Answer the task-specific question to distinguish this candidate from simpler and alternative approaches. Information must be extracted, classified or structured from documents.
Facts behind this choice
- Task discriminator: Unknown. Answer the task-specific question to distinguish this candidate from simpler and alternative approaches.
- Main task: Extract from documents
- Decision logic: Some judgment
- Repeatability: Yes
- Documented rules: Yes
- Required approval: Yes
- System access: Unknown
The human role
Require human approval at consequential actions and handle exceptions in a review queue.
Choose differently when
Stable templates may be handled by conventional parsing; interpreting the extracted text may need a separate LLM step.
These are capability-based candidates, not a verified vendor ranking. Confirm integrations, data terms, pricing and project fit before purchase. Official product sources reviewed 2026-10-05; this catalogue does not update live.
Compare other AI and non-AI approaches
These are alternatives to investigate when the stated conditions apply. They are not equally recommended for your current task.
Conversational AI
Use when: The task requires interactive dialogue, intent handling and a route to resolve a request.
Why not the current recommendation: Your selected task is “Extract from documents”; this option addresses “Handle customer or employee conversations”. Reassess if that better describes the work.
Simpler alternative: Use guided forms or a scripted chatbot for fixed paths; add RAG only when answers need approved knowledge.
Prerequisites: Representative conversations and approved answers; Tested human handoff, identity checks and channel access
Watch: A conversational interface does not authorize transactions or guarantee factual answers.
Microsoft Copilot Studio — Evaluate for business conversations, knowledge-grounded assistants and controlled actions in supported systems.
Amazon Lex — Evaluate for voice or text conversational interfaces with defined intents and business integrations.
Computer Vision
Use when: The task depends on recognizing visual patterns, objects or defects in images or video.
Why not the current recommendation: Your selected task is “Extract from documents”; this option addresses “Inspect images or video”. Reassess if that better describes the work.
Simpler alternative: Use conventional image processing or human inspection when a simple measurement is sufficient.
Prerequisites: Permitted representative images and labeled examples; Measured false-positive and missed-defect tolerances
Watch: Lighting, camera placement and rare defects can change accuracy; validate the real operating conditions.
Amazon Rekognition — Evaluate for supported image or video recognition tasks against your representative visual data.
Google Cloud Vision — Evaluate for supported image recognition and visual analysis capabilities.
Document AI + governed workflow
Use when: The process combines document extraction with validated downstream actions; it needs multiple capabilities rather than one generic assistant.
Why not the current recommendation: Your selected task is “Extract from documents”; this option addresses “Process documents and act across systems”. Reassess if that better describes the work.
Simpler alternative: Use parsing plus a conventional workflow if templates are stable. Add an agent only if next steps must adapt and a simpler workflow is insufficient.
Prerequisites: Representative documents with field-level validation; Supported system access, explicit routing rules and tested recovery; Approval before consequential or uncertain actions
Watch: An extraction error must not become an unchecked system update. Each stage needs its own acceptance test.
Azure Document Intelligence — Evaluate for extracting structured fields, tables and document content into a validated process.
Amazon Textract — Evaluate for extracting text, fields and tables from scanned documents in an AWS pipeline.
Microsoft Power Automate — Evaluate for explicit workflow rules, supported connectors or desktop RPA where appropriate.
No AI — process improvement or retain current process
Use when: A non-AI path was selected, or a tested simpler process already meets the requirement. No AI benefit is established.
Why not the current recommendation: Your selected task is “Extract from documents”; this option addresses “Improve or retain a process without AI”. Reassess if that better describes the work.
Simpler alternative: Clarify ownership, remove unnecessary steps, standardize handoffs or retain the working baseline.
Prerequisites: A measurable outcome and documented current process; Evidence that the retained or redesigned process meets the requirement
Watch: Do not assume AI savings or purchase a model without a demonstrated unmet need.
Rules / RPA
Use when: Repeatable transactions and structured handoffs suit explicit business rules.
Why not the current recommendation: Your selected task is “Extract from documents”; this option addresses “Move records or transactions”. Reassess if that better describes the work.
Simpler alternative: Use supported APIs or workflow automation before screen-based RPA; an LLM is unnecessary for an exact rule.
Prerequisites: Standardized inputs and explicit decision rules; A stable path, exception handling and an audit trail
Watch: Fragile interfaces and exceptions can create maintenance and residual human work.
Microsoft Power Automate — Evaluate for explicit workflow rules, supported connectors or desktop RPA where appropriate.
Predictive AI / Machine Learning
Use when: Historical patterns can support prediction, classification or forecasting of repeatable outcomes.
Why not the current recommendation: Your selected task is “Extract from documents”; this option addresses “Predict outcomes”. Reassess if that better describes the work.
Simpler alternative: Compare against a rules-based or statistical baseline; generative text is not a validated forecast.
Prerequisites: Representative historical data with known outcomes; A held-out evaluation set and measurable accuracy requirements
Watch: Data quality, bias, drift and overfitting can invalidate predictions.
Azure Machine Learning — Evaluate for building, validating and operating predictive models with a data-science team.
Amazon SageMaker AI — Evaluate for a managed machine-learning development and deployment workflow in AWS.
LLMs / Generative AI
Use when: The work involves interpreting, drafting, summarizing or restructuring language.
Why not the current recommendation: Your selected task is “Extract from documents”; this option addresses “Draft or summarize”. Reassess if that better describes the work.
Simpler alternative: Use templates for fixed wording; add retrieval when answers must be grounded in approved sources.
Prerequisites: Representative examples and clear output criteria; Human checking of facts and consequential outputs
Watch: Fluent language can contain unsupported claims; an LLM is not an evidence source.
ChatGPT Business / Enterprise — Evaluate for staff drafting, summarization and analysis in a managed workspace.
Claude — Evaluate for document-heavy drafting, synthesis and reviewed language work.
RAG / knowledge assistant
Use when: Answers need to remain anchored to approved policies, procedures or company knowledge.
Why not the current recommendation: Your selected task is “Extract from documents”; this option addresses “Answer from company knowledge”. Reassess if that better describes the work.
Simpler alternative: Conventional search may be sufficient when users only need exact source passages.
Prerequisites: Approved, current sources with appropriate access permissions; Tests of retrieval quality, citations and unanswered questions
Watch: Stale content, poor retrieval and unsupported synthesis still require checks.
Microsoft Copilot Studio — Evaluate for business conversations, knowledge-grounded assistants and controlled actions in supported systems.
Azure AI Search — Evaluate as the retrieval layer for a custom RAG solution over a governed document collection.
Digital Twin / Simulation — evaluate fit
Use when: A physical or operational system may benefit from a virtual model for testing decisions and predicting behavior.
Why not the current recommendation: Your selected task is “Extract from documents”; this option addresses “Model and simulate a physical system”. Reassess if that better describes the work.
Simpler alternative: Use a static scenario or conventional simulation when a synchronized model is unnecessary or the operational feed cannot be validated.
Prerequisites: A defined real-world system and behavior to model; A permitted operational data feed that can keep the model current; Validation against observed outcomes and a specific decision the model will improve
Watch: A dashboard or unvalidated what-if model is not a digital twin; stale inputs and model error can mislead decisions.
Azure Digital Twins — Evaluate for connected models of real assets and their relationships using operational data.
AI Agents — bounded and governed
Use when: The next action changes by case, so a bounded agent may coordinate actions across steps.
Why not the current recommendation: Your selected task is “Extract from documents”; this option addresses “Adaptive system actions”. Reassess if that better describes the work.
Simpler alternative: Prefer a predefined workflow when it can meet the requirement. Multiple systems alone do not justify an agent.
Prerequisites: Explicit permissions, action limits and human checkpoints; Tested escalation, stopping conditions, logging and recovery; Evidence that a simpler predefined workflow is insufficient
Watch: Action chaining and unclear permissions can let autonomy outrun governance.
Microsoft Copilot Studio — Evaluate for business conversations, knowledge-grounded assistants and controlled actions in supported systems.
Amazon Bedrock — Evaluate for custom generative applications and bounded agents with connected knowledge and tools.
Human expert decision — no AI role established
Use when: Nuance, ethics or accountability makes human judgment part of the product.
Why not the current recommendation: Your selected task is “Extract from documents”; this option addresses “Resolve nuanced cases”. Reassess if that better describes the work.
Simpler alternative: Retain expert judgment. If a separate drafting, retrieval or extraction task exists, assess that task to identify a specific supporting technology.
Prerequisites: A named accountable decision-maker; Review queues, escalation and traceable supporting evidence
Watch: Automation bias and false certainty can hide who actually made the decision.
