Automation In The Workplace

For years, AI agents in IoT felt like a distant promise, a theoretical concept. But what I'm seeing on the ground, across multiple industries, is that they're already here and fundamentally changing operations. We're moving from 'smart' to truly autonomous, and it's happening faster than many realize. Autonomous AI agents are powering real-time decisions, often without human intervention, and the impact is significant. Here's how they're reshaping the world in action, directly impacting efficiency, safety, and sustainability: ➞ Energy & Utilities AI agents manage utility meters, detect faults, balance renewable sources, and even forecast load for smarter grid control. We're seeing more resilient and efficient energy distribution. ➞ Smart Homes & Buildings From appliance alerts to voice-controlled lighting and HVAC systems, AI agents optimize energy and automate comfort, creating more adaptive living and working spaces. ➞ Industrial IoT (IIoT) Think workflow optimization, RPA, anomaly detection, and predictive maintenance  -  agents keep machines talking and plants running, minimizing downtime and maximizing output. ➞ Healthcare IoT Remote health tracking, early symptom alerts, emergency response triggers  -  AI agents act as silent guardians in care systems, providing continuous, proactive support. ➞ Smart Cities AI agents streamline traffic, parking, waste management, and air/water monitoring  -  building cities that think, adapt, and react to citizen needs. ➞ Supply Chain & Logistics From fleet tracking to warehouse automation and cold chain monitoring, agents ensure goods flow faster, safer, and smarter, optimizing complex global networks. ➞ Agriculture IoT Drones, sensors, and agents team up for pest detection, irrigation control, and yield forecasting precision farming at scale, driving higher productivity with less waste. This isn't just about adding a new feature; it's a change in approach. The true power of AI Agents in IoT isn't just intelligence, but its ability (with appropriate guard rails) to be situational, autonomous, and deliver industry-proven results. It's about devices that don't just report data, but actively manage and optimize their environments. Where are you seeing the most impactful applications of AI in the real world? ♻️ Repost if you see the future in AI agents, not just dashboards ➕ Follow me, Nick Tudor, for more real-world insights on AI + IoT

Building useful Knowledge Graphs will long be a Humans + AI endeavor. A recent paper lays out how best to implement automation, the specific human roles, and how these are combined. The paper, "From human experts to machines: An LLM supported approach to ontology and knowledge graph construction", provides clear lessons. These include: 🔍 Automate KG construction with targeted human oversight: Use LLMs to automate repetitive tasks like entity extraction and relationship mapping. Human experts should step in at two key points: early, to define scope and competency questions (CQs), and later, to review and fine-tune LLM outputs, focusing on complex areas where LLMs may misinterpret data. Combining automation with human-in-the-loop ensures accuracy while saving time. ❓ Guide ontology development with well-crafted Competency Questions (CQs): CQs define what the Knowledge Graph (KG) must answer, like "What preprocessing techniques were used?" Experts should create CQs to ensure domain relevance, and review LLM-generated CQs for completeness. Once validated, these CQs guide the ontology’s structure, reducing errors in later stages. 🧑⚖️ Use LLMs to evaluate outputs, with humans as quality gatekeepers: LLMs can assess KG accuracy by comparing answers to ground truth data, with humans reviewing outputs that score below a set threshold (e.g., 6/10). This setup allows LLMs to handle initial quality control while humans focus only on edge cases, improving efficiency and ensuring quality. 🌱 Leverage reusable ontologies and refine with human expertise: Start by using pre-built ontologies like PROV-O to structure the KG, then refine it with domain-specific details. Humans should guide this refinement process, ensuring that the KG remains accurate and relevant to the domain’s nuances, particularly in specialized terms and relationships. ⚙️ Optimize prompt engineering with iterative feedback: Prompts for LLMs should be carefully structured, starting simple and iterating based on feedback. Use in-context examples to reduce variability and improve consistency. Human experts should refine these prompts to ensure they lead to accurate entity and relationship extraction, combining automation with expert oversight for best results. These provide solid foundations to optimally applying human and machine capabilities to the very-important task of building robust and useful ontologies.

Most AI tool lists miss the point. The advantage doesn’t come from knowing more tools. It comes from knowing where they fit in your workflow. Right now most people use AI like this: → Try a tool → Generate something → Move on No structure. No repeatability. So the productivity gains stay small. The real leverage appears when you treat AI tools like a stack, not a collection of apps. Almost every modern AI workflow fits into four layers. If you understand these layers, you can build systems that run every week without starting from scratch. 1️⃣ Thinking layer Tools that help you clarify problems and structure ideas. → ChatGPT → Claude Use them to: → research unfamiliar topics → break down complex problems → outline strategies and plans → stress-test ideas before execution Most people jump straight to creation. The real value often starts one step earlier: better thinking. 2️⃣ Creation layer Tools that turn ideas into assets. → writing tools (Jasper, Writesonic) → design tools (Canva AI, Flair) → image tools (Midjourney, DALL-E, Stable Diffusion) → video tools (Runway, HeyGen, Synthesia) This layer turns raw ideas into: → presentations → visuals → videos → marketing assets → documentation Think of it as production infrastructure for knowledge work. 3️⃣ Automation layer Tools that connect steps together. → Zapier → Make → Bardeen Instead of repeating tasks manually, these tools: → move information between systems → trigger actions automatically → remove repetitive work Example: Research → draft → create visuals → publish. Automation turns that into a repeatable pipeline. 4️⃣ Deployment layer Tools that deliver work to customers and teams. → websites (Framer, Durable) → chatbots (Chatbase, SiteGPT) → marketing tools (AdCreative, Simplified) This is where work becomes: → websites → marketing campaigns → customer experiences → digital products Without deployment, great AI output never reaches the real world. If you run a business or lead a team, here’s a simple playbook. Step 1 Pick one tool per layer. You don’t need ten tools doing the same job. Step 2 Design one repeatable workflow. Example: → research with ChatGPT → draft content → create visuals in Canva → automate publishing with Zapier Step 3 Automate the steps that repeat every week. Anything you do more than three times should become a system. Step 4 Improve the workflow over time. Small improvements compound faster than constantly switching tools. The people getting the most value from AI right now are not the ones testing every new tool. They are the ones building simple systems that run every day. Tools will change. Workflows compound. 💾 Save this if you’re building your AI stack. ♻️ Repost to help others move from experimenting with AI to actually using it in their work. ➕ Follow Gabriel Millien for practical insights on AI execution and building real leverage with AI. Image credit: Aditya Goenka

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

Focusing on AI’s hype might cost your company millions… (Here’s what you’re overlooking) Every week, new AI tools grab attention—whether it’s copilot assistants or image generators. While helpful, these often overshadow the true economic driver for most companies: AI automation. AI automation uses LLM-powered solutions to handle tedious, knowledge-rich back-office tasks that drain resources. It may not be as eye-catching as image or video generation, but it’s where real enterprise value will be created in the near term. Consider ChatGPT: at its core, there is a large language model (LLM) like GPT-3 or GPT-4, designed to be a helpful assistant. However, these same models can be fine-tuned to perform a variety of tasks, from translating text to routing emails, extracting data, and more. The key is their versatility. By leveraging custom LLMs for complex automations, you unlock possibilities that weren’t possible before. Tasks like looking up information, routing data, extracting insights, and answering basic questions can all be automated using LLMs, freeing up employees and generating ROI on your GenAI investment. Starting with internal process automation is a smart way to build AI capabilities, resolve issues, and track ROI before external deployment. As infrastructure becomes easier to manage and costs decrease, the potential for AI automation continues to grow. For business leaders, identifying bottlenecks that are tedious for employees and prone to errors is the first step. Then, apply LLMs and AI solutions to streamline these operations. Remember, LLMs go beyond text—they can be used in voice, image recognition, and more. For example, Ushur is using LLMs to extract information from medical documents and feed it into backend systems efficiently—a task that was historically difficult for traditional AI systems. (Link in comments) In closing, while flashy AI demos capture attention, real productivity gains come from automating tedious tasks. This is a straightforward way to see returns on your GenAI investment and justify it to your executive team.

𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 𝗘𝗿𝗼𝘀𝗶𝗼𝗻 𝗧𝗵𝗿𝗼𝘂𝗴𝗵 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻: 𝗧𝗵𝗲 𝗖𝗼𝗺𝗽𝗹𝗮𝗰𝗲𝗻𝗰𝘆 𝗥𝗶𝘀𝗸 When AI handles duplicate detection, impact analysis, and traceability automatically, junior configuration managers never develop pattern-recognition skills by performing these tasks manually. The efficiency gains are real, but the cost manifests years later when organizations discover their CM professionals can't perform critical analysis without algorithmic assistance. Aviation already confronted this. Research on automation-induced skill fade shows that pilots who rely heavily on autopilot exhibit degraded manual flying skills. Recent studies show that pilots can lose manual-flying skills in as little as two months without practice. A 2025 survey revealed even experienced flight instructors struggle with basic manual flying when automation is disabled. One senior instructor, an examiner, who was asked to fly manually, "had a tough time doing it." The same dynamic threatens configuration management. When AI consistently provides correct answers, humans stop questioning those answers, stop developing judgment to recognize when AI recommendations are wrong, and gradually lose expertise that makes human oversight valuable. Consider requirements traceability. With AI generating trace links at 94% accuracy, reviews should be simple. However, a reviewer who manually creates links must evaluate semantic relationships and architecture, and recognize missed dependencies, whereas someone who only reviews AI suggestions relies on pattern matching: Is this reasonable? Experienced configuration managers develop intuition about which changes need scrutiny, which stakeholders to engage early, and where documentation gaps reveal issues. This intuition comes from mistakes and experience, whereas AI that prevents errors can hinder learning from them. Several approaches have emerged: 𝗚𝗿𝗮𝗱𝘂𝗮𝘁𝗲𝗱 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻: Junior managers perform tasks manually before gaining AI access, developing pattern recognition before algorithmic support. 𝗣𝗲𝗿𝗶𝗼𝗱𝗶𝗰 𝗺𝗮𝗻𝘂𝗮𝗹 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲: Require manual requirements tracing for at least one component per quarter, ensuring the ability to perform core functions when AI isn't available. 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺𝘀: When an AI system flags a potential impact between an ECU firmware change and thermal management, the explanation teaches a pattern they can apply independently. 𝗖𝗼𝗺𝗽𝗲𝘁𝗲𝗻𝗰𝘆 𝗴𝗮𝘁𝗲𝘀: Require demonstrating manual competency before using AI for critical functions. The goal isn't to prevent AI adoption, it's to ensure AI enhances, rather than replaces, human expertise. If your AI system went offline tomorrow, how many configuration managers could perform critical analysis manually, and how would you know before it's too late? What's your approach to preserving expertise while adopting automation? #ConfigurationManagement  #CM2 #ArtificialIntelligence #AI #PLM

The AI question most companies are still getting wrong. If you asked an enterprise leader two years ago, “Are you using #AI in your company?”, the question was legitimate. Back then, the goal was simple: replace a manual, repetitive task with an algorithm much like what #RPA has been doing for years. But the pace of technology has fundamentally shifted the stakes. The question is no longer about "usage"; it’s about foundation. The question leaders should be asking today is: “Are you operating at AI-First?” It might sound like semantics, but it’s a completely different ball game. Let’s look at a real-world example: Accounts Receivable (AR). In a traditional process, you have an invoice issued, a collector monitoring the status, follow-up calls, bank slip tracking, and finally, manual reconciliation to clear the invoice. Two years ago, the approach was: "How can we use AI to help the human with one or two of these steps?" The mistake there is automating a process that was originally designed for humans. An AI-First mindset goes back to first principles: - Input: Invoice issued. - Output: Cash collected. In that "gray area" between input and output, we don't just patch the old workflow. We build it as AI-Native. At the core, the AI handles the outreach, processes the follow-up notes, identifies the payment, and applies the cash end-to-end. AI-First isn’t about replacing existing steps. It’s about rethinking the entire process from the ground up where AI is the engine, not just an accessory. Have you thought about it? Share how you are leveraging AI in your organization.

Spent hours wrestling with reports that felt more like elaborate puzzles than useful data. For my team of 400 Infrastructure Support and Helpdesk Engineers, managing rosters and tracking performance against KPIs was, frankly, a nightmare. It was a manual grind, dependent on a few key people, and took up way too much valuable time each week just to get basic numbers. Analysis? Forget about it. I started thinking there had to be a smarter way. A way to actually use our data, not just generate it. So, we began mapping skills, understanding our business needs, and figuring out how to link the two logically. Then, we built a system that automatically generated rosters based on those connections. We also integrated our ITSM solution to capture tech and business metrics in real time. The result? We finally automated roster creation and reporting. It freed up about 20 man hours every single week. More importantly, it gave us actionable intelligence much faster, helping us actually *improve* operations instead of just reporting on them. It’s amazing what happens when you shift from manual data collection to automated insight generation. What are some of those time-consuming, manual processes in your field that you're itching to automate? I'd love to hear what you're tackling. #Automation #DataAnalytics #TeamManagement #OperationalExcellence #ITSM

Implementing IoT solutions for monitoring and managing energy consumption requires an integrated vision combining technology, data analysis, security, and sustainability to achieve significant efficiency and cost savings. Internet of Things (IoT) IoT refers to a network of physical devices that communicate via the Internet. These include sensors, smart meters, thermostats, and HVAC systems, all of which work together to collect and share real-time energy consumption data. Energy Consumption Monitoring Using smart sensors and meters allows real-time tracking of energy use, enabling the identification of inefficiencies and the implementation of immediate corrective measures to reduce unnecessary energy expenditure. Energy Management Automation systems in IoT can control lighting, heating, and cooling based on environmental data and occupancy. This optimization reduces energy waste without compromising comfort and operational needs. Data Analysis Advanced data analysis techniques, including big data and machine learning, help identify trends and consumption patterns. These insights drive long-term energy-saving strategies and continuous improvement in energy performance. Integration with Existing Systems Ensuring compatibility and seamless integration of new IoT devices with existing systems is crucial. Interoperability allows for smooth data exchange and functionality, enhancing overall system efficiency. Data Security Protecting the data collected by IoT devices is essential. Implement robust security measures, including encryption and access control, to safeguard sensitive energy data and ensure only authorized personnel have access. Economic and Environmental Benefits Efficient energy management leads to substantial operational cost savings, and reducing energy consumption supports corporate sustainability goals by lowering the organization’s carbon footprint. Implementation and Maintenance The implementation process includes planning, device installation, system integration, and staff training. Ongoing maintenance and regular updates ensure the IoT systems remain efficient and effective over time. Regulations and Standards Compliance with local and international energy management and IoT standards is vital. Certifications ensure the quality and security of the IoT solutions, meeting regulatory requirements and industry best practices. Staff Training Training staff on the use and maintenance of IoT systems is essential. Building an energy-conscious culture within the organization promotes efficient energy use and maximizes the benefits of IoT solutions. #IoT #EnergyManagement #BusinessEfficiency Ring the bell to get notifications 🔔

What connects Industrial IoT, Application and Data Integration, and Process Intelligence? During my time at Software AG, my attention has shifted in line with the company's strategic priorities and the changing needs of the market. My focus on Industrial IoT, moved into Application and Data Integration, and now I specialise on Business Process Management and Process Intelligence through ARIS. While these areas may appear to address different challenges, a common thread runs through them. Take a typical production process as an example. From raw material intake to finished goods delivery, there are countless interdependencies, processes and workflows, and just as many data sources. Industrial IoT plays a key role by capturing real-time data from machines and sensors on the shop floor. This data provides visibility into equipment performance, production rates, energy usage, and more. It enables predictive maintenance, reduces downtime, and supports continuous improvement through real-time monitoring and analytics. Application and Data Integration brings together data from across the value chain, including sensor data, manufacturing execution systems, ERP platforms, quality management systems, logistics, and supply chain management. Synchronising these systems with integration creates a unified, reliable view of production operations. This cohesion is essential for automation, traceability, quality management and responsive decision-making across departments and geographies. Process Management, including modelling, and governance, risk, and controls, takes a different yet equally critical perspective. Modelling helps design optimal process flows, while governance frameworks ensure controls are in place to manage quality, risk, and enforce conformance for standardisation. Process mining uncovers bottlenecks, rework loops, and compliance deviations. It focuses on how the production process actually runs, rather than how it was designed to operate. Despite their different vantage points, each of these domains works toward the same goal: aggregating, normalising, and structuring data to transform it into information that can be easily consumed to create meaningful, actionable insights. If your organisation is capturing process-related data through isolated tools, such as diagramming or collaboration platforms, quality management systems, risk registers, or role-based work instructions, it is likely you are only seeing part of the picture. Without a unified approach to integrating and analysing this data, the deeper insights remain fragmented or out of reach. By aligning physical operations, applications & systems, and business processes, organisations can move beyond surface-level visibility to uncover the root causes of inefficiency, unlock hidden potential, and govern change with clarity and confidence. #Process #Intelligence #OperationalExcellence #QualityManagement #Risk #Compliance