Where AI Fails in Engineering Drawings

Introduction: AI Isn’t Always Right — and That’s Okay

AI has come a long way in interpreting engineering drawings — detecting symbols, reading tag numbers, and even suggesting relationships between elements. But despite its progress, AI is not infallible, especially when it comes to complex, unstructured documents like P&IDs.

In fact, in engineering workflows, expecting AI to be 100% accurate can be dangerous.

This blog explores where AI typically struggles with P&IDs and other technical drawings — and why thoughtful human validation is essential for trustworthy outcomes.

1. Symbol Ambiguity and Uncommon Variants

Not all symbols are created equal. Some:

• Are too small or faint in scans
• Vary between vendors and projects
• Resemble other symbols (e.g., a pressure switch vs. a pressure gauge)

Even high-performing AI models can misclassify these, especially when they’ve been trained on limited or inconsistent datasets.

A human engineer may spot a subtle difference in context — AI might just guess.

2. OCR Errors in Tag Numbers

OCR (Optical Character Recognition) can extract text, but in engineering contexts:

• Tag fonts are often stylized or distorted
• Characters like O/0, 1/l/I, and S/5 are misread frequently
• Skewed scan angles or overlapping graphics make things worse

Without correction, this leads to:

• Broken data relationships
• Incorrect line-to-equipment mappings
• Cascading errors in downstream systems

3. Overconfidence in Context Inference

Large Language Models (LLMs) like OpenAI’s can help “guess” missing or malformed tag information. But they’re pattern matchers, not engineers.

For example:

• Inferring a tag like A1-FT-105 from context can work
• But LLMs may generate plausible-looking tags that never existed
• They might hallucinate connections where none exist

That’s why Storm Consulting treats LLM output as suggestions, never ground truth.

4. Line Walk Logic and Flow Errors

Tracing process flow through lines, tees, and chevrons requires:

• Understanding line direction
• Distinguishing piping from instrument signal lines
• Mapping flow continuity across page breaks

Even with graph-based logic, line walks can break if:

• A chevron is missing
• A dashed signal line is mistaken for process flow
• A symbol is misaligned

Human review is essential here — engineers know what “makes sense,” AI doesn’t.

5. Validation Is Not Optional — It’s a Design Principle

At Storm Consulting, we design all tools with validation in mind:

• Annotated outputs are editable and reviewable
• All changes are logged for audit and traceability
• Engineers stay in control of the final data

This hybrid approach — automation + manual validation — ensures:

• Faster annotation
• Fewer errors
• Greater trust

AI speeds up the work. Engineers sign off the result.

Conclusion: AI + Engineers = Real-World Reliability

AI is a powerful assistant, not a replacement. In the world of engineering drawings, human judgment remains irreplaceable — especially when safety, cost, and compliance are on the line.

The future isn’t fully autonomous — it’s collaborative. And that’s where AI can truly deliver value.