How Adlib’s Human-in-the-Loop Enhances AI Automation and Builds Trust

As AI adoption accelerates, many regulated industries are exploring how to scale automation without sacrificing trust, accuracy, or compliance.

BUT...
AI alone can’t meet enterprise standards for precision, especially when dealing with complex, high-volume, or sensitive documents.

Human-in-the-Loop (HITL) validation is key to ensuring trust and minimizing risk.

Adlib’s HITL approach empowers organizations to keep humans involved exactly where they’re needed most: validating AI-driven outputs and improving the quality of decisions. It’s not about replacing people, but about elevating their role.

Why HITL Matters in AI Workflows

While automation can process large volumes of data faster, AI is only as accurate as the data and logic behind it. In regulated sectors like life sciences, finance, and energy, decisions must be:

• Traceable
• Defensible
• Compliant
• Auditable

Adlib integrates HITL validation directly into its AI-powered document workflows to make that possible.

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Through built-in confidence thresholds and anomaly detection, Adlib flags low-certainty extractions and routes them to human reviewers via a clean, user-friendly interface. From there, business users validate outputs, make corrections, and approve final results, ensuring accuracy, audit-readiness, and trust in every document.

The Business Case for HITL: By the Numbers

Let’s look at what’s driving demand for this approach:

• 80% of enterprise data is unstructured and much of it lives in documents that are difficult for AI to interpret without preprocessing and validation.
  
• Businesses lose $1.6 trillion annually due to process inefficiencies tied to manual document handling.
  
• Manual document processes show 12–18% error rates, introducing risk into financial and regulatory workflows.
  
• Mid-level employees spend up to 2 hours per day searching for documents, resulting in lost productivity and delays in decision-making.
  

With HITL, organizations can shift from reactive document reviews to proactive quality assurance, catching issues before they cascade into audits, penalties, or rework.

Outcomes of Adlib’s Human-in-the-Loop

When integrated into intelligent document workflows, Adlib's HITL enables:

✔️ Higher accuracy
AI outputs are reviewed before being committed, ensuring confidence in every field and format.

✔️ Audit-readiness
Every action is logged, validated, and traceable, crucial for FDA, SEC, GDPR, and HIPAA compliance.

✔️ Stronger collaboration
Knowledge workers spend less time on repetitive tasks and more time making informed decisions.

✔️ Reduced rework
Errors are caught early in the process, reducing the need for downstream corrections or manual intervention.

✔️ Trust in AI
By giving humans visibility into and control over AI outputs, HITL increases user adoption and confidence across departments.

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What Does It Look Like In Real Life Scenarios

Life Sciences: Metadata Extraction for FDA Submission

A global pharmaceutical company is preparing an NDA (New Drug Application) for FDA submission. The application includes thousands of documents (lab reports, patient consent forms, and clinical summaries) originating from different authors, formats, and geographies.

With Adlib’s HITL: Adlib extracts structured metadata (e.g., author name, trial ID, study phase) and runs automated validations to check for mandatory fields. Low-confidence or anomalous data is routed to a reviewer who makes quick corrections in the intuitive HITL interface. The final package is 100% compliant, traceable, and FDA-ready, cutting submission time by 40%.

Insurance: Claims Processing with Audit-Grade Accuracy

An insurance provider receives tens of thousands of new claim submissions weekly, each with a mix of structured and unstructured content: PDFs, images, handwritten forms, and emails. Adlib classifies and extracts claim IDs, policyholder names, claim amounts, and supporting documentation details.

With Adlib’s HITL: Confidence thresholds trigger validation only when extractions fall below set thresholds. A claim handler quickly reviews flagged records and verifies key data points. As a result, claims are processed 30% faster, audit trails are preserved, and regulatory reporting is streamlined, all while freeing up staff from hours of manual review.

Energy: Regulatory Reporting with Confidence

An energy company must compile quarterly environmental and safety compliance reports for federal and state regulators (e.g., EPA, OSHA). Source data comes from handwritten inspection logs, scanned maintenance reports, and technical PDFs.

With Adlib’s HITL: Adlib processes unstructured inputs using multi-layer OCR and AI-enabled extraction. Any anomalies (like out-of-range values or unverified IDs) are flagged and presented to a compliance specialist for review. The end result is afully validated, audit-ready report, completed on time with zero compliance exceptions.

Bottom Line?

✅ AI is faster with automation
✅ Safer with HITL
✅ Smarter with Adlib

Adlib reduces risk, accelerates deployment, and increases ROI from every AI investment.  AI is better with Adlib. Period.