Introduction: AI is not magic, but a tool to expand the set of work patterns
When people talk about AI adoption, they often imagine amazing automation or dramatic productivity gains, but what actually works well in practice is to reliably speed up common tasks. The key is not to treat AI as an all-purpose coworker; instead, set up a package consisting of templates (patterns) + data + checks.
This article is organized by Marketing, Sales, HR, Accounting, Legal, and Customer Support (CS) to summarize highly effective use cases, implementation cautions, and example tools. At the end, you’ll find steps to start small tomorrow.
1. Marketing: Turn research, planning, and production into a high-speed loop
Common Challenges
- Market and competitive research takes too long
- Production of ads, landing pages, emails, etc. can’t keep up
- Campaign retrospectives are often handled by individuals, with learning not lasting
AI Use Cases
- Summarizing competitive and customer insights: Compile reviews, social posts, and open-ended survey responses to extract frequent themes and pain points. Quickly see what resonates and what turns customers off.
- Drafting persona / customer journey baselines: Provide your own customer data (purchase reasons, churn reasons, etc.) to generate hypotheses and accelerate meeting starts.
- Generating variations of ad copy and landing pages: Create numerous variations of the same messaging (short vs long text, addressing concerns vs emphasizing benefits) for A/B testing.
- SEO structure plans and rewrites: Organize search intent, propose headlines, and identify improvements for existing articles. However, ensuring E-E-A-T (Experience, Expertise, Authority, and Trust) is important.
- Automatic drafting of reports: Read numbers from GA4 and ad management dashboards, and articulate changing metrics, hypotheses about causes, and next actions.
Tools
- ChatGPT / Claude: Planning, summarization, copywriting, and turning analysis into language
- Perplexity: Cited research (primary sources must be verified)
- Notion AI / Google Workspace (Gemini) / Microsoft Copilot: Planning and summaries alongside internal documents
- Canva: Rough drafts for banners and proposals
Implementation Tips (Marketing)
Distill brand tone, disallowed expressions, and required elements into a short guide and share it with prompts to achieve consistent quality. Don’t rely on AI for numerical figures; final checks should be done by humans.
2. Sales: Reduce preparation and post-processing for proposals
Common Challenges
- Proposal materials and emails take up too much time
- Meeting notes整理 and CRM data entry get postponed
- Reasons for lost deals aren’t accumulated, hindering improvements
AI Use Cases
- Pre-meeting account research: Summarize company news, IR, hiring trends, and organizational structure to generate hypothesis topics and question prompts.
- Drafting the core of the proposal story: Automatically generate a structure that flows from problem → impact → remedies → implementation steps → ROI.
- Automatic summarization of meeting notes and To-Do extraction: From recordings/transcripts, extract decisions, tasks, and the next agenda.
- Drafts for emails/follow-ups: Adjust tone to be polite, concise, or persuasive as needed.
- Classification of lost deals / stalls: Aggregate CRM text and automatically tag by price, requirements mismatch, competitors, timing, approvals, etc.
Tools
- Salesforce / HubSpot: AI-assisted summaries, emails, forecasting (features vary by plan)
- Microsoft Copilot: Teams meeting summaries, email drafting
