The AI works in demos. It fails in production. The usual culprit? Integration with existing systems.

Most enterprises run on systems that predate AI by decades. ERPs from the 2000s. Mainframes from earlier. Custom systems nobody wants to touch. Getting AI to work with these systems is where implementation actually happens - and often fails.

The Integration Challenge

Enterprise AI doesn’t exist in isolation. It needs to:

• Read data from existing systems
• Write back decisions or updates
• Fit into existing workflows
• Maintain existing audit and compliance requirements
• Work within existing security constraints

Each of these is a potential failure point.

Common Integration Patterns

From deployments I’ve observed, certain patterns emerge:

API facade. Building modern APIs in front of legacy systems. The AI talks to the API; the API translates to legacy protocols. This works well when feasible but requires significant investment.

RPA bridge. Using robotic process automation to interact with legacy systems that lack APIs. The AI makes decisions; RPA bots execute them in legacy systems. Not elegant but practical.

Database integration. Reading from and writing to legacy databases directly. Risky if not careful - legacy database schemas are often poorly documented, with hidden dependencies.

File-based integration. Legacy systems that work with files (common in finance, healthcare, manufacturing). AI processes files in defined formats, placing outputs where legacy systems expect them.

Human bridge. Sometimes the integration is humans. AI provides recommendations; humans enter them into legacy systems. Not optimal but sometimes the path of least resistance.

What Makes Integration Hard

Several factors complicate AI-to-legacy integration:

Documentation gaps. Legacy systems often lack current documentation. Understanding what they do requires archaeology.

Undocumented dependencies. Changes that seem safe break things in unexpected ways. Systems have evolved through decades of patches.

Performance constraints. Legacy systems may not handle AI-induced load. Batch processes designed for overnight runs don’t appreciate real-time queries.

Security models. Legacy security approaches don’t match modern requirements. Service accounts, API keys, and OAuth don’t exist.

Availability windows. Some legacy systems have limited availability. Integration must accommodate maintenance windows and planned downtime.

Data quality. Legacy data is often inconsistent, incomplete, or incorrectly formatted. AI systems expecting clean input fail.

Getting Integration Right

From successful implementations:

Invest in discovery. Understand legacy systems before designing integration. Talk to people who maintain them. Document what you learn.

Build abstraction layers. Don’t couple AI systems directly to legacy protocols. Abstraction layers allow changing either side independently.

Plan for failure. Legacy systems fail. Network issues happen. Design AI workflows that degrade gracefully when integration fails.

Test extensively. Integration testing in production-like environments with realistic data. Edge cases in integration are where problems hide.

Monitor integration health. Continuous monitoring of integration points. Detect failures quickly.

Maintain expertise. Someone needs to understand both the legacy systems and the AI systems. This intersection is where problems get solved.

The Middleware Question

Should you build integration middleware or buy it?

Build when: Integration needs are specific to your systems, you have engineering capacity, and you need full control.

Buy when: Standard patterns exist, you need speed, and vendor solutions fit your systems.

Hybrid often works: Use platforms for standard integrations, build for specific needs.

The middleware market is evolving. Products like MuleSoft, Boomi, and others offer AI integration capabilities. But they don’t eliminate the need to understand your specific systems.

Organizational Considerations

Integration success depends on more than technology:

Cross-team collaboration. AI teams and legacy system teams need to work together. Organizational silos are integration barriers.

Ownership clarity. Who’s responsible when the integration fails? Clear ownership prevents finger-pointing.

Change management. Legacy system changes affect AI integration. Communication processes prevent surprises.

Skills development. Few people understand both AI and legacy systems. Developing this intersection is valuable.

The Temptation to Replace

When integration is hard, the temptation is to replace legacy systems entirely. This is sometimes right but often wrong.

Replace when: The legacy system is truly end-of-life, replacement cost is justified by broader benefits, and you can manage the transition risk.

Integrate when: The legacy system works adequately, replacement cost is prohibitive, and integration is feasible.

Most enterprises have legacy systems that will persist for years or decades. Learning to integrate AI with them is essential capability.

Working with AI consultants Sydney who have experience with legacy integration can help navigate these challenges, especially for systems that lack documentation or institutional knowledge.

The Unglamorous Truth

The exciting part of AI is the intelligence. The valuable part is often the integration.

AI that works in isolation provides limited value. AI that connects to existing systems, fits into existing workflows, and works with existing data provides real impact.

This work isn’t glamorous. It involves understanding old systems, building middleware, testing edge cases, and maintaining connections. It’s engineering work that makes AI practical rather than theoretical.

Organizations that invest in integration capability - either internal or through partnerships with firms like Team400 - turn AI potential into AI value. Those that focus only on the intelligence and neglect the integration get impressive demos and disappointing production systems.

The systems exist. The AI exists. The challenge is connecting them. That’s where real enterprise AI work happens.