Choosing Between OCR, NLP and LLMs for Data Automation

Decision-making about which AI technology to apply is crucial when automating document and data workflows. In our experience, organisations often struggle to select between OCR, NLP, and LLMs. Each technology serves a distinct purpose: OCR digitises text, NLP extracts structured meaning, and LLMs interpret complex context across diverse documents. Choosing the right approach ensures efficient, accurate automation and faster operational outcomes.

This post complements our pillar article AI for Document & Data Automation: From PDFs to Structured Insights.

Why should organisations carefully choose the right AI technology?

Using the wrong tool can lead to slow processing, inaccurate data, or unnecessary costs. We observed a logistics client using only OCR for contract analysis. While the text was digitised, critical clauses were missed, delaying compliance checks. Applying the right combination of NLP and LLMs alongside OCR significantly improved accuracy and insight extraction.

Semantic variants include intelligent automation, AI agents, and workflow automation software.

How do OCR, NLP, and LLMs differ and complement each other?

1. OCR (Optical Character Recognition) – Converts scanned documents or images into machine-readable text. Best for static PDFs, handwritten notes, and forms.

2. NLP (Natural Language Processing) – Extracts entities, sentiment, and relationships from text. Ideal for structured analysis, tagging, and summarisation.

3. LLMs (Large Language Models) – Interpret context, handle ambiguous or complex text, and generate insights across multiple documents. Perfect for summarising, question-answering, and anomaly detection.

In practice, we combine these technologies in layered workflows. For instance, OCR digitises scanned engineering reports, NLP identifies critical components and measurements, and LLMs summarise trends for engineering managers.

See also Extracting Actionable Insights from Unstructured Data Using AI for related techniques.

Implementation Steps

How can organisations choose and implement the right AI technology for data automation?

1. Assess Document Complexity

Identify types, formats, and volume of documents to determine processing needs.

2. Define Required Output

Clarify whether simple digitisation, structured extraction, or contextual analysis is required.

3. Match Technology to Task

• OCR for digitisation.

• NLP for entity recognition and tagging.

• LLMs for summarisation, interpretation, and anomaly detection.

4. Pilot Testing

Deploy on representative datasets, compare outputs, and refine models iteratively.

5. Integration

Feed processed data into dashboards, ERPs, or reporting systems for operational use.

6. Continuous Monitoring

Track accuracy, retrain models, and incorporate human feedback to maintain reliability.

For further guidance, see Automating PDF and Form Extraction with Large Language Models.

Example 

We assisted a construction client in automating tender evaluation. OCR first digitised PDF proposals. NLP identified budget lines, timelines, and contractor details. LLMs generated comparative summaries highlighting risks and inconsistencies. Human reviewers focused only on flagged items, reducing evaluation time by 55% and improving decision confidence.

Common Pitfalls

What challenges should organisations anticipate?

• Overcomplicating the Workflow – Using LLMs for simple digitisation can be inefficient.

• Inconsistent Input Quality – OCR accuracy drops with poor scans or handwriting.

• Overreliance on Automation – Human oversight remains necessary for critical decisions.

We observed that a phased approach with human-in-the-loop validation mitigates these risks effectively.

Conclusion

Choosing the right AI technology - OCR, NLP, or LLMs - is key to efficient, accurate data automation. Layering these tools based on task complexity enables organisations to reduce errors, accelerate workflows, and unlock insights from documents.

For more detail, link to our AI Automation Services page or book a 30-minute strategy call – no cost, no pitch.

FAQs

When should I use OCR only?
For simple digitisation of scanned PDFs, forms, or images where no context is needed.

When is NLP more suitable?
For structured extraction, entity recognition, and tagging of textual data.

When are LLMs necessary?
For complex, ambiguous, or multi-document analysis requiring summarisation and contextual interpretation.

Can these technologies be combined?
Yes, layered workflows often combine OCR, NLP, and LLMs for optimal results.