The Internet of Things (IoT) and artificial intelligence (AI) are two of today’s most disruptive technologies. While each has considerable potential, they’re most effective when working together – a concept known as AIoT.
Machine learning and IoT devices already work closely together in many use cases. The IoT provides the data machine learning models need to provide accurate insights. The AIoT goes further by bringing AI analytics to the IoT endpoint instead of analysing it in a separate environment. That combination has some impressive advantages.
1. Faster analytics
Bringing AI and the IoT together means that data has less distance to travel before you can use it. That can be helpful because many of the largest data centres are likely to be situated a different territortory to you. As a result, your information must cover a lot of ground between its origin, processing and application, introducing latency problems.
Combining the IoT and AI removes that distance. Smart devices can analyse their own information without sending it to a data centre located on the other side of the world, enabling much faster speeds and lower latencies.
Those improvements, in turn, make many machine learning and IoT applications more convenient.
This low latency could let self-driving vehicles, for instance, recognise obstacles in a fraction of a second, enabling safe navigation. Supply chain leaders could get alerts about incoming disruptions when the data suggests a change. Regardless of the specifics, faster analytics means faster decision-making, which is a plus in any industry.
2. Improved cybersecurity
AI can also address some of the IoT’s biggest challenges, the most prevalent being cybersecurity. Conventional IoT systems are infamously difficult to secure, due to devices’ lack of built-in security measures and the way they increase attack surfaces. As a result, there were 57 million IoT malware attacks in the first half of 2022 alone, but AIoT poses a solution.
Intelligent algorithms can actively monitor for suspicious activity or unauthorised access on IoT devices. Many companies already use AI for continuous monitoring in IoT networks, but AIoT brings this process closer to the endpoints.
With lower latencies and faster speeds, these algorithms can catch potential hacks sooner, mitigating the damage. Quicker responses from AI already save $3.05 million on average in data breaches, so these improvements are significant.
3. Flexible automation
Combining machine learning and IoT can also make automation more flexible. One of the biggest downsides to robots today is their struggle to adapt to changing situations. You could work past that issue by letting them communicate with each other and recognise changes.
IoT-connected robots can send data about their workflows — including any unusual situations they encounter — to each other. Bots with AI functionality could interpret that information to understand how to adapt to their changing environment.
These adaptations would make automation more practical and handle disruption better. You could then expand automation to new heights or safely invest in robotics to meet seasonal demand spikes when labour is short.
4. Increased scalability
Bringing AI to the IoT can make both technologies more accessible and scalable. IoT devices are only as helpful as your ability to analyse their data, and AI is only useful if you have enough information for it to study. Consequently, combining the two produces better results than using separately.
AI algorithms can pick out the most important IoT data and summarise it before sending it to other devices. That compression lowers network requirements, making large-scale IoT environments more viable. Networks can distribute workloads through edge computing, reducing hardware needs for advanced AI analytics.
At least 70% of companies will use AI in some form by 2030. Those that don’t capitalise on this technology may fall behind the competition, and AIoT makes it more accessible, enabling that shift for smaller businesses.
5. Reduced human error
Combining machine learning and IoT technology will also help businesses minimise mistakes. In many modern workflows, you must take data from IoT systems and move it to another location for AI analysis. However, moving that analysis closer to the information would minimise human involvement in the process, reducing the chance of errors.
Data with more phases to go through or locations to move between encounters more points where something can go wrong, like a data entry error. AIoT eliminates this risk by analysing information where it arises. Decreased movement and fewer changes of hands mean less room for mistakes.
According to Forbes, businesses lose millions of dollars annually from human error, and as data grows more valuable, these mistakes could become more costly. Considering those numbers, AIoT’s error reduction possibilities are hard to ignore.
To conclude, AI and loT are both game-changing technologies in their own right. If you want to make the most of them, you must bring them together. Combining machine learning and IoT will unlock the potential of both technologies. This will become the new standard for data-driven organisations as the trend grows and technology advances. That shift will change the business landscape for the better.
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