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
Adoption tips (sales)
First, shortening the flow of "minutes then CRM entry" has a big effect. The caveat is the handling of customer information. Decide operations after checking internal rules (information OK to input) and the tool's data-use scope.
3. HR: Run Recruiting and Development "Fairly and Fast"
Common challenges
- Job descriptions become person-dependent and quality varies
- Applicant handling is heavy and replies are delayed
- Interview evaluation tends to lean on subjectivity
AI use cases
- Improving job descriptions: separate required and preferred requirements, reduce ambiguous expressions, and propose competitive appeal per role.
- Preparing applicant-handling templates: quickly format scheduling, screening guidance, and replies on declines.
- Standardizing interview questions: generate questions on a competency (behavioral-trait) basis and align evaluation viewpoints.
- Drafting training content: creating onboarding materials, FAQs, and role-play scripts.
- Writing explanations of internal rules/systems: break down difficult text into something easy to understand for internal use.
Tool examples
- Google Workspace / Microsoft 365: summarization/drafting based on internal documents
- Notion: aggregating and searching HR knowledge
- ATS (applicant tracking): depending on the vendor, AI screening/suggestion features
Adoption tips (HR)
The most important thing in recruiting is fairness and explainability. Using AI output not as "a judgment" but for organizing judgment material is reassuring. Since candidate personal data is sensitively handled, clarify the input scope and make rules including log management.
4. Accounting: Redesign Journaling, Billing, and Reconciliation to Be "Exception-Handling Centered"
Common challenges
- Invoice processing, expense settlement, and voucher checks are many
- Monthly closing becomes person-dependent and the close is delayed
- Inquiry handling (payment date, line items, etc.) occurs frequently
AI use cases
- Invoice reading (OCR) + auto journal-entry candidates: produce candidates for description, account, and tax category, so people concentrate on approval and exception handling.
- Reconciliation support: detect mismatches in deposit clearing and the three-way match of order/acceptance/invoice, and present candidate causes.
- Fraud/anomaly detection in expense settlement: flag duplicate applications, amounts outside rules, and bias toward specific patterns.
- Putting monthly reports into prose: write the factors of year-over-year and budget-vs-actual differences as text to create the base for management materials.
Tool examples
- freee / Money Forward: journal automation and voucher processing (features vary by plan)
- Bill One: invoice receipt and data conversion
- Workday / SAP / Oracle: core systems for large enterprises (AI features depend on the module)
Adoption tips (accounting)
For AI adoption, a design that reduces exceptions is a shortcut, rather than "automate everything." First, prepare the partner master and account-rule rules to create a state where AI doesn't get confused, and effects emerge.
5. Legal: Raise the Initial Speed of Review and Detect Risk Without Omission
Common challenges
- Contract-review requests are many and tend to stagnate
- Past knowledge of similar contracts is hard to find
- Fear of overlooking risk lowers speed
AI use cases
- Contract summarization and issue extraction: list issues such as payment, termination, damages, confidentiality, personal data, and governing law.
- A starter for clause alternatives (markup proposals): propose revisions in line with your company's template.
- Detecting risky clauses: present check viewpoints such as one-sided clauses, no liability cap, auto-renewal, and the strength of audit clauses.
- First-line answers to internal consultations: prepare internal FAQs on anti-social checks, sealing, NDAs, copyright, etc. to reduce inquiries.
Tool examples
- LegalOn / Lexto / GVA assist: contract-review support (features for Japanese legal affairs)
- Microsoft 365 / Google Workspace: document summarization/drafting
- Knowledge DB (Notion/Confluence): aggregating templates and judgment criteria
Adoption tips (legal)
AI is convenient, but in legal affairs the correctness of the conclusion is most important. Don't make AI "the judge"; using it to accelerate surfacing issues and comparative study is safe. Further, always check whether it can be used for training data (a setting where input information is not sent externally).
6. Customer Support (CS): Align First-Line Quality and Grow the Knowledge Base
Common challenges
- Inquiries are many and replies are delayed
- Answer quality varies by agent
- The FAQ isn't updated, so the same questions don't decrease
AI use cases
- Drafting reply text: summarize the inquiry content and create a polite answer. Tone (polite/casual/strict) is also easy to align.
- Auto-classification (tagging) and priority judgment: classify into failure, billing, cancellation, usage, etc., and route according to the SLA (response deadline).
- Knowledge search (RAG): present answer candidates by referring to internal procedure manuals, past tickets, and release notes.
- VOC analysis: extract improvement requests, dissatisfaction, and suspected bugs from inquiry logs, and shape them into a form that can be handed to the product team.
Tool examples
- Zendesk / Intercom: AI agents, summarization, help-center integration (features depend on plan)
- Freshdesk: ticket management and automation
- Your own RAG: a mechanism to answer by searching internal documents (vector DB + LLM)
Adoption tips (CS)
What works in CS is "having it reference the correct information source." As a countermeasure for the "hallucination" where AI produces a plausible-looking answer, a design of answers with reference links and escalating to a human when confidence is low is reassuring.
Cross-Cutting Cautions: Crush the Common Failures First
- Ambiguous about information that must not be input: make rules for handling personal, customer, and confidential information (e.g., redact name, email, contract amount).
- Prompts become person-dependent: prepare a short team template (purpose, premise, output format, prohibitions).
- Production deployment without verification: first measure error rate, hour reduction, and customer impact in a 2–4 week trial.
- No logs remain: keeping who had what output and how it was fixed makes improvement cycle.
How to Start From Tomorrow: 3 Steps to Start Small and Build a Template
- Narrow to one task: e.g., for sales "minutes then summary then CRM entry," for CS "reply draft," etc.
- Templatize: fix the input (premise information) and output (format). For example, unify into the template "conclusion then reason then next action."
- Decide check viewpoints: make explicit the points a human checks at the end, such as matching of numbers, proper nouns, legal expressions, and internal rules.
A recommended first move: across all roles, "summarization" and "drafting" tend to produce effects easily. Keep judgment and approval with humans and lean AI toward "preparation and formatting" to be less failure-prone.
Conclusion: For AI Utilization, "Reproducibility" Wins Over "Speed"
When AI fits well, it suddenly makes things easier. But what is truly strong is when you can create a state where anyone who uses it produces a certain level or more of results. First small, but surely. From your team's "tedious standard tasks," try handing just one to AI.
