AI Is Coming for Document-Heavy Compliance Workflows First — Here's Why

Nasdaq's acquisition of Dasseti brings AI specifically to due diligence questionnaires, requests for proposals, and ongoing manager monitoring — historically manual, document-heavy workflows in institutional investing. That's a notably different target than the AI use cases that dominate finance industry conversation, like algorithmic trading or research synthesis. It's also, for many organizations, a more practical near-term place to start.

Why Document-Heavy Workflows Are a Practical First Target

Workflows built around structured or semi-structured documents — questionnaires, compliance forms, monitoring reports, contract review — tend to have clearer inputs and outputs than open-ended tasks like strategic research or creative work. That makes it easier to define what 'correct' looks like, easier to measure AI performance against a human baseline, and easier to build in human review checkpoints for higher-stakes decisions. These same properties make document-heavy compliance and due-diligence workflows attractive first targets for AI automation across industries, not just in financial services specifically.

What to Look for When Evaluating This Category of AI Tool

• Does the tool actually understand the structure and context of the specific document types involved, or is it applying generic text processing that misses industry-specific nuance
• How does the tool flag genuine ambiguity or missing information rather than confidently producing a plausible-but-wrong answer — this matters more in compliance-adjacent workflows than almost anywhere else
• What is the audit trail for AI-assisted decisions — in regulated or compliance-relevant workflows, being able to show how a conclusion was reached is often as important as the conclusion itself
• How does the tool handle the volume and scale claimed — a platform covering 17,000 asset managers, as in Nasdaq eVestment's case, needs to perform consistently at that scale, not just in a curated demo

The Practical Takeaway

If your organization is looking for a practical, lower-risk starting point for AI adoption, document-heavy compliance and monitoring workflows — due diligence, contract review, regulatory filing preparation — are often a better first target than more open-ended, judgment-heavy use cases. The structure inherent to these workflows makes both the AI's performance and its failure modes easier to evaluate before expanding scope to higher-stakes decisions.