Best Use Cases for Claude AI

Anthropic quietly published how its own teams actually use Claude Code. I expected engineering examples. The non-engineering examples were more interesting. Their teams use it for: Kubernetes debugging Terraform reviews Data workflows API debugging Onboarding Product design Legal work Growth experiments Documentation One example stuck with me. Finance team members write a plain text workflow like: “query this dashboard, get information, run these queries, produce Excel output” Claude Code runs the workflow. That is not “AI writes code faster.” That is a different interface for internal work. Another example: Their infra team used screenshots of Kubernetes dashboards. Claude walked through the Google Cloud UI, found pod IP address exhaustion, then gave the commands to fix it. This is the part people still underrate. Claude Code is not only a coding assistant. It can become a layer over all the messy internal workflows that live across docs, dashboards, terminals, and one person’s memory. The best use case may not be “write this function.” It may be: “Turn this painful internal process into something anyone on the team can run.” I'm Shrey Shah & I teach AI assisted coding and agents.

Most executives are using tools like Claude as better search engines: Ask a question. Get an answer. Move on. But the highest-leverage leaders are beginning to use AI differently. → Not as a chatbot. → Not as a content generator. → As an operating layer for their work. When AI can synthesize your documents, prepare your meetings, draft your communications, organize your files, and surface decisions before they become bottlenecks, it starts to function less like a search bar and more like a chief of staff. Here are 5 ways senior leaders can use Claude Cowork to create more executive capacity: 1/ Build a morning intelligence briefing Instead of starting the day buried in email, use AI to review your calendar, relevant messages, meeting notes, and priority documents. The output should be a short executive brief: → What needs your attention → What changed overnight → What decisions are required → What can be delegated → What risks are emerging The goal is starting the day with judgment, not noise. 2/ Turn meetings into decisions Most meetings create more work because the thinking is not captured cleanly. Use AI to turn transcripts, notes, and follow-up threads into: → Executive summaries → Action items → Owner assignments → Follow-up drafts This is where AI becomes more than a note-taker. 3/ Manage communication before it manages you → Executives spend enormous cognitive energy deciding what needs a response, what needs escalation, and what can be delegated. → AI can help by drafting responses in your voice, preparing talking points, summarizing long threads, and identifying which messages truly require your attention. The leadership payoff is better communication with less context switching. 4/ Create decision briefs, not research dumps Executives already have too much information. The value is that AI can turn scattered inputs like market notes, competitor pages, internal documents, customer feedback, and financial context into a structured decision brief. A useful brief should answer: → What is happening? → Why does it matter? → What are the options? → What are the risks? → What decision is needed now? That is a very different workflow from asking AI for “research.” 5/ Create a leadership brain file Build a single reusable context file that captures: → Your leadership style → Your company priorities → Your team structure → Your OKRs → Your communication preferences → Your decision criteria Then use that context every time you ask AI to draft, summarize, prepare, analyze, or recommend. With the right context, AI starts producing work that reflects how you actually lead. The next productivity gap will come from who knows how to operationalize it. The CXOs falling behind are not ignoring AI. They are using it too small. Want the Executive Prompt Kit I use to help leaders build their own AI Chief of Staff? Get it here: https://lnkd.in/gD7ggCYy Save this for future reference.

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

Claude for Small Businesses came out last month. I’ve played around with it for a few hours. Best use cases I found so far: 1. Connect Claude to your QuickBooks and PayPal. It pulls your cash position, checks what's still outstanding, cross-references incoming settlements, and builds a 30-day cash forecast in one run. Then it ranks which invoices are overdue and queues the follow-ups for you to approve. 2. Before you sign anything, it reviews contracts and flags what you need to know: bad termination clauses, automatic renewals, liability exposure. Connect DocuSign and it handles the outbound too: sends for signature, tracks status, and files the executed copy when it comes back. 3. Plug in HubSpot and Canva. It ranks incoming leads by likelihood to close, finds the slow periods in your revenue, drafts a campaign strategy around them, and generates the assets in Canva ready to go live. It’s one of the most useful things I've seen come out of the AI space for SMB owners. This is just scratching the surface. Will post updates as I find more.

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.

I use these 6 Claude Code features on repeat. The last one is my favorite. 1. Auto Mode. Claude works autonomously while you're away. I turned it on, went to get coffee, came back to a complete analysis of 76 pages of source material. 5 agents had run in parallel, extracted every usable data point, and compiled it into a structured document. The human bottleneck isn't thinking. It's sitting there watching AI work. 2. Custom Skills. You can build reusable commands. I built one called /write-exec-intro. One slash command, and Claude researches the target company's annual report, maps regulations to our product, checks competitors, and writes a board-level introduction message. What used to take my team a full day runs in minutes. And it gets better every time I refine the skill. 3. MCP Servers.                                                                                                     Claude can connect directly to external tools. I connected Notion. Now I tell Claude to pull last week's meeting notes, and it reads them live. Then I run one of our skills /build-presentation. Claude takes the raw notes and builds a fully designed slide deck. From Notion to (almost 😉) finished presentation without me touching anything in between.  4. Parallel Agents. Claude can launch multiple agents at once. I use this for research. Here is an example for sales 7 agents running simultaneously: one on the annual report, one on regulatory mapping, one on competitive landscape, one on communication. The output is deeper than what most sales teams produce in a week. 5. Screenshot Reading. I built my personal website by giving it one screenshot of a cool website as a design reference. Claude looked at it, built a first version, then screenshotted its own output using Playwright, compared the two, and iterated until it is extremely close to the input. I didn't touch anything. 7 rounds of visual feedback with zero input from me. That's what you want AI to do generally -> create loops where AI iterates on its own input without your interference. These are examples, you can use the logic for any work you touch. Is there any workflow I should create a step-by-step guide for? #automation #AI #claudecode #anthropic

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

"Which AI Should I Use?" The Question Every Non-Tech CXO Asks Me (And Why the Answer Changes Monthly) Recently, a Chief Revenue Officer(CRO) asked me during a conference: "Shashwat, I keep hearing about ChatGPT, Claude, and Gemini. Which one should I be using for my daily work?" My answer surprised her: "It depends on what you're writing." Here's what most business leaders miss: there is no single "best" AI for text tasks. The real question isn't which AI to use – it's which AI for which type of writing or thinking. LMArena recently updated its Text Arena leaderboard (October 16, 2025) with over 4.2 million community votes across 258 models. These rankings are based on real users comparing text-based responses – exactly what you're doing when you ask an AI to draft an email, draft proposals, analyse competitor strategies, prepare board presentations, or help think through a strategy. Looking at the leaderboard across different text categories, we see dramatic performance variations: 📊 Coding: Claude Sonnet 4.5 leads with a score of 1527. Even if you're not a developer, you might ask AI to write Excel formulas or help with basic automation scripts. 🧮 Mathematical Reasoning: Claude Sonnet 4.5 tops at 1469, with Gemini 2.5 Pro at 1459. When you're asking AI to analyze financial data or work through quantitative scenarios, the model choice matters. ✍️ Creative Writing: Gemini 2.5 Pro excels at 1451, with Claude Sonnet 4.5 at 1444. Drafting compelling pitch decks, marketing copy, or customer communications? Different models shine here. 💬 Multi-Turn Conversations: Both Claude Sonnet 4.5 and Claude Opus 4.1 tie at 1473. Perfect for those extended back-and-forth sessions when you're refining a strategy document or working through complex ideas. 🎯 Instruction Following: Claude Opus 4.1 leads at 1459. Critical when you need the AI to follow specific formats – like "write this in three bullet points" or "summarize this in executive language." 🔥 Hard Prompts (complex, domain-specific challenges): Claude Opus 4.1 scores 1494. When you're tackling sophisticated business problems that require deep reasoning. 📝 Longer Query: Claude Opus 4.1 leads at 1483. When you need to provide extensive context or ask complex questions that require detailed responses. Why This Matters to Your Daily Work: Using the wrong tool means you're leaving quality and time savings on the table. Here's the critical part: these rankings change quite often. The leaders getting the most value from AI aren't necessarily using the "best" model overall – they're matching the right model to each specific type of text work they're doing. Feel free to drop a comment below or DM me if you'd like to discuss optimizing your personal AI toolkit. #B2BLeadership #AIStrategy #ExecutiveProductivity #GTMStrategy #AIforSales