Integrating LLM’s into Legacy Enterprise Systems 

What’s Inside

This whitepaper explores the real challenges of integrating LLMs into legacy enterprise systems, focusing on architecture, risk, and scalability rather than just model capabilities.

Inside, you’ll learn how to:

• Understand the mismatch between deterministic systems and probabilistic AI models
• Identify common LLM integration failure modes in enterprise environments
• Apply proven patterns like AI sidecar services and asynchronous workflows
• Design systems where AI acts as an advisor, not a decision-maker
• Implement human-in-the-loop controls for high-risk use cases
• Address data challenges, including fragmentation and governance
• Mitigate risks related to security, hallucinations, and prompt injection
• Manage cost, latency, and operational complexity at scale
• Establish clear governance and ownership models for AI systems

The paper also outlines key principles for building AI-tolerant architectures that can evolve safely with changing models and business needs.

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