reads historical job costs from the lakehouse and suggests pricing — meeting people where they are instead of forcing a tool change.
Engineering knowledge mining
Mold designs, change orders, and SOWs flow through Unstructured.io into the lakehouse — indexed, embedded, and searchable. Mold-flow analysis is a multi-year build; we set the foundation now, not the expectations.
ERP write-back via human-approved batches
When AI suggests a record change, it lands in a review queue — never in DB2 directly. Approved batches flow back through controlled jobs, preserving the AS/400's stability.
An air gap between the agents and the AS/400.
The principle Andrew stated — push out, never reach in — rendered as an architecture rule, not a hope.
The boundary
- Inbound only. CDC streams from DB2 push into the lakehouse. Agents never query the AS/400 directly.
- Review queue, not write-back. Every agent-proposed change to ERP records lands in a human-approved batch.
- Policy at the gateway. watsonx.governance enforces what models can be called, on what data, by whom.
- AI security. Prompt-injection, PII, and IP-exfiltration guardrails on every model call — GPT, Claude, or Granite.
Visualizing the air gap
Twelve months, three phases, one governed substrate.
Each phase delivers value on its own. None of them require ripping out the IBM i, Sterling, or the work the AI lead is already doing on Azure.
Foundation
- Stand up watsonx.data on IBM Fusion
- CDC pipelines from DB2 + Sterling via Confluent
- Unstructured.io pipeline for SOWs and engineering PDFs into the lakehouse
- watsonx.governance baseline: catalog, lineage, policies
- Rebuild the executive 27-KPI dashboard on lakehouse views
- AI security guardrails for the existing Claude / Copilot usage
Forecasting & governed AI
- IBM Planning Analytics live for sales forecasting and S&OP
- watsonx.orchestrate pilot: SOW comparison productized
- Multi-model routing: GPT, Claude, Granite via the gateway
- watsonx.data intelligence rolled out across business domains
- First write-back review queue (low-risk records)
Scale & agents
- Estimating copilot in production, Excel-native
- Human-in-the-loop agentic workflows for production scheduling
- Engineering knowledge graph fed by Unstructured.io across SOWs and design docs
- Foundation for mold-flow ML (multi-year program)
- Joint operating model with the Azure-side AI team
What we'll measure — and what we refuse to compromise on.
Soft-ROI ranges below are directional estimates for a $1B manufacturer; we'll firm them up with NYX finance during the workshop.
The Azure work continues. The data layer should be IBM's.
This isn't a vendor war. The AI lead's Azure prototypes can — and should — read from the same governed lakehouse. The question is where the substrate lives.
A two-and-a-half hour working session — then a Phase 1 SOW.
No long sales cycle. The April 20 conversation already established intent. The next meeting designs Phase 1.
Proposed workshop agenda
- 0:00Recap of April 20 conversation & confirm guiding principles
- 0:20Deep-dive: lakehouse on Fusion — sizing, deployment model, Iceberg
- 0:50Governance & AI security walkthrough on NYX-shaped data
- 1:20Planning Analytics demo against synthetic NYX shipment data
- 1:50Joint plan with the Azure-side AI lead — interfaces, not turf
- 2:15Phase 1 statement of work, cost envelope, success criteria
Stakeholders
- Andrew FritchNYX — IT leadership, sponsor
- NYX AI leadNYX — Azure-side AI program
- NYX estimating leadNYX — pilot owner for estimating copilot
- Mangat RaiIBM — watsonx.data
- Eyal AbrahamIBM — watsonx.governance & AI
- Chris McCoolPartner — program lead
- Jack KadenPartner — engagement coordination
Glossary
Prepared for NYX Inc. · Based on the April 20, 2026 working session with IBM and partner teams.