​How AI Changes The Way Organizations Deal With Messy Data

A compliance manager at a manufacturer receives evidence of regulatory compliance in multiple formats: a scanned PDF, a photographed test report, a specialized spreadsheet, and an email. While the information exists, traditional software requires manual entry before it can be used—a scenario that plays out across countless organizations. Kunal Chopra, CEO of Certivo, notes that capable employees spend excessive time retyping information that already exists elsewhere.

Traditional systems have operated on the assumption that data must be structured before processing. However, businesses don't produce information this way—MIT Sloan estimates that 80% to 90% of enterprise data is unstructured. This disconnect forces organizations to invest in supplier portals and data-cleansing projects, leading to records that lag behind source documents and inconsistent interpretations.

AI-Native Software

AI-native systems now accept information in any format—PDFs, spreadsheets, emails—and make sense of it without requiring restructuring. Unlike earlier extraction tools that depended on templates and broke with layout changes, these systems handle variation like a knowledgeable person would, understanding what the document is and how it relates to organizational knowledge.

This capability, known as contextual intelligence, extracts only what the task requires rather than everything on a page. In compliance contexts, it recognizes different document types and matches them against appropriate regulations while linking related information across sources. The same principle is transforming contract review, claims adjudication, and customer due diligence.

Implementation Challenges

Reading unstructured input doesn't lower standards for output. Extracted values must remain accurate, traceable, and repeatable. Companies should evaluate AI-native systems based on four questions: Can it show the source of every extracted value? Does the same document produce consistent results? How does it handle unfamiliar document types? What happens when errors occur and how quickly are they identified?

Trade-offs exist—systems can't recover information suppliers never send, and extraction quality varies with document complexity. Early deployments require human verification until the system earns trust. Additionally, AI-native layers typically feed rather than replace existing ERP or PLM systems, meaning integration work remains necessary.

Workforce Transformation

This technology fundamentally changes how people work with systems. Teams no longer need clean data before starting—the system builds the picture with context far faster than manual structuring. Once assembled, the system reports what's missing, allowing humans to focus on judgment-based gap closure rather than data entry.

People whose expertise involved finding information need new roles, and approval processes built for slower workflows become bottlenecks when results arrive in days rather than months. Leadership must determine which determinations the system can handle independently. This transformation enables automatic compliance as a realistic operating posture, where the system of record becomes a system of action, requesting missing declarations and routing exceptions.