Many manufacturers talk about “adopting AI,” but few have a clear plan for where and how to apply it. For technical writers and documentation teams, AI is far more than an auto-writer. It is a strategic partner that can transform every stage of the documentation process—from research to continuous improvement. Below are four key application areas, drawn from real manufacturing scenarios, where AI can deliver measurable value.

1. Research and Regulatory Intelligence

Faster market entry through AI-assisted research
Early planning often consumes the most time. AI can compress days of investigation into hours, providing a quick foundation for compliant and up-to-date manuals.

Example – Forklift maker entering the U.S. market
A forklift manufacturer needed to revise its operator manuals to meet U.S. safety requirements but lacked clear internal guidance on which standards applied. Using AI, the team:

1. Asked: “What U.S. safety standards apply to forklifts?” → Identified ANSI/ITSDF B56.1.
2. Requested a summary and extraction of manual-specific requirements.
3. Generated a writer’s checklist directly from the standard.

Other research tasks suited to AI
AI is equally valuable beyond product-specific standards:

• Emerging regulatory trends – Track new mandates such as digital accessibility (WCAG, ADA), sustainability labeling, or environmental disclosures and summarize what must appear in manuals.
• Documentation best practices – Collect recent articles, conference papers, and training materials on plain language, modular authoring, or AI-driven content workflows to inform internal style guides.
• Market trend intelligence – Identify emerging documentation practices and summarize competitive features observed in public manuals without detailed side-by-side analysis.

2. Document Analysis and Benchmarking

Objective evaluation of existing manuals
AI enables data-driven comparisons of your manuals against global standards or competitor documentation.

Example – Medical-device manual vs. global leader
A U.S. medical-device maker received dealer feedback that its manual was “hard to follow” and inconsistent in safety warnings. By uploading both its manual and a competitor’s, the team used AI to:

• Compare TOC structure, user flows, and visual elements.
• Analyze language style (technical vs. action-oriented).
• Produce a side-by-side improvement matrix and a proposed style guide.

Typical analysis tasks

• Evaluate existing manuals against key standards such as IEC 82079-1, ANSI Z535.6, or ISO/IEC 26514 to pinpoint compliance gaps.
• Check consistency across multiple language versions.
• Verify that safety messages meet ANSI/ISO visual and wording standards.
• Evaluate information hierarchy, readability, and searchability.

3. Authoring and Content Generation

From one source to many outputs
AI can restructure, simplify, or repurpose content for different audiences without sacrificing accuracy.

Example – Restructuring a maintenance manual for user-flow clarity
A heavy-equipment manufacturer needed to reorganize an operation/maintenance manual that was overly technical and hard to navigate. The goal was not to change the audience but to transform a standards-heavy, engineering-oriented structure into a logical task-oriented flow for maintenance engineers. Using AI, the team:

1. Reorganized chapters into a maintenance-workflow sequence (Inspection → Setup → Operation → Troubleshooting → Scheduled Maintenance).
2. Rewrote complex engineering sentences in clearer, controlled language while preserving critical safety terminology.

Typical authoring tasks

• Rewrite expert-level instructions into plain-language guides for new engineers or cross-functional teams.
• Draft multi-format deliverables—Quick Start Guides, FAQs, training slides, or video scripts—from a single source document.
• Convert manuals into chatbot-ready Q&A text, web content, or text-to-speech (TTS) formats.
• Produce alternative sentence versions to meet regional terminology preferences or controlled-language standards (e.g., Simplified Technical English).

4. Data-Driven Continuous Improvement

Closing the loop with real user data
Documentation must evolve with customer feedback. AI can transform raw support data into actionable improvements.

Example – FAQ generation from service logs
A home-appliance company analyzed months of support tickets with AI. The tool clustered recurring questions (“Wi-Fi drops frequently,” “How do I reset?”), pinpointed where the answers existed in the manual, and proposed clearer placement and simpler language.

Typical improvement tasks

• Classify customer inquiries to identify missing or hard-to-find information.
• Analyze search logs to optimize keyword usage and content placement.
• Track ticket trends to recommend structural updates and validate revision impact.

The Takeaway: AI as a Strategic Partner

The four areas we explored—research, analysis, authoring, and continuous improvement—are not tasks that AI completes on its own. They become powerful only when experienced technical writers apply their subject expertise and use AI as an accelerator, guiding prompts and verifying outputs to achieve reliable results.

AI is no longer a future experiment. For manufacturers producing technical and user documentation, it is a present-day tool that accelerates research, reveals quality gaps, generates multi-channel content, and drives continuous improvement.

At Hansem Global, we treat AI not as a shortcut but as a partner in creating documentation that is faster to produce, easier to translate, and clearer for every user. Ready to explore AI-enabled documentation? Hansem Global can help you build a practical AI strategy from research to final delivery.