AI Workflow Enhancement

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.

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

How I Built My Personal AI Assistant That Runs My Entire Business As entrepreneurs, we're drowning in repetitive tasks. But what if I told you my AI reads hundreds of articles daily, manages my expenses, schedules meetings, and handles emails - all automatically? Here's the exact n8n workflow running my business: ✅ Daily AI news summaries from 3+ sources ✅ Automated expense tracking with smart categorization ✅ Email management with contact verification ✅ Task creation in Notion with priority setting ✅ Calendar integration preventing double-booking Cost: <$10/month (vs hiring a VA) Time saved: 15+ hours/week ROI: Immediate This isn't just automation - it's business transformation. The future belongs to entrepreneurs who leverage AI to amplify their capabilities, not replace their creativity. What business process would YOU automate first? #Automation #AI #Entrepreneurship #n8n #BusinessEfficiency #NoCode

I spend a huge part of my week just managing my calendar — finding free slots, rescheduling meetings, dealing with recurring events, and juggling multiple time zones. It’s tedious and eats into real work. That’s why I decided to build my own solution: a Google Calendar AI agent powered by Google’s Agent Development Kit. This agent can: 👉 Understand plain English commands like “Schedule a 1-hour call with Alex next Tuesday morning”. 👉 Suggest free time slots based on my existing calendar. 👉 Handle recurring events, cancellations, and attendees automatically. 👉 Work across time zones without any manual conversion. While building this, I learned something crucial: AI isn’t just about generating text — it can actually perform actions that solve real problems. Designing this agent taught me how to bridge natural language understanding with real-world API actions. I wrote a detailed step-by-step blog, including code snippets and logic, so anyone can replicate this setup or build their own AI productivity assistant: https://lnkd.in/dsDhtcMr #AIAgents #AgentDevelopmentKit Google Cloud #GoogleAI #GoogleCalendar #CalendarManagement #AgenticAI

Staying updated as an SAP Consultant in the era of AI involves several proactive steps to ensure you remain relevant and competitive: 1. Continuous Learning : AI is rapidly evolving, and so is SAP's integration of AI technologies. Commit to ongoing learning through SAP's official training programs, online courses (like SAP Learning Hub), or through recognized educational platforms offering AI and machine learning courses. 2. Stay Abreast of SAP's AI Initiatives : Follow SAP's updates on their AI capabilities and solutions. Stay informed about how AI is being integrated into SAP products like SAP S/4HANA, SAP Leonardo, and SAP Analytics Cloud. 3. Networking and Community Engagement : Join SAP user groups, forums, and communities where AI topics are discussed. Participate in conferences, webinars, and seminars focused on AI and SAP to network with peers and stay updated on industry trends. 4. Explore Industry Use Cases : Understand how AI is being applied in different industries using SAP solutions. This knowledge can help you anticipate client needs and position yourself as an informed consultant. 5. Develop AI Skills : While not mandatory to be a data scientist, having a basic understanding of AI concepts, such as machine learning algorithms and natural language processing, can enhance your ability to work with AI-powered SAP solutions. 6. Collaborate Across Disciplines : AI often requires interdisciplinary collaboration. Engage with data scientists, AI specialists, and business analysts to understand diverse perspectives and foster cross-functional teamwork. 7. Monitor Technological Advancements : Keep an eye on advancements in AI technologies outside of SAP. Understanding broader trends can help you anticipate future developments within SAP's ecosystem. 8. Adapt to New Roles : As AI influences SAP's offerings, new roles and responsibilities may emerge. Be flexible and prepared to adapt your skills and expertise to these evolving demands. By taking these steps, you can position yourself as a knowledgeable and adaptable SAP Consultant in the era of AI, equipped to deliver value-added solutions to your clients.

This one’s for Founders & Sales folks. I built an AI agent that cut my sales follow-up time by 90%. Not kidding. From 30 minutes per email... to 2 minutes. And I actually enjoy it now. Let me back up. I hate writing sales follow-ups. → Re-reading call notes → Trying to remember context → Spending hours wordsmithing Even with my system of organized ChatGPT folders with custom deal context, it still took forever. So I did what any founder would do. I built a tool. It sounds much harder than it actually was. I hadn’t built an AI agent before and it only took me 2 hours end to end. Here’s what I used and how it works. ⚙️ Built with: Relay.app (shoutout to Jacob Bank - love what you’re building!) Step 1: I trigger Relay to follow up with a particular deal in Hubspot. Step 2: Relay retrieves deal context from Hubspot (it’s made me much more diligent about making sure my data is up-to-date here) Step 3: Agent reviews the deal and decides if a follow-up is needed. It gives me the following output: Is a follow up required? Yes / No response What kind of follow-up is required? General check-in email, breakup email, nudge with resources (I provided these options for it to choose from). Why did it make this decision? This is really helpful because it gets me up to speed on the deal quickly—when did we last check in, what were their objections or concerns, when is the next expected touch point, and so on. Step 4: I approve or tweak. I tell the agent if it’s right or wrong, or provide context it may not have. Step 5: AI writes a draft email. The first draft hits me within ~20 seconds. I give high-level feedback (e.g., “focus more on timeline urgency”) if necessary. Step 6: AI revises the draft based on by input. At this stage I have an almost perfect draft. I make minor edits if at all and hit send. The whole process takes 2–3 minutes max. Are we all getting replaced by AI in 2 years? Probably. But for now, I’ve outsourced an annoying part of sales and it's amazing.

How AI SDRs actually work? AI SDRs do more than just send emails—they: - identify leads - analyze intent - personalize outreach - qualify prospects - book meetings all at scale. Here’s how they work And the tools that power them: 1️⃣ Lead Identification & Data Enrichment: AI finds and enriches leads using: RB2B → Identifies anonymous website visitors. Breakcold → Checks CRM to avoid duplicate outreach. Trigify.io & Leadspicker → Tracks job changes, funding rounds, and hiring trends. AI scans LinkedIn, job boards, and databases to find buyers Adds context like company news, and ensures outreach is fresh. 2️⃣ Understanding the Prompt & Generating Outreach: AI SDRs don’t just send generic messages. They follow structured prompts that define: - Goal (book a call, follow up, gather info). - Lead details (company, role, activity, pain points). - Tone & personalization (casual, direct, professional). AI also retrieves past conversations to maintain context. 3️⃣ Prioritization & Intent Detection: Not every lead is worth chasing! AI qualifies and ranks prospects based on engagement. How It works: - AI analyzes email opens, LinkedIn engagement, and CRM signals to score intent. - High-intent leads move to a high-touch sequence with immediate follow-ups. - Unresponsive leads are dropped or nurtured passively to avoid wasting time. 4️⃣ Handling Conversations & Lead Qualification: AI SDRs respond over email, chat, or voice and qualify leads using: Humanlinker & Amplemarket → Video and voice note-based LinkedIn outreach. BANT framework: to assess Budget, Authority, Need, and Timeline. If a lead fits, AI books a meeting or routes it to a human SDR. 5️⃣ Automated Follow-Ups & Smart Nurturing: Follow-ups are adjusted based on engagement: HeyReach → LinkedIn DMs. Drippi.ai → Twitter outreach. Cold DM → Instagram messaging. Smartlead / Instantly.ai → Cold email AI changes messaging angle and outreach channel (email → LinkedIn → Twitter) to improve response rates. 6️⃣ CRM Integration & Learning: Persana AI / Airscale / Clay → Optimize targeting and refine outreach. Tracks what’s working and adjusts messaging automatically. 7️⃣ Multichannel Outreach: If email fails, AI shifts to another channel where the lead is active. They find, engage, and qualify leads automatically, Allowing human reps to focus on closing deals. If you're not using AI for outbound, you're already behind. #ai #aisales

Using #AI to extract information from documents to put it into the system is not a new discipline…   …and it has gotten much easier to scale with #generativeAI. With SAP Document AI, we already process billions of documents per year, handling over 50 document types such as invoices or contracts, and being able to understand more than 100 languages. However, a big gap remains: You never get 100% accuracy out of the box, because the remaining 10-20% are a last-mile-problem, slowing down teams and limiting adoption. Sometimes, even a human being has a hard time figuring out in a document where the material number is located.   For example, our customer Tyrolit Group, a leading manufacturer of grinding and drilling tools, had already an excellent out-of-the-box accuracy of Document AI of 91%. But the remaining 9% had still to be corrected and entered manually in the system. A huge gap! So, we were wondering, what if your document processing could learn from every correction - instantly? With instant learning within SAP Document AI, we’re closing exactly that gap - for good. Now, when a user corrects something, the system learns instantly. No retraining. No finetuning. No waiting. Fix it once — and it’s fixed for everyone. This isn’t just an upgrade. It’s a breakthrough.   The benefits: ✅ Automate document handling within SAP apps ✅ Enhance accuracy with AI that adapts in real-time ✅ Simplify operations with seamless integration and built-in compliance   Check out the system in action and watch this real-world demo video from our customer Tyrolit Group! 📹

I spent 300 hours researching 1,500+ software. Here's how to upgrade your tech stack with AI: 1️⃣ Use AI apps for building leads lists Your ability to put the message in front of the right person is the biggest predictor of your outreach campaign's success. Some AI applications make the process easier by helping you: - Build lead lists - Deeply research prospects - Segment & score prospect Examples include: - Relevance AI Prospect Researcher and Enrichment agents - Clay’s AI Agent and Integration with 100+ data platforms. - Exa’s AI Research & Data Sourcing Agent 2️⃣ Use AI to orchestrate complex workflows Running cold email campaigns used to be fragmented across several platforms and involved moving around .csv files from one tool to another. Workflow builders & AI agents now help you: - Build a list  - Score your leads - Find their Emails - Verify these Emails - Personalise your messaging with AI - And import to leads to a sales engagement platform … all that from a single tool. Examples for these tools include Clay, Relevance AI, Common Room & Unify. 3️⃣ Use AI to improve your deliverability A message that doesn’t get read, doesn’t get replied to. Every year, deliverability is getting harder to crack. Thankfully, a few sending platforms are leveraging AI to help with: - Secondary domains & mailboxes setup - Automated Spintaxxing - Email warm-ups Examples include: Instantly.ai, lemlist & Woodpecker.co. 4️⃣ Use AI to help close your deals We’re still far from seeing AI successfully replacing humans in Google Meet. But the AI bots are already listening to these sales conversations. They already help by: - Taking notes from convos - Surfacing insights from individual conversations - Surfacing aggregate insights from MANY conversations For example, one thing I like to do is ask Attention what our prospects have asked most in our latest 100+ sales conversations. Once I know that, I can address these points in our messaging. P.S: Any unheard-of use case you’ve seen with AI applied to go-to-market?

Recently, a client reached out to us expressing frustration with the RAG (Retrieval-Augmented Generation) application they had implemented for customer support emails by a different AI agency. Despite high hopes of increased efficiency, they were facing some significant problems: The RAG model frequently provided wrong answers by pulling information from the wrong types of emails. For example, it would respond to a refund request email with details about changing an order - simply because those emails contained some similar wording. Instead of properly classifying the emails by type and intent, it seemed to just perform a broad embedding search across all emails. This created a confusing mess where customers were receiving completely irrelevant and nonsensical responses to their inquiries. Rather than streamlining operations, the RAG implementation was actually making customer service much worse and more time-consuming for agents. The client's team had tried tuning the model parameters and changing the training data, but couldn't get the RAG application to accurately distinguish between different contexts and email types. They asked us to take a look and help get their system operating reliably. After analyzing their setup, we identified a few key issues that were derailing the RAG performance: Lack of dedicated email type classification The RAG model needed an initial step to explicitly classify the email into categories like refund, order change, technical support, etc. This intent signal could then better focus the retrieval and generation steps. Noisy, inconsistent training data The client's original training set contained a mix of incomplete email threads, mislabeled samples, and inconsistent formats. This made it very difficult for the model to learn canonical patterns. Retrieval without context filtering The retrieval stage wasn't incorporating any context about the classified email type to filter and rank relevant information sources. It simply did a broad embedding search. To address these problems, we took the following steps with the client: Implemented a new hierarchical classification model to categorize emails before passing them to the RAG pipeline Cleaned and expanded the training data based on properly labeled, coherent email conversations Added filtered retrieval based on the email type classification signal Performed further finetuning rounds with the augmented training set After deploying this updated system, we saw an immediate improvement in the RAG application's response quality and relevance. Customers finally started getting on-point information addressing their specific requests and issues. The client's support team also reported a significant boost in productivity. With accurate, contextual draft responses provided by the RAG model, they could better focus on personalizing and clarifying the text - not starting responses completely from scratch.