AI Workflow Enhancement

I asked 195 B2B go-to-market leaders about where they're placing their bets for 2026. The top channel bet: AI discovery aka AEO. Wow, things escalated quickly... One company that got a head start on AEO is Webflow, the website building scaleup. Their stats via VP of growth Josh Grant: 1. 10% of signups now come from AI discovery, growing 4x year-on-year. (This is actual LLM-referred traffic, which likely understates things.) 2. 91% of LLM referrals come from ChatGPT alone. 3. ChatGPT traffic converts at 24% (!), 6x higher than Google. 4. For conversions referred by an LLM, two-in-three convert within 7 days. 3 tactics from Webflow you can apply in the next 24 hours (& one to avoid): Avoid: Add an llms.txt file - The Webflow team tried it. They haven’t seen any significant lift. - The takeaway: focus on content optimizations instead. Tactic 1: Automate content refreshing at scale - AI reshuffles answers constantly. Refresh velocity can be the difference between staying on top and missing out. - Webflow built an AI-driven workflow with AirOps to 5x their refresh frequency. Tactic 2: Turn every webinar into 10 pieces of expert content - Webinars can make great source material. Repurposing makes them fresh, structured, and consistently discoverable by both people and AI. - Webflow automates this by transcribing webinars (AirOps), using LLMs to identify themes & soundbites, generating assets & adding an editorial review. Tactic 3: Automate FAQs and schema content for AI discovery - FAQ sections answer long-tail questions and help LLMs get a more granular understanding of the product. ChatGPT can essentially "borrow" your FAQ answers in their repsonses. - Webflow automates this by scraping what people are asking via Reddit & Google (AirOps), generating new FAQs & answers (GPT-5, Claude), pushing updates into the CMS & then tracking any visibility shifts before/after. --- The full story is out NOW in Growth Unhinged: https://lnkd.in/eXP-gnFN Hope you find it useful 🙏 #aeo #marketing #chatgpt

AI is rapidly moving from passive text generators to active decision-makers. To understand where things are headed, it’s important to trace the stages of this evolution. 1. 𝗟𝗟𝗠𝘀: 𝗧𝗵𝗲 𝗘𝗿𝗮 𝗼𝗳 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗙𝗹𝘂𝗲𝗻𝗰𝘆 Large Language Models (LLMs) like GPT-3 and GPT-4 excel at generating human-like text by predicting the next word in a sequence. They can produce coherent and contextually appropriate responses—but their capabilities end there. They don’t retain memory, they don’t take actions, and they don’t understand goals. They are reactive, not proactive. 2. 𝗥𝗔𝗚: 𝗧𝗵𝗲 𝗔𝗴𝗲 𝗼𝗳 𝗖𝗼𝗻𝘁𝗲𝘅𝘁-𝗔𝘄𝗮𝗿𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 Retrieval-Augmented Generation (RAG) brought a major upgrade by integrating LLMs with external knowledge sources like vector databases or document stores. Now the model could retrieve relevant context and generate more accurate and personalized responses based on that information. This stage introduced the idea of 𝗱𝘆𝗻𝗮𝗺𝗶𝗰 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗮𝗰𝗰𝗲𝘀𝘀, but still required orchestration. The system didn’t plan or act—it responded with more relevance. 3. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜: 𝗧𝗼𝘄𝗮𝗿𝗱 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 Agentic AI is a fundamentally different paradigm. Here, systems are built to perceive, reason, and act toward goals—often without constant human prompting. An Agentic system includes: • 𝗠𝗲𝗺𝗼𝗿𝘆: to retain and recall information over time. • 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴: to decide what actions to take and in what order. • 𝗧𝗼𝗼𝗹 𝗨𝘀𝗲: to interact with APIs, databases, code, or software systems. • 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆: to loop through perception, decision, and action—iteratively improving performance.    Instead of a single model generating content, we now orchestrate 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗮𝗴𝗲𝗻𝘁𝘀, each responsible for specific tasks, coordinated by a central controller or planner. This is the architecture behind emerging use cases like autonomous coding assistants, intelligent workflow bots, and AI co-pilots that can operate entire systems. 𝗧𝗵𝗲 𝗦𝗵𝗶𝗳𝘁 𝗶𝗻 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 We’re no longer designing prompts. We’re designing 𝗺𝗼𝗱𝘂𝗹𝗮𝗿, 𝗴𝗼𝗮𝗹-𝗱𝗿𝗶𝘃𝗲𝗻 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 capable of interacting with the real world. This evolution—LLM → RAG → Agentic AI—marks the transition from 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 to 𝗴𝗼𝗮𝗹-𝗱𝗿𝗶𝘃𝗲𝗻 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲.

Tech Stack of an AI Research Agent: The complete architecture that powers intelligent research automation. Building effective AI research agents requires more than just selecting a good LLM. The real challenge is coordinating multiple specialized components that work together smoothly to deliver accurate and thorough research results. Here's the essential tech stack breakdown: 🔹 1. LLM Backbone drives the core intelligence : GPT-4o excels at multimodal tasks and summarization. Claude 3 handles long-context document analysis very well. Mistral or Llama 3 offer open-source flexibility when you need full control over your deployment. 🔹 2. Memory and Context Management prevent information loss : LangChain or LlamaIndex manage context and effectively handle document chunks. Vector databases like Pinecone, Weaviate, or Chroma store embeddings and allow for semantic search across large document collections. 🔹 3. Web Browsing and Retrieval capabilities gather live information : Search APIs such as Serper, Brave Search, and Bing fetch reliable real-time results. Browser automation tools like Selenium or Playwright scrape dynamic content when static APIs fall short. 🔹 4. Tool Abstractions and Agents coordinate complex workflows : AutoGen enables collaboration among multiple agents. CrewAI provides role-based organization for task-specific responsibilities. LangGraph manages stateful workflows between agents. 🔹 5. Task Routing and Planning handle smart decision-making : Function calling via OpenAI or Claude APIs manages tool selection. ReAct or AutoGPT-style planners support iterative search, analysis, and synthesis processes. 🔹 6. Document Understanding extracts structured information : PDF parsers like Unstructured.io handle content extraction. OCR tools like Tesseract process scanned documents and images. 🔹 7. Output Generation creates professional deliverables : Notion API or Google Docs API generate formatted reports. Whimsical API and Mermaid.js create diagrams and visual summaries. The sample flow showcases the complete cycle: query processing, task breakdown, web search, document parsing, vector storage, summarization, source citation, and final output generation. Success comes from choosing components that integrate well, not just relying on individual tool capabilities. #aiagent

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.

If you’re in leadership, you need to understand *how* genAI will transform your organization, and what that means for restructuring teams. Here's what we're learning: BREAKTHROUGH IN AI IDEATION OpenAI is getting ready to launch new AI models (o3 and o4-mini) that can connect concepts across different disciplines ranging from nuclear fusion to pathogen detection. (Reporting from The Information's Stephanie Palazzolo and Amir Efrati). Molecular biologist Sarah Owens used the system to design a study applying ecological techniques to pathogen detection and said doing this without AI "would have taken days." THE NEW TEAMMATE EMERGES Remember the HBS study with 776 Procter & Gamble professionals? It showed that genAI functioned as an actual teammate. Individuals using AI performed at levels comparable to traditional human teams, achieving a 37% performance improvement over solo workers without AI. Teams using AI were three times more likely to produce top-quality solutions while completing tasks 12.7% faster and producing more detailed outputs. BREAKING DOWN SILOS That study showed that AI also dissolves professional boundaries. Without AI, R&D specialists created technical solutions while Commercial specialists developed market-focused ideas. With AI, both types of specialists produced balanced solutions integrating technical and commercial perspectives. A NEW KIND OF TEAM AI users reported higher levels of excitement and enthusiasm while experiencing less anxiety and frustration. Individuals working alone with AI reported emotional experiences comparable to those in human teams. That's wild. RESTRUCTURING FOR ADVANTAGE The HBS study showed that AI reduces dominance effects in team collaboration. When genAI translates between roles, it accelerates iteration at a pace that there’s no way traditional teams could match. ++++++++++++++++++++ THREE THINGS YOU SHOULD BE DOING NOW: 1. Upskill your entire workforce: Develop a fundamental behavioral shift in how teams interact with AI across every task. This only works if everyone is doing it. (We work with enterprise to upskill at scale - more below.) 2. Experiment with new team structures: Test different AI-team combinations. Try individuals with AI for routine tasks and small teams with AI for complex challenges. Find what works best for your specific needs. 3. Redefine success metrics: Set new standards for what good work looks like with AI. Track not just productivity but also idea quality, knowledge sharing across departments, and team satisfaction—all areas where AI shows major benefits. ++++++++++++++++++++ UPSKILL YOUR ORGANIZATION: When your company is ready, we are ready to upskill your workforce at scale. Our Generative AI for Professionals course is tailored to enterprise and highly effective in driving AI adoption through a unique, proven behavioral transformation. It's pretty awesome. Check out our website or shoot me a DM.

I've watched organizations rush to implement AI tools across their revenue functions, often with mixed results. Today, I'm sharing a crucial insight: the companies seeing transformative results are not those with the most advanced tech stacks. Instead, they deploy AI with surgical precision at the intersection of efficiency and trust. In my latest piece, I break down specific AI tools reshaping revenue operations and offer strategic guidance on implementing them without eroding the customer trust that underpins sustainable growth. Key takeaways: 🎯 Conversation Intelligence Platforms (Gong, Chorus): Not just for call analysis, but for scaling successful behaviors while maintaining authentic customer interactions 🎯 Predictive Lead Scoring (MadKudu, 6sense): Allowing targeted deployment of human capital against high-probability opportunities (with critical guardrails) 🎯 Personalization Engines (Mutiny, Optimizely): Creating tailored experiences without increasing operational complexity or crossing the "creepy line" 🎯 Content Generation (Jasper.AI, Copy.ai, Claude.ai): Achieving velocity without sacrificing quality (but still requires human oversight to be more, well, human). 🎯 Customer Journey Orchestration (Drift, a Salesloft company, Qualified): Creating guided buying experiences that feel personalized while operating at scale 🎯 AI Assistants (Grok, ChatGPT): Rapid iteration and testing of multiple approaches before committing resources The most successful revenue organizations aren't those using the most AI but those using AI most strategically. There is a competitive advantage in knowing where NOT to automate - in preserving human connection where it creates differentiating value. What AI tools are you implementing in your revenue operations? And more importantly, how are you measuring their impact beyond efficiency metrics? Read more here: https://lnkd.in/e4Ang6Nj __________ For more on growth and building trust, check out my previous posts. Join me on my journey, and let's build a more trustworthy world together. Christine Alemany #Strategy #Trust #Growth

🚀 AI is transforming how we work—but so much of the focus is on the individual—individual AI use cases, individual productivity gains, and the individual skills required to capitalize on AI. Too often, we miss the bigger picture. What happens to our teams and org structures when AI enters the workplace? I’m excited to share new research (link in first comment below 👇) that my colleagues and I recently published in CSCW. Our 10-month ethnographic study of a fast-growing digital retailer unpacked how AI challenges traditional org charts and structures. Here's the problem: 🗂️ Traditional org charts divide work into silos—sales, marketing, product lines, etc. This structure is decades old, designed to keep complexity manageable by clearly assigning who does what. 🤖 But AI doesn’t like silos. 🔎 In our study, the algorithms couldn’t fully optimize because the org chart kept decision-making locked in silos. Once those constraints were lifted, AI delivered far better results—spotting trends and opportunities no single team could see on its own. Our research suggests that to get the most out of AI, organizations need to rethink three key areas: 1️⃣ Break Down Silos Don’t box in your AI—or your teams. AI is most powerful when applied at the cross-functional level, connecting insights across departments and uncovering trends no single team can see on its own. 2️⃣ Rethink Your Data Systems Rigid, fragmented data systems are AI’s kryptonite. Shifting to flexible, connected data systems ensures AI can analyze patterns across the entire organization. If your data’s stuck in fragmented systems, your AI will be stuck too. 3️⃣ Rethink Your Org Chart—Or At Least How It Might Be Constraining Your AI Build teams and processes that aren’t limited by static org charts. Rethink how roles and responsibilities are assigned. When you’re building your next team, don’t just grab the org chart. Look at what needs to get done and use AI to help you figure out the right roles. Have you thought about how AI might reshape your org chart? We know that hierarchy matters a lot—history shows us that too many attempts to dismantle it over the years have flopped. But with AI in the picture, I expect that rethinking parts of our org charts won’t be optional—it’ll be inevitable. So honored to collaborate with an incredible team of superstars on this piece: Amanda Pratt, Melissa Valentine, and Michael Bernstein.

𝐇𝐨𝐰 𝐝𝐨 𝐈 𝐬𝐭𝐚𝐲 𝐫𝐞𝐥𝐞𝐯𝐚𝐧𝐭 𝐢𝐧 𝐭𝐡𝐞 𝐀𝐈 𝐞𝐫𝐚? The question I keep getting from professionals across every function — engineering, marketing, finance, operations: "What should I be doing right now to enhance my chances of keeping and flourishing in my job?" Having watched this shift play out across our portfolio companies, here is how I think about it. 𝐁𝐮𝐭 𝐟𝐢𝐫𝐬𝐭, 𝐨𝐧𝐞 𝐡𝐚𝐫𝐝 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧. Before you re-skill, ask whether the company you work for has a future in the AI era. If your company's core product is being replaced by AI — not enhanced, not contested, but replaced — reskilling inside that company may not be enough. Getting out early is not disloyalty. It is career survival. Assuming you are in the right place — three things, in order. 𝐒𝐡𝐢𝐟𝐭 𝐟𝐫𝐨𝐦 𝐞𝐱𝐞𝐜𝐮𝐭𝐨𝐫 𝐭𝐨 𝐨𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐨𝐫. Your value is no longer in doing the work — it is in knowing what work to do, why, and whether the output is right. The person who can break a problem down, delegate to AI, and judge the result is more valuable than the person who can execute a single step perfectly. This is a fundamental shift in identity — from "I am good at X" to "I know when X is done well." 𝐁𝐮𝐢𝐥𝐝 𝐀𝐈 𝐟𝐥𝐮𝐞𝐧𝐜𝐲 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐝𝐚𝐢𝐥𝐲 𝐮𝐬𝐞, 𝐧𝐨𝐭 𝐜𝐨𝐮𝐫𝐬𝐞𝐬. Stop taking "AI for professionals" courses. Start using AI tools in your actual work, every day. Draft with it, analyze with it, review with it. Fluency comes from repetition, not theory. The people pulling ahead are the ones who integrated AI into their daily workflow six months ago. 𝐃𝐞𝐞𝐩𝐞𝐧 𝐲𝐨𝐮𝐫 𝐝𝐨𝐦𝐚𝐢𝐧, 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐲𝐨𝐮𝐫 𝐭𝐨𝐨𝐥𝐬. AI commoditizes execution. What it cannot replicate is your understanding of why things work the way they do in your industry — the exceptions, the judgment calls, the context. When you can see the full picture of how outcomes are produced, you start thinking in terms of improving those outcomes, decreasing cycle times, and removing friction. That is where AI becomes a force multiplier — not on isolated tasks, but across workflows. 𝐈𝐌𝐏𝐎𝐑𝐓𝐀𝐍𝐓: Ask the hard question about your company first. Then shift your mindset from executor to orchestrator. Build AI fluency through daily use, not courses. And deepen the domain expertise that no model can replace. The window to build these habits is now — not next year. What has worked for you in re-skilling for AI? Would love to hear.

People on LinkedIn: “Prompt Engineering is dead!” Me as a person actually building complex GenAI agents for the biggest GenAI provider in the world: “Prompt Engineering is an integral part of my workflow”. Does that make people on LinkedIn wrong? Well, not really. We have done hard work to take the prompting requirements off the end user and have prioritized doing and improving upon this work in the backend. This way, we increase ease of use for you while ensuring our systems are more robust and less likely to go off the rails. Prompt Engineeing is more than tuning prompts, it’s about doing prompt experimentation, orchestration of LLM agents, tool incorporation, versioning prompts and their iterations as a part of model evaluation, testing prompt approaches for best results, working with user research to create appropriate system rules for specific use cases, applying Responsible AI principles, etc. In short, prompt engineering is a part of the scientific method in applied LLM building. Once again, prompt engineers still have to put the “science” in Data Science. It’s not easy work to create robust agents. So if this is the work you want to do, there are no shortcuts- take those linear algebra, multivariate calculus, research/quantitative methods and programming courses seriously. While the techniques themselves may change, the underlying science, DS&A principles and mathematics hold true.