Mentioned in 16 AI use cases across 9 industries
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).
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.
Think of this as turning the power grid into a ‘smart internet of energy’ 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 putting a smart AI control room directly on drilling rigs and remote mining sites so they can analyze sensor data and equipment status locally, make decisions fast, and keep working even when the internet is slow or offline.
Think of this as a playbook of ways to use AI as the ‘brains’ of a modern factory—helping machines predict failures, optimize production lines, and improve quality with less human guesswork.
Think of this as a super-smart ad trader that watches billions of people’s clicks in real time and automatically decides which ad to show, to whom, at what price, and on which platform to get the best return—far faster and more accurately than any human team could.
This is like giving your power plant or energy equipment a “check engine” light that warns you days or weeks before something breaks, instead of after it fails. Sensors continually watch vibration, temperature, pressure, etc., and machine‑learning models learn the normal patterns so they can flag early signs of trouble.
Think of every patient as a unique garden: their genes are the soil, epigenetics is how the soil has been treated over time (fertilizer, pollution, stress), and the microbiome is the mix of plants and microbes living there. This work is about using data and models to understand how all three together affect health and how people respond to medicines, so treatments can be tailored to each person’s “garden” instead of using one-size-fits-all drugs.
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 this as a ‘medical weather forecast’ system powered by AI: it looks at a huge mix of patient data (labs, scans, genetics, history) to predict who is likely to get which disease and which treatment is most likely to work for each person.
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.
Imagine a blood pressure clinic that treats each patient the way a tailor makes a custom suit: it uses your genes, lifestyle, gut bacteria, and medical history—analyzed by AI—to pick the drug and dose that fit you best instead of guessing and adjusting over months.
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) don’t get overloaded or damaged.
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 “health monitoring and early-warning system” 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.
Think of this as giving pharma companies a super-smart digital lab assistant and paperwork robot rolled into one. The assistant can sift through mountains of scientific data to suggest promising new drugs faster, and it can also take over a lot of the routine documentation and admin work that bogs down scientists and health‑care workers.