Why Regulated Industries Need Purpose-Built AI
Life sciences, insurance, and manufacturing organisations are under pressure to modernise with AI, but generic tools rarely meet their compliance and audit obligations. In these sectors, documents such as clinical trial files, First Notice of Loss emails, or multi-hundred-page engineering specifications must be not only digitised, but also validated, traceable, and defensible under regulatory scrutiny. Traditional AI deployments often stall because the underlying data is incomplete, inconsistently classified, or impossible to audit, turning promising pilots into operational risks. Regulated AI solutions are emerging to close this gap by treating document extraction compliance and enterprise AI auditability as first-class requirements, not afterthoughts. They emphasise provenance, human oversight, and secure integrations with existing content repositories. The result is a new class of AI that can power natural language workflows and automation while still producing a clear, regulator-ready record of how each document was processed and how every AI-assisted decision was reached.
Adlib Transform 2026.1: Fixing Documents Before They Hit the Model
Adlib Transform 2026.1 is designed specifically for highly regulated enterprises that need every document feeding their AI systems to be accurate, validated, and traceable. Rather than focusing on models, it addresses the document problem at the source. New capabilities include an AI Model Builder that configures extraction models from sample documents in minutes, reducing the need for lengthy prompt engineering cycles. Object Separation decomposes complex, multi-modal files—such as CAD drawings, adverse event forms, and RFQs—into text, tables, images, and diagrams, improving extraction accuracy for visual and structured content. Source citations in Chat with Documents trace every AI-generated answer back to its originating file, delivering full provenance for legal and compliance teams. Human-in-the-loop classification triggers embed expert review at any workflow step, capturing a detailed correction log, while Large Document Stitching handles massive regulatory filings and specifications without performance degradation.
Connecting Regulated Workflows Across Life Sciences, Insurance, and Manufacturing
Transform 2026.1 extends its compliant document extraction to the repositories that regulated sectors already rely on. In life sciences, a Veeva Vault connector brings clinical documents directly from the system of record into AI-ready pipelines, while the AI Model Builder compresses deployment of clinical extraction from weeks to hours. Human-in-the-loop corrections are fully documented to support GxP and other audit trails, and source citations in AI chat provide defensible evidence for regulatory submissions. Insurers benefit from an MS Exchange connector that automates intake of email-based First Notice of Loss, combined with Object Separation to process mixed-format police reports and medical bills, with every adjuster action audit-logged. Manufacturing and energy organisations use Object Separation and Large Document Stitching to handle CAD-intensive RFQs, SOPs, and engineering specifications, with connectors to platforms like M-Files ECM and SharePoint ensuring validated outputs flow into existing quality, PLM, and content management workflows.
Laserfiche AI Agents: Natural Language Workflows with Governance Built In
While Adlib focuses on making documents AI-ready, Laserfiche is turning natural language workflows into a governed reality. Its new AI agents operate within the Laserfiche content platform, following integrated security rules and compliance requirements so sensitive data remains protected. Users interact through Smart Chat, directing AI agents with simple language to perform one-time actions such as locating specific contract clauses, identifying late invoices, or re-filing HR records based on employee attributes. The agents use generative LLM reasoning to bridge the gap between fully automated workflows and manual tasks, handling document data analysis and triggering context-aware actions without bypassing access controls. Legal teams can have agents flag inconsistencies in contracts before routing them for human review, while departments like Accounts Payable and HR benefit from automated detection and routing of critical records. This blend of AI agents and AI-driven content analysis lets organisations act on their repositories without sacrificing governance.
Auditability as the Differentiator for Enterprise AI
Across both Adlib and Laserfiche offerings, a consistent theme emerges: auditability and traceability are becoming defining features of enterprise AI in regulated environments. Natural language interfaces and AI agents reduce friction, allowing staff to query repositories and trigger actions conversationally. Yet every step—from document ingestion and classification to AI-generated answers and automated routing—must be explainable and reviewable. Transform 2026.1 delivers this through source-cited responses, human-in-the-loop checkpoints, and detailed extraction provenance, ensuring document extraction compliance even for complex, multi-modal content. Laserfiche AI agents complement this by executing natural language workflows strictly within established security and governance frameworks, tied to user permissions. Together, these regulated AI solutions illustrate how enterprises can modernise operations, cut manual document handling, and scale AI initiatives, all while maintaining a defensible, regulator-ready record of how information was processed and how decisions were influenced by AI.