AI in Knowledge Work Productivity

Everyone's talking about LLMs. I went a different direction 🧠 While everyone's building RAG systems with document chunking and vector search, I got curious about something else after Prof Alsayed Algergawy and his assistant Vishvapalsinhji Parmar's Knowledge Graphs seminar. What if the problem isn't just retrieval - but how we structure knowledge itself? 🤔 Traditional RAG's limitation: Chop documents into chunks, embed them, hope semantic search finds the right pieces. But what happens when you need to connect information across chunks? Or when relationships matter more than text similarity? 📄➡️❓ My approach: Instead of chunking, I built a structured knowledge graph from Yelp data (220K+ entities, 555K+ relationships) and trained Graph Neural Networks to reason through connections. 🕸️ The attached visualization shows exactly why this works - see how information naturally exists as interconnected webs, not isolated chunks. 👇🏻 The difference in action: ⚡ Traditional RAG: "Find similar text about Italian restaurants" 🔍 My system: "Traverse user→review→business→category→location→hours and explain why" 🗺️ Result: 94% AUC-ROC performance with explainable reasoning paths. Ask "Find family-friendly Italian restaurants in Philadelphia open Sunday" and get answers that show exactly how the AI connected reviews mentioning kids, atmosphere ratings, location data, and business hours. 🎯 Why this matters: While others optimize chunking strategies, maybe we should question whether chunking is the right approach at all. Sometimes the breakthrough isn't better embeddings - it's fundamentally rethinking how we represent knowledge. 💡 Check my script here 🔗: https://lnkd.in/dwNcS5uM The journey from that seminar to building this alternative has been incredibly rewarding. Excited to continue exploring how structured knowledge can transform AI systems beyond what traditional approaches achieve. ✨ #AI #MachineLearning #RAG #KnowledgeGraphs #GraphNeuralNetworks #NLP #DataScience 

A lot of AI engineers (even sharp ones) get seduced by the cool factor of vector databases. Cosine similarity, ANN search... it all sounds cutting-edge. But when you're building a Retrieval-Augmented Generation (RAG) pipeline, you're not just doing retrieval. You're orchestrating a semantic symphony between memory, context, and reasoning. And that's where many go off the rails. ❌ The Mistake: Vector First, Think Later Vector DBs are fantastic if: • Your knowledge is flat, unstructured, and mostly text • You want fast nearest-neighbor search over embeddings • You're okay with opaque black-box retrieval But the moment your domain knowledge has structure, hierarchies, relationships, or rules that need to be preserved across hops... vector search starts hallucinating. Hard. Because embedding space flattens knowledge. It smears out the sharp logic. It doesn't understand that "Paris is the capital of France and a city in Europe and has museums related to Impressionism." Vector DB just knows "Paris" is semantically close to "Eiffel Tower." Wow. Groundbreaking. 🧭 What You Should Be Using: Knowledge Graphs If your use case has: • Ontologies (types, classes, hierarchies) • Multi-hop reasoning (A→B→C) • Causality or directionality (X leads to Y, not just related to) • Entity disambiguation (which "Apple" are we talking about?) • Need for traceability and explainability (the why behind the answer) Then a Knowledge Graph (KG) is your divine weapon. Graphs don't just store facts. They encode logic, preserve causality, and let you do symbolic + neural hybrid search. They let you model the world like the world actually works... not just as a soup of cosine-clustered tokens. 🧪 Real-World Case: Ask a medical LLM powered by a vector DB: Can ibuprofen be taken with aspirin? You might get a generic answer scraped from a webpage. Ask the same question in a KG-powered RAG. The graph knows: Ibuprofen is an NSAID. Aspirin is an antiplatelet. There's a potential drug interaction due to increased bleeding risk. This depends on patient profile → age → comorbidities → other meds It can trace a path through nodes and edge types to construct a reasoned answer. This is not just retrieval. This is inference. 🔮 Where This Is Going The future of RAG is hybrid: 🔸️Embeddings for semantic breadth 🔸️Graphs for logical depth You'll embed the leaves of the tree... but you'll walk the branches with graph logic. 🎯 TLDR for the Impatient: Vector DBs are great for fuzzy recall. Knowledge Graphs are necessary for precise reasoning. And most AI engineers forget that precision is not optional in high-stakes domains like medicine, law, or finance. If your system needs to think, not just parrot, start with the graph. #database #vector #embeddings #knowledgegraphs #algorithms #computerscience #software #tech #medicine #law #finance #AI #RAG #LLM

How can leaders transform their teams to be AI-first? It starts with mindset. An AI-first mindset means: Seeing AI as an opportunity, not a threat. Viewing AI as a tool to augment teams, not just automate tasks. Using AI to reimagine work, not just optimize work. As leaders, it’s on us to build this mindset within our teams. Here are 5 ways we do this at HubSpot: Use AI daily: Lead by example—trust grows when teams see leaders embrace AI themselves. I use it everyday and share very specific use cases with our company on how I use it. Now every leader is doing the same with their teams. The result is that we will have almost everyone in the company use AI daily by the end of year. Apply constraints: Give clear, focused challenges. We kept headcount flat in Support while growing the customer base by 20%+. Result - the team innovated with AI and over achieved the target. Smart constraints drive innovation. Establish tiger teams: Empower small, agile groups to experiment, innovate, and teach the organization. We have AI Tiger teams in every function - they share progress in Slack channels and there is so much energy with small groups experimenting and learning. Be a learn-it-all: Foster a culture of continuous learning. Share openly about successes and failures alike. We have dedicated 2 full days to learning and scaling with AI this quarter as a company - we have lined up great speakers, ways to experiment and gamified learning. Measure progress and share it: Measure which teams are completing learning modules, using AI everyday and share that openly. A little healthy competition goes a long way in driving AI-fluency. AI isn’t just a technology shift. It’s fundamentally reshaping how work gets done—and that requires shifting our mindset first. Leaders who embrace AI now will unlock creativity, performance, and impact. Are you building an AI-first mindset with your team? #Leadership #AI #Innovation #Mindset #FutureOfWork

Adopting AI tools is easy. Reimagining how we work with them is the real transformation. Across many organizations, teams are being asked to “adopt AI” without the time, training or clarity they need to feel confident. When that happens, progress becomes fragmented—some people race ahead, others hesitate, and morale drops under the weight of confusion. Real AI transformation requires more than deploying technology. It demands deeper shifts that help people work differently and unlock value: → Change management to guide teams through new ways of working → Skilling to empower every employee to thrive in an AI-powered environment → Process understanding to ensure AI augments what matters most → Technology that’s usable, ethical and aligned with business goals As this Forbes article shares, the organizations that succeed will be the ones that treat AI adoption as a human journey, not just a technical one. When teams feel equipped, supported and included in shaping the path forward, that’s when AI truly delivers. What support are you giving your teams to learn and experiment with AI? https://lnkd.in/g2pXBtjm

Ambient AI is no longer a future concept in healthcare, it’s already reshaping how care is delivered. AI-enabled clinical documentation is changing how physicians experience technology, making it feel supportive rather than burdensome. By reducing the administrative load of documentation, clinicians can spend more time practicing medicine instead of managing systems. At the same time, clinical documentation, which has long been a source of friction, burnout, and risk, has the potential to become a powerful source of real-time clinical insight. At Elevance Health, we’re focused on applying digital technologies, such as ambient and clinical insights - responsibly - not just to document care, but to enable earlier intervention, better coordination, and more effective cost management. Several principles guide our approach: 🚣 Move upstream: Embed payer intelligence, such as risk signals and care gaps, directly into clinical workflows rather than surfacing insights after the fact. 🕵 Focus on moments that matter: Earlier detection of risk allows action before acute events occur. 🩺 Keep humans in the loop: AI should support clinical decision-making, not replace clinical judgment. 🔃 Reduce friction, not add it: Seamless data flow means less manual work for providers and faster, more comprehensive care. By integrating real-time clinical documentation with actionable insights, ambient AI can help surface relevant information at the moment of care, supporting more comprehensive diagnosis, improved coordination, and more affordable outcomes without increasing burden or compliance risk. The opportunity ahead isn’t about adding more AI tools. It’s about turning data into action at the right time, in the right workflow, for the right member. I look forward to continued collaboration across payers, providers, and technology partners as we shape what responsible, AI-enabled healthcare should look like.

Achieving AI productivity gains usually means you have to slow down in order to speed up. Across Amazon, teams are using AI to get more done across a variety of functions, including software development. Teams that treat AI as a drop-in replacement or expect immediate gains without restructuring how they work consistently underperform. In recent months, we've been experimenting across hundreds of engineering teams and noticing where AI is delivering the most value. The largest productivity gains across the business have come from what we call frontier teams, and they usually took one of three paths: a pathfinder initiative with experts tackling a challenge, a structured sprint to execute on a well-defined plan, or an in-situ experiment splitting teams between existing approaches and AI-adapted workflows. The paths differ in structure but converge on the same insight. Teams achieved 4.5x, in some case more than 10x, productivity gains. They achieved this by reducing barriers to context for agentic workloads and increasing the surface area of work that can be done independently. Here's what I think are five ways to build an AI-native team: 1. Patience. Frontier teams that get the most productivity gains invest time in building agent context. When teams skip this step, agents keep making the same mistakes. At AWS, the Bedrock infrastructure team placed all code and documentation into a monorepo and kept the inline commentary that AI agents generated — treating it as persistent memory. 2. More patience. Push through learning curves and restructure to capture cross-functional expertise. The teams that quit this early never see the compounding acceleration that's achievable after a couple of weeks.  3. Feed agents instead of babysitting them. We saw one principal engineer ship a complete change with only 'a couple of hours of contiguous time' because the agent worked while the engineer moved between code reviews, operational support, and meetings. 4. Be very clear. Teams need to make intent explicit before code gets written. Teams that have clear context about what "done" looks like report that they handwrite only 1-2% of their code. This opens the door to push more commits per person per week. 5. "Shift testing left." Frontier teams build tooling so agents can run all integration tests locally and self-correct before code ever reaches the pipeline. The first few weeks of this process are going to feel slow. Start with a small, deliberate pilot before broadening this across your business. Take learnings and develop playbooks that your entire organization can use and build from. Frontier teams are possible for any organization, here's more on how we're building them at Amazon https://lnkd.in/gpY5UjCz

In the world of Generative AI, 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚) is a game-changer. By combining the capabilities of LLMs with domain-specific knowledge retrieval, RAG enables smarter, more relevant AI-driven solutions. But to truly leverage its potential, we must follow some essential 𝗯𝗲𝘀𝘁 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀: 1️⃣ 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗮 𝗖𝗹𝗲𝗮𝗿 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲 Define your problem statement. Whether it’s building intelligent chatbots, document summarization, or customer support systems, clarity on the goal ensures efficient implementation. 2️⃣ 𝗖𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗕𝗮𝘀𝗲 - Ensure your knowledge base is 𝗵𝗶𝗴𝗵-𝗾𝘂𝗮𝗹𝗶𝘁𝘆, 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱, 𝗮𝗻𝗱 𝘂𝗽-𝘁𝗼-𝗱𝗮𝘁𝗲. - Use vector embeddings (e.g., pgvector in PostgreSQL) to represent your data for efficient similarity search. 3️⃣ 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗠𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺𝘀 - Use hybrid search techniques (semantic + keyword search) for better precision. - Tools like 𝗽𝗴𝗔𝗜, 𝗪𝗲𝗮𝘃𝗶𝗮𝘁𝗲, or 𝗣𝗶𝗻𝗲𝗰𝗼𝗻𝗲 can enhance retrieval speed and accuracy. 4️⃣ 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗲 𝗬𝗼𝘂𝗿 𝗟𝗟𝗠 (𝗢𝗽𝘁𝗶𝗼𝗻𝗮𝗹) - If your use case demands it, fine-tune the LLM on your domain-specific data for improved contextual understanding. 5️⃣ 𝗘𝗻𝘀𝘂𝗿𝗲 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 - Architect your solution to scale. Use caching, indexing, and distributed architectures to handle growing data and user demands. 6️⃣ 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 𝗮𝗻𝗱 𝗜𝘁𝗲𝗿𝗮𝘁𝗲 - Continuously monitor performance using metrics like retrieval accuracy, response time, and user satisfaction. - Incorporate feedback loops to refine your knowledge base and model performance. 7️⃣ 𝗦𝘁𝗮𝘆 𝗦𝗲𝗰𝘂𝗿𝗲 𝗮𝗻𝗱 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝘁 - Handle sensitive data responsibly with encryption and access controls. - Ensure compliance with industry standards (e.g., GDPR, HIPAA). With the right practices, you can unlock its full potential to build powerful, domain-specific AI applications. What are your top tips or challenges?

Knowledge and expertise are human. Yet used well, AI can assist people in acquiring knowledge, transfering expertise from experienced seniors to juniors, and developing true organizational intelligence. The intent must be not just to capture and institutionalize knowledge, but to enable the flows of human to human knowledge that are at the heart of all expertise development and the foundation of a dynamic, flourishing organization. This compact report provides a framework and distills some of the most useful approaches used by NASA Jet Propulsion Laboratory Wärtsilä Morgan Stanley IBM Siemens Unilever Bank of America Moderna for others to learn from. The 8 techniques: 1️⃣ Knowledge Extraction and Codification AI draws tacit expertise out of people through interviews, walkthroughs, and conversation, then structures it into searchable assets that capture both actions and underlying reasoning. 2️⃣ Iterative Expert Encoding AI progressively absorbs experts’ knowledge and decision patterns over time so non-specialists can be guided through complex decisions without needing constant direct expert input. 3️⃣ Conversational Knowledge Repository AI makes organizational knowledge accessible in plain language by synthesizing information across documents, policies, past decisions, and expert outputs. 4️⃣ Knowledge Discovery AI maps who knows what across the organization by analyzing signals such as work patterns and outputs, revealing expertise, risks, concentrations, and gaps. 5️⃣ Knowledge Routing AI delivers the right expertise, content, or expert connection at the point of need without employees having to know where to look or whom to ask. 6️⃣ Augmented Mentoring and Tandem Learning AI strengthens learning relationships by pairing more and less experienced employees, surfacing timely content, and making the exchange more productive. 7️⃣ Simulation and Experiential Practice AI compresses the learning curve by creating realistic practice environments where people can build judgment, pattern recognition, and confidence before real-world consequences apply. 8️⃣ Expertise Extension AI enables domain-adjacent employees to perform work that once required deeper specialist expertise, while still relying on human judgment and foundational knowledge. Lots more useful content coming, follow to keep on the edge of how AI can amplify organizational success. 🙂

It's not sexy to say, but most of AI transformation has nothing to do with AI. There are 10 steps in the sequence of making an internal process or external product AI-native. Only 1 step is AI, and ironically, the other 9 steps are the far harder part. Step 1: Identify the problem - Find the manual process worth automating. turn your brain off autopilot & turn on your "suck meter". - Funny enough, your company becomes more efficient just by mapping out your processes even if you don't introduce AI. Step 2: Understand the workflow - Map how people actually work today. grab an 8.5x11 piece of paper or Excalidraw and create a flow chart of the workflow from beginning to end. - Least sexy part, but generally where the people driving transformation (FDE, GTM engineer, etc) should spend the majority of their time. Step 3: Collect the data - Gather sample inputs, documents, edge cases - Example: for my content machine ai workflow, I gathered past slack messages/notion transcripts to test automated ideation Step 4: Build the prototype [The AI Part] - Whether its engineer-led or SME-led the goal is to test your hypothesis that there's a better way of doing things for yourself as customer zero. Don't worry about code cleanliness, don't worry about scalability. Step 5: Test & iterate - Before you take the process from single player (only you using it) to multiplayer (many users), you want to beat it up with as many rounds of work & feedback + edge cases as possible. Turning every process into a self-improving loop before scaling is key. Step 6: Integrate with systems - Point-in-time data is good for testing the workflow, but live data is necessary before going into production. Step 7: Roll out & train - Whether the new process lives on a live link, on GitHub or an internal library, next step is hand-holding your peers/users through the onboarding process of your new workflow/product. Step 8: Drive adoption - Embed the workflow in your culture where adoption is tracked, ideas & feedback are celebrated, and new/creative use cases become social currency in your business. Step 9: Empower contribution - Treat your new process like an opensource project. Allow users to become contributors. Whether they are literally pushing code or are simply empowered to add ideas/feedback to a kanban board that gets serviced by engineers, make everyone feel like a builder. Step 10: Measure & capture value - If you're in the experimental phase of AI adoption in your company, fuck ROI. The goal is to empower people to throw a lot of shit at the wall & see what's worth focusing on. You don't need to be scientific during this process. - If you're in the scale-up phase of AI in your business, and you need to realize hard ROI, you need to reskill employees attached to this process, undershoot your approved hiring roadmap, or measurably increase ACV/conversion rate/sales cycle speed.

Dear Stakeholders and Executive Leaders: The easiest way to improve the data and AI team’s productivity is to stop scheduling meetings with them. If technical team members have over 3 internal meetings per week, something’s wrong. That’s not collaboration or communication. It’s overhead. Most meetings can be handled via email or Slack message. We’re moving meetings, emails, and DMs into NotebookLM. Each team member has an “Info I know that you may need to know…” notebook. They drop information into it, thinking, “Vin would want to know about this,” or “This will be important in 3 months.” Each client and project has one with meeting recordings, emails, documents, diagrams, where to find data, and whatever else. We handle most information requests by asking questions there first. Everyone has a status notebook. They add updates at the beginning and end of the day. They can talk it out, write it down, take pictures of a whiteboard…it all works the same. I have a “How to do…” collection. It has processes for invoicing, SOW creation, managing difficult client scenarios, etc. Whenever someone asks me, or I ask them ‘how to,’ it’s recorded for a new notebook. For now, Google’s data-sharing policy works for us. We are evaluating Notebook Llama in case it changes. Get used to doing something once, documenting it in the easiest mode, and adding it to an LLM-supported knowledge base. LLMs can help transform it into a knowledge graph that more efficiently represents the workflows and expertise required to run the business. Businesses will only benefit from AI when they rethink and innovate existing workflows. Start with the ones that add the most overhead, like meetings. #ArtificialIntelligence #GenAI #Productivity