Best Use Cases for Claude AI

Everyone talks about using AI for writing. I use Claude to run my day. It’s not a tool. It’s an operations partner—if you give it the right prompts. Here’s exactly how I use Claude as my assistant (connected to Gmail, Drive, and Calendar): 1. Morning Briefing Prompt Start the day with clarity. “Check my calendar, unread emails, and recent docs. Summarize today’s meetings with prep notes. Pull any open loops or tasks from emails. Suggest a time-blocked plan for deep work + admin. Flag anything urgent or out of alignment.” I open Claude before I open my email. 2. Pre-Meeting Prep Prompt No more last-minute scrambling. “I have a meeting with [Name] about [Topic]. Pull key context from emails, docs, and last calendar invite. Extract action items from last call. Draft talking points and 3 smart questions to ask.” Perfect for client calls or collabs. 3. Research & Synthesis Prompt Working on a project? Claude becomes your researcher. “I’m working on [project]. Pull relevant threads from Gmail. Scan docs with [keyword] and summarize insights. Build a timeline of progress + open items. Draft a quick project update I can send or post.” This alone has saves me 3 hours a week. 4. Workspace Organization Prompt Your brain, but with folders. “Find all docs related to [project]. Suggest categories or themes. Create a folder/tag structure that makes sense. Highlight outdated files or duplicated info. Build a cheat sheet with links + purposes.” Perfect if your Google Drive looks like a tornado. 5. Smart Inbox Prompt Catch up without the chaos. “Find unread emails from VIP contacts. Summarize key threads and flag what’s urgent. Draft quick replies where possible. Link any emails to related docs or calendar events. Build a follow-up plan so nothing slips.” It’s triage for your inbox—with logic. Claude isn’t just for content. It’s for operations, decisions, and daily momentum. Want more tips like this? Join 3,400+ readers of 9-To-Thrive → https://lnkd.in/gXMzXweK

I use AI every single day, so my bar for being "impressed" is high.  But last weekend, I genuinely had a moment of disbelief… I watched an AI agent navigate the NY court system, analyze 3 different cases, and organize 54 legal documents autonomously. Anthropic has just released Claude Cowork, and here is what I had it do: * Look up 3 cases on the NY state court website * Find the one that matches the criteria I outlined It opened the court’s website in a browser, clicked through the search screens, entered queries, reviewed the case files, and evaluated all three cases in the background. Finally, it told me which case was the best fit. Next step. I downloaded the filings from that case myself: 54 documents in total. (The AI can’t do the downloading yet, but that gap will close soon.) Once the files were on my computer, I asked it to: 1. Rename the files to a standardized naming convention (e.g. “Date - Name - Case Number”) 2. Organize the files into litigation folders (e.g. “Motions”, “Pleadings”, etc.) 3. Create a Word document Executive Summary of the case 4. And then the same but in PowerPoint format Lo-and-behold, it completed all those tasks. WITHOUT any more input from me, fully autonomously. The one big downside of Claude still, compared to Gemini, is harder verification (there are no inline citations). But for admin tasks, this is truly a game-changer. These AI agents are also not very fast right now, it took Claude ~20 minutes to complete all of the tasks above. But that’s not a huge concern as these can be run in the background, and even several AI agents operating at the same time. The possibility of having AI “employees” working in your firm is suddenly closer than what everyone expected. See the demo below of how it works.

I reverse engineered Claude Claude to figure out what makes it so damn good. Here’s the secret sauce behind the best AI tool in the world & how you can steal it for your own AI workflows: 1. Show Up Briefed Idea: The model works best when it already knows who it’s working for and what success looks like. Steal: Paste a reusable header at the top of every chat — your audience, offer, tone, KPI, and source links. Tell it: “Treat this as context. Ask before writing anything.” 2. Give It Tools, Not Just Prompts Idea: Don’t just chat — connect it to real data. Steal: Let it read from and write to a Google Sheet, Notion page, or API. Define exactly when it should pull info, update it, or ask permission. 3. Plan → Do → Track Idea: Claude manages itself with checklists. Steal: Make it outline a plan before acting, give quick status updates after each step, and add a “REMINDER:” line if it drifts off-track. 4. Split the Work, Then Combine It Idea: Multiple focused chats beat one messy one. Steal: Run 2–3 side chats (market, product, channels). Then use an “Aggregator” prompt to score each idea on impact, confidence, cost, time, and risk — and return one ranked decision with next steps. 5. Remember Rules, Not Rambles Idea: Keep your decisions consistent. Steal: Create a simple `Decisions & Rubrics.md` file. When you change direction, have the model propose a short “diff” — what changed and why. --- Copy-Paste Starters --- 1. Strategy Workshop - use this when you need to design or prioritize your company’s AI roadmap. ``` You are my AI strategy partner. Treat this chat as a working session to design an internal AI roadmap. Context: [Company Name], [Industry], [Team Size], [Core KPI]. Deliverables: (1) 3–5 high-impact AI initiatives ranked by ROI and feasibility, (2) draft 90-day rollout plan. Ask clarifying questions before outputting anything. ``` 2. Content Engine: Use this when you want to turn raw ideas into ready-to-post LinkedIn content. ``` You are my editorial co-pilot. Treat this chat as an always-on content system for LinkedIn. Context: [ICP], [Offer], [Tone], [KPI]. Task: Turn my raw notes into 5 post drafts using the Project OS format (Hook → Insight → Takeaway). After each, ask: “Publish, refine, or queue?” ``` 3. Product Discovery: Use this when you need to extract insights or opportunities from customer research. ``` You are my product research analyst. Goal: Find, cluster, and rank user pain points for [target persona or segment]. Inputs: I’ll paste raw notes or transcript text. Output: A table with columns (Pain Point, Frequency, Impact, Root Cause, Example Quote). After summarizing, suggest 3 potential AI-powered solutions. ``` Most people use ~5% of what AI can do. Don’t just chat with it, run it like an operating system.

Claude Code is the first AI tool that genuinely feels like it moved past "answering" and into shipping. Not in a hype way. In a "this changes how engineering work gets done" way. 𝗪𝗵𝗮𝘁'𝘀 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁: Most AI tools live inside a chat box. Claude Code lives next to your codebase: → Reads real project context → Edits multiple files safely → Runs terminal commands → Debugs with feedback loops → Keeps state across long tasks Think of it like an agentic execution layer: Intent → Plan → Tools → Codebase → Tests → PR → Deploy Once you see it this way, you stop prompting for "code snippets"… and start delegating workflows. 𝗪𝗵𝗲𝗿𝗲 𝗶𝘁'𝘀 𝘂𝗻𝗿𝗲𝗮𝗹 𝗶𝗻 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲: Here are real patterns that compound fast: ✅ "Scan the repo, find all dead code paths, propose deletions with a PR" ✅ "Refactor a module across 200 files + update tests + run lint" ✅ "Turn meeting notes into PRD → tickets → acceptance criteria → release checklist" ✅ "Fix the bug, write the regression test, then document the edge case" 𝗧𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝘂𝗻𝗹𝗼𝗰𝗸: 𝗠𝗖𝗣 MCP is basically USB-C for agents. Once Claude can securely connect to tools like GitHub, Jira, Slack, Notion, Postgres, and Sentry… You go from "assistant" to automation teammate. 𝗛𝗼𝘄 𝗜 𝗿𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗶𝘁 (𝗳𝗮𝘀𝘁): 1. Start with one repo you know well 2. Give it a single, scoped mission 3. Force the loop: plan → execute → validate → checkpoint 4. Add MCP only after the base workflow is stable The best AI tools don't just answer questions. They ship code.

Last week, a fellow operator and I caught up. My favorite question: "How are you using AI every day?" Here's my breakdown: 1️⃣ Competitor Intelligence Before: Hours of stalking competitor websites and socials. Now: Perplexity search "[Competitor] + GTM strategy 2024" for pricing and positioning. Follow up with specific questions. Time saved: 2-3 hours per analysis. 2️⃣ Campaign brief development Old process: Start with a blank doc, spend 45+ minutes outlining. New process: Create a Claude AI project with prompt "Create a campaign brief template for [specific campaign type]," then refine with ICP context, goals, and key messages. Ask Claude to ask you questions to help it improve the output. Result: 80% complete briefs in under 15 minutes. 3️⃣ Meeting summaries people actually read Team complaint: "No one reads meeting notes." Solution: Record meetings in Fathom - AI Meeting Assistant, upload transcript to Claude with prompt: "Extract key decisions, action items with owners, and critical insights. Format as bullet points by topic." Result: People remember what was discussed and act on it. 4️⃣ Feedback delivery Challenge: Giving constructive feedback while incorporating input from 7 stakeholders. Solution: Paste stakeholder comments into ChatGPT: "Consolidate these points into themes. Help craft a constructive delivery approach." Then: "Role-play this conversation with me. Play the role of the team member receiving feedback." Result: Most productive feedback session I've had. The recipient called it the most actionable feedback they'd received. 5️⃣ Weekly planning when overwhelmed List all projects, subtasks, and personal commitments (exercise, family time) Prompt Claude: "Create a realistic weekly schedule with 30-min breaks, 1-hour lunch, and 2-hour daily buffer." Ask: "What high-leverage activities should I protect at all costs?" Result: 50% less planning stress while maintaining boundaries and hitting deliverables. My biggest learning: AI isn't just about speed... it's about improving work quality and decision-making while protecting your time and energy. What operational challenge could AI help with in your role? #AIforOperations #GTMStrategy #ProductivityHacks

I wish this Claude Code guide existed a few months ago. Most people think of Claude as just another AI chat tool. But Claude Code is very different. It’s closer to an AI operating system for developers than a chatbot. Instead of only answering questions, it can: • Access your project files • Run commands • Analyze large codebases • Connect to tools through MCP • Execute multi-step tasks autonomously That’s when things start to get interesting. For example, you can ask it to: → Review your repository and explain the architecture → Refactor a module across multiple files → Generate documentation from your codebase → Analyze customer feedback and create insights → Connect to tools like GitHub, Slack, or Notion One part that stood out to me from this guide is MCP (Model Context Protocol). Think of it like USB-C for AI tools. It connects Claude to external systems so it can interact with your workflows, data, and tools in real time. Another underrated concept here is prompting with structure, not just questions: • Give context • Define constraints • Assign a role • Ask for clear outputs This turns AI from a search engine into an actual collaborator. The biggest takeaway for me: The future of AI tools isn’t just chatting with them; it’s integrating them into your workflow so they can actually do the work. If you're exploring AI for coding, automation, or workflows, this guide is honestly one of the clearest visual breakdowns I've seen. Curious: How many people here are already experimenting with Claude Code or MCP servers in their workflow?

Are you using Claude AI fully or only scratching the surface? Many people use Claude like a smarter search box. One prompt in, one answer out, then they move on. That works, but it misses where the real leverage lives. Claude becomes far more powerful when you use it as a system for thinking, building, and execution. Here’s a practical Claude AI cheat sheet 👇 1. Pick the Right Model Use stronger models for deep reasoning and complex work. Use faster ones for everyday tasks. 2. Improve Your Prompts Add roles, examples, constraints, style, and step-by-step instructions. 3. Use Prompt Patterns Zero-shot, one-shot, and few-shot prompting can improve consistency fast. 4. Build Projects Keep work organized with saved context, files, instructions, and topic-based workflows. 5. Use Artifacts Create documents, apps, visuals, dashboards, and interactive outputs. 6. Connect External Tools Link files, docs, calendars, databases, and business systems. 7. Use Claude Code Debug, edit files, write features, run commands, and manage coding workflows. 8. Create Reusable Skills Turn repeated tasks into reusable prompt systems. 9. Start with Better Prompts Examples: analyze data, compare options, rewrite tone, build roadmap, fix code. 10. Combine With Other Platforms Use design, automation, presentation, and research tools alongside Claude. 11. Use Shortcuts Save templates, reuse memory, chain tasks, and iterate quickly. 12. Think Beyond Chat The best users build workflows, not just conversations. Final Insight The gap between casual users and power users is rarely intelligence. It’s workflow design. Don’t just ask better prompts. Build better systems. What’s one thing Claude could automate for you today?

I've been using AI heavily in finance for a year. Here's what I've found actually matters: 1️⃣ If your accounting system doesn't connect easily with an external AI (ChatGPT, Claude, etc.), you're using the wrong accounting system. Built-in AI tools in accounting systems like QBO are rarely as capable as the real thing. 2️⃣ Know the privacy risks. My rule: strip out identifying information before uploading any document. 3️⃣ Treat it like a new staff accountant. You have to check its work. You have to train it. It will make mistakes. 4️⃣ Direct connectors to Google Drive or cloud storage are hit or miss. If accuracy matters, export the reports and upload manually to the AI model. 5️⃣ Before you review anything, ask it to double-check its own work first. It catches its own errors more often than you'd think. 6️⃣ Reconciliations and matching tasks are where it shines. Add agents into the mix, and you're automating a significant chunk of staff-level accounting work. 7️⃣ The future belongs to finance professionals who understand how these models think, know roughly what the answer should look like, and can verify the math. Your experience-based gut instinct matters more now, not less. 8️⃣ Cash flow forecasting is where I'd start for most businesses. It's the single highest-impact use case I've found. 9️⃣ For FP&A work specifically, Claude is the clear winner — and worth the paid upgrade. 🔟 Information overload is real. My fix: ask it to cut the output by 50%, then ask it to simplify further. Forces it to distill down to what actually matters. AI won't replace experienced finance professionals. But it will replace those who don't know how to use it. #Finance #AI #Accounting #FPandA #CFO

I suspect many people have yet to develop an intuition for the Claude Code/Cowork model of AI. Here's a non-coding task I accomplished with Claude Code that illustrates it well. Last week, I needed to model the marginal AWS costs of onboarding another 100 users given existing usage patterns. This is a complex, multi-variable problem. Many of our AWS costs are fixed and amortize with scale. Some are step-function costs that amortize until hitting a critical inflection point, at which point we need to scale up. Many are variable but nearly impossible to calculate bottom-up so we can understand "one redline costs us X in AWS spend." So I asked Claude Code to help me construct a model. I gave it read access to the AWS CLI to fetch our billing data from the past quarter, and access to the Google BigQuery CLI to read from our raw, anonymized analytics data. It analyzed all of our spend categories and quickly identified the fixed costs. For each variable cost category, it ran a linear regression on the fly to correlate spend to usage increases across a variety of key datapoints. With that, it had developed a function for projecting the marginal cost of key actions in the application such as handling file uploads, redlining documents, etc. It then used BigQuery to construct a usage distribution to estimate the added usage from a marginal 100 users. With all of those pieces in place, it created a report estimating the marginal cost of the next 100 users. Following that, my cofounder Kevin recommended I have Claude create a script I could execute in the future to recalculate with fresh data. Now we have a deterministic script we can run whenever we want to understand our marginal costs. Before Claude Code, it might have made sense for either Google BigQuery or AWS to expose a chat interface directly in their UIs to interact with their data. Or it might have made sense for an "AI infra costs" startup to build integrations to both data sources and expose them with a chat interface in *their* UI — perhaps packaging the linear regression modeling as a "skill" that its AI is predisposed to use. But in the Claude Code model, none of that is necessary. All Claude Code needs is the CLI of both tools for accessing the data, and it can handle everything else.

I’m currently testing Claude Code as a local AI agent running directly on my computer, and it’s already changing the way I work as an automation engineer. Instead of remembering terminal commands or switching between tools, I can simply describe what I want to do in plain English, for example checking network connectivity to a Siemens HMI panel. Claude Code translates that intent into real system commands, executes them locally, and immediately returns clear technical feedback such as latency and reachability. What makes this powerful is that it is not just advice from a chatbot. This is a local AI agent interacting with a real engineering environment. TIA Portal is open, an HMI project is on the screen, and live communication checks happen in the background. This approach removes friction from everyday engineering tasks and allows me to stay focused on PLC logic, system behavior, and industrial processes. For me, AI is not about replacing engineers. It is about reducing cognitive load, speeding up workflows, and supporting engineers so they can focus on what really matters. What do you think about supporting engineers with AI in daily automation and PLC work? #IndustrialAutomation #PLC #Engineering #AIforEngineers #ClaudeCode #TIAportal #HMI #OT #Industry40