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.

Most AI tool lists miss the point. The advantage doesn’t come from knowing more tools. It comes from knowing where they fit in your workflow. Right now most people use AI like this: → Try a tool → Generate something → Move on No structure. No repeatability. So the productivity gains stay small. The real leverage appears when you treat AI tools like a stack, not a collection of apps. Almost every modern AI workflow fits into four layers. If you understand these layers, you can build systems that run every week without starting from scratch. 1️⃣ Thinking layer Tools that help you clarify problems and structure ideas. → ChatGPT → Claude Use them to: → research unfamiliar topics → break down complex problems → outline strategies and plans → stress-test ideas before execution Most people jump straight to creation. The real value often starts one step earlier: better thinking. 2️⃣ Creation layer Tools that turn ideas into assets. → writing tools (Jasper, Writesonic) → design tools (Canva AI, Flair) → image tools (Midjourney, DALL-E, Stable Diffusion) → video tools (Runway, HeyGen, Synthesia) This layer turns raw ideas into: → presentations → visuals → videos → marketing assets → documentation Think of it as production infrastructure for knowledge work. 3️⃣ Automation layer Tools that connect steps together. → Zapier → Make → Bardeen Instead of repeating tasks manually, these tools: → move information between systems → trigger actions automatically → remove repetitive work Example: Research → draft → create visuals → publish. Automation turns that into a repeatable pipeline. 4️⃣ Deployment layer Tools that deliver work to customers and teams. → websites (Framer, Durable) → chatbots (Chatbase, SiteGPT) → marketing tools (AdCreative, Simplified) This is where work becomes: → websites → marketing campaigns → customer experiences → digital products Without deployment, great AI output never reaches the real world. If you run a business or lead a team, here’s a simple playbook. Step 1 Pick one tool per layer. You don’t need ten tools doing the same job. Step 2 Design one repeatable workflow. Example: → research with ChatGPT → draft content → create visuals in Canva → automate publishing with Zapier Step 3 Automate the steps that repeat every week. Anything you do more than three times should become a system. Step 4 Improve the workflow over time. Small improvements compound faster than constantly switching tools. The people getting the most value from AI right now are not the ones testing every new tool. They are the ones building simple systems that run every day. Tools will change. Workflows compound. 💾 Save this if you’re building your AI stack. ♻️ Repost to help others move from experimenting with AI to actually using it in their work. ➕ Follow Gabriel Millien for practical insights on AI execution and building real leverage with AI. Image credit: Aditya Goenka

Agentic AI can get expensive fast. Not because the model is always wrong. Because every agent step can add more context, more tool calls, more reasoning, and more tokens. If you want scalable agents, token efficiency becomes architecture. Here are 6 ways to save tokens in agentic workflows: → 𝗣𝗿𝗼𝗺𝗽𝘁 𝗖𝗮𝗰𝗵𝗶𝗻𝗴 Reuse repeated system prompts, instructions, and context instead of processing the same input again. Best for stable instructions, long policies, and repeated workflows. → 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗖𝗮𝗰𝗵𝗶𝗻𝗴 Reuse previous answers when a new query has the same meaning, even if the wording is different. Best for support, FAQs, internal knowledge, and repeated user requests. → 𝗥𝗼𝘂𝘁𝗶𝗻𝗴 Send each task to the right model, tool, or workflow based on complexity, cost, and accuracy needs. Simple tasks should not use your most expensive model. → 𝗞𝗲𝗲𝗽𝗶𝗻𝗴 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗖𝗹𝗲𝗮𝗻 Remove irrelevant, outdated, duplicated, or noisy information before sending context to the model. Clean context improves both cost and output quality. → 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗖𝗼𝗺𝗽𝗮𝗰𝘁𝗶𝗼𝗻 Compress long conversations or documents into shorter summaries while preserving critical facts. Useful when agents need memory without carrying the entire history. → 𝗟𝗮𝘇𝘆-𝗟𝗼𝗮𝗱𝗶𝗻𝗴 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 Load only the information needed at the moment instead of dumping everything upfront. The lesson is simple: More context is not always better. Better context is better. Token savings come from smarter caching, cleaner context, better routing, and loading only what the agent actually needs. Save this if you are building AI agents, RAG systems, automation workflows, or production GenAI apps.

I see many people struggling or confused when switching into AI. Don’t jump straight into frameworks like LangChain or LangGraph. Frameworks are accelerators, not starting points. Without foundations, you’ll end up building fragile demos instead of production-grade systems. Here’s a step-by-step path to transition your career into Generative AI: 1. Build Core Foundations --Python (APIs, JSON, virtual envs, packaging) --Git, Docker, Linux basics --Databases: Postgres + pgvector, or FAISS for embeddings 2. Learn Just Enough Math & Data --Vectors, cosine similarity, probability --Tokenization, chunking, normalization 3. Understand LLM Basics --How transformers work at a high level --Different types of models: base vs. instruct, hosted vs. local --Prompt engineering patterns (instruction, few-shot, tool-use) 4. Get Hands-on with RAG (without frameworks first) --Ingest → chunk → embed → store → retrieve → re-rank → generate --Add logging, caching, retries --Evaluate outputs with ground-truth sets 5. Learn Evaluation & Safety --Handle hallucination, PII, toxicity --Define and track metrics (accuracy, latency, cost) 6. Explore Reliability & MLOps --CI/CD for prompts/config --Observability, tracing, cost dashboards --Error handling and fallbacks 7. Then Explore Agents --Start simple: one-tool agents --Add planning and memory only when metrics prove value 8. Finally → Use Frameworks Wisely --Adopt LangChain, LangGraph, or LlamaIndex as orchestration layers --Keep your core logic framework-agnostic 9. Showcase Projects --Document QA system with metrics --Structured extraction pipeline with redaction --A small but reliable agent automating a real workflow 10. Be Interview-Ready --Explain RAG pipelines on a whiteboard --Compare models and providers --Justify design choices (chunking, caching, re-ranking) Learn the primitives first. Frameworks make you faster after you understand what’s under the hood. That’s how you build systems that last.

Most developers are missing the real value of Claude Code. It is not just an AI that writes code. The actual power comes from how you configure it around your engineering workflow. After exploring it deeply, one thing became very clear to me: prompting is only a small part of the picture. What actually makes Claude Code powerful is understanding when to use what. Here’s the mental model that made it click for me: 1. CLAUDE.md Your project memory. Best for stack, architecture, coding conventions, and commands Claude should always know. 2. Skills For recurring patterns that should automatically activate when relevant. If you repeatedly say “remember to do this,” it probably belongs here. 3. Commands For workflows you intentionally trigger, like code reviews, scaffolding, debugging, or repetitive tasks. 4. Subagents Specialized assistants for focused responsibilities like security reviews, testing, or architecture discussions without polluting the main context. 5. MCP Servers The bridge to external systems. Useful for live docs, GitHub, databases, APIs, Jira, Slack, and other tools Claude normally would not access. 6. Hooks Deterministic automation. Great for formatting, validations, running tests, or preventing risky edits automatically. 7. Plugins A way to package workflows and reuse them across projects or teams. One thing I feel developers may get wrong early is trying to configure everything at once. The smarter approach is to start lean. Begin with CLAUDE.md, then commands, then skills. Add MCPs, hooks, and subagents only when friction starts appearing in your workflow. That’s when Claude stops feeling like just another coding assistant and starts feeling more like an engineering partner. Have you explored Claude Code yet? Curious what has actually improved your workflow.

Companies lose weeks turning contracts into cash. Orb's Contract-to-Cash is set to revolutionize this. Here’s how it usually goes: Sales closes the deal. Finance gets the contract. Then comes the chaos - PDFs, spreadsheets, usage data, manual billing. Invoices go out late. Cash gets stuck. DSO creeps up. Orb decided to fix that with Contract-to-Cash: From signed deal to invoice in minutes. No scripts. No engineering. No waiting for implementation windows. How it works: 1. Upload your signed contract PDF. 2. Orb’s AI extracts key billing terms automatically. 3. Add usage if needed. 4. Generate ready-to-send invoices - instantly. That’s it. Finance can now turn contracts into cash without manual reconciliation or spreadsheet chaos. The result? A) DSO drops. B) Forecasts get cleaner. C) Invoices go out 4–6 days faster on average. D) The same team handles 10× more contracts - with zero extra headcount. Orb Contract-to-Cash flips the billing model: You start with the contract, not the system. Finance gets speed, accuracy, and control: Built on the same engine that powers Orb Billing and Orb Simulations. Because monetization shouldn’t slow growth - it should power it. Contracts in. Invoices out. Cash collected. That’s how modern finance should look. Want to understand better how it works? Get the guide here: https://lnkd.in/ejv-B33j

A nice review article "Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation" covers the scope of tools and approaches for how AI can support science. Some of areas the paper covers: (link in comments) 🔎 Literature search and summarization. Traditional academic search engines rely on keyword-based retrieval, but AI-powered tools such as Elicit and SciSpace enhance search efficiency with semantic analysis, summarization, and citation graph-based recommendations. These tools help researchers sift through vast scientific literature quickly and extract key insights, reducing the time required to identify relevant studies. 💡 Hypothesis generation and idea formation. AI models are being used to analyze scientific literature, extract key themes, and generate novel research hypotheses. Some approaches integrate structured knowledge graphs to ground hypotheses in existing scientific knowledge, reducing the risk of hallucinations. AI-generated hypotheses are evaluated for novelty, relevance, significance, and verifiability, with mixed results depending on domain expertise. 🧪 Scientific experimentation. AI systems are increasingly used to design experiments, execute simulations, and analyze results. Multi-agent frameworks, tree search algorithms, and iterative refinement methods help automate complex workflows. Some AI tools assist in hyperparameter tuning, experiment planning, and even code execution, accelerating the research process. 📊 Data analysis and hypothesis validation. AI-driven tools process vast datasets, identify patterns, and validate hypotheses across disciplines. Benchmarks like SciMON (NLP), TOMATO-Chem (chemistry), and LLM4BioHypoGen (medicine) provide structured datasets for AI-assisted discovery. However, issues like data biases, incomplete records, and privacy concerns remain key challenges. ✍️ Scientific content generation. LLMs help draft papers, generate abstracts, suggest citations, and create scientific figures. Tools like AutomaTikZ convert equations into LaTeX, while AI writing assistants improve clarity. Despite these benefits, risks of AI-generated misinformation, plagiarism, and loss of human creativity raise ethical concerns. 📝 Peer review process. Automated review tools analyze papers, flag inconsistencies, and verify claims. AI-based meta-review generators assist in assessing manuscript quality, potentially reducing bias and improving efficiency. However, AI struggles with nuanced judgment and may reinforce biases in training data. ⚖️ Ethical concerns. AI-assisted scientific workflows pose risks, such as bias in hypothesis generation, lack of transparency in automated experiments, and potential reinforcement of dominant research paradigms while neglecting novel ideas. There are also concerns about the overreliance on AI for critical scientific tasks, potentially compromising research integrity and human oversight.

Most AI workflows overpromise & undersell. But one of my favorites has (actually) driven hundreds of thousands in incremental revenue. The CEO of Zapier—who’s the homie—shared it with me, and I’ve been hooked ever since. Think of it as an AI SDR, who qualifies, organizes, and engages sales leads. Here are all of the steps my sales sidekick takes: 1) Extracts the name, email, company, role, and website for any lead that fills out a sales form on our website 2) Researches the lead online to gather the following info: - Company website & recent news - Linkedin profile and background - Company size, industry, and estimated funding/revenue/growth indicators - Specific pain points related to my company’s service 3) Compares lead info against ideal ICP criteria I’ve set: - US-based company - VP-level & up  - Revenue: $10m-$500m annually  - Company size: >50 employees 4) Scores the lead as “Great Fit,” “Possible Fit,” or “Poor Fit” based on ICP comparison 5) Adds a new record to our CRM with the following details: - Contact details (name, email, company, role) - Research findings (company size, revenue, industry) - ICP fit score - Date submitted 6) Conditional logic based on Lead Fit IF lead is “Great Fit” Draft a personalized email in Gmail incorporating: - Their specific company challenges identified in research - Relevant case studies from similar companies - Clear next steps for a discovery call IF lead is “Possible Fit” Send direct message in Slack to me with:  - A summary of lead and research findings - Reasons for uncertainty regarding ICP fit - A recommendation with supporting data - The question: “Should I draft a response email for this lead?” IF response is “yes”: follow great fit action  IF response is “no”: no response Update CRM for this lead based on action taken in Step 6. Let me know if you have any questions—and if you take it for a spin—let me know what you think. #ZapierPartner

𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗔𝗜 AI doesn’t replace traditional architecture frameworks, it enhances them. Take, for example, the TOGAF Standard's #ADM. AI can act as a force multiplier for each phase. 🔸 𝗣𝗿𝗲𝗹𝗶𝗺𝗶𝗻𝗮𝗿𝘆 𝗣𝗵𝗮𝘀𝗲: Rapidly scan and synthesize architectural documentation to highlight recurring pain points. AI tools also support capability assessment. Skills inventories and role descriptions can be analyzed to identify gaps in the team’s abilities. 🔸 𝗣𝗵𝗮𝘀𝗲 𝗔: Simulate business scenarios based on real enterprise data. AI can model the impact of implementing predictive maintenance, intelligent customer service, or algorithmic procurement. AI tools can analyze stakeholder communication to identify sentiment trends and key concerns. This allows architecture teams to tailor the vision to what stakeholders care about. 🔸 𝗣𝗵𝗮𝘀𝗲 𝗕: Ingest workflow logs, screen interactions, and system traces to automatically map how business processes actually work, not how they are documented. These real-world models make it easier to identify inefficiencies, bottlenecks, and opportunities. 🔸 𝗣𝗵𝗮𝘀𝗲 𝗖: Assist by automatically profiling data sources to assess their readiness for machine learning and analytics use cases. On the application side, AI models can recommend integration points for new capabilities. 🔸 𝗣𝗵𝗮𝘀𝗲 𝗗: Simulate various deployment architectures and predict performance characteristics. This is especially useful in balancing on-premise and cloud strategies or designing hybrid environments. 🔸 𝗣𝗵𝗮𝘀𝗲 𝗘: Use-case prioritization can be supported with scoring models that assess feasibility, ROI, risk, and stakeholder alignment. AI design assistants can generate architecture artifacts: draft diagrams and interaction flows. This dramatically reduces the time required to prepare solution documentation. 🔸 𝗣𝗵𝗮𝘀𝗲 𝗙: Creating and continuously refining dependency graphs that reflect system interconnections, change risks, and stakeholder constraints. AI tools can also simulate different roadmap paths. E.g., how would a regulatory change impact the timeline? 🔸 𝗣𝗵𝗮𝘀𝗲 𝗚: Monitor project progress and detect misalignments with architecture specifications. This operates in near real-time, integrating with project management tools. Architecture compliance reviews become continuous and intelligent. 🔸 𝗣𝗵𝗮𝘀𝗲 𝗛: Monitor change signals (evolving regulations, new technologies, etc.) and surface emerging trends, risks, or opportunities. Feedback from users of AI-enabled systems can also be analyzed at scale. Applying AI to the ADM is about elevating the practice of Enterprise Architecture. The use of AI accelerates execution without losing structure. The methodology remains the same. The difference lies in how intelligently, quickly, and adaptively it can now be applied. ADM inset: © The Open Group #EnterpriseArchitecture #EA #TOGAF #OpenGroup #AI

AI didn’t take my job. It gave me back the part of it that actually mattered - understanding people. For three decades, I believed I was doing "people work." I was wrong. My team was reviewing 50 resumes daily but never truly seeing candidates. Scheduling 20 interviews weekly but not preparing meaningful conversations. Drafting policy documents and communication instead of understanding employee concerns. With AI, now I can spend:  → Spend 2 hours weekly in deep career conversations with high-potential employees  → Conduct stay interviews that uncover real retention drivers  → Design onboarding experiences that create genuine belonging  → Make nuanced decisions about team dynamics and cultural fit  → Build mentorship programs based on individual aspirations If you’re in HR or leadership, here’s how to make the same shift: Step 1: Map your week. List every recurring task, from screening résumés to sending feedback reports. Mark what requires pattern spotting (AI’s domain) versus empathy or nuance (your domain). Step 2: Automate the repeatables. Let AI handle interview scheduling, résumé shortlisting, and pulse surveys. This frees up 10 to 15 hours that you can reinvest where human connection drives outcomes. Step 3: Guard human time. Block at least two hours every week to mentor, check in, or resolve team friction. These are the kinds of conversations no bot can replicate. Step 4: Track the intangibles. Instead of only measuring time saved, track retention, engagement, and internal referrals. That’s the real ROI of emotional bandwidth. It removed the excuse that administrative tasks were strategic work. Now I'm finally doing what HR was always meant to be about: understanding people. What is the biggest change you’ve made with AI?