Enterprises in industries like law, healthcare, insurance and finance have millions of files with unstructured data such as text, images, or audio, making it challenging to extract valuable information without specialized AI tools.
Extractive AI, a subset of AI, aims to locate and extract data from diverse data sources. Though it also relies on deep learning, it is quite different from generative AI which enables computers to use existing content like text, audio and video files, and images, to create new content.
What makes extractive AI more valuable than generative AI?
The focus on accuracy and efficiency makes extractive AI the right solution for industries where decisions need to be reliable (i.e. based on real data rather than generated content).
1. Factual accuracy
Enterprises, especially those in highly regulated industries, prioritize data integrity and verifiable outputs, making extractive AI ideal for applications that require high levels of factual accuracy.
Extractive AI is hallucination-free and operates by directly pulling pre-existing information from structured or unstructured datasets. This ensures accuracy, especially in industries where factual precision is essential (e.g., legal research, financial, and healthcare sectors).
2. Efficiency
Automation is almost always more efficient than manual work and allows enterprises cut down manual labor and focus resources on higher-value tasks.
Extractive AI is also more efficient than other forms of automation like RAG-powered generative AI since extractive AI typically relies on more power-efficient machine learning models with fewer parameters.
3. Transparency
Extractive AI models, by design, retrieve specific, verifiable data from existing sources. Enterprises can track exactly where the extracted information comes from, ensuring that all data is traceable and can be audited. This is essential for industries where data provenance is crucial for regulatory compliance (e.g., GDPR in Europe or HIPAA in healthcare).
Table: Generative models vs extractive models
| Feature | Generative AI | Extractive AI |
|---|---|---|
Primary function | Create new content from learned patterns. | Retrieve specific, existing information from data. |
Output | New text, images, or media not present in data. | Snippets, key phrases, or summaries from data. |
Example models | GPT-4, DALL·E, MuseNet | Lazarus AI, LawGeex |
Use cases | Chatbots, creative writing, art generation | Legal document analysis, data extraction, summarization |
Best For | Tasks requiring novel content or creativity | Tasks needing factual, precise information |
20 Extractive AI Use cases
Legal and compliance
1. Contract review: Legal professionals spend countless hours reviewing documents, contracts, and case law to find relevant clauses or precedents.
Real-life examples:
Extractive AI tools like Kira Systems claim to achieve a recall rate of 90%, higher than human reviews, showing that their false positive rate is lower than humans. This helps legal teams extract relevant clauses from contracts faster.1
Lawgeex, a contract review platform, uses extractive AI to analyze legal contracts and highlight key terms such as liabilities, and termination clauses.2
Read more: See legal AI software to learn about contract drafters and reviewers.
Healthcare
2. Clinical data processing: Almost six hours of the workday are devoted to primary care physicians working with the EHR (electronic healthcare records). 3
The healthcare industry generates massive amounts of unstructured data in the form of clinical notes, patient records, and research papers. Extractive AI in healthcare can help healthcare providers uncover key insights for decision-making in multiple areas:
3. Electronic healthcare record (EHR) documentation management
4. Medical bill automation
5. Medical recruiting and credential-checking
6. Clinical trial data management
Real-life examples:
Google Cloud’s Healthcare API uses Extractive AI to analyze unstructured sensitive data such as clinical notes, lab reports, and EHRs (Electronic Health Records). This improves diagnostic support by identifying key information for healthcare providers.4
IBM Watson Health leverages extractive AI to extract clinical data from medical literature and patient records to support healthcare professionals in diagnoses and treatment plans.5
Video: Pulling information from large documents
Source: IBM6
Financial services
7. Extracting insights from reports: In the financial sector, the ability to extract meaningful data from reports, filings, and contracts is critical for compliance, risk management, and investment decisions. 100% of U.S. polled financial reporting leaders say they will either use or pilot artificial intelligence (AI) in financial data reporting within the next three years.7
Audit, tax, and advisory firms have started using extractive AI tools for dealing with vast amounts of unstructured data. For example extractive AI can help automating:
8. Invoice processing
9. Invoice capturing
10. Financial reporting (daily, monthly, annual)
11. Reconciliation automation (e.g. extracting relevant data from these statements.
12. Claims processing (e.g. extracting demographics, name, policy type, policy number data.
13. Claims validation (e.g. classifying insurance coverage according to the documentation input)
For more on different types of invoices, feel free to read our article on invoices.
Media and news summarization
14. Extracting key information from news articles or research papers and summarizing it for readers.
Real-life example: Google Cloud Document AI is an AI tool that extracts the most important information from news articles and presents concise summaries. This is particularly useful for professionals needing to keep up with multiple sources of news without spending time reading entire articles.8
Retail
15. Analyzing and identifying retail trends: Extractive AI is useful in the retail sector, particularly in analyzing and identifying trends by extracting relevant insights from massive amounts of unstructured data such as customer reviews, social media content, sales reports, and market research documents. See key use cases:
16. Customer sentiment analysis from reviews: Extractive AI can process large volumes of customer reviews and extract insights related to product preferences, emerging trends, and pain points.
17. Dynamic pricing and market analysis: Extractive AI can automatically pull competitive pricing information from online sources, helping retailers identify pricing trends across competitors.
18. Supply chain and inventory management: Extractive AI can extract and analyze supply chain data from reports to help retailers track inventory levels.
Video: Pulling data from a purchase order document to Excel
Source: AlgoDocs9
19. Customer self-service: Platforms like Zendesk implement extractive AI to automatically retrieve answers from vast FAQ databases, improving response times for customer queries.
20. AI assistants for customer service agents: Customer service agents benefit from copilots that extract relevant details about customer enquiries in real time, helping them understand customer issues faster.
Intelligent document processing flow with extractive AI
- Data ingestion: Data ingestion is collecting and moving data from various sources to a central location for processing.
- Pre-processing & OCR: Following document collection, the documents go through several pre-processing procedures to remove errors for content extraction using OCR and computer vision.
- Document classification: Document classification categorizes documents based on the document’s language, structure, or content.
- Validation: After data is extracted, it is crucial to verify the accuracy of the data. During a validation step, extractive AI tools compare the information retrieved with the relevant paperwork (e.g.comparing the products listed on the invoice and the receipt).
- Data enrichment: As a more complex type of validation, data can also be cross-checked and enriched using internal systems. This involves sending the extracted data, together with the response in its final result, to an API endpoint.
- Data integration: The structured document’s output is forwarded to the appropriate systems for additional processing. This could start more (autonomous) business operations or provide information for reporting and insight flows.
AI technology is often involved in the document processing flow, see the key approaches:
- Extractive AI
- Optical character recognition (OCR): Digitizes physical records, such as government documents or historical archives.
- Machine learning (ML): Automatically classifies and identifies patterns in large language models, providing predictive insights.
Read more: Intelligent document processing.
Feel free to check out our document capture software list.
Further reading
- 5 Steps to OCR Training Data
- Image Annotation: Definition, Importance & Techniques
- Computer Vision: In-Depth Guide
External Links
- 1. ”Leverage human-centred AI for contract and document analysis”. Kira Systems. 2024. Retrieved on September 11, 2024.
- 2. ”Automate your contract review process”. Lawgeex. 2024. Retrieved on September 11, 2024.
- 3. ”Analyses of electronic health records utilization in a large community hospital”. National Library of Medicine. 2020. Retrieved on September 11, 2024.
- 4. ”Cloud Healthcare API”. Google Cloud. 2024. Retrieved on September 11, 2024.
- 5. ”Clinical language understanding and extraction (CLUE)”. IBM. 2024. Retrieved on September 11, 2024.
- 6. ”watsonx.ai: Extract”. IBM. 2024. Retrieved on September 11, 2024.
- 7. ”Navigating The AI Era In Financial Reporting”. KPMG. 2024. Retrieved on September 11, 2024.
- 8. ”Try Document AI”. Google Cloud. 2024. Retrieved on September 11, 2024.
- 9. ”Intelligent Document Processing for your Business”. TextRazor. 2024. Retrieved on September 11, 2024.
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