Innovative Workflow Optimization

𝗥𝗲𝗱𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗳𝗼𝗿 𝗔𝗜: A new INSEAD/Harvard field experiment with 515 startups proves that profitable AI isn't about faster tasks, but about redesigning entire workflows. Both groups got identical tech access. The only difference? One group learned how to remap their whole production process around AI. The results were staggering: 🔍 44% more AI use cases discovered across the value chain ⚡ 12% more internal tasks completed in the same timeframe 💰 𝟵𝟬% 𝗵𝗶𝗴𝗵𝗲𝗿 𝗿𝗲𝘃𝗲𝗻𝘂𝗲 than the equally equipped control group 🏗️ 40% less capital needed to hit milestones, with zero change in headcount The takeaway: speeding up one step in a 10-step workflow barely moves the needle. The real gains come from removing handoffs, parallelizing work, moving humans to exceptions, and adding evaluation loops. AI isn't a technology problem, but a design and strategy problem. The control group had the exact same AI; they just didn't know how to map it onto their operations. 🛑 Stop asking "How can AI do this task faster?" ✅ Start asking "How would we redesign this entire process if AI were native?" Profitable AI = workflow redesign, not task optimization. My full article on how to redesign workflows for AI 👉 https://lnkd.in/e_nbd8Gj

Traditional surrogate-based design optimization (SBDO) is hitting a wall, especially with high-dimensional, complex designs. In this new paper, Dr. Namwoo Kang presents a next-gen framework using generative AI, integrating three key models: - Generative model (design synthesis) - Predictive model (performance estimation) - Optimization model (iterative or generative) Rather than optimizing directly in a high-dimensional design space (x), the workflow introduces a low-dimensional latent space (z) learned via generative models. ➡️ z → x → y z = latent variables x = CAD geometry y = performance (drag, stress, etc.) This means we’re no longer hand-coding design parameters or doing trial-and-error with simplified surrogate models. 🧠 Why this matters: - Parametric modeling is no longer a bottleneck - Complex shapes are learned directly from CAD - Dynamic and multimodal performance data (1D, 2D, 3D) can be used - Near real-time optimization is possible #AI #GenerativeDesign #CAE #DesignOptimization

Walk into a bookstore’s self-help section , rows of productivity systems : Getting Things Done, Bullet Journal, Zettelkasten. Each promises transformation—if you commit weeks. The market thrives , hundreds of new titles annually. But something new is emerging. We’re discovering we can redesign workflows instantly with AI prompts instead of studying methodologies for weeks. Three recent experiments hint at what’s coming. First, Paper2Agent transforms research papers into working code agents in 30 minutes to 3 hours. Ask it to implement AlphaGenome’s genomic analysis from a published paper & it processes the code, builds the environment, runs the tutorials. Previously: read the paper (2 days), understand the methodology (1 day), code the implementation (3 days). Now: point AI at the repository, get a working agent by lunch. Second, when I discovered Google’s Agent Development Kit research on tool design patterns, I asked AI to redesign my automation tools following those principles. The result : a 41% reduction in AI operation costs, implemented while I was answering emails in another browser tab. Third, over the weekend I read Jesse Vincent’s architect/implementer workflow via Simon Willison’s write-up. The approach splits AI coding into two sessions: one architect to design, one implementer to execute. No weeks of trial & error to understand the nuances. Read, implement, test, done. The pattern emerging : instant implementation. We can now test productivity systems like trying coffee drinks. Monday : architect/implementer (cappuccino—structured , classic). Tuesday : traditional single-session (flat white—simpler , maybe better?). Wednesday : hybrid workflow (chai latte—wait , is this even coffee?). By Thursday , the data shows which saves 3 hours per week. The self-help aisle promised transformation through commitment. AI promises transformation through experimentation.

Case Tuesday: Workflow Optimization It’s a typical morning in a large hospital radiology department: Dozens of CT, MRI, and X-ray studies waiting to be read Some cases are routine follow-ups, others are life-threatening emergencies Radiologists must balance speed, accuracy, and communication with busy clinical teams The challenge: Critical cases can get buried in a growing worklist Reporting bottlenecks delay clinical decision-making Communication gaps slow down patient care across departments #AI is stepping in to help beyond diagnosis itself: Automatically prioritizing urgent cases (e.g., suspected stroke, hemorrhage, PE) Routing studies to the right subspecialist radiologist Streamlining reporting and reducing administrative burden Integrating with hospital systems to ensure faster handoff to clinicians The radiologist is still at the center of the process but now supported by an ecosystem that reduces friction and ensures patients get the right care, faster. The impact: More efficient workflows that free up radiologists to focus on complex cases Shorter turnaround times for critical findings Improved collaboration across multidisciplinary teams As Chief Medical Officer at GE HealthCare, I see workflow optimization as the “hidden superpower” of AI; less visible than detecting a tumor or a clot, but just as vital for ensuring patients get the care they need at the moment they need it. Where do you see the greatest potential for AI in radiology helping with diagnosis, or transforming the workflow itself? #CaseTuesday #RadiologyWorkflow #AIinHealthcare #FutureOfMedicine #GEHealthcare

Many healthcare organizations are trying to optimize their workflows without a clear strategy, and that’s where things can go wrong. While serving as the US Navy's chief medical informatics officer (CMIO), I learned important lessons about workflow optimization, strategy, and technology integration. Here’s the truth: Healthcare workflows are intricate and multifaceted. Without the right approach, there’s a risk of: ⏳ Wasting valuable time on redundant tasks 💸 Incurring unnecessary costs 😟 Compromising patient experiences But it doesn’t have to be this way. 🔍 Here’s what you need to know to streamline and optimize your healthcare workflows with AI: 1️⃣ Identify Bottlenecks. First, not all workflow issues are created equally. Some are more critical than others. → Start by pinpointing the areas where inefficiencies are costing you the most. 2️⃣ Leverage AI for Automation. AI can handle routine tasks like appointment scheduling and data entry. → Free up your staff to focus on patient care and complex decision-making. 3️⃣ Enhance Decision-Making with AI. Insights AI can quickly analyze vast amounts of data, offering insights that improve patient outcomes. → Use AI to support clinical decisions and personalize treatment plans. 4️⃣ Improve Communication Channels. AI-driven tools can streamline communication between departments and with patients. → Ensure everyone is on the same page, reducing errors and enhancing patient satisfaction. 5️⃣ Monitor and Adjust Regularly. AI is powerful, but it is not set and forgotten. Continuous monitoring and adjustments are key. → Regularly review your workflows and tweak AI tools for ongoing optimization. Healthcare is challenging enough. Don’t let outdated workflows add to the stress. With a strategic approach, AI can transform your healthcare operations, making them more efficient, cost-effective, and patient-centered. 👉 Are you ready to explore how AI can elevate your healthcare workflows? Let’s discuss the possibilities.

In modern software development, we don't just guess if our code works. We write unit tests, run integration tests, and build CI/CD pipelines. We replaced manual guesswork with rigorous, automated validation. So why are many of us still in the "guesswork" phase with LLM prompts? The common workflow is a manual loop : tweak a prompt, test it, eyeball the result, and tweak it again. This is artisanal, slow, and doesn't scale. A prompt that works today might break tomorrow with a slight model update. It’s not an engineering discipline. The paradigm shift we need is Systematic Prompt Optimization. This is the move from "prompt art" to "prompt science." It’s about treating a prompt not as a magic incantation, but as a key component of a system that can be algorithmically tested, measured, and improved. The framework for this is surprisingly simple and powerful: 1./ Hypothesis (Your Base Prompt): Your initial, best-guess prompt. 2./ Ground Truth (An Evaluation Dataset): A set of inputs and ideal outputs that define success for your use case. 3./ Objective Function (An Evaluator): A measurable score for success (e.g., accuracy, semantic similarity, factuality). 4./ Optimizer: An algorithm that intelligently searches the vast space of possible prompt variations to find the one that maximizes your objective function. This approach is a repeatable, data-driven process. It allows you to prove why one prompt is better than another and ensures your system is robust. I've been exploring frameworks that enable this, and Comet's Opik is a fascinating, concrete example of this principle in action. It provides the optimizer and structure to automate this entire loop. Check here: https://lnkd.in/dZEfCW6S By adopting this mindset, we're not just writing better prompts. We're building more reliable, maintainable, and predictable AI systems. What steps is your team taking to bring more engineering discipline to your work with LLMs? #llm #ai #optimization #agents

Stop Automating Chaos: Why Process Optimization Must Precede Technology Buying expensive software to fix a broken workflow is a classic error. It happens constantly. Executives sign a contract for a new ERP or CRM and expect immediate results. The results never arrive. Instead, confusion grows. Automating a bad process does not yield efficiency. It yields high-speed chaos. We call this "paving the cowpaths". You solidify bad habits in code, making them expensive and difficult to change later. Your digital strategy must follow a strict sequence. People define the culture. Processes define the work. Technology supports both. You must map the actual reality of your operations first. Talk to the teams doing the work. Use Design Thinking to see the friction points from the user's view. Apply Lean principles to cut waste and simplify steps. Only then should you introduce any tool like AI. Technology amplifies what already exists. If your backbone is weak, software breaks it. If your process is solid, technology scales it. Reduce your operational risk by focusing on the workflow before the tool. A clean process builds the stability required for strategic growth. Stop looking for a software savior. Let Digital Transformation Strategist optimize your operations first.

Versur-GH and Versur-RH are now live. Versur can now run workflows directly inside Rhino and Grasshopper. This builds on the same idea - workflows as callable units - now accessible directly inside the design environment. You can build workflows in Versur - simple ones, chained processes, or full agentic workflows - and execute them directly inside your model. These workflows can range from image generation to more pragmatic ones. Natural language–based solvers, compliance checks, and others. The structure is the same. What they do is up to you. One important part of this is the solver. Instead of relying on predefined logic or optimization strategies, you can define how the system reasons about your Grasshopper model directly in Versur.ai. Goals, constraints, and evaluation logic can be described and executed as part of the workflow. In that sense, it behaves more like a programmable layer guiding the model. In Grasshopper: - load and access your Versur workflows - run them directly on the viewport - visualize results - chain multiple workflows - use the solver to define goals, optimize parameters, and run evaluations In Rhino: - run workflows through a chat-style panel - operate directly on the active model - optional viewport input - session persistence across runs Workflows stop being something you run outside and bring back in. They become callable components that operate directly within the design environment.