AI gets all the press coverage. But amid the talk of intelligence in your computer and agents on your network, the world is on the brink of an even bigger revolution.
Bigger than the car. Bigger than electricity. Bigger even, perhaps, than the internet.
As you’d guess, it involves AI – but with an extra twist. It’s AI in robotics and machine building. Because robotics adds a factor missing from LLMs: it lets AI experience the world the way we do.
Moving around. Looking at things. Manipulating objects by feel and grip. Whether it’s a manipulator harvesting soft fruit or a hydraulic arch painting a vehicle, pairing robots with AI lets a machine act on its external environment, in ways a cloud LLM cannot. And that means they’re less like equipment – and more like employees.
Because AI-powered robotics and smart machinery aren’t following coded sequences of instructions. They’re making their own decisions about how to get things done, from adjusting the pressure needed for a firm grip to getting up when they fall over. You know, the same way *people* learn.
AI visionaries like ex-Meta’s Yann LeCun are already creating “world-based” AIs for robotics, developed with the physics of objects in the real world rather than the predictive text of Large Language Models. So as the two technologies of artificial intelligence and smart machinery come together, there’s a huge opportunity for companies that can apply it successfully – and ICT Strypes is ready to help.
Let’s look at some strategies that’ll help you thrive in this fast-arriving future!
AI in robotics and machine building: the next industrial shift
Let’s set expectations first. Dancing humanoid robots look great on TikTok, but most robots don’t resemble humans – and never have. The shoebox-sized insecticide sprayer in a greenhouse or the pumping column milking a cow are as much “robots” as C3PO from Star Wars. The form factor is suited to the job; the competitive advantage is the intelligence inside. So as we explore some strategies, recall that very few use cases are human-shaped.
Strategy #1: Think adaptive control, not scripted control
Traditional industrial automation is “deterministic”: PLC logic, position-action-repeat, yes/no criteria. By contrast, the right strategy for AI-powered robotics is adaptive – policies that let the robot learn and adjust in real time. Think grip pressure on a fragile component, weld parameters as material varies, path replanning when a part is 3mm off. Intelligent machines handle variance, instead of faulting on it – dealing with the nuances of the real world the same way a human employee would.
What it means for you: it’s a quality thing. Fewer automatic rejections, less lost to scrap, and longer viable pathways for complex products where a scripted machine’s chance of failure would rise exponentially as time goes on.
So, for our robotic future, code your behaviours the same way you’d teach a human – showing them the way, letting them learn, and trusting them to get the job done. In time, micromanaging may be as unpleasant for robots as it is for people.
Strategy #2: Give the machine eyes – and let it use them
The best way to teach a skill? “Show, don’t tell”. And industrial artificial intelligence in manufacturing is no different. It’s more than putting cameras on a robot. It’s about letting the AI inside use information from all kinds of sensors: images, thermal maps, movement patterns, chemical concentrations … whatever you need to gather information on. A system that can be shown a new part and taught by example – rather than programmed for it from scratch – changes the economics of short-run and high-mix manufacturing.
What it means for you: the mindset change is from traditional rules-based inspection (“Is this part 0.006cm tolerance?”) to model-based (“Does this new part fit the engineering goals?” Just as you’d trust an experienced human engineer. Make sure your people make this change.
As well as machine vision, your future intelligent machines may use many sensors to “see” things humans can’t – making these robots a fantastic addition to your workforce. Let them shine.
Strategy #3: Predict failure per-part instead of scheduling per-class
Every manufactured part has an MTBF: Mean Time Before Failure, the point at which you’re supposed to replace it. But environment matters hugely. In a cool dry semiconductor cleanroom, abrasion and rust risk may be near-zero; follow MTBF and you’ll be spending money long before you need to. In a hot, humid, soil-scattered greenhouse, that part will fail much faster; MTBF is just a dream. The most cost-effective strategy is predictive maintenance – and AI-powered robotics lets you do that at the level of the individual part.
What it means for you: it lets you treat maintenance as a business-level optimisation strategy, not just an operational one. You can build product lifetimes and failure risks into individualised service contracts, uptime guarantees customised by customer, and revenue projections case-by-case, even when you’ve got thousands of customers doing different things. With predictive maintenance, every part performs to its maximum potential – and that means cost performance, too.
(Incidentally, predictive maintenance is a field we have deep experience with at ICT Strypes. Why not ask us how we’ve made it perform in areas as diverse as farming and chipmaking?)
Strategy #4: Build the machine twice … and train on the virtual one
Ever heard of a “Digital Twin”? It’s a representation of your real-world equipment or system – but in software, often using the same data. These days, many complex machines can be simulated in software, and behave the same in their virtual world as they would in the real one. Needless to say, this makes testing-to-destruction a lot cheaper. And what your intelligent machines learn as a digital twin can then be applied to the production environment, without risking expensive equipment in the real world.
What it means for you: there are advantages all the way down. A simple metal cut simulated in your DT generates information on shavings, blade life, waste heat and so on – all helping to optimise the real-world process. And the more information and parameters your DT learns, the more effective your production – turning it into a competitive asset for the long term.
One of ICT Strypes’ biggest clients makes the most complex machines in the world: equipment for making chips, where a single part can cost millions. Digital twins have been part of their strategy for years now – and they’re among the world’s most successful companies because of it.
Strategy #5: Leverage the data from your robot army
An AI-powered robot isn’t just a worker doing tasks; it’s a sensor array generating fresh data – data you can use for training and improvement. So that’s our fifth strategy: always be looking for ways to make that data work for you. It’s often called “fleet learning”, where a situation experienced by one machine improves the whole organisation’s knowledge.
What it means for you: engineering has always been about continuous improvement, or CI – and data delivers it. Remote equipment monitoring supplies gigabytes that can be mined with AI for insights; remote diagnostics (an ICT Strypes specialty) cuts engineer travel time to far-flung sites; robots on site can even repair and replace parts without human involvement. If your business covers large areas or hard-to-reach places, that matters.
The business with 500 connected AI-powered robots learns faster than the competitor with none. And the gap compounds over time – meaning there’s a competitive advantage there waiting for you.
Strategy #6: Treat the machine as you would a software product!
With AI making decisions, sensors as data pipelines, over-the-air updates, and the world as their operating environment, AI-powered robots in industrial automation feel a lot like software themselves – and that’s a key insight. Don’t treat your robots as set-and-forget – treat them like software applications, maintaining and upgrading them to always perform at their best.
What it means for you: this approach stops you forgetting that technology doesn’t stand still. AI-powered intelligent machines are in the early stages of a decades-long revolution; every year will bring a new robot, a new AI paradigm, a new application for your existing investments. And it’ll pay to keep up.
Machine builders are becoming software companies, whether they like it or not. The strategic choice is whether you build that capability inhouse, buy it in from outside, or partner for it. (Of course, ICT Strypes favours the last one.)
Robots are the future – and that future starts now
AI in robotics and machine building isn’t science fiction. It’s all happening now. And if your business involves making things, servicing equipment, or operating in non-uniform environments, it’ll be part of your future.
Let’s note this isn’t about replacing people. Since it’ll let your business grow faster, it’s likely you’ll need more people over time, not fewer. But those humans will be far more productive and creative – because the repetitive tasks of industrial automation have been assigned to intelligent machines instead. So why not start your robotic journey today?
After all, we may be moving into a world of robots – but the most important component will always be human. Talk to a real person at ICT Strypes today.