Every scenario follows the same four stages. An AI agent takes the input — the information that comes in — through judgment (organizing and analyzing it) to execution (drafts and draft responses). The final approval always stays with a person. Each use case below shows exactly where that line falls for its industry.
- Input
- Judgment
- Execution
- Human approval
Seven use cases
EC operations & consulting
From monthly report to the next move
A scenario where the monthly report doesn't stop at aggregation — it moves through to the next move. An AI agent pulls together Shopify, ad, inventory, and CS data, then drafts anomaly detection, cause hypotheses, and improvement proposals. Prioritization and the final recommendation to the client stay with the account manager.
約60%
月次レポート作成の工数削減
Illustrative
当日中
改善提案の提示速度
Illustrative
Multi-store MEO
Turning multi-store reviews into action
A scenario that turns multi-store reviews from a pile that gets ignored into raw material for action. An AI agent classifies per-store reviews, reply history, Google Business Profile activity, and ranking data, then prepares urgency flags, reply drafts, and cross-store issues. Store-specific calls — escalations, individual compensation — stay with a person.
下書きまで自動
返信案の作成
From a real engagement (anonymized)
多店舗を横断
一次対応の範囲
From a real engagement (anonymized)
SEO & content support
A quality gate before you write with AI
A scenario for building a pipeline where quality survives before you write with AI — instead of volume that thins out originality. An AI agent reads Search Console, competitor primary sources, and existing articles, then handles opportunity extraction, outlines, source checks, and fact-checking. Original perspective and the decision to publish stay with the editor.
約50%
記事制作リードタイム短縮
Illustrative
約70%
公開前チェックの自動化率
Illustrative
Professional services (tax, licensing, grants)
Return document-hunting time to case judgment
A scenario that returns time spent hunting for documents back to the judgment the case actually needs. An AI agent reads grant guidelines, client materials, past applications, and inquiries, then prepares guideline diffs, a list of missing documents, a question sheet, and a draft application. Legality, likelihood of approval, and final submission stay with the licensed professional.
約75%
必要書類の洗い出し工数削減
Illustrative
当日中
要領変更の差分チェック
Illustrative
RPO & recruiting
Faster candidate response, human judgment kept
A scenario that speeds up candidate response while leaving the judgment with people — because the longer a reply takes, the more good candidates drop off. An AI agent organizes job requirements, candidate information, and interview notes, then prepares candidate summaries, interview questions, and message drafts. Evaluation, relationship-building, and hire decisions stay with the recruiter.
当日中
候補者への一次返信
Illustrative
約65%
候補者要約の作成工数削減
Illustrative
B2B sales support
From research to first proposal, one company at a time
A scenario for going deep one company at a time, from research to first proposal — because volume sends kill reply rates, and depth wins meetings. An AI agent researches company sites, public materials, CRM, and past deals, then prepares issue hypotheses, per-company messages, and response logging. Send approval, the meeting itself, and relationship calls stay with the salesperson.
約75%
一社あたりの調査工数削減
Illustrative
全件
個社別に仕上げる提案文
Illustrative
Creative & marketing agencies
More outsourcing without diluting the standard
A scenario that keeps quality from thinning as outsourcing grows — before the variance and the checking all pile onto one person’s head. An AI agent reads briefs, client feedback, brand materials, and schedules, then prepares instruction sheets, assignment proposals, reviews, and change summaries. Concept, aesthetic judgment, and client negotiation stay with people.
約50%
外注指示書の作成工数削減
Illustrative
約60%
レビューの確認漏れ削減
Illustrative