Findings from The Forrester Wave™: Document Mining And Analytics Platforms, Q2 2026

2026-05-21 · Source: Featured Blogs - Forrester · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Emerging Technologies & Innovation · Depth: Intermediate, quick

Summary

The Forrester Wave™: Document Mining And Analytics Platforms, Q2 2026, reveals a fragmented and rapidly evolving market where success hinges on aligning solutions to specific use cases, document types, and architectural choices. While agentic AI and LLM innovation accelerates, organizations face practical realities regarding time to value, cost, and accuracy. The report emphasizes that DMAP is not a single market, with capabilities spanning enterprise content management, intelligent automation, and search. It advises understanding pricing dynamics, which range from approximately \$0.05 to \$0.20 per page, and setting pragmatic accuracy expectations, typically starting around 60%-plus and improving to the high-90% range with tuning. Expect about six months to reach MVP.

Key takeaway

For Directors of AI/ML evaluating document mining and analytics platforms, prioritize aligning your solution to specific document types and use cases before vendor selection. Understand that achieving a Minimum Viable Product typically takes six months, not weeks, and factor in variable costs from \$0.05 to \$0.20 per page. Design for architectural flexibility to integrate diverse ML models and plan for human-in-the-loop processes to achieve high accuracy.

Key insights

Success in Document Mining and Analytics Platforms (DMAP) hinges on precise alignment to use cases, document types, and architectural flexibility.

Principles

• DMAP is not a single market; segment by use case.
• Document type significantly impacts vendor suitability.
• Agentic AI architecture flexibility is critical for cost-effective task matching.

Method

Start with use case, then shortlist vendors within the right segment; refine use case early based on document type; plan for ~6 months to MVP; understand pricing dynamics and accuracy expectations.

In practice

• Use premium LLMs for complex extraction, lighter models for summarization.
• Incorporate human-in-the-loop processes for production deployments.
• Utilize the Forrester Wave evaluation spreadsheet for platform selection.

Topics

• Document Mining
• Analytics Platforms
• Agentic AI
• LLM Architectures
• Enterprise Content Management
• Intelligent Automation

Best for: CTO, Executive, AI Architect, Director of AI/ML, VP of Engineering/Data, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by Featured Blogs - Forrester.