The Problem with Calling Everything AI

Defense contractors and government manufacturing systems face a specific problem right now. Program managers see “AI” on a roadmap and assume all artificial intelligence works the same way. They don’t. Machine learning models and large language models have fundamentally different architectural requirements, failure modes, and integration patterns.

This matters because the distinction affects how you design data pipelines, handle compliance boundaries, and plan capacity. A manufacturing execution system using ML for predictive maintenance has different CMMC requirements than a warehouse management system using an LLM for natural language queries. The data flows differently. The failure modes look different. The security controls you need aren’t the same.

I’ve seen teams architect systems assuming they can swap ML for LLM later. They can’t, not without significant rework. The inference patterns are different. The context requirements are different. The way you handle sensitive data in FedRAMP environments changes based on which approach you’re using.

Two Architectural Patterns, Not One

Traditional ML models learn patterns from structured data and make narrow predictions. They’re deterministic within their training domain. LLMs process natural language across broad contexts and generate text. They’re probabilistic and general-purpose.

This creates three architectural distinctions that affect system design: data pipeline architecture, inference patterns, and failure mode handling.

Data Pipeline Architecture

ML models need structured, labeled training data specific to their task. A quality control model in a MES environment trains on sensor readings, defect classifications, and production parameters. You can version this data, validate it against schemas, and trace it back to specific production runs. The pipeline looks like extract, transform, validate, train, deploy.

LLMs work differently. They need massive text corpora for pre-training, then smaller task-specific datasets for fine-tuning. You’re not building a data pipeline from scratch. You’re starting with a foundation model and adapting it. The architecture focuses on prompt engineering, retrieval-augmented generation, and context management rather than feature engineering and data labeling.

This affects your compliance posture. With ML in manufacturing, you control the entire training pipeline. You know what data went in, how it was processed, and what the model learned. You can document this for CMMC audits. With LLMs, you’re using a model trained on internet-scale data you didn’t control. Your compliance boundary starts at fine-tuning and inference, not at initial training.

The data residency requirements differ too. ML training data for defense manufacturing often contains controlled unclassified information. You keep it in your FedRAMP-authorized environment and train models there. LLMs complicate this. If you’re using a third-party API, you’re sending data outside your boundary. If you’re self-hosting, you need infrastructure that can handle billions of parameters and context windows measured in thousands of tokens.

Inference Patterns and System Integration

ML inference is fast and cheap. A defect detection model processes an image in milliseconds. It returns a classification with a confidence score. You can run thousands of inferences per second on modest hardware. This makes real-time integration with production systems straightforward. Your WMS scans a barcode, the model classifies the item condition, and the system routes it to the correct location. The latency is predictable.

LLM inference is different. Processing a context window takes seconds, not milliseconds. Token generation happens sequentially. You’re paying for compute proportional to both input and output length. This changes how you architect integrations. You can’t call an LLM in a tight loop during production operations. You need async patterns, caching strategies, and fallback logic.

The context window constraint matters for system design. An ML model has no memory constraints beyond its input size. You feed it a sensor reading, it returns a prediction. An LLM has a fixed context window. GPT-4 handles 128k tokens. Claude handles 200k. That sounds like a lot until you’re trying to give it context about a complex manufacturing process with multiple work orders, bill of materials, and quality specifications. You need context management strategies: summarization, retrieval systems, hierarchical prompting.

This affects how you handle state in manufacturing workflows. ML models are stateless. Each inference is independent. LLMs benefit from conversation history and shared context. If you’re using an LLM to help operators troubleshoot production issues, you need to maintain conversation state across multiple interactions. That’s a different integration pattern than point-in-time ML predictions.

Failure Modes and Observability

ML models fail in predictable ways. They perform poorly on out-of-distribution data. They overfit to training data patterns. They degrade when production data drifts from training data. You can detect these failures with statistical monitoring. Track prediction confidence, input distribution shifts, and ground truth accuracy over time.

LLMs fail differently. They hallucinate. They produce plausible-sounding text that’s factually wrong. They’re sensitive to prompt phrasing. They can leak training data or generate harmful content. These failures are harder to detect programmatically. You can’t just monitor confidence scores. You need human review loops, output validation against structured data sources, and prompt injection defenses.

The observability requirements differ too. For ML in MES environments, you monitor model performance metrics: accuracy, precision, recall, latency. For LLMs, you need to trace prompts, responses, token usage, and context retrieval. You’re debugging why the model misinterpreted an operator’s question or why it suggested an incorrect procedure. That requires different instrumentation than monitoring ML prediction accuracy.

Design for the Architecture You Need

System architects in defense manufacturing need to understand these distinctions before they commit to an approach. ML and LLM aren’t interchangeable. They solve different problems with different trade-offs.

Use ML when you need fast, deterministic predictions on structured data in real-time production workflows. Use LLMs when you need natural language understanding, broad generalization, or synthesis across unstructured information sources.

The architectural implications ripple through your entire system: data pipelines, compliance boundaries, inference patterns, integration approaches, failure handling, and observability. Design for the pattern you actually need, not the one that sounds more innovative in a program review.