Everyone talks about AI transforming how we work with documents. But between the hype and the horror stories, what can AI actually do reliably today?
We've analyzed the latest peer-reviewed research and industry benchmarks to give you a clear picture. The findings reveal a nuanced reality: AI excels at some document tasks while struggling with others. Understanding these differences can help you use AI more effectively and avoid costly mistakes.
AI excels at finding specific facts in your documents
When you need to extract specific information from a single document, today's AI models perform remarkably well. Recent benchmarks show that top models achieve near-human performance on document question-answering tasks, with accuracy rates approaching 85-90% when the document is well-formatted and the AI can cite its sources.
Think of AI as an exceptionally fast reader with excellent recall. Ask it to find a specific clause in a contract, pull key metrics from a report, or identify important dates in a project plan, and it will typically deliver accurate results in seconds.
This high accuracy applies to various document types:
- PDFs with clear text and structure
- Word documents and presentations
- Spreadsheets with labeled data
- Even scanned documents, thanks to improved OCR technology
The key factor? These tasks involve finding and extracting information that already exists in the document. The AI isn't interpreting or calculating—it's locating and presenting facts.
Multi-step reasoning shows promise but needs careful oversight
The picture becomes more complex when AI needs to combine information from different parts of a document or perform calculations. Research on financial document analysis shows that when AI must pull numbers from tables and text, then perform multi-step calculations, accuracy drops to around 72-73%.
This might sound concerning, but consider the alternative. Manual analysis of complex documents often takes hours or days. AI can provide a solid first draft in minutes, flagging areas that need human verification.
Common challenges include:
- Calculations involving multiple data points
- Unit conversions and normalizations
- Time period comparisons (quarterly vs. annual data)
- Adjustments between different accounting standards
We've found that the most effective approach treats AI as a capable assistant rather than an autonomous analyst. Let AI handle the heavy lifting of initial analysis, but always verify calculations and complex reasoning.
Cross-document research remains firmly in collaboration territory
When tasks require synthesizing information across multiple documents, current AI accuracy drops significantly. The latest Finance Agent benchmark shows even the best models achieving only 46-51% accuracy on realistic multi-document research tasks.
This might seem disappointing if you expected AI to replace entire research teams. But reframe it as augmentation rather than replacement, and the value becomes clear. A 50% accurate AI assistant that works at superhuman speed can dramatically accelerate research workflows.
Consider a typical research project involving:
- Searching through document archives
- Identifying relevant sources
- Extracting key information
- Synthesizing findings into insights
AI can help with each step, but human judgment remains essential for:
- Validating source relevance
- Checking factual accuracy
- Ensuring logical consistency
- Making strategic connections
Why citations have become non-negotiable
One of the most important findings from recent research: AI systems that provide citations are significantly more reliable than those that don't. Even with advanced retrieval-augmented generation (RAG) systems, hallucinations remain frequent in long-form summaries.
Citations serve multiple critical functions:
Verification pathway: Every claim can be traced back to its source, making fact-checking efficient.
Confidence indicator: AI systems typically cite sources when they're drawing from actual documents rather than generating plausible-sounding content.
Legal protection: In professional settings, being able to show the source of information protects against liability.
Quality control: Citations make it obvious when AI is interpolating rather than extracting.
We recommend treating any AI-generated content without citations as a creative writing exercise rather than factual analysis. The extra seconds it takes to verify citations can save hours of correction later.
A practical framework for trusting AI outputs
Based on the research, we've developed a simple framework for deciding when to trust AI with document tasks:
Green light tasks (80-90% accuracy)
Automate with confidence, but maintain periodic reviews:
- Extracting specific facts from single documents
- Finding named entities (people, companies, dates)
- Pulling data from clearly structured tables
- Answering questions with direct quotes
- Creating initial document summaries
Yellow light tasks (65-80% accuracy)
Use AI for acceleration, but require human review:
- Multi-step calculations from documents
- Cross-referencing information within a document
- Comparing data across time periods
- Generating analytical insights
- Creating detailed reports from templates
Red light tasks (below 65% accuracy)
Treat as drafts requiring significant human input:
- Complex research across many documents
- Strategic analysis and recommendations
- Financial projections and modeling
- Legal or compliance determinations
- Any task where errors have serious consequences
What this means for your daily work
Understanding AI's current capabilities helps you use it more effectively. Here's how to apply these insights:
Start with high-confidence tasks. Use AI for document search, fact extraction, and initial summaries. These tasks offer immediate productivity gains with minimal risk.
Build verification into your workflow. For analytical tasks, use AI to create first drafts but always verify calculations and check reasoning. The time saved on initial creation more than compensates for review time.
Demand citations. Whether using AI tools or reviewing AI-generated content from others, insist on source attribution. No citations should mean no confidence.
Layer human expertise appropriately. Match the level of human review to the task complexity and stakes. A meeting summary might need just a quick scan, while a board presentation requires thorough verification.
Set clear expectations. When sharing AI-generated content, be transparent about what was automated and what was reviewed. This builds trust and ensures appropriate scrutiny.
Different AI models excel at different document tasks
The research reveals that no single AI model dominates across all document tasks. OpenAI's models excel at structured reasoning, achieving over 91% accuracy on business fundamentals tasks. Anthropic's Claude leads in multi-tool research scenarios, while Google's Gemini handles very long documents most effectively.
This suggests a portfolio approach to AI tools:
- Use specialized models for their strengths
- Don't assume one tool fits all use cases
- Test different options for your specific needs
- Consider cost-performance tradeoffs
For most business users, the differences between top models matter less than having clear processes for verification and review. Choose tools that integrate well with your workflow and provide the transparency you need.
The path forward balances automation with judgment
Current AI technology offers tremendous value for document processing, but it's not the autonomous solution some vendors promise. The research makes clear that effective use requires understanding both capabilities and limitations.
The most successful teams we work with treat AI as a force multiplier rather than a replacement. They've identified tasks where AI excels and built workflows that leverage those strengths while maintaining human oversight where needed.
Key principles for success:
Be specific about use cases. Generic "AI will transform everything" thinking leads to disappointment. Identify specific document tasks where AI can help today.
Measure and iterate. Track accuracy for your specific use cases. What works for financial analysis might not work for legal documents.
Invest in verification. The time spent building good review processes pays dividends in accuracy and confidence.
Stay current. AI capabilities improve rapidly. What requires heavy human review today might be highly automated next year.
Looking ahead with realistic optimism
The research paints a picture of AI that's incredibly capable in specific domains while still developing in others. This isn't a failure of technology—it's a natural evolution. Today's AI can already transform how we work with documents, just not in the way many expected.
Rather than replacing human judgment, AI amplifies it. Rather than eliminating work, it shifts focus from mechanical tasks to strategic thinking. Rather than providing perfect accuracy, it offers incredible speed with good-enough precision for many use cases.
Understanding these realities helps you make better decisions about when and how to use AI. The teams seeing the best results aren't waiting for perfect AI—they're building smart workflows around current capabilities while preparing for what's next.
The future of document processing isn't human or AI. It's human and AI, each doing what they do best. And that future is already here for teams ready to embrace it.