AI-Ready Document Automation: How aviation can build supply-chain resilience under tariff pressure

What changed in 2025 (and why it matters)

In April 2025 the U.S. applied 10% duties on nearly all imported commercial aircraft, jet engines, and parts, with industry groups warning of cost and safety risks if tariffs climb further. In May, the Commerce Department launched a Section 232 national-security investigation into these imports, opening the door to additional duties. A July U.S.–EU framework moved to protect aerospace flows between the blocs (zero-for-zero on aircraft/parts) even as broader EU goods face a 15% baseline tariff, so exposure now depends on trading partner and product mix.

Implication: Volatility is now a planning assumption. Aviation OEMs and suppliers need structural cost controls and faster cycle times that don’t depend on tariff reprieves.

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The hidden blocker: unstructured, siloed content

Across regulated industries, the majority of enterprise information is unstructured; over half of corporate data is stale. That’s a recipe for low AI accuracy, brittle audits, and slow handoffs across engineering, quality, and procurement.

In aviation, those “documents” span CAD and PMI-rich drawings, supplier CoCs, test logs, MRB packages, work instructions, service bulletins, and export-controlled files, often scattered across PLM, MES, ERP, and shared drives. If AI and IDP can’t ingest them reliably, trust (and ROI) collapses.

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Adlib’s role: make aviation documents AI-ready (and audit-ready)

Adlib transforms messy, multi-format content into structured, machine-navigable outputs that downstream systems and LLMs can trust. It layers into existing PLM/QMS/MES/ERP environments to modernize from within (no rip-and-replace).

“Adlib can be deployed in any product lifecycle management system or manufacturing ecosystem. The end result is a formatted file you can use to drive AI automation.”
- Erikjan Franssen, GM of EMEA & OEM

What that looks like in practice

• Automated intake & classification: Normalize inbound supplier packages; apply metadata; enforce naming.
• File conversion at scale: Process 300+ file types, including complex CAD, into compliant, searchable renditions and AI-ready JSON/XML.
• Technical & regulatory formatting: Validate fonts, links, stamps, headers/footers; standardize sustainability and supplier-audit docs; create audit-ready repositories.
• Extraction you can trust: Use AI-enhanced OCR and templates to pull BOMs, specs, approvals, improving first-pass yields and shrinking HITL review windows.
• Interoperate across the stack: Feed PLM/QMS/MES/ERP and analytics without disrupting source systems.

“We convert your document, add stamps and security, validate for compliance… Wouldn’t it be nice if you got all of that extra value in addition to extraction?”
- Anthony Vigliotti, CPO

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Building AI trust in a safety-critical industry

Aviation can’t tolerate “close enough.” You need deterministic document fidelity and traceability before model inferences.

“If a pilot flies at 98% accuracy, 2% of flights fail. That’s not acceptable.” — Erikjan Franssen, CRO

Adlib’s document-first approach improves AI precision by eliminating upstream noise (format chaos, stamps, tables, legacy scans) and supplying structured, provenance-rich inputs to your LLMs/RAG pipelines, reducing hallucinations and the need for heavy HITL.

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Four automation plays that move the needle under tariff pressure

External-doc control (suppliers & MRO partners)
Classify, rename, convert, and validate inbound packages automatically; enforce metadata and version rules; push clean artifacts back into PLM/MES for touchless routing. Result: fewer delays, fewer chargebacks, faster PO-to-dock-to-line.

Compliance-by-construction
Continuously check structural elements (fonts, links, page geometry, watermarks), assemble binders, and enforce policy rules to create audit-ready records, cutting prep time dramatically and improving first-pass audit rates.

Security & risk posture for export-controlled data
Keep sensitive docs inside enterprise boundaries while reducing manual handling with system-to-system workflows and consistent version control across engineering, quality, and supply chain.

Scale across formats (present and legacy)
Normalize the long tail of formats, drawings, scanned records, office files, so AI and analytics can consume them consistently. Support for 300+ file types reduces costly rework and cleans up the last mile that breaks most AI initiatives.

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Change leadership: what winning programs do

• Name an internal owner to drive AI-readiness (scope, budget, schedule).
• Run an OCM motion: map processes → harmonize data → cleanse → integrate, before scaling pilots.
• Measure what matters: first-pass yield, rework rate, cycle time, audit findings, % touchless flows.
• Design for complementarity: “We’re unobtrusive and complementary to your infrastructure,” as Adlib’s CEO Cris Huff put it.

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Outcomes to target (and how to quantify them)

Tie ROI to tariff-exposed cost centers and schedule adherence:

• Efficiency: Fewer stoppages from document defects; lower expediting and rework.
• Productivity: Engineering and SQE time shifted from formatting/assembling to root-cause and CI.
• Throughput: Better release cadence from design to handover—fewer escapes.
• Forecasting: Cleaner histories improve model accuracy and commit reliability.

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Why now

• Tariff exposure is real and dynamic.
• 80%+ of the info you need sits in unstructured documents; 54% of corporate data is stale. Clean it or pay for it, in accuracy, time, and cash.
• Document automation is the fastest path to de-risk AI while lowering cost to serve.

“Adlib acts as the connective layer that makes your tools work better, faster, and smarter.”
- Anthony Vigliotti, CPO