Integrating AI In Engineering Solutions

I stopped using Claude as a chatbot. I started using it as my Product Design team. 🚀 Most designers use AI for generating copy, rewriting text, or creating random UI ideas. That's only scratching the surface. While working on an AI Interview Engine project, I realized Claude can contribute to almost every stage of product design from problem discovery to developer handoff. Today, my workflow looks very different. How I use Claude to ship AI-powered products 🌱1. Discover & Research - User pain points - Competitor analysis - Market research - Interview questions - Research synthesis Instead of spending hours organizing notes, Claude helps me identify patterns and opportunities faster. 💡 2. Product Thinking & Strategy - PRDs - Feature prioritization - User journeys - Edge cases - Success metrics This is where Claude becomes powerful. Not because it gives answers. Because it helps me ask better questions. 🎯 3. UX Flows & Information Architecture - User flows - Task flows - Journey maps - States and scenarios - Error handling Many UX problems appear before a single screen is designed. 🎨 4. Claude Design + UI Creation - Screen concepts - UX critiques - Design system recommendations - Interaction ideas - Accessibility checks Claude helps me explore more possibilities before committing to a direction. ⚡ 5. Claude Code: This changed my workflow completely. I use it for: - Frontend prototypes - Design system implementation - UX validation - Product simulations - Documentation generation Seeing ideas come alive in code helps uncover issues much earlier. 🌻6. Validation & Iteration - Heuristic reviews - UX audits - Edge case testing - Scenario generation - Accessibility review The goal is not to validate designs. The goal is to validate decisions. 🚀 7. Handoff & Delivery - Functional requirements - Developer documentation - Acceptance criteria - Component behavior - Interaction specifications Developers get more clarity and fewer assumptions. The biggest lesson? AI didn't replace my design process. It amplified it. The more product thinking, judgment, and decision-making I bring, the better the output becomes. That's why I believe the future belongs to designers who can combine: 🧠 Product Thinking 🤖 AI Leverage 🎨 Design Craft 📈 Business Understanding Not just screen design. What part of your design process are you currently using Claude for? 👇 I'd love to learn from your workflow too. #uxdesign #productdesign #uidesign #claudeai #claudecode #artificialintelligence #designsystems #uxresearch #figma #designleadership #aidesign #productdesigner #userexperience #designthinking #aitools #aiindesign #ai #aidesigntools #designercommunity # #juniordesigners #learning #linkedin #creator #uiux

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

Industrial Engineering is entering a new era. #IndustrialEngineers in 2026 will not only understand Lean, OEE, RCA, FMEA, KPI management, and Operational Excellence. They will know how to combine them with AI, data analytics, strategy, and business excellence frameworks. Most Industrial Engineers and Operations teams still spend significant time on: • Manual analysis • Repetitive reporting • KPI consolidation • Standardization efforts • Data cleaning and formatting • Building presentations and dashboards • Recreating the same operational templates repeatedly AI is transforming that workflow completely. Today, AI can support: 1. Industrial Engineering workflows 2. Data analytics and performance insights 3. KPI standardization and reporting structures 4. OEE and Six Big Loss analysis 5. FMEA and risk prioritization 6. Root Cause Analysis and 5 Whys 7. SOP generation and process documentation 8. Capacity planning and line balancing 9. Lean waste identification and Kaizen opportunities 10. Strategy deployment and operational roadmaps 11. Sustainability reporting and ESG analysis 12. Business Excellence and continuous improvement initiatives 13. Executive summaries and transformation reporting The real advantage is not replacing engineering expertise. It is accelerating execution, improving decision-making, and enabling teams to focus on higher-value operational impact. AI significantly reduces the time spent on repetitive analytical work, allowing engineers to focus more on impact and execution. Before Claude: VSM in Visio → 2–3 days FMEA → entire afternoon (manual) SOPs → written from scratch RCA → 2-hour whiteboard session OEE → manually after every shift After Claude: VSM with takt time & bottleneck → ~40 min FMEA → ranked by RPN, review-ready SOPs → generated in minutes RCA → structured 5 Whys + Fishbone OEE → instant loss-driver breakdown The future of Industrial Engineering will be AI-assisted, data-driven, and execution-focused. The organizations that combine Operational Excellence with AI-enabled decision-making will move faster, optimize better, and create stronger long-term value. The engineers who adapt early will lead the next generation of Operational Excellence and Manufacturing Transformation. #IndustrialEngineering #DataAnalytics #KPIManagement #KPIStandardization #OperationalExcellence #BusinessExcellence #LeanManufacturing #ContinuousImprovement #Sustainability #ESG #Strategy #ManufacturingEngineering #DigitalTransformation #AIinManufacturing #IndustrialAI #ProcessImprovement

Making design exploration with AI feel natural again Traditional CAD and BIM tools are powerful, but during the conceptual phase they can feel rigid. Lines, layers, commands. Precise, but not always intuitive. In FORMAS.AI, we explore how AI can support a more fluid way of 𝐭𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐚𝐧𝐝 𝐝𝐞𝐬𝐢𝐠𝐧𝐢𝐧𝐠. • Intuitive space blending : moving beyond static blocks toward continuous spatial transitions that evolve in real time. • Orchestrated AI models : combining multiple deep learning systems to maintain architectural logic while expanding the range of formal exploration. • Localized notes as design drivers : embedding semantic and visual intent directly into specific zones of the model, instead of writing long global prompts. • Realtime procedural shapes : adjustable geometries that respond instantly, keeping iteration fast and exploratory. • Visually pleasant interface : reducing friction. Less command-line logic, more direct spatial and physical manipulation. A workspace that feels closer to sketching than drafting. The goal isn’t to replace precision tools like AutoCAD. It’s to extend the conceptual layer with a high level of control, where design should feel responsive, spatial, and alive. AI becomes less of a renderer, and more of a 𝐭𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐩𝐚𝐫𝐭𝐧𝐞𝐫 𝐚𝐧𝐝 𝐢𝐧𝐭𝐞𝐧𝐭 𝐞𝐧𝐚𝐛𝐥𝐞𝐫. If you want to know more about it, just comment below!

𝗙𝗿𝗼𝗺 𝗦𝘁𝗿𝗮𝗶𝗴𝗵𝘁 𝗙𝗶𝗻𝘀 𝘁𝗼 𝗔𝗜-𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝗱 𝗚𝗲𝗼𝗺𝗲𝘁𝗿𝘆: The illustration below highlights three generations of thermal optimization: (𝟭) 𝟭𝗗 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 – 𝗗𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 Engineers optimize basic parameters such as: • Fin height • Fin thickness • Fin spacing • Fin length 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲𝘀 • Simple design and manufacturing • Low cost • Suitable for conventional air-cooled heat sinks 𝗟𝗶𝗺𝗶𝘁𝗮𝘁𝗶𝗼𝗻𝘀 • Limited design freedom • Lower heat transfer efficiency • Higher thermal resistance at extreme heat fluxes    (𝟮) 𝟮𝗗 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 – 𝗙𝗶𝗻 𝗚𝗲𝗼𝗺𝗲𝘁𝗿𝘆 Instead of only changing dimensions, engineers optimize fin shapes using: • Pin fins • Offset fins • Louver fins • Wavy fins This increases: • Surface area • Turbulence • Coolant mixing • Heat transfer coefficient The result is significantly improved cooling performance while still using conventional manufacturing methods. (𝟯) 𝗧𝗼𝗽𝗼𝗹𝗼𝗴𝘆 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 – 𝗠𝗮𝘁𝗲𝗿𝗶𝗮𝗹 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 This represents the next generation of thermal engineering. Rather than designing individual fins, optimization algorithms determine 𝘄𝗵𝗲𝗿𝗲 𝗺𝗮𝘁𝗲𝗿𝗶𝗮𝗹 𝘀𝗵𝗼𝘂𝗹𝗱 𝗲𝘅𝗶𝘀𝘁 𝗮𝗻𝗱 𝘄𝗵𝗲𝗿𝗲 𝗶𝘁 𝘀𝗵𝗼𝘂𝗹𝗱 𝗯𝗲 𝗿𝗲𝗺𝗼𝘃𝗲𝗱 to maximize thermal performance while minimizing pressure drop and weight. Using 𝗖𝗙𝗗, 𝗙𝗗𝗔, 𝗙𝗘𝗔, 𝗮𝗻𝗱 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗱𝗲𝘀𝗶𝗴𝗻, engineers create complex organic structures that would be nearly impossible to design manually. 𝗞𝗲𝘆 𝗯𝗲𝗻𝗲𝗳𝗶𝘁𝘀 • Maximum heat transfer per unit volume • Lower junction temperatures • Uniform coolant distribution • Reduced pumping power • Lightweight, high-strength structures • Optimized pressure drop vs. thermal performance These geometries are typically manufactured using 𝗺𝗲𝘁𝗮𝗹 𝗮𝗱𝗱𝗶𝘁𝗶𝘃𝗲 𝗺𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴 (𝗦𝗟𝗠/𝗗𝗠𝗟𝗦), enabling cooling solutions beyond the capabilities of traditional machining. 𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 Thermal management has become one of the primary constraints on AI infrastructure performance. As chip power densities continue to rise, the industry is moving beyond incremental fin optimization toward 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺-𝗱𝗿𝗶𝘃𝗲𝗻 𝘁𝗵𝗲𝗿𝗺𝗮𝗹 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀. The future of AI cooling will be designed not only by engineers—but also by optimization algorithms that simultaneously solve for 𝗵𝗲𝗮𝘁 𝘁𝗿𝗮𝗻𝘀𝗳𝗲𝗿, 𝗳𝗹𝘂𝗶𝗱 𝗱𝘆𝗻𝗮𝗺𝗶𝗰𝘀, 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗮𝗹 𝗶𝗻𝘁𝗲𝗴𝗿𝗶𝘁𝘆, 𝗺𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗰𝗼𝘀𝘁. #𝗧𝗵𝗲𝗿𝗺𝗮𝗹𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 #𝗗𝗮𝘁𝗮𝗖𝗲𝗻𝘁𝗲𝗿 #𝗔𝗜𝗖𝗼𝗼𝗹𝗶𝗻𝗴 #𝗟𝗶𝗾𝘂𝗶𝗱𝗖𝗼𝗼𝗹𝗶𝗻𝗴 #𝗖𝗼𝗹𝗱𝗣𝗹𝗮𝘁𝗲𝘀 #𝗛𝗲𝗮𝘁𝗧𝗿𝗮𝗻𝘀𝗳𝗲𝗿 #𝗖𝗙𝗗 #𝗙𝗘𝗔 #𝗧𝗼𝗽𝗼𝗹𝗼𝗴𝘆𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 #𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲𝗗𝗲𝘀𝗶𝗴𝗻 #𝗔𝗱𝗱𝗶𝘁𝗶𝘃𝗲𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴 #𝗛𝘆𝗽𝗲𝗿𝘀𝗰𝗮𝗹𝗲 #𝗛𝗣𝗖 #EngineeringInnovation

AI use cases in engineering span the entire product development lifecycle and have the potential to accelerate engineering processes in a truly sustainable way. One use case that, in my view, still receives far too little attention is the AI-supported transformation from EBOM to MBOM at the interface between Engineering and Manufacturing. The handover from the Engineering BOM (EBOM), which reflects the functional and design intent, to the Manufacturing BOM (MBOM), which represents the production-ready view including assemblies, routing logic, and manufacturing constraints, is time-consuming, resource-intensive and heavily dependent on expert knowledge. Yet most manufacturing companies already possess a wealth of historical transformation data and mapping rules that have been developed over years. AI agents can leverage these assets (past EBOM→MBOM mappings, domain ontologies and mapping rules) to propose MBOM structures automatically or semi-automatically. By doing so, they can massively speed up the engineering-to-manufacturing transition and help ensure that Engineering and Manufacturing teams “speak the same language.” Typical tasks AI agents can support include: - inferring manufacturing assemblies from engineering components, - identifying missing manufacturing attributes and - validating consistency across versions and product variants. However, this requires that historical transformation data becomes machine-readable, and that ontologies, mapping rules and human-in-the-loop checkpoints are embedded into agent-based workflows. The raw data exists in many organizations! Now it’s about preparing it, structuring it and developing robust workflows to unlock these high-value use cases. The potential is enormous: faster handovers, fewer inconsistencies, less manual rework and a scalable way to capture engineering–manufacturing knowledge for future generations. Vlad Larichev | Laurin Prenzel | Rick Bouter | Jülich Sebastian | Jiangyue Zhao #AI #EBOM #MBOM #Manufacturing #Engineering #DigitalThread #ProductDevelopment #SmartManufacturing

𝐌𝐨𝐬𝐭 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 𝐚𝐫𝐞 𝐬𝐭𝐢𝐥𝐥 𝐮𝐬𝐢𝐧𝐠 𝐀𝐈 𝐚𝐬𝐬𝐢𝐬𝐭𝐚𝐧𝐭𝐬 𝐥𝐢𝐤𝐞 𝐚𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐚𝐮𝐭𝐨𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞 𝐭𝐨𝐨𝐥𝐬. The strongest engineers in 2026 are building AI-native engineering workflows. That is the real shift. Tools like Claude are no longer only helping developers write code faster. They are changing how engineering execution itself is structured. 𝐓𝐡𝐞 𝐜𝐨𝐧𝐯𝐞𝐫𝐬𝐚𝐭𝐢𝐨𝐧 𝐢𝐬 𝐞𝐯𝐨𝐥𝐯𝐢𝐧𝐠 𝐟𝐫𝐨𝐦: “How do I use AI during development?” To: “How do I architect development workflows around AI systems?” 𝐓𝐡𝐚𝐭 𝐜𝐡𝐚𝐧𝐠𝐞𝐬 𝐡𝐨𝐰 𝐦𝐨𝐝𝐞𝐫𝐧 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐭𝐞𝐚𝐦𝐬 𝐭𝐡𝐢𝐧𝐤 𝐚𝐛𝐨𝐮𝐭: → Context management → Workflow orchestration → Multi-agent collaboration → Permission-aware execution → Structured task planning and rollback control Because as AI systems become more capable… Engineering leverage increasingly comes from workflow design, not only coding speed. The organisations moving ahead are not simply adopting AI coding tools. They are creating repeatable AI-assisted engineering systems that improve execution quality, operational consistency, and development velocity over time. This is where the next layer of competitive advantage is emerging. Not through isolated AI usage. But through AI-integrated engineering operations. The future engineering stack will not only include infrastructure and CI/CD pipelines. It will include intelligent orchestration layers coordinating how software gets designed, executed, reviewed, and evolved. P.S. Many engineers still measure AI tooling by how much code it generates. The more mature teams measure how much operational leverage it creates across the entire engineering lifecycle. Follow Ashish Sahu for more insights

I told my design team to use AI. Their reaction shocked me... As a design agency owner, I'm often asked: "Aren't you worried about AI replacing designers?" My response? Absolutely not. In fact, I'm excited about AI's potential to supercharge our creative process. AI isn't here to replace designers – it's here to enhance them. Think of AI as a powerful new tool in a designer's toolkit. It's not about replacement; it's about augmentation and efficiency. Here's how AI is empowering designers today: ↳ Rapid prototyping with AI-assisted wireframing ↳ Streamlining asset creation with generative fill ↳ Boosting ideation through AI-powered brainstorming ↳ Automating tedious tasks like resizing and formatting The result? Our designers now have more time to focus on what truly matters: creativity, innovation, and solving complex design challenges. And our clients get all the benefits. AI excels at handling repetitive tasks, but it still struggles with: ↳ Understanding nuanced brand contexts ↳ Generating truly original concepts ↳ Emotional intelligence in design ↳ Developing a unique creative voice That's why the human touch remains irreplaceable in design. By embracing AI, we're not replacing designers – we're elevating them. It allows us to iterate faster, explore more options, and deliver better results for our clients. The designers who thrive will be those who see AI as a collaborator, not a competitor. How are you integrating AI into your design process? What tasks has it helped you streamline? Share your experiences below!

Our deep dive into AI for circuit board design revealed a crucial insight: AI's true power lies in its ability to gather data and generate code, rather than in direct design tasks. This finding came from a study of AI vs an expert design from Texas Instruments for a microphone pre-amplifier. We found that: - AI excels at extracting information from complex documents like datasheets - It can generate high-level code for circuit design more effectively than low-level netlists - The combination of data gathering and code generation can significantly accelerate the design process However, AI still struggles with nuanced decision-making and original design synthesis. That's where human expertise remains irreplaceable. This realization is shaping how we develop our code-based design tool. We're focusing on creating a symbiotic relationship between AI and human engineers, where AI handles data extraction and initial code generation, while engineers focus on critical design decisions and refinement. The future of circuit board design isn't AI replacing engineers - it's AI empowering engineers to work more efficiently and creatively. Can AI revolutionize circuit board design? Our latest article pits GPT-4o, Claude 3 Opus, and Gemini 1.5 against real EE challenges - see the surprising results: https://hubs.la/Q02F3S1g0 #AI #ElectricalEngineering

The traditional R&D and engineering cycle is painfully slow. Moving from initial customer requirements to an optimized design often takes months of manual iteration, searching through old files, and trial-and-error testing. AI is fundamentally compressing this timeline from months to days. Here is how the AI Processing Layer transforms the workflow: 1. Input: We start with customer requirements (performance targets, constraints, costs). 2. Knowledge Mining: Instead of engineers spending weeks digging through archives, AI retrieves past solutions in under 30 seconds—scanning 10,000+ historical designs, test reports, and patent literature. 3. Predictive Modeling: The AI predicts performance (yield, durability, thermal, cost) across 50+ candidate designs instantly. 4. Output: The engineer receives ranked, optimized design recommendations complete with risk scores and engineering rationale. AI doesn't replace the engineer; it gives them a superpower to iterate faster and design better. How much time does your R&D team spend searching versus actually designing? #Engineering #ResearchAndDevelopment #IndustrialAI #Manufacturing #ProductDesign #Innovation