Introduction: AI Is Not "Magic" but a Tool That Adds "Templates" to Work
When people hear "AI utilization," they tend to imagine amazing automation or dramatic productivity gains, but what actually works in the field is "making common tasks stably faster." The point is not to treat AI as an "all-powerful colleague" but to prepare templates + data + check procedures as a set.
In this article, divided into marketing, sales, HR, accounting, legal, and customer support (CS), we summarize use cases that work well, cautions at adoption, and examples of usable tools. At the end, we also include steps to "start small from tomorrow."
1. Marketing: Turn Research, Planning, and Production Into a "High-Speed Loop"
Common challenges
- Market and competitor research takes time
- Production of ads, LPs, emails, etc. can't keep up
- Initiative retrospectives become person-dependent and learnings don't remain
AI use cases
- Summarizing competitor/customer insights: aggregate reviews, SNS posts, and free-text survey responses to extract frequent themes and dissatisfaction points. "What hits and what is disliked" becomes visible quickly.
- Making starters for personas/customer journeys: hand over your customer data (reasons for purchase, reasons for cancellation, etc.), build hypotheses, and speed up the start of meetings.
- Generating ad-copy and LP variations: for the same appeal, mass-produce "short/long," "anxiety-resolving/benefit-emphasizing," etc., and run them through A/B tests.
- SEO structure proposals and rewrites: organizing search intent, heading proposals, extracting improvement points of existing articles. However, ensuring E-E-A-T (experience, expertise, authoritativeness, trustworthiness) is important.
- Auto-drafting reports: read numbers from GA4 or the ad management screen and put "changed metrics," "factor hypotheses," and "next actions" into prose.
Tool examples
- ChatGPT / Claude: planning, summarization, copywriting, verbalizing analysis
- Perplexity: research with citations (but primary-source confirmation is essential)
- Notion AI / Google Workspace (Gemini) / Microsoft Copilot: planning and summarizing together with internal documents
- Canva: rough production of banners and proposal materials
Adoption tips (marketing)
Make "brand tone," "NG expressions," and "required elements" into a short guide and distribute it together with prompts to align quality. Don't leave numbers to the AI; have a human do the final check.
2. Sales: Reduce the "Preparation" and "Post-Processing" of Proposals
Common challenges
- Time is taken by making proposal materials and writing emails
- Organizing meeting notes and CRM entry are put off
- Lost-deal reasons don't accumulate, so improvement doesn't cycle
AI use cases
- Account research before a meeting: summarize a company's news, IR, hiring trends, and org structure, and build hypothesis issues and question proposals.
- Building a proposal-story skeleton: auto-generate a structure proposal in the flow issue then impact then measure then adoption steps then ROI.
- Auto-summarizing minutes and extracting ToDos: from recording/transcription, format decisions, homework, and the next agenda.
- Drafting email/follow-up text: adjust tone to the temperature, such as "polite," "concise," "give a push."
- Classifying reasons for lost/stalled deals: aggregate CRM text and auto-tag into price, requirement mismatch, competitor, timing, internal approval, etc.
Tool examples
- Salesforce / HubSpot: AI assist (summarization, email, prediction, etc.; varies by plan)
- Microsoft Copilot: Teams meeting summary, email drafting
- Zoom AI Companion: meeting summary (usage conditions depend on plan)
- Notion / Google Docs: integration with proposal templates
