AI Tools For Communication

Gmail’s AI email assistant writes like a committee of lawyers designed it. Pete Koomen’s recent post Horseless Carriages explains why: developers control the AI prompts instead of users. In his post he argues that software developers should expose the prompts and the user should be able to control it. He inspired me to build my own. I want a system that’s fast, accounts for historical context, & runs locally (because I don’t want my emails to be sent to other servers), & accepts guidance from a locally running voice model. Here’s how it works: 1. I press the keyboard shortcut, F2. 2. I dictate key points of the email. 3. The program finds relevant emails to/from the person I’m writing. 4. The AI generates an email text using my tone, checks the grammar, ensures that proper spacing & paragraphs exist, & formats lists for readability. 5. It pastes the result back. Here are two examples : emailing a colleague, Andy (https://lnkd.in/gtjt3BPp), & a hypothetical founder (https://lnkd.in/gDwM4f22). Instead of generics, the system learns from my actual email history. It knows how I write to investors vs colleagues vs founders because it’s seen thousands of examples. The point isn’t that everyone will build their own email system. It’s that these principles will reshape software design. - Voice dictation feels like briefing an assistant, not programming a machine. - The context layer - that database of previous emails - becomes the most valuable component because it enables true personalization. - Local processing, voice control, & personalized training data could transform any application, not just email, because the software learns from my past uses We’re still in the horseless carriage era of AI applications. The breakthrough will come when software adapts to us instead of forcing us to adapt to it. Centered around a command line email client called Neomutt (https://neomutt.org/). The software hits LanceDB, a vector database with embedded emails & finds the ones that are the most relevant from the sender to match the tone. The code is here (https://lnkd.in/gZ-AaAWa).

How I actually use AI at work (Real examples from my day) When people ask me how I use AI at work, I don’t start with the flashy stuff. It’s really the everyday things that actually make the difference. 🧠 Brainstorming clarity When I hit mental blocks writing product docs or framing complex engineering trade-offs, I’ll prompt LLMs to “explain this to a PM with no infra background” or “turn this email thread into a design doc.” It helps me get unstuck. 📥 Inbox triage Between 0 to 1 podcast guests, content campaigns, and Google work, my inbox gets overwhelming. I use AI to quickly summarize long threads or help me draft professional but human replies. A game-changer. 🎙️ Content repurposing After recording a podcast episode, I drop the transcript into AI to generate ideas for titles, hooks, and social copies. It saves 2–3 hours per episode, and lets me focus on the creative direction instead. 🔍 Prompt engineering = the new shortcutting I don’t copy/paste AI outputs blindly, but I’ve learned to prompt better, faster, and more intentionally over time. Learning how to collaborate with AI is the real unlock. — 🗓️ I’m excited to share this as part of LinkedIn’s inaugural #AIinWork Day! If you’re curious how AI fits into your workflow, I’d love to hear what’s working for you 👇 #AIinWork #ArtificialIntelligence #FutureOfWork

Yesterday I had a call with 2 founders who wanted to make sales outreach more efficient without it sounding like AI slop. Together, we found a counterintuitive way to make that possible. Their sales team was spending hours crafting personalised emails. "Surely AI can help us?" seemed like the obvious question. But equally, they were painfully aware of being on the receiving end of AI generated sales emails and wanted to avoid that. So what could we do? - The starting point for any AI project is doing a process breakdown. So I asked them to walk me through their actual process. Step by step. Turns out they were spending 10 minutes checking data from multiple different places before even opening Gmail. The bottleneck wasn't writing the emails at all. It was gathering data. So instead of discussing how to use AI to write the emails, we discussed how to use it to solve the data issue. - The team was non-technical, but AI has opened up a world for non-technical people to start building technical solutions themselves. They had already experimented with asking Claude to write little bits of code (and asking Claude to teach them how to run it). Now is their opportunity to take that a step further. We discussed building a tool which brings all the relevant data together so the sales rep has everything they need to craft that perfect, personal email. We discussed how this tool could also help them better prioritise who was urgent to contact. And for bonus points, we also discussed using AI transcription tools like Superwhisper or Wispr Flow so the rep can 'speak' the email instead of typing. - With all this, I reckon we could get a 15 minute process down to a couple of mins PLUS potentially even making the emails BETTER by making all the context more accessible AND it's also way more fun for the sales team, who get to focus on building relationships instead of trawling spreadsheets. AND it's also better for the customer, saving them from yet more sales slop in their inbox win win win win! - Not good for my sales numbers though, as I gave them the DIY solution instead of pitching something complicated for Artanis to build for them ;) I think the best AI implementations are often invisible. They're solving the real, hidden inefficiencies that create bottlenecks, and enabling humans to do more of what they do best. Does this spark any idea for hidden bottlenecks AI could solve for you? -- 👋 Hi, I'm Laura Rosenberger I write about: • getting into AI if you're non-technical • building AI (that actually works) with Artanis • creating genuine impact in your business from AI

AI is going to revolutionize meetings and it's going to do so four ways: 1️⃣ Meeting Efficiency We already have tools such as Calendly to improve scheduling efficiency. AI will make that faster and easier as well. Also important is identifying exactly who needs to be there for the active meeting. There are still too many meetings we attend where we are essentially "cc" attendees, interested in the content/outcome but not necessarily part of the discussion. 2️⃣ Meeting Effectiveness AI will help create agendas based on the meeting's objectives, attendees and related content/threads/conflicts seen in other channels/formats. That agenda will be dynamic - some agenda items might disappear right before the meeting for example if the issue has already been resolved in email or no longer requires the full group's attention. AI can also help with time management, assigning time slots for topics based on depth of discussion needed and give gentle reminders when it's time to move on (AI could also gently redirect the meeting if the discussion gets off topic or goes into a rabbit hole). 3️⃣ Meeting ROI Too often we rush from one meeting to the next and a lot of action items are lost, forgotten or ignored. AI will automatically capture and assign action items and help hold attendees accountable for follow-up. This will flow into whatever work management systems you use - Adobe Workfront, Asana, Outlook Tasks, whatever. 4️⃣ Meeting Kaizen In other words, how can we make the next meeting better? AI will give constructive feedback on how to improve all above elements - whether the meeting was necessary at all, whether it was successful, how to manage agenda/attendees/discussion/follow-up next time. Doing all of this manually would make meetings far more effective today. AI will do the hard work for us in the not so distant future.

How I use Claude.ai to save time and hassle chasing submissions from businesses We’ve all had to wait on forecast information, or received incomplete submissions and then had to chase. The good news is that automating this task, which isn’t my favourite(!) is quick and easy using AI. To give an example, I’ve generated a script using a simple prompt in Claude.ai to read reports (in this case csv, word, pdf and powerpoint) and query unclear items or where an explanation is needed. This saves time and allows me to move onto other things. Here’s what the script does: Reads reports in CSV, Word (DOCX), PDF, and PowerPoint (PPTX) formats Automatically detects the file type and processes accordingly Analyzes the report content for unclear items or those needing explanation Generates a formatted HTML email with the findings Sends the email to relevant stakeholders Saves a copy of the email content as an HTML file for record-keeping This versatile automation can save hours of manual work across different document types and ensure that important issues are promptly addressed. And it’s not just useful for treasurers, it can be used by project managers, team leads, and anyone dealing with regular reporting processes using various file formats. Key features of the script: Uses pandas for data manipulation Implements file type detection and appropriate reading methods Utilizes libraries like python-docx, PyPDF2, and python-pptx for different file formats Generates clean, formatted HTML emails using Arial font Easily customizable for different report formats and email content I've included the full code in the comments below. Feel free to adapt it to your needs or reach out if you have any questions! This is just one example of the type of automation that we’re focussing on at Your Treasury - AI your way I’d encourage you to go to Claude.ai and try creating something similar yourself. #Python #Automation #ProjectManagement #DataAnalysis #EmailAutomation #DocumentProcessing ——————————————————- Example prompt Create a Python program where a user uploads a file and python then detects the file type (doc, excel, pdf, PowerPoint) and Python then reads a report and send out emails to relevant stakeholders if anything is unclear or needs more explanation. Produce example code to show this, including the report and a sample email which should be saved as an html file and formatted in Arial text. Prompt me if you have any questions

The Hidden Cost of 'Professional' Emails (And Why I Stopped Writing Them). I've never been one for the polished, perfectly crafted emails. You know the ones - filled with fluff, niceties, and a whole lot of nothing. But even I found myself slipping into the trap of using AI to make my emails sound "just right." Here's the kicker: That 'professionalism' often meant sacrificing authenticity, and the results weren't pretty. AI is fantastic for generating ideas and organizing thoughts, but it can also lead to emails that sound more like a corporate script than a human conversation. Every time I leaned into crafting the "ideal" email with AI, it felt like I was putting on a suit that didn't fit. The more I tried to play the game, the less I connected with the people on the other side and the lower my response rates got. I get TONS of AI generated emails and no matter how "personalized" they are, I can always tell they're written by AI and the only way I can explain it is that they just don't have a soul. Let me give you an example. There was a time when I was trying to write the perfect email to hold someone accountable for ghosting me. I tried a "professional persistence" version, hoping to sound polished and firm. But then, in a moment of frustration and honesty, I simply typed "Did I lose you?" in the subject line and hit send. I got an immediate response. Why? Because it was genuine. It cut through the noise and spoke directly to the person on the other end. (type "ghost" in the comments if you want the link to how i use that email to get an 80%+ response rate when being ghosted) So I ditched the act. I went back to basics - clear, direct communication with a personal touch. My emails are back to being an extension of my conversations, where the only polish was in the sincerity of my words. And guess what? My response rates improved. People didn't respond to the professionalism of the email. They responded to the realness. So here's a thought: Next time you hit 'compose,' ask yourself if you're writing to impress or to connect. Trust me, when you speak from the heart, people listen. #MakeItHappen #Sales #Authenticity #AI

What kills collaboration faster than conflict? Silence. How AI can fix it.     We've all been there: a meeting ends, everyone nods, no one asks questions... and yet, the project still goes sideways. The truth? Silence doesn’t mean clarity. Silence in teams can feel like alignment, but it's often confusion in disguise. It usually means someone didn’t feel safe or empowered to ask for it.   Even the best teams hit roadblocks:   Misunderstandings from assumptions Hesitation to ask questions Miscommunication that leads to rework   These challenges aren't new, but the way we tackle them can be.   This is where AI can quietly transform how your team collaborates. By acting as a neutral, judgment-free assistant, AI makes it easier for people to understand questions, clarify tasks, and stay aligned without fear of “looking dumb.”    Here's how:   ✅ Clarify complexity – AI can quickly summarize dense threads, documents, or meeting notes. ✅ Encourage curiosity – With the right prompts, AI makes it safe and easy to ask “obvious” questions. ✅ Keep teams in sync – AI can reinforce shared goals and priorities without sounding repetitive. It’s like adding a smart, impartial facilitator to every meeting, every teams thread, every project doc.   💡 Try this prompt to get started: "You are a helpful team assistant. Whenever I ask a question, respond with a reasonable amount of detail to help the team work together effectively." Simple but powerful to make missing information to all team members visible.     Ready to bring this into your team culture? Start with these steps:   1. Pick one team ritual (e.g., weekly meeting, retros, or docs) and layer in AI support. Let AI summarize, generate follow-up questions, or identify unclear points. 2. Encourage “clarifying questions” as a norm, not a nuisance. Use AI to increase curiosity and good inquiry. 3. Train with prompts. Craft a few go-to prompts your team can use in AI tools like Co-Pilot or whatever tool you use.   Collaboration doesn’t break down because people don’t care. It breaks down when people don’t feel clear and get frustrated.

When your most capable (and most expensive) minds spend half their day in meetings, but outcomes rely on memory, you’re essentially running your business on Snapchat, where communication is ephemeral and actionability hinges on recall. Agentic AI transforms ephemeral conversations into persistent, strategic assets that compound in value over time. Leaders can now extract insights automatically, generate agendas from past discussions, and invite only the necessary people, while ensuring the output is broadly accessible. Instead of vanishing into thin air, meetings become a searchable, mineable system of record that fuels organizational intelligence — whether or not you were in the room. https://lnkd.in/g5F4prwH via GeekWire, Mark Briggs, Read AI

Everyone has the same AI tools, and that’s the problem. Clay. Apollo. Seamless.ai. Jason AI... The list goes on. Your competitors are using the exact same "personalization" tools you are. Result? Your prospects are getting 47 emails that all mention their recent LinkedIn post about Q4 planning. All "hyper-personalized." All generated by AI. All sounding exactly the same. We democratized the tools but not the strategy. This is happening across company stages: Teams think buying better AI tools will fix their outbound problem. It won't. Here's why AI-first outbound is broken: 1. Same inputs = same outputs Everyone's scraping the same LinkedIn posts, company news, and tech stack data. Your "unique" insight isn't unique. 2. AI optimizes for volume, not relationship These tools help you send 1000 emails that sound personal. They don't help you have 10 conversations that matter. 3. Recipients can smell automation When your "personalized" email mentions their job change but gets their new company name wrong—you're done. The solution isn't better AI. It's better human-AI collaboration. Here's what could actually work for you going forward 👇🏼 Use AI for research, humans for insight. Let AI pull the data. But you need to interpret what it means for their business. Use AI for first drafts, humans for authenticity. AI can write the structure. You add the perspective that only comes from real experience. Use AI for scale, humans for key accounts. Automate the mass outreach. But your biggest opportunities deserve human attention. Unfortunately, most teams use AI to avoid the hard work of understanding their prospects. They'd rather send 100 AI-generated emails than spend 30 minutes researching 5 key accounts. But here's what separates winners from spammers: → Winners use AI to do the research faster, then apply human judgment to create genuine insights. → Spammers use AI to avoid thinking altogether. Your prospects can tell the difference. When everyone has the same tools, execution becomes the differentiator. Not the email you send. The thinking behind it.

Stop blaming ChatGPT, Claude , or Grok for bad outputs when you're using it wrong. Here's the brutal truth: 90% of people fail at AI because they confuse prompt engineering with context engineering. They're different skills. And mixing them up kills your results. The confusion is real: People write perfect prompts but get terrible outputs. Then blame the AI. Plot twist: Your prompt was fine. Your context was garbage. Here's the breakdown: PROMPT ENGINEERING = The Ask CONTEXT ENGINEERING = The Setup Simple example: ❌ Bad Context + Good Prompt: "Write a professional email to increase our Q4 sales by 15% targeting enterprise clients with personalized messaging and clear CTAs." AI gives generic corporate fluff because it has zero context about your business. ✅ Good Context + Good Prompt: "You're our sales director. We're a SaaS company selling project management tools. Our Q4 goal is 15% growth. Our main competitors are Monday.com and Asana. Our ideal clients are 50-500 employee companies struggling with team coordination. Previous successful emails mentioned time-saving benefits and included customer success metrics. Now write a professional email to increase our Q4 sales by 15% targeting enterprise clients with personalized messaging and clear CTAs." Same prompt. Different universe of output quality. Why people get this wrong: They treat AI like Google search. Fire off questions. Expect magic. But AI isn't a search engine. It's a conversation partner that needs background. The pattern:  • Set context ONCE at conversation start • Engineer prompts for each specific task  • Build on previous context throughout the chat Context Engineering mistakes:  • Starting fresh every conversation  • No industry/role background provided  • Missing company/project details • Zero examples of desired output Prompt Engineering mistakes:  • Vague requests: "Make this better" • No format specifications  • Missing success criteria • No tone/style guidance The game-changer: Master both. Context sets the stage. Prompts direct the performance. Quick test: If you're explaining your business/situation in every single prompt, you're doing context engineering wrong. If your outputs feel generic despite detailed requests, you're doing prompt engineering wrong. Bottom line: Stop blaming the AI. Start mastering the inputs. Great context + great prompts = consistently great outputs. The AI was never the problem. Your approach was. #AI #PromptEngineering #ContextEngineering #ChatGPT #Claude #Productivity #AIStrategy Which one have you been missing? Context or prompts? Share your biggest AI struggle below.