Integration Hell: Connecting Legacy Systems to AI

The AI is the easy part. The SOAP API from 2008 is the hard part.

The Brownfield Reality

In Silicon Valley demos, data lives in clean JSON files or modern Postgres databases.
In the mid-market, data lives in:

An on-premise ERP (SAP, Oracle, NetSuite).
A custom SQL Server maintained by one guy named Dave.
A folder of CSVs on a shared drive.

If your AI strategy doesnt account for Legacy Integration, you dont have a strategy. You have a toy.

Pattern 1: The Sidecar Database (The Safe Bet)

Do not let the AI touch the legacy production database directly.

Risk: The AI runs a heavy query and crashes the ERP during payroll.
Solution: ETL (Extract, Transform, Load) data out of the legacy system nightly into a modern Sidecar database (Postgres/Snowflake).
Benefit: The AI can query the Sidecar as fast as it wants without risking ops.

Pattern 2: The API Wrapper (The Facade)

Legacy APIs are often SOAP, XML, or weird proprietary formats. LLMs hate XML.

Solution: Build a lightweight Python/FastAPI layer that wraps the legacy API.
Input: Clean JSON from the Agent.
Action: Python translates JSON - XML - Legacy System.
Benefit: You abstract the ugliness away from the AI logic.

Pattern 3: The Robotic Process Automation (RPA) Bridge

Sometimes there is NO API. The only way in is a Windows GUI.

Solution: Use an RPA bot (UiPath/Power Automate) to do the clicking, and expose that bot as a webhook.
Workflow: Agent - Webhook - RPA Bot - Legacy GUI.
Warning: This is fragile. Use only as a last resort.

Conclusion

Dont wait for your legacy systems to be modernized before adopting AI. You will be waiting forever. Build the bridges instead.

Next: [Building an AI-Ready Organization](/blog/building-ai-ready-organization)

How This Article Was Made

This article is a live example of the AI-enabled content workflow we build for clients.

Stage	Who	What
Research	Claude Opus 4.5	Analyzed current industry data, studies, and expert sources
Curation	Tom Hundley	Directed focus, validated relevance, ensured strategic alignment
Drafting	Claude Opus 4.5	Synthesized research into structured narrative
Fact-Check	Human + AI	All statistics linked to original sources below
Editorial	Tom Hundley	Final review for accuracy, tone, and value

The result: Research-backed content in a fraction of the time, with full transparency and human accountability.

Why We Work This Way

Were an AI enablement company. It would be strange if we didnt use AI to create content. But more importantly, we believe the future of professional content isnt AI vs. Human—its AI amplifying human expertise.

Every article we publish demonstrates the same workflow we help clients implement: AI handles the heavy lifting of research and drafting, humans provide direction, judgment, and accountability.

Want to build this capability for your team? Lets talk about AI enablement →