Automating Repetitive Work Tasks

We sent 4,495 AI SDR emails in 2 weeks and achieved the #1 response rate on our platform. But here's what nobody tells you about making AI SDRs actually work... The Metrics: ✅ 4,495 personalized messages sent in 14 days ✅ Highest response rate on our entire platform ✅ $700,000 of pipeline opportunities opened ✅ Meetings booked daily (literally got one this morning) ✅ Outperformed all our historical human SDR averages — mostly ✅ Better results than some of our human AEs The Reality Check First We had unfair advantages. SaaStr has been around since 2012, we've sold $100,000,000 in sponsorships, and people know our brand. We targeted our existing database—website visitors, past attendees, lapsed accounts—not cold lists. We spent 2 weeks doing basically nothing else: 90 minutes every morning, 1 hour every evening training our AI, plus real-time responses throughout the day. 👉What Actually Works: 1️⃣ Your AI has to add real value, not just volume There's no way we could send 4,495 good emails ourselves manually in two weeks. The key is each one has to be at the level we would write ourselves. Bad: "Hey [NAME], saw you visited our website" Good: "Congrats on your new VP role at Oracle. Since you attended SaaStr London last year, thought you'd want to know about our 2025 VC track with speakers from a16z and Sequoia..." 2️⃣ Your data is messier than you think We trained our AI on 20+ million words of SaaStr content, but still found: - Opportunities never logged in Salesforce - Missing context from AEs who never used the system - Customer relationships that existed nowhere in our CRM We literally spend time every day finding things that were missing and manually adding them to AI's knowledge base. 3️⃣ Human-in-the-loop isn't optional When prospects respond to your AI, YOU have to respond instantly at the same quality level. We have it hooked up to Slack—our phones go off at all hours because SaaStr is global. The AI creates an expectation of responsiveness. You better match it or they'll know it was "just an AI email." 5️⃣ This is additive, not replacement We still do personal emails, marketing campaigns, and have human SDRs. Results by campaign type: - Website visitors: Hit or miss - Cold outbound: Ranked 4th out of 4 campaigns - Lapsed renewal accounts: Really good results 🏋🏽♀️ The Uncomfortable Truth: It's MORE work, not less. You get 10x better output, but it requires S-tier human orchestration. E.g., we're running 30+ personas across different campaigns. 🔮 Bottom line: AI SDRs work incredibly well, but only with proper training and orchestration. After 60 days of daily improvements, you'll have something you're proud of. But you can't skip the daily 30-45 minute audit process. Full breakdown with all our tools and processes at link in comments.

Early in my customer success journey, I was tasked with onboarding a massive conglomerate. - Multiple business units - Varied requirements - Distributed teams - Hundreds of stakeholders This was before we even started building their onboarding plan. And if you've ever onboarded an enterprise client, you know the chaos: - Conflicting asks - Email threads that never end - People looping in and out without context We didn’t want that. We wanted every stakeholder to feel “This was the smoothest onboarding we’ve experienced.” So we rolled up our sleeves and built one of the most detailed onboarding plans we’ve ever created. No fancy tools. Just a spreadsheet. And honestly? It worked. But it came at a cost: Manual updates. Constant syncs. High risk of misalignment. Fast forward to today, I wouldn’t recommend doing it the same way. Because now, tools like Flowla 🌊 exist that can automate a LOT of things. Instead of juggling spreadsheets, Slack threads, and email chains, you can give every client a single link that does the heavy lifting: - Aligns internal and external teams - Triggers onboarding tasks automatically - Flags blockers before they escalate Keeps everyone on track without micro-managing. From chaos to clarity, in one collaborative workspace. For CS teams managing complex accounts, this is a game-changer. -- ♻️ Reshare if this might help someone. ▶️ Join 2,485+ in the Tidbits WhatsApp group → link in comments

I love having AI review specific types of emails I get to see if they're worth my time. Today I helped my friend Alec Cheung - a fellow marketer in the CMO Coffee Talk community I'm a member of - set up one of these AI automations and it turned out awesome, so I wanted to tell you about it (with his permission of course). Here's the backstory: Going through website form submissions was eating up too much marketing team time. The submissions (which have a place for open-ended inputs that make them hard to screen via traditional methods) flow through Pardot and trigger emails that required human review to figure out if they were genuine leads or just spam. Tons turn out to be junk, creating unnecessary work for Alec's team. Others are genuine leads (or actual paying clients) that a human definitely needs to follow up with. Alec posted in our CMO Coffee Talk AI channel a few weeks ago asking for ideas on how to improve their process with AI. This is a super simple automation, so I offered to help if he'd let me share what we did. He was in! Our fix was AI-powered automation using Zapier. Here's the flow: 1️⃣. A visitor submits a form on Alec's company's website. 2️⃣. Pardot sends an email with the form contents to a designated address. 3️⃣. Zapier kicks off the automation. 4️⃣. GPT-4o reads, analyzes, and categorizes the submission. 5️⃣. Based on the categorization, the automation filters the next steps. 6️⃣. If a human needs to attend to the email, it emails them (along with the appropriate categorization, directly in the flag email's subject line). If it does not require a human, then nobody is notified about the form submission. The result is a far more streamlined workflow that will save a lot of monotonous manual effort. And it's CHEAP. It looks like this automation will cost Alec around $0.10-$0.20 per day in AI usage to run - yes, cents, not dollars. It's an incredibly affordable solution for such a big time (and headache)-saver. Alec was new to using Zapier for this kind of task, so we had 2 calls. First, we planned the planned process, and I gave him some homework, and then today we finished everything. (And he gave me the okay to share with you!) While the setup is pretty straightforward, it does require a base knowledge of Zapier, and how to write solid AI prompts (this isn't something for AI newbies to take on alone). For those in CMO Coffee Talk, I'll go much deeper into this automation during our 2/19 mid-week Zoom "What Every CMO Needs to Know About How AI is Evolving in 2025: Automated Workflows, Agents, and Reasoning Models." Also - if you're a marketer and want to explore AI automations, I do get into that in my Foundations of Generative AI for B2B Marketing course (🔗 in bio). --- UPDATE: A recording of the webinar for CMOs I mentioned above is now available! More details at: https://lnkd.in/gxe9EQ69

Everyone is talking about AI SDRs being the next hot thing in Outbound. No one’s talking about what they can actually do (and how they work). Here are the real questions: → What does an AI SDR Outbound workflow look like? → How are top sales teams using AI SDRs right now? → Can AI SDRs really replace human reps (or will they just support them?) → How do you balance AI automation with personalization in Outbound? → Which AI SDR tools are actually delivering results (and which aren’t?) → What do AI SDR's help us with? speed, accuracy, or something else? These are the typical AI SDR workflows for an Outbound motion (so far): 🔎 Find & Contact Leads → AI can build prospect lists based on your ICP. ⚡️ Enrich Data → It can verify emails, phone numbers & LinkedIn profiles. 🧑🏻💻 Scrape Websites → It can also analyze technographics, firmographics, services, and other data like hiring trends, company size etc 🚨Track Intent Signals → It can monitor funding news, job postings & social activity. ✅ Qualify Leads → It can score prospects on fit & engagement. 📧 Send Personalized Emails → It can deliver context-rich messages at scale. 💙 Categorize Responses → AI can sort replies: interested, follow-up, not now. 📅 Book Meetings → It can also schedule qualified calls. 🔂 Update CRM → And ofcourse it can ensure smooth handoff to human reps by updating everything in your CRM Just keep in mind that some AI SDRs can manage the entire workflow, while others can focus only on specific tasks (e.g RevReply). Here are some other cool 🥶 features AI SDR's have: ✅ Custom Training → AI can adapt to your ICP, messaging & CRM insights. ✅ Multi-Channel Campaigns → AI can run LinkedIn, email & Outbound at scale. ✅ Industry-Specific AI → Some AI SDRs have pre-trained models for Marketing, HR, finance, SaaS, & eCom etc ✅ Smart CRM & Slack Sync → AI can learn from interactions & adjust on the spot. And here are a few AI SDRs that the SalesCaptain team has been testing: ↪ Artisan ↪ AISDR (YC Backed) ↪ 11x ↪ Patagon AI ↪ Regie.ai ↪ RevReply ↪ Swan AI ↪ settr. ↪ Topo (YC W24) So here's my conclusion on AI SDR's: ✨ AI SDRs are not here to replace human reps (yet). ✨ They are good at automating simple, repetitive workflows. ✨ We still need reps to be on top of AI. ✨ At the end of the day, performance depends on how you are leveraging them. Have you tested any AI SDRs? What’s working (or not) for you? Drop me a comment below! 👇 #aisdr #salesautomation #outboundsales #leadgeneration

“𝘛𝘩𝘢𝘯𝘬𝘴 𝘧𝘰𝘳 𝘳𝘦𝘢𝘤𝘩𝘪𝘯𝘨 𝘰𝘶𝘵! 𝘞𝘦’𝘭𝘭 𝘨𝘦𝘵 𝘣𝘢𝘤𝘬 𝘵𝘰 𝘺𝘰𝘶 𝘢𝘴 𝘴𝘰𝘰𝘯 𝘢𝘴 𝘱𝘰𝘴𝘴𝘪𝘣𝘭𝘦.” That’s the sentence @Matthew killed first when he became VP of Support. He said: “If your first message doesn’t help the customer… it’s just noise.” So they replaced the auto-reply with something smarter. Now, before a human even sees the ticket, AI jumps in to: 🧠 Summarize the issue in plain English ⚠️ Flag urgency based on tone and keywords 🔁 Suggest the most likely next step It’s not trying to resolve the issue. It’s teeing it up—so the agent can. And that first message goes out within seconds. Not a deflection. Not a promise. A head start. Here’s what happened next: - Agents jumped in with real context - Customers stopped rewriting the same tickets twice - Resolution time went down, even with fewer people on shift Then they went one step further. If the AI doesn’t find enough context to summarize the issue, it automatically asks the customer for a screen recording—via Birdie. That way, agents get a short video of the issue, plus network and console logs, all in one go. Less guessing. Fewer follow-ups. More first-contact resolutions. Auto-replies are dead. Modern teams use AI to triage for humans, not replace them. If you're still using “𝘞𝘦’𝘭𝘭 𝘨𝘦𝘵 𝘣𝘢𝘤𝘬 𝘵𝘰 𝘺𝘰𝘶 𝘴𝘰𝘰𝘯,” You're wasting the most valuable message in the entire conversation. Anyone else rebuilding that first reply? I’d love to hear what you’re trying.

Retry Pattern is Good for Resilience, But Only if You Do It Right. Here are 4 things to remember when using the Retry Pattern. The Retry Pattern is a design approach. It enhances reliability and resilience by automatically reattempting a failed operation or request.  𝟏. 𝐒𝐞𝐭 𝐚 𝐑𝐞𝐚𝐬𝐨𝐧𝐚𝐛𝐥𝐞 𝐑𝐞𝐭𝐫𝐲 𝐋𝐢𝐦𝐢𝐭: Determining the right number of retries is critical.  Too few retries might prevent the resolution of temporary issues, while too many retries could lead to excessive load or long delays in recognizing a persistent problem.  I never go over 3 retries.  𝟐. 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭 𝐄𝐱𝐩𝐨𝐧𝐞𝐧𝐭𝐢𝐚𝐥 𝐁𝐚𝐜𝐤𝐨𝐟𝐟: Instead of retrying immediately, implement an exponential backoff strategy.  This means that after each failed attempt, you increase the time before the next retry, not overwhelming the system and giving it time to recover.  Exponential backoff helps avoid a stampeding herd effect, in which all failed requests suddenly hit the system simultaneously after a short time. 𝟯. 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗥𝗲𝘁𝗿𝗶𝗮𝗯𝗹𝗲 𝗘𝗿𝗿𝗼𝗿𝘀: Not all errors are worth retrying. Focus on retrying only transient errors: • 408 Request Timeout • 5XX (Server did something bad) Avoid responses like: • 400 (Bad Request) • 403 (Forbidden) They are not recoverable, so the retry logic shouldn't retry them. 𝟰. 𝗖𝗼𝗺𝗯𝗶𝗻𝗲 𝘄𝗶𝘁𝗵 𝗖𝗶𝗿𝗰𝘂𝗶𝘁 𝗕𝗿𝗲𝗮𝗸𝗲𝗿: The Retry Pattern works well when combined with a circuit breaker mechanism.  A circuit breaker monitors a service's health and prevents repeated calls to a failing service.  If a certain threshold of failures is reached, the circuit breaker opens, temporarily preventing further requests. This gives the service time to recover before attempting retries again. When done correctly, the Retry Pattern minimizes disruptions and optimizes system performance!

Top U.S. sports retailer levels up with GenAI.  How they improved email personalization: Angela Jing & May Khine's infographic breaks down the opportunity, methodology, and results. Opportunity: Dick’s Sporting Goods (DSG) sends one to two daily emails to its subscribers. Previously, these emails were created manually with limited personalization. To enhance efficiency and personalization, DSG sought to combine Generative AI with its demographic, loyalty data, email templates, interactions, and transaction history. Here’s how they did it: 1\ Identify customer segments ↳ Cluster customers by purchase history to identify their content preferences. 2\ Generat email templates ↳ Create personalized email templates for customer segments using an LLM (DBRX) with Retrieval-Augmented Generation (RAG). 3\ Recommend templates for customers ↳ Rank LLM-generated templates and recommend the best for each customer. Results: Deliverables ↳ New LLM-based automated email generation pipeline. ↳ User-friendly web application. ↳ Clear prompt guidelines. Metrics ↳ 65% predicted increase in email relevance. ↳ 18% predicted increase in clicks. ↳ 29% estimated reduction in creation time. P.S. The graphic dives deeper on data scope and methodology. --- ♻️ Repost to help your network! 📌 Want to level up with Generative AI? 1. Just follow me Lewis Walker ➲ 2. Subscribe to my free newsletter. #generativeai #ai

Using n8n with local #AI and #Ollama to categorise emails is proving to be really valuable for me. I've been meaning to sort out my email inbox for, well, let's just say it's been on the to-do list for years. We're talking about a backlog of around 11k emails that needed sorting. I'd been keen on using AI for this task for ages, but the idea of paying API providers didn't sit well with these numbers. So today, I invested a bit of time in building an automation to do the heavy lifting. It reads my emails, tags them with predefined categories, and moves them to the appropriate folders. Invoices, receipts, junk, newsletters, action items - all sorted automatically. For this setup, I'm using the Qwen 2.5 14b model. It's working well with the right prompt engineering. I'm putting together a YouTube video with a template, which should be up shortly. The whole system is set up to run in Docker on Windows using the Windows Subsystem for Linux. If you're interested, I posted a precursor video on YouTube yesterday showing how to get n8n running locally on Windows in preparation: https://lnkd.in/euZUiK9N 😉

Email automation isn’t about spamming inboxes, it’s about creating a thoughtful, trust-building journey that leads to conversions. Here’s a streamlined approach to getting email right: 1. Set a Consistent, Friendly Cadence Nurturing works best with a consistent rhythm. Start with every 2-3 days, then shift to every 5 days after the first month. Aim for around 10 emails in those first 30 days to stay top of mind. If someone unsubscribes, they likely weren’t the right fit anyway. 2. Use Tools That Make Automation Easy Popular tools like Beehiiv, High Level, Salesforce, Pipedrive, and MailChimp offer powerful yet user-friendly options. Choose one that fits into your workflow, so your team can quickly adapt based on what resonates with your audience. 3. Focus on Value Before the “Ask” The best email strategies follow the “Jab, Jab, Jab, Right Hook” model—giving value before asking for anything. For SaaS, this means: - Add value: Share insights, case studies, and helpful content that establish trust. - Mix in a CTA: After a few value-driven messages, add a clear call-to-action—whether it’s a demo, trial, or quote request. - Plan for 7-10 touchpoints: SaaS buyers often need multiple interactions before they’re ready to commit. Patience and consistency pay off. Effective email automation is about staying relevant, delivering value, and building a relationship over time. When done right, you’ll earn not just a sale but a loyal customer who trusts your brand. #SaaSMarketing #DemandGen #B2BMarketing

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!