AI Workflow Enhancement

Last week, I described four design patterns for AI agentic workflows that I believe will drive significant progress: Reflection, Tool use, Planning and Multi-agent collaboration. Instead of having an LLM generate its final output directly, an agentic workflow prompts the LLM multiple times, giving it opportunities to build step by step to higher-quality output. Here, I'd like to discuss Reflection. It's relatively quick to implement, and I've seen it lead to surprising performance gains. You may have had the experience of prompting ChatGPT/Claude/Gemini, receiving unsatisfactory output, delivering critical feedback to help the LLM improve its response, and then getting a better response. What if you automate the step of delivering critical feedback, so the model automatically criticizes its own output and improves its response? This is the crux of Reflection. Take the task of asking an LLM to write code. We can prompt it to generate the desired code directly to carry out some task X. Then, we can prompt it to reflect on its own output, perhaps as follows: Here’s code intended for task X: [previously generated code] Check the code carefully for correctness, style, and efficiency, and give constructive criticism for how to improve it. Sometimes this causes the LLM to spot problems and come up with constructive suggestions. Next, we can prompt the LLM with context including (i) the previously generated code and (ii) the constructive feedback, and ask it to use the feedback to rewrite the code. This can lead to a better response. Repeating the criticism/rewrite process might yield further improvements. This self-reflection process allows the LLM to spot gaps and improve its output on a variety of tasks including producing code, writing text, and answering questions. And we can go beyond self-reflection by giving the LLM tools that help evaluate its output; for example, running its code through a few unit tests to check whether it generates correct results on test cases or searching the web to double-check text output. Then it can reflect on any errors it found and come up with ideas for improvement. Further, we can implement Reflection using a multi-agent framework. I've found it convenient to create two agents, one prompted to generate good outputs and the other prompted to give constructive criticism of the first agent's output. The resulting discussion between the two agents leads to improved responses. Reflection is a relatively basic type of agentic workflow, but I've been delighted by how much it improved my applications’ results. If you’re interested in learning more about reflection, I recommend: - Self-Refine: Iterative Refinement with Self-Feedback, by Madaan et al. (2023) - Reflexion: Language Agents with Verbal Reinforcement Learning, by Shinn et al. (2023) - CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing, by Gou et al. (2024) [Original text: https://lnkd.in/g4bTuWtU ]

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.

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.

It’s easy to think of AI as a time-saver that streamlines workflows and accelerates output. But the deeper opportunity lies in how it’s reshaping the nature of work itself. A new study from Harvard Business School’s Manuel Hoffmann followed more than 50,000 developers over two years, with half using GitHub Copilot. The results were striking: developers shifted away from project management and toward the core work of coding. Not because someone told them to, but because AI made it possible. With less need for coordination, people worked more autonomously. And with time saved, they reinvested in exploration—learning, experimenting, trying new things. What we’re seeing here isn’t just productivity. It’s a shift in how work gets done and who does what. Managers may spend less time supervising and more time contributing directly. Teams become flatter. Hierarchies adapt. This is just one signal of how generative AI is changing our org charts and challenging us to rethink how we structure, support, and lead our teams. The future of work isn’t just faster. It’s more fluid. And if we get this right, it’s a whole lot more human. https://lnkd.in/gaUgXnRY

Last quarter, I worked with the MD of a heavy equipment manufacturer who believed AI would make status reports clearer and give leadership better visibility into project progress, but while the dashboards improved and the data looked sharper, the actual profit margins did not improve because delays were still being identified too late to prevent cost overruns. By the time problems appeared in reports, the financial impact had already occurred, and in 2026, with tighter compliance requirements and thinner operating buffers, that delay between issue and action is no longer affordable. What has truly changed is not reporting quality but execution speed, because AI systems can now reallocate resources, adjust schedules, and flag bottlenecks immediately instead of waiting for weekly or monthly review cycles; in plant upgrade programs and supplier transitions, I have seen problems addressed at the point of occurrence rather than after escalation. When corrective action happens closer to where the issue starts, delivery risk declines and cycle times shorten, since decisions are triggered by live data rather than by meetings or manual coordination. The main weakness I continue to see is governance, because many AI agents operate on fragmented data sources without clear ownership of decision rights, which leads teams to override outputs they do not trust and reintroduce manual controls that slow everything down, creating a false sense of stability where dashboards remain green but margin pressure builds quietly underneath. Two mistakes appear repeatedly. The first is treating AI as an advanced reporting layer, because manufacturing projects depend on operational control rather than visibility alone, and insight does not prevent delay unless the system is allowed to act within clearly defined boundaries. The second is deploying AI without defining who owns the decisions it influences, because manufacturing plants rely on accountability structures, and when escalation paths are unclear, agents can create conflicting actions that slow adoption and reduce confidence across teams. If you are beginning this journey, start by mapping a single workflow where approvals consistently delay progress, such as change requests during shutdown planning, and introduce AI only where decision rules are already stable and measurable, while avoiding areas that depend on negotiation or human judgment.  #AIInProjectManagement #AgenticAI #ExecutiveLeadership #FutureOfWork #OperationalExcellence0 #DecisionIntelligence #EnterpriseAI #ProjectGovernance #DigitalTransformation #AIForCEOs #BusinessExecution #AIStrategy

Most people drown in the endless sea of new AI tools. But the truth is - you don’t need hundreds of tools to stay ahead in 2026. You only need to master the 10 categories that actually drive business results, automation, and career acceleration. This guide breaks them down with clarity: what you need, why it matters, and the real impact each category delivers. Here’s the snapshot: 🔹 1. Advanced LLMs (Your New Thinking Models) ChatGPT, Claude, Gemini, Llama, DeepSeek → These become your operating system for reasoning, analysis, writing, coding, planning, and problem-solving. 🔹 2. AI Automation Tools (Workflow Builders) Make.com, n8n, Zapier, Pipedream → The backbone of automated sales, onboarding, support, content pipelines, and internal systems. 🔹 3. AI Agents & Orchestration Tools CrewAI, LangChain, LlamaIndex, AutoGen, OpenAI → 2026 is about multi-step workflows and self-correcting agents that function like digital employees. 🔹 4. Vector Databases (Memory for AI Systems) Pinecone, Weaviate, ChromaDB, Milvus → The foundation of RAG applications, internal chatbots, and knowledge automation. 🔹 5. Knowledge Management + Document Intelligence Notion AI, Airtable AI, Secoda, Glean, Elastic AI → Instant summaries, automated documentation, and searchable intelligence hubs for faster decision-making. 🔹 6. AI Video & Avatar Tools Synthesia, HeyGen, Runway, Pika → Training, marketing, and onboarding videos created in minutes - video becomes the default communication layer. 🔹 7. AI Data Tools (Analytics + Insights Engines) ClickUp AI, Tableau AI, PowerBI AI, Amplitude AI, Akkio → Automated dashboards, predictive insights, and analytics without needing SQL or code-heavy workflows. 🔹 8. AI Design Tools (Visual Experience Builders) Canva AI, Adobe Firefly, MidJourney, Figma AI → Branding, ads, UI/UX, infographics, thumbnails - all created 10× faster through prompting. 🔹 9. AI Coding Tools GitHub Copilot, Cursor, Replit AI, Codeium → Faster builds, fewer bugs, and better architecture. Developers shift from code writers to solution architects. 🔹 10. AI Search & Personal Intelligence Tools Perplexity, LexisNexis AI, Adobe Ask → Instant reports, automated research, competitor analysis, and conversational search. This is the real AI stack for 2026. Not hype. Not noise. Just the tools that will genuinely move your business, your work, and your career forward. Which category are you focusing on next?

I built a self-hosted AI architecture that runs without internet, no API cost, no cloud. AI works when network doesn't. This was the toughest project I’ve ever worked on and I did it to answer one question: Can we talk to AI when the internet is down and can we trust AI with sensitive data which cannot leave the building? Short answer: Yes. Meet Secure AI Lab. What it does: Works like ChatGPT, but lives on your computer and runs without internet Reads your own documents (protocols/policies) to answer with context. Automates tasks (save files, generate PDFs, log entries) locally. Runs fully offline after setup no cloud, no API keys, no telemetry. In the video, I switch Wi-Fi OFF and ask: “What medications are used for cardiac arrest??” OpenWebUI (local chatbot) answers from my local knowledgebase. n8n (local workflow) auto-creates a file on my disk with the summary. Every step happens on localhost. Nothing leaves the machine. ⚠️ Demo ≠ diagnosis. The medication shown is mock data; this is a clinical support example, not medical advice. Why this matters: Emergency Departments (ED) during downtime: keep triage guidance, protocol recall, and order prep running when EHR/internet is down. Hospitals, banks, factories: when privacy and reliability matter, local beats cloud. Cost control: one-time setup vs. indefinite per-token bills. How it works (simple flow) Inside the Lab: Local Brain – AI model (Ollama) generates answers on device. Your Documents – RAG reads your PDFs (protocols/policies) locally. Local Robot – n8n automations save files, generate PDFs, log to SQLite, print if needed. Not just Ollama offline. I built a complete offline system: chat UI + local RAG over my PDFs + automations that create PDFs/logs on disk, with Wi-Fi OFF and no egress. It’s a product, not just a model. I have added File-Watcher: when OpenWebUI saves a new answer, n8n auto-detects it and creates a PDF/log instantly, still with no internet. Stack at a glance OpenWebUI – local chat UI + RAG Ollama – runs the AI models on device n8n – no-code automations (write files, PDFs, logs) Docker – isolated, reproducible setup RAG – reads your docs; answers with citations SQLite/Files – local logs & artifacts (no cloud) This was my toughest build yet. I spent many weeks planning and stitching everything together to prove AI can run fully offline and still be useful in emergencies.

𝐑𝐞𝐚𝐥 𝐨𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐲 𝐟𝐨𝐫 𝐀𝐈 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐧𝐠 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 I have been meeting with many enterprise CXOs and AI advisory firms about AI adoption over the last few months. Almost all of them start the same way: 1. Map the current workflows. 2. Identify the manual steps. 3. Find where people are spending time. 4. Layer AI on top to automate or accelerate the work. This is the default playbook. And it is not wrong. It is the safe, best way to test and show quick results. A great entry point for AI. 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐬𝐮𝐩𝐩𝐨𝐫𝐭 𝐰𝐨𝐫𝐤𝐟𝐥𝐨w 1. Customer calls in. 2. L1 agent picks up, follows a script. 3. Cannot resolve. Escalates to L2. L2 reads the notes, asks the customer to repeat the problem, checks the knowledge base. Maybe escalates to L3. 4. Resolution happens 3 handoffs and 48 hours later. Most enterprise AI deployments in customer support follow the same default playbook: 1. Automating L1 with a voicebot 2. L2 with AI-assisted responses 3. Giving L3 a copilot. Same tiers, same structure, just faster and cheaper. 𝐖𝐡𝐲 𝐝𝐨 𝐭𝐡𝐞𝐬𝐞 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 𝐞𝐱𝐢𝐬𝐭 𝐢𝐧 𝐭𝐡𝐞 𝐟𝐢𝐫𝐬𝐭 𝐩𝐥𝐚𝐜𝐞? Most processes were designed around human limitations — quality, consistency, onboarding, training, error containment. 𝑩𝒖𝒕 𝒘𝒐𝒓𝒌𝒇𝒍𝒐𝒘𝒔 𝒂𝒓𝒆 𝒏𝒐𝒕 𝒕𝒉𝒆 𝒈𝒐𝒂𝒍. 𝑻𝒉𝒆𝒚 𝒂𝒓𝒆 𝒂 𝒎𝒆𝒂𝒏𝒔 𝒕𝒐 𝒕𝒉𝒆 𝒈𝒐𝒂𝒍. The goal was never "route through 3 tiers." If AI can access the full knowledge base, understand context, and maintain quality — why not give the customer or a single agent an AI tool that resolves it directly? Three tiers collapse into one. 𝐓𝐡𝐞 𝐫𝐞𝐚𝐥 𝐨𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐲 is to return to the original objective and move from multi-step process to single-step outcome as confidence builds. This is also where the biggest opening exists for new AI startups — not workflow automation, but outcome-based automation. 𝐈𝐌𝐏𝐎𝐑𝐓𝐀𝐍𝐓: Before you automate your current workflows, ask why they exist. The enterprises that will get the biggest AI wins are the ones redesigning toward outcomes — not just making existing steps faster.

AI Agents - or shifting chat bots into do bots, is the next big thing AI development currently in the hype stage. This article discusses a responsible framework. Taking the leap from having generative AI to do simple tasks to exploring workflows is one step. But AI agents goes beyond that to automating a department or team workflow. That requires some readiness steps including: 1) Identify Repetitive Tasks for Automation: Identify routine and time-consuming tasks that AI agents can handle. These might be some of the simple tasks that you are using generative AI for right now. But you want to put those in the context of a whole workflow using process mapping. 2) Small Controlled Team or Dept. Pilot: Identify a pilot that is low-risk. Better places to start are on internal workflow processes. Identify a metric for success - time savings or work quality improvement? 3) Ensure Human Oversight: While AI agents can handle many tasks autonomously, it's crucial to maintain human oversight, especially for tasks requiring nuanced judgment or ethical considerations. These should be identified during process mapping. And, once the pilot is up and running, set up bias checks, audits, and steps to address issues. 4) Invest in Training and Development: Equip people with the necessary skills to work alongside AI agents. This includes training in prompting, data management, and understanding AI functionalities. Agents are not a pot-roast, set it and forget technology. They require preparation, planning, and monitoring. https://lnkd.in/gh5rXDfH

I attend 30+ data and AI conferences every year, and for the longest time, outreach was complete chaos. Spreadsheets everywhere, notes scattered across tools, follow-ups slipping through, and the worst part was sending generic emails that got ignored. It did not matter how many events I attended, the system just did not scale. So I rebuilt everything inside Airtable. I created a simple but structured system with conferences, sponsors, and contacts all connected in one place. Now I could actually see who I met, where, and what needed to happen next. That alone made things cleaner, but it still required a lot of manual work. The real shift happened when I connected it with Claude. Now I start my workflow in Claude. It pulls context directly from my Airtable base, understands the sponsors I am targeting, the events I am attending, and the history of interactions. Then it goes out, does research on each company, looks at what they have recently announced, and brings back insights that actually matter. From there, it writes everything back into Airtable. New sponsor ideas get added. Outreach emails are drafted with real context. Follow-ups are created automatically based on status. Everything stays structured, tracked, and easy to act on. The biggest change for me is I am no longer jumping between tools or starting from scratch every time. I think in Claude, execute in Airtable, and then go back to Claude to refine messaging or strategy. That back and forth is what makes this powerful. This is how I now manage conference partnerships at scale. Not by adding more tools, but by connecting the right ones in a way that actually works. Learn more about it here – https://lnkd.in/gFCDbR7T #airtablepartner #data #ai #claude #theravitshow