Generative AI for Improving Operations

Continuing with my series on Gen AI, we had recently assisted a leading global company in unlocking cognitive insights generation at scale. The client faced significant obstacles in accessing and analysing critical performance metrics and market intelligence. They relied on disparate data sources—including multiple tables, external datasets, and competitor insights from websites and news articles—which made the process slow and complicated. Business leaders spent significant time gathering data and insights, often requiring help from tech teams leading to delays in decision-making and reduced agility.   Recognising the need for transformation, we collaborated closely with the client to design, deploy, and scale a GenAI-driven platform, empowering business leaders to track the performance of business divisions. The platform was based on a module with two kinds of datasets: structured KP datasets and unstructured textual datasets. Our GenAI solution enabled the client to conduct real-time computations, extract insights, and generate visual answers from both structured tabular data and unstructured text—allowing users to “converse” with the data. Leveraging advanced LLM models and text embeddings, the system performs at least eight distinct computations in response to queries, while summarising information from multiple sources seamlessly.   The impact of this solution has been significant. Leaders can now access critical information in seconds, changing their decision-making process from reactive to proactive. The client realised key benefits such as:   - Rapid access to critical insights: The solution reduced the effort for business managers to generate insights by 90%, while also minimising the risk of missed insights, enabling accurate and timely data-driven decisions. - Accelerated decision-making: The rapid analysis of data augmented by textual insights has led business leaders to make timely decisions, enabling them to respond to market dynamics instantly - Significantly improved operational efficiency: By automating routine tasks such as calculations and data summarisation, operational efficiency has improved significantly, with a reported 30% reduction in time spent on manual data gathering - Conversational interface: By enabling users to interact directly with the underlying data and insights, the organisation has fostered a self-service culture, significantly improving access to information across all levels   This case is a compelling case of how Generative AI could transform the insights generation process, delivering business decision support. Currently, the solution supports business leadership and has been scaled up across almost all global business units, with plans to cover most of the organisation in the future.   #GenAI #GenAISeries #Innovation #Consumer #GenAIInnovation #InsightGeneration #ConversationalAI

Remember the last time you tried to map a business process? You probably started with optimism, sticky notes, and endless coffee. Hours turned into days. Stakeholder interviews stretched on forever. The whiteboard filled up, got photographed, and then came the dreaded task of transcribing everything into a proper diagram. For decades, this has been the reality of process mapping – a bottleneck rather than a driver of innovation. But what if you could skip the painful parts? What if a simple conversation, rough whiteboard sketch, or pile of old procedure documents could become a perfect, formal process diagram in minutes? This isn't science fiction. It's Generative AI transforming how we understand and improve business operations. The traditional approach is broken: → Endless interviews with subject matter experts → Manual transcription prone to human error → Specialized tools requiring technical expertise → Long review cycles and frustrating revisions The result? Companies are left with outdated diagrams that don't reflect how work actually happens. Generative AI flips this model completely. It acts as the perfect translator between how people talk about their work and the technical language of process diagrams. You provide the raw material – interview transcripts, SOPs, emails, even whiteboard photos. The AI identifies actors, actions, systems, and decision points. Then it connects the dots using semantic analysis to understand logical flow. In seconds, you get a clean, structured, accurate process model in BPMN 2.0 format. This level of speed represents a competitive advantage. Instead of waiting months to identify bottlenecks, you spot them in an afternoon. Instead of one improvement project per quarter, you can run several. Full blog: https://lnkd.in/e5meRWn6 What's been your biggest challenge with traditional process mapping? #ProcessMapping #BusinessProcessManagement #ArtificialIntelligence #DigitalTransformation #ProcessImprovement

In my work with back-office and customer support teams in logistics and other B2B service areas, I find they’re often overwhelmed.   They’re fielding endless “Where’s my shipment?” emails, entering the same data into multiple systems, and chasing updates across siloed platforms. Most of these teams are doing heroic work — but they’re stuck in reactive, manual workflows.   Over the past decade, traditional AI tools — including machine learning models, regression analysis, and optimization algorithms — have made operations smarter and more efficient. We’ve seen them drive real value through things like: * Route optimization * Demand forecasting * Inventory and network planning * Load consolidation Dynamic pricing   But generative AI opens a new frontier — one that transforms how customer-facing teams interact, communicate, and respond.   We now have AI tools that can:   * Instantly answer tracking and status questions * Generate proactive updates before the customer even asks * Interpret and summarize internal systems in plain language * Draft accurate, personalized responses at scale   This isn’t just automation — it’s a fundamental shift in how we serve customers. It improves response times, eliminates bottlenecks, and significantly reduces the cost to serve.   The organizations that adopt these tools thoughtfully — and integrate them into real workflows — will gain a serious competitive advantage.   Are you rethinking how your teams serve customers? #LogisticsTech #GenerativeAI #DigitalTransformation #CustomerExperience #AIinBusiness #Automation #OperationsExcellence

🔧 Rewiring Maintenance with Generative AI: The Next Industrial Revolution? 🔧 Maintenance operations are evolving rapidly as industries face increasing complexity, aging workforces, and pressure to maximize uptime. Generative AI is emerging as a game-changer, transforming traditional maintenance practices into proactive, data-driven strategies that reduce downtime, optimize resources, and preserve institutional knowledge. How Gen AI is Reshaping Maintenance: 🚀 Enhanced Efficiency – AI-driven automation of routine tasks and data analysis is freeing up skilled workers for higher-value activities. ⚙️ Predictive Maintenance – Instead of reacting to failures, AI is now predicting them before they happen, significantly reducing unplanned downtime. 📚 Knowledge Retention – AI-powered assistants are capturing and sharing expertise, addressing the challenge of workforce retirements and skill gaps. Real-World Impact: 🔹 An oil and gas company used Gen AI to automate Failure Modes and Effects Analysis (FMEA)—cutting equipment downtime and improving operational efficiency. 🔹 A consumer goods manufacturer implemented an AI-powered troubleshooting assistant, leading to faster issue resolution and minimized production disruptions. What’s Holding Companies Back? Despite these benefits, many organizations struggle with AI adoption. The most common barriers include: ❌ Lack of AI-ready data – Maintenance data is often unstructured, siloed, or incomplete. ❌ Change resistance – Technicians and engineers may be hesitant to trust AI-driven recommendations. ❌ Integration challenges – Legacy systems weren’t designed for AI, requiring significant investment in modernization. Critical Questions for Business Leaders: 💡 How can companies effectively integrate Gen AI into their existing maintenance processes without overhauling legacy systems? 💡 What strategies can organizations use to upskill their workforce and drive AI adoption among frontline technicians? 💡 Will Gen AI fully replace human decision-making in maintenance, or is its true power in augmenting human expertise? The potential for AI-driven maintenance transformation is massive, but the real challenge lies in execution. Organizations that successfully leverage Gen AI, predictive analytics, and human expertise together will gain a significant edge in operational resilience and efficiency. 🚀 Is your company exploring AI-powered maintenance solutions? What challenges or successes have you seen? #GenerativeAI #PredictiveMaintenance #Industry40 #AIInnovation #Manufacturing #SupplyChain #DigitalTransformation

Let’s Use Generative AI to Build American Business, Not Tear It Down The same technology businesses have been using to disassemble their companies they could be using to expand and accelerate them. Generative AI, as it exists today, can almost always unsuccessfully, or it can be used to empower individuals and entire organizations to level up. When approached with imagination and courage, AI becomes an augmentation platform, a way to partner with people and make them infinitely greater at what they do. It’s not just about improving bottom-line efficiency. It’s about unlocking top-line growth by creating smarter, more adaptive, and more human-centered businesses. Here are 3 ways to build your business with GenAI: 1. Build Organizational Infrastructure for Augmentation Forget isolated AI pilots. Think integrated ecosystems where AI supports every function—marketing, sales, ops, HR, R&D, creating seamless cross-functional collaboration and strategic clarity. McKinsey found that companies using integrated AI strategies see *35% higher cross-functional productivity and 25% faster time-to-market.¹ 2. Level Up Human Brilliance Generative AI isn’t here to replace human intelligence, it’s here to multiply it. By augmenting creativity, decision-making, and knowledge-sharing, companies can unlock new ideas and solutions. BCG reports that AI-augmented teams achieve *3x faster innovation cycles and 20% higher revenue growth.² 3. Operationalize Ethics and Monetize Shared Values Embedding ethics into AI operations isn’t just a safeguard, it’s a strategic differentiator. Companies that align AI initiatives with shared values (including those of employees and customers) build trust and drive sustainable advantage. PwC found that organizations with strong AI governance outperform peers by *20% in long-term shareholder returns.³ The truth is, we don’t need more technology in American business. We need more imagination, and a belief that by working together, we can build a much stronger, more human-centered future. ******************************************************************************** The trick with technology is to avoid spreading darkness at the speed of light Stephen Klein is Founder & CEO of Curiouser.AI, the only Generative AI platform and advisory focused on augmenting human intelligence through strategic coaching, reflection, and values-based decision-making. He also teaches AI Ethics at UC Berkeley. Learn more at curiouser.ai or connect via Hubble https://lnkd.in/gphSPv_e Footnotes McKinsey & Company, "The State of AI in 2024" Boston Consulting Group, "AI as a Driver of Innovation" PwC, "AI Governance as a Business Advantage" Gartner, "AI-Driven Foresight in the Future Enterprise" Forrester Research, "AI and Employee Engagement"

Unlocking Operational Excellence with GenAI, LLMs, and RAG Deploying cutting-edge technologies like Generative AI (GenAI), large language models (LLMs), and Retrieval-Augmented Generation (RAG) is no longer a luxury—it’s a necessity. These tools transform organizations' operations, enabling more intelligent decision-making, streamlined processes, and enhanced customer experiences. What does this look like in action? Customer Support Revolution Imagine a customer service team equipped with an LLM fine-tuned on your company’s knowledge base. With RAG, the model retrieves real-time, context-specific data to provide accurate answers. This reduces resolution times and boosts customer satisfaction.  Knowledge Management At Fokker, an aerospace company, RAG-powered LLMs allow employees to instantly access critical contract details or warranty terms, saving hours of manual searching and ensuring accuracy.  Predictive Maintenance: In manufacturing, AI-driven systems analyze equipment data to predict failures before they occur, minimizing downtime and cutting costs.  How to Get Started:   1️⃣ Define Your Goals: Identify areas where AI can drive the most value—customer service, operations, or innovation.   2️⃣ Leverage Existing Data: Use RAG to integrate proprietary data into LLMs, ensuring relevant and actionable outputs.   3️⃣ Start Small, Scale Fast: Pilot projects in specific departments to demonstrate ROI before scaling across the organization.  The future of operational efficiency is here. Are you ready to lead the charge? #Leadership #AI #Innovation #OperationalExcellence  

Generative AI, a subset of artificial intelligence that focuses on creating new content, is poised to significantly impact the passive telecommunications infrastructure industry in 2025 & beyond. Here are some key areas where generative AI can make a substantial difference: 1. Predictive Maintenance and Network Optimization Predictive Analytics: By analyzing vast amounts of data from sensors & network performance metrics, generative AI can predict potential equipment failures and network bottlenecks. Optimized Network Design: AI-powered tools can generate optimal network designs, considering factors like traffic patterns, geographic constraints, cost-effectiveness. Automated Fault Detection and Isolation: AI algorithms can rapidly identify and isolate network faults, minimizing downtime and improving service quality. 2. Enhanced Site Acquisition and Permitting Site Selection Optimization: AI can analyze various factors like population density, zoning regulations, infrastructure availability to identify optimal site locations. Automated Permitting Processes: By streamlining the complex permitting process, AI can significantly reduce time & costs associated with site acquisition. Real-time Regulatory Compliance: AI-powered systems can monitor regulatory changes and ensure compliance, mitigating legal risks. 3. Improved Supply Chain Management Demand Forecasting: AI can accurately predict future demand for infrastructure components, optimizing inventory levels & reducing costs. Supply Chain Optimization: AI-driven algorithms can identify the most efficient supply chain routes, minimizing transportation costs & lead time Risk Mitigation: By analyzing real-time data on supply chain disruptions, AI can proactively identify & mitigate potential risks. 4. Innovative Product Design & Development Accelerated Design Processes: AI-powered design tools can automate repetitive tasks, allowing engineers to focus on innovative solutions. Optimized Product Performance: AI can simulate various design scenarios to identify the optimal configuration for specific use cases. Personalized Product Recommendations: By analyzing customer data, AI can recommend tailored product solutions, enhancing customer satisfaction. 5. Enhanced Customer Experience Intelligent Chatbots: AI-powered chatbots can provide 24/7 customer support, answering queries & resolving issues efficiently. Personalized Service Offerings: AI can analyze customer behavior & preferences to offer personalized services and solutions. Proactive Issue Resolution: By monitoring network performance and customer feedback, AI can proactively address potential issues before they impact service quality. As the technology continues to evolve, its potential to revolutionize the industry will only grow stronger from here. Indian Institute of Management, Indore DIGITAL INFRASTRUCTURE PROVIDERS ASSOCIATION Cellular Operators Association of India - COAI NIIF Infrastructure Finance Limited #AI #IOT #Telecom

𝗬𝗢𝗨𝗥 𝗔𝗜 𝗜𝗦 𝗟𝗜𝗦𝗧𝗘𝗡𝗜𝗡𝗚. 𝗪𝗵𝗮𝘁 𝘆𝗼𝘂 𝗽𝗿𝗼𝗺𝗽𝘁 𝘁𝗼𝗱𝗮𝘆 𝘄𝗶𝗹𝗹 𝘀𝗵𝗮𝗽𝗲 𝘆𝗼𝘂𝗿 𝗳𝘂𝘁𝘂𝗿𝗲 𝘁𝗼𝗺𝗼𝗿𝗿𝗼𝘄. Equipment downtime, inefficiencies, and rising costs—sound familiar? Generative AI is transforming industrial manufacturing, energy, water, agriculture - but 𝘀𝘂𝗰𝗰𝗲𝘀𝘀 𝗱𝗲𝗽𝗲𝗻𝗱𝘀 𝗼𝗻 𝗼𝗻𝗲 𝘁𝗵𝗶𝗻𝗴: 𝗽𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴. How you guide AI determines the quality of insights and outcomes. From 𝟱𝟴 𝗽𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 𝘁𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀, here are the top 8 categories driving change in industrial operations—and why they matter: 1️⃣ 𝗖𝗵𝗮𝗶𝗻-𝗼𝗳-𝗧𝗵𝗼𝘂𝗴𝗵𝘁 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 Breaks tasks into logical steps, ideal for root cause analysis and troubleshooting. Example: Pinpointing 𝘄𝗵𝘆 𝗮 𝘀𝗲𝗻𝘀𝗼𝗿 𝘁𝗿𝗶𝗴𝗴𝗲𝗿𝘀 𝗿𝗲𝗽𝗲𝗮𝘁𝗲𝗱 𝗳𝗮𝗹𝘀𝗲 𝗮𝗹𝗮𝗿𝗺𝘀. 2️⃣ 𝗙𝗲𝘄-𝗦𝗵𝗼𝘁 𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 Adapts AI to new tasks 𝗹𝗶𝗸𝗲 𝗮𝗻𝗼𝗺𝗮𝗹𝘆 𝗱𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 𝘂𝘀𝗶𝗻𝗴 𝗺𝗶𝗻𝗶𝗺𝗮𝗹 𝗵𝗶𝘀𝘁𝗼𝗿𝗶𝗰𝗮𝗹 𝗱𝗮𝘁𝗮. 𝗣𝗲𝗿𝗳𝗲𝗰𝘁 𝗳𝗼𝗿 𝗽𝗹𝗮𝗻𝘁𝘀 𝘄𝗶𝘁𝗵 𝗹𝗶𝗺𝗶𝘁𝗲𝗱 𝗹𝗮𝗯𝗲𝗹𝗲𝗱 𝗱𝗮𝘁𝗮𝘀𝗲𝘁𝘀. 3️⃣ 𝗗𝗲𝗰𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 Splits multi-layered problems into smaller parts, making it 𝗲𝗮𝘀𝗶𝗲𝗿 𝘁𝗼 𝘀𝗼𝗹𝘃𝗲 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗹𝗶𝗸𝗲 𝗲𝗻𝗲𝗿𝗴𝘆 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗼𝗿 𝗰𝗵𝗲𝗺𝗶𝗰𝗮𝗹 𝗱𝗼𝘀𝗶𝗻𝗴. 4️⃣ 𝗦𝗲𝗹𝗳-𝗖𝗿𝗶𝘁𝗶𝗰𝗶𝘀𝗺 𝗮𝗻𝗱 𝗩𝗲𝗿𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 Ensures AI outputs are reliable and compliant, 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗳𝗼𝗿 𝘀𝗮𝗳𝗲𝘁𝘆-𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝗮𝘂𝗱𝗶𝘁 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀. 5️⃣ 𝗘𝗻𝘀𝗲𝗺𝗯𝗹𝗶𝗻𝗴 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 Combines multiple reasoning paths for robust decisions, whether in supply chain planning or sensor data aggregation. 6️⃣ 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 Refines instructions to 𝘁𝗮𝗶𝗹𝗼𝗿 𝗔𝗜 𝗼𝘂𝘁𝗽𝘂𝘁𝘀 𝗳𝗼𝗿 𝘁𝗮𝘀𝗸𝘀 𝗹𝗶𝗸𝗲 𝗳𝗶𝗹𝘁𝗲𝗿 𝗺𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗼𝗿 𝘁𝗿𝗮𝗰𝗸𝗶𝗻𝗴 𝗞𝗣𝗜𝘀 in real time. 7️⃣ 𝗭𝗲𝗿𝗼-𝗦𝗵𝗼𝘁 𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 Deploys AI rapidly without pre-training, useful for responding to unexpected equipment failures or new operational challenges. 8️⃣ 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲𝘀 𝘁𝗲𝘅𝘁, 𝘀𝗲𝗻𝘀𝗼𝗿, 𝗮𝗻𝗱 𝗶𝗺𝗮𝗴𝗲 𝗱𝗮𝘁𝗮 to provide holistic insights for real-time monitoring and decision-making. 𝗪𝗵𝘆 𝗜𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀: 𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 𝘁𝘂𝗿𝗻𝘀 𝗔𝗜 𝗶𝗻𝘁𝗼 𝗮 𝗽𝗿𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝘁𝗼𝗼𝗹 𝗳𝗼𝗿 𝗿𝗲𝗱𝘂𝗰𝗶𝗻𝗴 𝗱𝗼𝘄𝗻𝘁𝗶𝗺𝗲, 𝗶𝗺𝗽𝗿𝗼𝘃𝗶𝗻𝗴 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆, 𝗮𝗻𝗱 𝗱𝗿𝗶𝘃𝗶𝗻𝗴 𝘀𝘂𝘀𝘁𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆. These techniques aren’t just abstract concepts—they’re the foundation of smarter, more reliable operations. Let’s explore how prompting can transform industrial AI efforts. #IndustrialAI #GenerativeAI #PromptEngineering #PredictiveMaintenance #Efficiency #Sustainability #Agriculture #Energy #Water

Most companies are only scratching the surface with generative AI. In my experience, many organizations are stuck on the first, most basic capability: using it for Information Compression. They're leveraging GenAI as a super-powered search engine or a summarization tool. While useful, that’s just level one. This is exactly why I developed the "Building an AI-First Snack Company" exercise. It's a hands-on approach to helping leaders overcome that initial hurdle, and I'm thrilled that it has now been taught to thousands of executives, enabling them to see the full picture. The real competitive advantage comes from understanding the other four possibilities and empowering your teams to imagine how they can redesign core operations around them. Beyond a better search bar, generative AI is a powerful engine for: 🎨 Content Creation & Synthesis: Instantly generating novel designs, marketing copy, or functional code, moving beyond human bottlenecks. 🔄 Information Transformation: Not just summarizing, but completely restructuring data, translating a complex report into a simple memo, or instantly adapting communications to different audiences. 📈 Simulation & Prediction: Modeling future market scenarios, predicting operational failures before they happen, or simulating new product launches to de-risk investment. 🧠 Complex Reasoning & Problem-Solving: Tackling multi-step strategic challenges, from optimizing a global supply chain to planning a complex market entry strategy. The crucial question for leaders isn't "How can AI make us faster?" It's "How can these new capabilities allow us to operate in ways that were previously impossible?" Link to the exercise is in the first comment. #GenerativeAI #AIStrategy #Leadership #DigitalTransformation #Innovation #BusinessStrategy #Experimentation

Check our latest blog in the AWS Smart Machines series! This time exploring the synergies between Generative AI and IoT in #SmartMachines powered by Amazon Web Services (AWS). Every week, I am exploring with customers how #GenerativeAI and #IIoT work together to enhance smart equipment capabilities, monetization, servicing and customer experiences. Both with classic Generative AI and with #AgenticAI. For many, this combination sounds either futuristic and advanced or unclear on use cases. But it should not! Thus, we wrote this introductory blog. 😉 In this blog we explain some common use cases, architectures and best practices for how to combine IoT and Generative AI today in Software Defined Machines. Working towards a vision of #SelfOptimized machines and #Autonomous systems. 🎯 Four practical use cases we explore: 1. Assisted Diagnosis and Troubleshooting When equipment issues arise, GenAI enriches IoT sensor alerts by analyzing equipment manuals, SOPs, maintenance records, and spare parts history. The result? Complete problem context with step-by-step repair guidance, specific spare parts recommendations and ordering, and even voice-enabled support for hands-free operations. 2. Enhanced Field Service Operations AI-generated remote diagnostic reports help field teams prepare better and reduce site visits. 3. Machine Fleet Analysis for OEMs OEMs can query fleet data in natural language to identify failure trends and guide design improvements. 4. AI-Generated Diagnostic Reports AI-generated reports synthesize operational data into strategic insights, enabling premium services to customers. 🧱The Technical Guidance: 
The #architecture leverages AWS IoT #SiteWise and AWS IoT Core with Amazon #Bedrock - connecting equipment data with generative AI capabilities, incl. Agentic AI. 💬 In the blog you can also find the quick insights from four #AWS Smart Machines System Integrator #AWSpartners: Deloitte, SoftServe,Twisthink and Green Custard Ltd. and #customer videos with KONE and HP. 🙏 Thanks to the dear colleagues who co-authored this blog with me: Gary Emmerton and Gabriel Verreault and our key contributors: Yuri Chamarelli (GenAI-IIoT), Channa Samynathan (#IntelligenceEdge), Vijay Karthick Baskar (#VoiceInteraction) and Emily Pacheco O’Kelly, MBA (Industrial PMM). 🚀 For Equipment OEMs, Component Manufacturers & Industrial Solution Providers: Understanding and implementing this combination in your products can differentiate your equipment offerings, reduce cost of serving, create new revenue streams, new insights and strengthen customer relationships. Can’t wait to see what our customers and partners will build! 💫 What’s your experience with #GenAI in #Connectedequipment? I’d love to hear your thoughts! 👇 👉 https://lnkd.in/eAME5kkd AWS for Industrial AWS for Industries #NewBlog #GenAIoT #SmartMachines #IoT #AIoT #AWSIoT #DimitriosIoT