In-House AI Adoption by General Affairs Teams Reshaping Catalog Production Bids for SME Manufacturers

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Introduction: Changes Visible from Losing a Three-Company Competitive Bid

In the catalog production scene for small and medium-sized enterprises, cases of reduced outsourcing to external production companies are increasing. When one production company heard the reason for losing a bid in a three-company competition, the answer was, “Because the general affairs department started making it in-house using AI and tools.” This change suggests that the spread of generative AI and design support tools is promoting in-house production by general affairs departments. Real-world cases show workflows that previously required external vendors now being completed internally.

📑Table of Contents

1. Introduction: Changes Visible from Losing a Three-Company Competitive Bid
2. Background and Reality of In-House AI Tool Adoption by General Affairs Departments
3. Specific Reasons External Production Companies Lose Bids
4. Impact on Quality Control and Brand Consistency
5. Countermeasures for Small and Medium-Sized Production Companies
6. Summary: The Future of Catalog Production in the AI Era

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Background and Reality of In-House AI Tool Adoption by General Affairs Departments

General affairs departments are moving toward in-house production of catalogs and promotional materials to reduce costs and improve speed. According to reports from ITmedia and others, combining generative AI with tools like Canva enables in-house visual creation without relying on external vendors. For example, designs that previously cost tens of thousands of yen per sheet can now be done for a few hundred yen, and creating multiple patterns has become easier.

According to araya.org cases, image generation AI utilizing RAG and LLM has significantly shortened the time for creating internal documents and proposals. In general affairs and HR departments, AI is used for responding to internal regulations and contract reviews, reducing inquiries to specialized departments by approximately 60%. Marketing teams generate multiple creative patterns for SNS ads automatically, while EC sites produce product image variations from text instructions in minutes. In product development, multiple design proposals are auto-generated, cutting initial review time by about 30%. Sales email personalization is automated, shortening material creation time by roughly 50%.

Item	Outsourcing	In-House (AI Tools)
Cost	Tens of thousands of yen per sheet	A few hundred yen
Creation Time	Several days	Minutes to hours
Number of Patterns	Limited	Multiple patterns easy
Modification Flexibility	Low	High
Inquiry Reduction	–	~60% (HR/General Affairs)
Review Time Reduction	–	~30% (Product Development)
Material Creation Time	–	~50% (Sales Emails)

Source: ITmedia reports and araya.org cases (as of 2026). Facts drawn from independent non-Hatena sources.

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Specific Reasons External Production Companies Lose Bids

The main reason external production companies lose in competitive bids is the cost and time advantages of in-house production. By introducing AI tools, general affairs departments make the three-company competitive bidding process itself unnecessary in some cases. Even non-specialist staff can produce materials of a certain quality using tools, reducing dependence on external experts. With RAG-enabled proposal and catalog generation spreading internally, opportunities for external orders are diminishing.

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Impact on Quality Control and Brand Consistency

The expansion of in-house production may bring challenges to quality and brand consistency. AI-generated images and text may not align with corporate brand guidelines, and corrections can be time-consuming. Industry surveys point out that many companies lack sufficient checking systems to maintain brand consistency. Workflows led by general affairs departments require careful guideline setup and final human review.

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Countermeasures for Small and Medium-Sized Production Companies

Small and medium-sized production companies need to actively utilize AI tools themselves and provide higher-value-added services. For example, they can differentiate by combining brand strategy proposals with quality checking and correction services for AI-generated content. Offering AI utilization consulting and templates for general affairs departments is also effective. Supporting RAG-based learning from past projects for tailored proposals represents a shift toward specialized services.

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Summary: The Future of Catalog Production in the AI Era

With the spread of AI tools, the nature of catalog production is changing. While in-house production by general affairs departments brings cost benefits, challenges remain in terms of quality. External production companies can maintain competitiveness by leveraging AI while providing services that utilize human expertise. Going forward, the division of roles between AI and humans will be key. Assisting with internal tool adoption and building quality management flows are emerging roles for production companies.

FAQ

Q: Does quality drop when general affairs departments use AI to create catalogs?

It depends on the tools, but combining brand guideline settings and final human checks can maintain a certain level of quality. Many companies are using it as an auxiliary tool rather than full automation. RAG-based learning from company data improves industry-specific accuracy in some cases.

Q: How should external production companies respond going forward?

By operating AI tools in-house and adding strategic proposal and quality management services, differentiation is possible. Shifting from simple production to consulting-type services is being seen in some cases. Providing templates and workflow support for general affairs departments is effective.

Q: What is the cost reduction effect of in-house production?

In some cases, the cost per sheet has been reduced from tens of thousands of yen to a few hundred yen, and creating multiple patterns has become easier. However, initial tool introduction and learning costs are separate. araya.org reports confirm significant efficiency gains in visual material creation.

Q: How is brand consistency ensured?

It is recommended to strictly set templates and guidelines before AI generation and implement a flow where humans review after generation. AI assists with internal regulation responses and contract reviews, reducing the burden on specialized departments.

Q: Is this trend limited to small and medium-sized enterprises?

While reports mainly feature cases in SMEs, large enterprises are also introducing AI as efficiency tools for general affairs departments. RAG and LLM adoption within companies is spreading regardless of company size.

Q: What is the effect of RAG-powered proposal creation?

By learning from past transaction records and product catalogs, industry-specific proposals can be auto-generated from customer data. Sales email personalization is also automated, with some cases showing about 50% reduction in creation time.

Q: What are examples of use in EC sites and SNS?

Product image variations with different backgrounds and angles are generated automatically from text instructions. Multiple creative patterns for SNS ads can be created in a short time without external outsourcing, enabling flexible operations.

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