Siemens AG is a German technology company focused on industrial automation, electrification, and digitalization across manufacturing, infrastructure, transport, and energy systems. It provides software and hardware for industrial operations and grid management, and applies AI/ML to improve reliability, efficiency, and predictive maintenance in critical assets.
Kayros is like a super-smart air traffic controller for your factory. It constantly looks at all your machines, orders, and constraints, then automatically figures out the best possible production plan and scheduleâand keeps adjusting it when things change.
This is like giving factory robots a brain that learns from experience, so they can move faster, make fewer mistakes, and adapt when something on the production line changesârather than just blindly following a fixed script.
This is about using AI as a super-smart control center for factories and supply chains. It watches machines, inventory, orders, and logistics in real time, then predicts problems before they happen and suggests the best way to run production so you waste less time, material, and money.
This is like giving your car factory a super-smart assistant that watches everything on the line, spots problems before they happen, and suggests small tweaks that make the whole plant run faster, cheaper, and with fewer defects.
Think of generative design as an AI-powered junior architect/engineer that, instead of drawing one design, generates hundreds or thousands of options that all meet your rulesâlike budget, materials, safety codes, and space limitsâthen shows you the best ones to choose from.
Think of todayâs big AI models as brilliant general doctors who know a little about everything but arenât yet safe or precise enough to treat complex, highârisk patients. This paper is about how to retrain and constrain those general doctors so they can safely become topâtier specialists in specific medical tasks, like reading scans, summarizing patient records, or supporting treatment decisions.
This is like putting smart ears and eyes on your machines so they can tell you when something sounds or looks wrongâbefore it breaks. Small sensor boxes sit on the equipment, watch and listen in real time, and warn you early so you can fix problems during planned downtime instead of after a costly failure.
This is like a smart weather-and-power crystal ball: it looks at recent weather and production data and uses machine learning to predict how much solar and wind power will be generated in the next few hours.
This is like a smart mechanic for power-plant valve actuators: it watches sensor data, predicts when parts are likely to fail, and also explains in plain engineering terms why it thinks a failure is coming (e.g., which pressures, temperatures, or vibrations are driving the risk).
This is like a smart assistant that reads a patientâs electronic medical record and quietly taps the doctor on the shoulder to say, âBased on all this history and lab data, this patient looks like theyâre at high risk for X in the next few hoursâhereâs why and what to watch out for.â
This is like putting a smart âcheck-engineâ light on every critical asset in an oil & gas operation. Instead of waiting for something to break, software constantly watches sensor data and warns you in advance when a pump, compressor, or pipeline component is likely to fail, so you can fix it during planned downtime.
Think of this as putting a âsmart brainâ on top of every critical piece of oil & gas equipment. It constantly listens to sensors, learns what ânormalâ looks like, and warns you before something breaks so you can fix it at the best possible time.
This is like giving the power grid a very smart weather forecast, but instead of predicting rain, it predicts how much electricity people will use so green energy sources can be used more efficiently.
This is like giving oil and gas equipment a digital âcheckâengine lightâ that predicts problems before they happen. It watches sensor readings, work orders, and maintenance history and then tells you which assets are likely to fail and when, so you can fix them in a planned shutdown instead of during a costly emergency.
Imagine your entire oil and gas operationâwells, pipelines, refineriesâcovered in smart sensors and watched by an alwaysâawake digital control room. That digital brain constantly learns from data, spots problems before they happen, and quietly adjusts valves, pumps, and schedules so you produce more oil and gas with less downtime, waste, and risk.
This is like giving doctors a super-smart assistant that has read millions of medical cases and guidelines, then quietly whispers, âHere are the likely diagnoses and what to check nextâ while the doctor is still seeing the patientâespecially to catch diseases earlier than usual.
Think of this as a smart traffic conductor for Bangkok: cameras and sensors watch the roads, an AI brain predicts where jams will form, and then it recommends how to adjust traffic lights and routes so cars and buses flow more smoothly.
Think of this as turning the power grid into a âsmart internet of energyâ where 5G connects all the equipment and AI acts like a traffic controller, constantly balancing where electricity should go, when to store it, and how to avoid waste or outages.
This is like giving ER triage nurses a smart calculator that looks at a patientâs vital signs and symptoms and helps decide how urgent their case is, so the sickest people are seen first and fewer patients are mis-prioritized.
Think of this as a digital mechanic that constantly listens to your vehicles, trains, or equipment, predicts when something is about to break, and tells you exactly when to bring it in for service so you avoid breakdowns and warranty fights.
This is like giving your factory a smart air-traffic controller that constantly looks at all your machines, workers, and orders, then automatically decides the best sequence of jobs so everything ships on time with minimal idle time and overtime.
Think of this as a playbook of ways to use AI as the âbrainsâ of a modern factoryâhelping machines predict failures, optimize production lines, and improve quality with less human guesswork.
This is like giving radiologists a super-smart assistant that looks at heart MRI scans and automatically measures how well the heart is working, then flags patterns that match different heart diseasesâmuch faster and sometimes more consistently than a human reading every image by hand.
Think of AI in oil and gas as a super-smart control room operator that never sleeps. It constantly watches wells, pipes, and equipment data, predicts when something will break, and suggests how to squeeze more oil and gas out of the ground at lower cost and risk.
Think of this as a smart coâpilot for radiology departments: it sits on top of imaging systems, helps route and prioritize scans, spots patterns, and surfaces the right information so radiologists and hospitals can move faster and make fewer mistakes.
Think of this as a smart scheduling assistant for hospital operating rooms that learns from past data and live conditions (staffing, emergencies, cancellations) to constantly reshuffle the theatre list so more patients get treated on time with fewer lastâminute surprises.
This is like giving a commercial buildingâs heating and cooling system a smart autopilot. It watches how energy is used, learns building patterns (people coming and going, outside weather, peak loads), and automatically tunes HVAC settings to keep tenants comfortable while using less electricity.
Think of Cohesion as a digital command center for large office or mixedâuse buildings. It connects elevators, HVAC, security, access control, and occupancy data into one intelligent system so building operators can see whatâs happening in real time and let software make many of the small adjustments people used to make manually.
Think of a large office building as a living body. In the past, the heating, cooling and lighting were like organs running on fixed schedules, whether people were there or not. AI turns the building into a âsmart bodyâ that can sense where people actually are, how hot or cold it is, what energy costs right now, and then automatically adjusts everything in real time to stay comfortable while using far less energy.
This is like giving the power company a very smart weather forecast, but instead of predicting rain or sunshine, it predicts how much electricity people will use in the next few hours or days using machine learning.
Think of a smart city as a city with a digital nervous system. AI is the brain that helps it see traffic jams, power usage, crime hotspots, and public service demand in real time, then quietly adjusts lights, signals, and services to keep everything running smoother and safer.
This is a blueprint for turning todayâs hospital workflows from paper-and-phone based routines into a mostly digital, AI-assisted assembly line for patient care. Think of it as redesigning how doctors, nurses, and staff work together so computers do the repetitive checking, routing, and documentation, while humans focus on medical decisions and patient interaction.
This is like giving every doctor an alwaysâon digital colleague that has read every medical textbook, guideline, and journal article, and can quickly suggest possible diagnoses and treatments while the doctor is seeing a patient.
This is like giving ER doctors a super-fast, data-driven second opinion that watches the patientâs information in real time and quietly flags risks or suggests next steps, without replacing the doctorâs judgment.
Think of this as giving the emergency department a very fast, very experienced digital assistant that helps doctors and nurses notice critical problems sooner, choose better tests and treatments, and move patients through the system more efficiently â especially when things are chaotic and time-sensitive.
Think of this as a field guide to all the ways computers can learn from medical and pharma dataâlike a tireless junior doctor and data analyst rolled into oneâto help spot diseases earlier, pick better treatments, and run hospitals and clinical trials more efficiently.
This is like giving an AI a chest X-ray or MRI scan and having it write the first draft of the radiologistâs report, instead of the doctor starting from a blank page. The doctor still reviews and edits, but the AI does the heavy lifting of describing what it sees.
Think of this as a smart coâpilot for nurses: it watches patient data, compares it to whatâs happened with thousands of similar patients before, and then suggests what to watch out for and what actions might be neededâwhile the nurse stays in full control.
Think of a cityâs road network as a giant, messy orchestra. This use case is about putting an AI âconductorâ in charge that can see whatâs happening on the roads in real time (via cameras and sensors), predict where jams and accidents might happen, and then adjust traffic lights, signals, and routing instructions to keep everything flowing smoothly.
Think of this as a âself-optimizing factory brainâ for mines: it watches every step of crushing, grinding, and separating ore, learns what settings give the best results, and then continuously tweaks the knobs to squeeze out more metal with less waste, energy, and downtime.
This is like giving your buildingâs heating and cooling system a smart autopilot. It watches how your building behaves, learns patterns of occupancy and weather, and then constantly tweaks HVAC settings to keep people comfortable while cutting energy waste and emissions.
Think of this as a smart co-pilot for buildings: it watches how your facilities are used, how equipment behaves, and what work orders come in, then suggests what to fix first, when to schedule maintenance, and how to run the building cheaper and smoother.
This is like a supercharged planning sandbox for delivery routes and vehicle schedules: you can try different ways of assigning trucks and drivers to trips on a computer, see how each plan performs, and then pick the best one before you spend real money on the road.
This is about teaching factories to "take care of themselves." Machines learn to warn you before they break, adjust their own settings for quality and efficiency, and eventually coordinate with each other so the whole plant runs with less human babysitting and fewer surprises.
This is like a smart autopilot for renewable power plants that mixes solar, wind, and batteries. It predicts how much energy youâll get from the sun and wind, how much your customers will use, and then automatically decides when to store, sell, or buy electricity to save money and keep the lights on.
This is like giving your power plant or energy equipment a âcheck engineâ light that warns you days or weeks before something breaks, instead of after it fails. Sensors continually watch vibration, temperature, pressure, etc., and machineâlearning models learn the normal patterns so they can flag early signs of trouble.
This is like a smart mechanic for fleets and industrial equipment. It constantly âlistensâ to machines, spots early warning signs of failure, and tells you when to service them before they break down.
Think of a city where every bus, traffic light, and parking space can talk to each other in real time, and an AI âtraffic conductorâ continuously listens and adjusts things so people and goods move faster and more safely with less waste.
This is like giving the power grid a smart navigation system that can instantly reroute electricity around traffic jams and accidents so the lights stay on and the roads (power lines) donât get overloaded or damaged.
This is like a weather forecast, but for how much energy a building will use. It learns from past data about the building (design, materials, historical meter readings, weather) and then predicts future consumption so you can plan and optimize better.
Think of a smart building as a self-driving car for energy and operations: sensors constantly watch whatâs happening (people, temperature, light, equipment), and AI decides when to heat, cool, light, or ventilate each space so you use the least energy without sacrificing comfort.
Think of this as a smart mechanic that lives inside your machines. It constantly listens and watches for early signs of trouble, tells you what is likely to break and when, and even recommends the best time and way to fix it so you avoid unplanned downtime.
This is like a smart air-traffic controller for a factory: it looks at all your orders, raw materials, machines, and people, then constantly rearranges the schedule so everything runs smoothly, on time, and at the lowest cost.
This is like hiring a smart mechanic that constantly listens to all your vehicles and equipment, predicts whatâs about to break, and schedules repairs before anything actually fails or delays service.
Imagine your factory is a busy kitchen with many different dishes to cook. This system is like a superâsmart head chef that constantly reorders which dishes to make first so the ovens are always full, the cooks never wait around, and customers still get their meals on time.
This is like giving a car or engine a brain that learns to âlistenâ to its own sensors and predict how much life it has left before something fails. Instead of engineers handcrafting dozens of rules and features, the model learns directly from raw sensor data when parts will wear out.
Think of this as a buildingâs "autopilot for energy": it constantly watches how the building is being used, how hot or cold it is, what the weather and prices look like, and then automatically adjusts heating, cooling, lighting and other systems to keep people comfortable while using as little energy (and money) as possible.
This is like giving railway tracks and equipment a âcheck engine lightâ powered by AI. Instead of waiting for something to break, the system watches sensor data, predicts when parts will fail, and tells maintenance teams exactly where and when to intervene.
This is like putting a smart âcheck engineâ light on every aircraft part and piece of ground equipment. Instead of waiting for something to break, Azureâs AI watches sensor data and tells you in advance when a component is likely to fail so you can fix it during planned downtime.
Think of this as a citywide âcontrol towerâ that watches whatâs happeningâtraffic, utilities, emergency calls, citizen requestsâand then uses AI to suggest faster, cheaper, safer ways to run city services.
Think of this as giving power plants and grids a smart brain that constantly watches operations, predicts future demand and equipment issues, and suggests optimal ways to run everything more safely and cheaply.
This is like a âhealth monitoring and early-warning systemâ for industrial equipment in energy operations. It watches sensor data from machines, predicts when something is likely to break, and suggests when to repair or adjust operations before failures happen.