AI Tools For Communication

🔬 #ArtificialIntelligence 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴: 𝗧𝗲𝗮𝗰𝗵 𝗬𝗼𝘂𝗿 𝗧𝗲𝗮𝗺 𝗜𝗻𝗯𝗼𝘅 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁! 𝗦𝘁𝗼𝗽 𝗱𝗿𝗼𝘄𝗻𝗶𝗻𝗴 𝗶𝗻 𝗲𝗺𝗮𝗶𝗹. 𝗛𝗲𝗿𝗲'𝘀 𝗵𝗼𝘄 𝘁𝗼 𝘁𝗿𝗮𝗶𝗻 𝘆𝗼𝘂𝗿 𝘁𝗲𝗮𝗺 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗟𝗟𝗠 𝗽𝗿𝗼𝗺𝗽𝘁𝘀 𝘁𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘁𝗮𝗺𝗲 𝘁𝗵𝗲 𝗶𝗻𝗯𝗼𝘅. You know the feeling. 100+ emails a day, half of them need sorting, three need immediate action, and the rest? Noise. Your employees waste hours on inbox triage that an AI could handle in seconds—if they knew how to ask it the right way. That's where Context Engineering for Email comes in. 𝗕𝗮𝗱 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 Most people don't realize that a bad prompt sounds like this: ==> "Organize my emails" 𝗕𝗲𝘁𝘁𝗲𝗿 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 But a good prompt sounds like this: "Review my inbox and categorize each email as: (1) Urgent—needs response today (2) Action—do this week, or (3) FYI—read later. For each, list the sender and main ask in one sentence. Output as a table." 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 See the difference? The second one tells the AI exactly what you want, how to structure it, and how to deliver it. Start with the 3-step framework: 1.     What: Name the exact task ("Summarize this thread so I only read the key points") 2.     How: Tell the AI how to structure the output ("Use bullet points, max 3 bullets") 3.     Why: Give context so it understands the intent ("I need this to spot action items quickly") Then give them a real-world email prompt they can use today: "I receive 50+ emails a day. Create a daily digest that: lists emails needing my direct response (with sender name and action), groups FYI updates by topic (no more than 3 lines per group), and flags anything urgent in red. Sort by priority. Use a simple table format." 𝗧𝗵𝗲 𝗠𝗮𝗴𝗶𝗰 Your team writes this prompt once, reuses it daily, and suddenly has three hours back each week. They'll see immediately how specificity beats vagueness, how structure beats rambling, and how context beats guessing. 𝗧𝗵𝗲 𝗥𝗲𝗮𝗹 𝗪𝗶𝗻 Once they nail email, they use these same skills for proposals, reports, brainstorms, and customer responses. Prompt engineering becomes a career skill, not a party trick. Start this week. Pick one person on your team, walk them through the framework with their actual inbox, and let them build the prompt together with you. Watch them light up when the AI nails it on the first try because they asked the right way. Please follow me for more tips! T. Scott Clendaniel

I built an “AI Decoder Ring” with my son to turn messy Slack threads and vague emails into clear checklists, dependencies, and paste-ready confirmations. The post shares the exact prompts, a copy-and-paste template, and a simple Project setup that keeps everything scoped, private, and consistent. If you’re a creative who struggles with organization, this will help you work calmer and faster—read the full story and steal the system.

The new ChatGPT connectors are really useful! Chat can now access Gmail, Google Cal, and Drive, so it can: -Skim unread emails & give a summary -Summarize threads + draft replies -Pull key info from old convos -Do meeting prep + agendas Before I get into some prompts you can copy, quick reminder that enabling connectors gives ChatGPT view access to your private data. I'm an AI power user and it's part of my job to test everything, so personally, I make the sacrifice for the extra features. But please be careful if your job is data-sensitive! 1. Email Intelligence Get ChatGPT to skim your recent unread emails, give you a summary, and rank them in importance: "Summarize all my unread emails in Gmail that I received over the past 48 hours. Rank it in order of importance." 2. Search & Data Extraction Get ChatGPT to find threads and draft in your tone "Find all my emails with [Jennifer], then compare it to my most recent email from [Jennifer]. Give me the takeaways in bullet points. Then, draft a short reply in my tone based on that context." 3. Inbox Memory Get ChatGPT to pull key info from old convos and compare to recent emails: "When is OpenAI DevDay this year and when was it during the previous years (2023, 2024)? What makes this year different. Use a mix of internet information and my email inbox context" 4. Meeting Briefs / Agendas Get ChatGPT to look at your Google Cal and your email to prep for upcoming meetings "Look at my next Google Calendar event. Give me a rundown of the person and the event with context from my Gmail so I come prepared. Also suggest a meeting agenda." I'm just the surface here on how you can use these connectors, but they've been very useful so far as someone who spends hours in my inbox every day If you enable them, I would love to hear your use cases! Will be featuring the top responses in The Rundown (1M+ readers!)

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

This is how I use ChatGPT for my daily software engineering tasks and end up saving 20 hours per week. Nothing fancy. Very simple. But when integrated into your workflow, it saves time, and clears a lot of mental load. 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗙𝗮𝘀𝘁𝗲𝗿  • Slack and Teams threads piled? – Copilot. No more scrolling through chaos.  • Long email chains? – One clean summary and I’m instantly caught up.  • Missed a meeting? – AI gives me the recap + action items. 𝗖𝗼𝗱𝗲 𝗦𝗺𝗮𝗿𝘁𝗲𝗿  • Generate edge test cases – Always catches one or two I didn’t think of.  • Refactor messy functions – It gives 2–3 cleaner versions with reasoning.  • Boilerplate – Handles class templates, config files, repetitive setup.  • Catch logical bugs – Just explain it to AI, it spots issues. 𝗪𝗿𝗶𝘁𝗲 𝗕𝗲𝘁𝘁𝗲𝗿 & 𝗙𝗮𝘀𝘁𝗲𝗿  • Write README files – Explain the project casually, AI formats it like a pro.  • Generate API docs – Paste code, get clean documentation in Markdown.  • Turn comments into diagrams – Use GPT + Mermaid to visualize instantly.  • Write JIRA & PR summaries – Rough bullets in, clean descriptions out.  • Respond to tricky emails – Start in Hinglish or native lang, AI fixes the tone.  • Draft cold emails or intros – AI helps me phrase what I used to overthink. 𝗧𝗵𝗶𝗻𝗸 & 𝗘𝘅𝗽𝗹𝗮𝗶𝗻 𝗖𝗹𝗲𝗮𝗿𝗹𝘆  • Brainstorm solutions – Ask GPT for 2–3 design options with pros/cons.  • Simplify concepts – “Explain like I’m new” always gets the job done.  • Create onboarding guides – Dump notes, AI turns them into clean docs. 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗲 & 𝗣𝗿𝗲𝘀𝗲𝗻𝘁  • Turn bullets into slides – Use tools like Napkin to create visual decks fast.  • Write posts or announcements – Start with bullets, let AI expand clearly. 𝗖𝗹𝗮𝗿𝗶𝘁𝘆-𝗗𝗿𝗶𝘃𝗲𝗻 𝗛𝗮𝗯𝗶𝘁𝘀  • Use AI as a thinking partner.  • To get a good answer, you first have to ask a good question.  • That act forces clarity.  • Even before AI replies — you already understand better.  • I call this the 𝘾𝙡𝙖𝙧𝙞𝙩𝙮 𝙇𝙤𝙤𝙥. This isn’t the future. This is today’s reality. Include AI in every possible way in your workflow and become a 10X Dev. Future is for 10X Devs. P.S.: How are YOU using GenAI in your workflow? 

The moment you find a way to give agentic AI like Claude Code access to all of your corporate tools and context, everything changes. New AI drops every week. Cerebras, Opus 4.6, OpenClaw, Moltbook. I'm sure several more launched while I was writing this post! I'm overwhelmed trying to keep up while actually trying to stay on top of my job. So here's one concrete thing that's actually changed how I work. The problem with every ChatGPT-style tool is that you are the integration layer. You copy from Jira, paste into the chatbox. Copy from Slack, paste into the chatbox. The AI is smart, but it's totally blind. You're doing all the research so the AI can do the thinking. So... a couple weeks ago I started using Claude Code with MCP connections to my actual work tools — Gmail, Calendar, Slack, Jira, Google Docs, Snowflake, etc — and something clicked. It's not just that it has context (though that matters a lot). It's that it's actually agentic. It doesn't just answer questions — it takes actions. It searches, cross-references, drafts, creates (with your permission, of course!). It operates across your systems instead of waiting for you to spoon-feed it. I asked Claude: "What's the current state of the current migration project? Check Jira, Slack, and the design doc." And of course, Claude just did the work: searched my real systems. Pulled threads together on its own, no 15-minute research manual context gathering phase before I could even start thinking. The bottleneck hasn't been model intelligence for a while now! It's context and agency. Once the agent can see your systems and act on them, the whole workflow inverts. You stop doing the work and start directing and refining the work. "Draft a follow-up email to yesterday's design review — pull the notes from the doc and the attendee list from the calendar invite." It does the research, writes the draft, you ask for revisions, make your edits, remove the still-too-real AI slop effect as much as you can, and send. That shift matters most for managers. Our days can sometimes feel like 70% or more context-gathering and 30% judgment calls. AI that can't see our systems only helps with the 30%. An actual agent eliminates the entire gathering phase. There's a million AI tools fighting for your attention right now. Ignore most of them. Connect one agent to your real work systems and see what happens.

SO many times I've wished I could talk to my Gmail and ask questions. But since Google hasn't set up those tools yet (get crackin' Alphabet!), I created a fairly easy AI workaround to make it possible. The idea with this is that you'll use a Google App Script (don't worry, you don't need to know code!) to analyze your messages. Here's how it works: 1. Put a Google label on any emails that you want to review. That might be all emails from a certain date range, or just the ones sent to a certain person or company. This is an important first step, because AI can't read your entire inbox at once. 2. Ask ChatGPT to create a Google App Script for you using this exact prompt: “Can you create a Google Apps Script that pulls all Gmail messages with the label ‘[Your Label Name]’ into a spreadsheet with columns for Date, To, From, Subject, and Body?” You’ll get a clean, copy-paste-able script that you can drop into https://script.google.com. When you run the script, it’ll export every email with that label into a Google Sheet — no coding experience required. 3. Once your messages are in your Sheets (or an exported spreadsheet) you can ask ChatGPT to help you analyze them. You can say things like: How many times did I email this person in June?” OR “Summarize my back-and-forth with this client.” OR "Are there any pending to dos for this account?" This AI workflow is one of the most helpful ways I’ve found to review my own communication, whether I’m wrapping up a project, prepping for a meeting, or just making sure I didn’t forget to follow up. It's literally saved me half a day of analysis every months. Happy to share the exact script and prompts I've use if you want to try it!

When I ask journalists how they're using ChatGPT (or other LLMs), many/most are quick to go on the defense and tell me they've never used it. But honestly? That's a mistake, because these tools can help you do some really cool things — like managing your biggest stressor (your inbox). Three examples from my week: I always see the Gemini box pop up on my Gmail, but have never typed anything into it. I asked it to try two things, and got brilliant results. Prompt 1: Please analyze any email pitches that have come in between March 1-April 14 that mention golf, and suggest a few unique story angles for publications based on the contents. Result: I got three really smart angles here, and Gemini even included publication suggestions. Keep in mind, I've literally never used Gemini before, so this tool has no "training" from me, but recommendations felt quite reasonable. Prompt 2: Can you please pull out any email received in 2026 that includes the phrase "press trip invite"? Please include the subject line of the email, the date received, the location of the trip and trip dates. Result: It perfectly extracted these from my inbox and put them together in a spreadsheet for me, even pulling in an invite I'd missed because the press trip part of the email wasn't mentioned in the subject line. Whew! Prompt 3: Can you please go through my inbox and find any single-day event invite emails from the past two weeks, then craft a short and sweet reply that respectfully declines? (All of these are not in my home base, FYI.) Result: It drafted quick replies tailored specifically to each one, formatting them in a way that's easy to copy and paste, and including hyperlinks to the specific emails so I can click through, paste and send. I'm not going to use AI to reply to most emails, but for batching tasks like this (and not leaving people hanging), it saved me a ton of time. Would you try any of these prompts for analyzing emails? What other ways are you using AI to hack your inbox?

One thing that has always felt broken about AI at work is this: The model can sound smart, but most of the time it has no clue how your company actually works. In partnerships, the real value is not just finding an answer. It’s being able to create something useful from context scattered across emails, docs, tickets, and past decisions. For me, that could be an exec brief before a partner meeting, a joint account plan, or a follow-up note that actually reflects the product history, open issues, prior commitments, and where the relationship really stands. The hard part is not writing. It’s making sure what you create is grounded in reality. The announcement is simple but important: Glean's MCP server now brings real company context directly into ChatGPT and Claude! I've put more info in comments. With Glean’s MCP server, ChatGPT and Claude can work against real company context: docs, tickets, emails, decisions, with permissions intact and information staying up to date. That matters a lot. Instead of copying sensitive information into a chat window and hoping for the best, teams can ask things like: • What changed in the Q1 roadmap? • What did we decide in the last review? • Can you summarize this project and point me back to the source? And what comes back can be grounded in the actual source material, not just a polished guess. What makes this different for me is simple: this is not AI pretending to understand the business. It can work from the actual context, point back to the source, and stay inside the same permission model your company already trusts. That is what makes it useful. And that is what makes it real for work. #WorkAI #EnterpriseAI #MCP #ChatGPT #Claude #Glean

We built an AI agent system that takes 40% of inbound work off our GTM team   Most teams don’t struggle with inbound replies because writing is hard.   They struggle because replies show up without context, without prioritization, and without a clear way to decide what should happen next.   This is the AI agent orchestration we use so humans only touch the conversations that actually matter.   Here’s how it works, step by step.   STEP 1: CAPTURE ALL REPLIES → Email replies, LinkedIn replies, and form replies are captured in one place using Masterinbox.com → This removes inbox fragmentation and ensures no conversation is missed   STEP 2: SCORE THE LEAD → Each reply is scored from 1 to 10 based on intent and fit using AI scoring in Clay or n8n → This creates an early priority signal before a human ever reads the message   STEP 3: ATTACH CONTEXT → Each reply is linked to the correct contact, account, deal, and last message using CRM data from HubSpot or Attio → This ensures responses are grounded in full conversation history   STEP 4: CLASSIFY INTENT → Replies are classified as interested, not now, out of office, spam, or other using ChatGPT or Gemini → The output is a clear label that downstream logic can reliably act on   STEP 5: PICK THE RESPONSE PATH → Intent and score together determine whether to auto-reply, ask follow-up questions, book a meeting, or route to a human → Decision rules are managed in n8n or Clay   STEP 6: SEND THE RESPONSE DRAFT → Responses are generated using GPT or Gemini → Messages are either sent automatically or prepared for human approval based on risk and value   STEP 7: LOG THE OUTCOME → Conversation outcomes are written back to the CRM in HubSpot or Attio → Attribution is updated so future decisions improve over time   That’s the full orchestration.   Not an AI that replaces humans. Not a generic reply automation.   Just a system that keeps humans focused on the conversations that actually matter.