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Home / Insights / Blog / The Convergence of AI and IoT: Key Benefits in the Business World
October 30, 2019 - by Anish Purohit
If there are two technologies that are constantly creating a buzz in the business world, they are AI and IoT. Today, every critical aspect of the business is being shaped by AI with IoT; together, they are building efficient and impactful business solutions. However, the convergence of AI and IoT at the edge is taking efficiency a notch higher.
According to Forbes, the convergence of IoT and AI is one of the hottest trends shaping 2019. By capturing and processing data near the edge of network, this convergence is decentralizing the process of data analysis, allowing for faster and more accurate decision-making.
With enormous volume of data being generated from machines, sensors, and other smart devices, IoT has been enabling businesses to make important, evidence-based, data-driven decisions. AI, on the other hand, keeps track of historical trends with account of any anomaly and makes use of machine learning algorithms to predict future outcomes.
The constant stream of IoT data, when analyzed and consumed by AI-based models and applications, offers a 360-degree view of advanced analytics that utilizes data captured in real-time as well as that captured historically to predict any failures in the future.
When this is done at the edge, the combined capabilities of both these advanced technologies accelerate the process of data analysis, and improves the efficiency of real-time decision-making. Processing millions of terabytes of data every second at the edge eliminates the problem of low bandwidth and high latency, while helping organizations save precious resources and costs.
The fusion of AI and IoT at the edge (edge computing is when data is analyzed on devices, that is, at the edge of the network, rather than in the cloud itself) is quickly becoming key to success for companies in various sectors.
Since it allows businesses to efficiently capture, process, and store data right where it is generated, it eliminates the need for devices to send captured data anywhere, thus eliminating the need to have a centralized, remote cloud take care of all the processing.
This decentralized handling and processing of data leads to a number of advantages: improved network efficiency because of reduced load on bandwidth, lower data traffic, improved device efficiency and performance, reduced operational costs, unlimited scalability, and improved security – as the chances of a single security incident taking down the entire network get greatly reduced.
That said, here’s how the convergence of IoT and AI at the edge is impacting different business domains:
Manufacturing is probably one of the best use cases of convergence of AI and IoT at the edge. Since devices can collect and analyze data in real-time, they reduce the complexity of interconnected systems and make data collection and analysis a cakewalk, especially in remote sites where network connectivity is intermittent and unpredictable.
Data can be gathered and analyzed locally, with only critical information transmitted back to the central network; such analysis helps streamline industrial processes, optimize supply chains, and optimize the smart factory setting.
For example, using AI algorithms, manufacturing organizations can constantly analyze IoT data to assess the condition of critical equipment, and speed up (or slow down) operations to reduce the likelihood of system downtime, enhance efficiency, and optimize usage – without the assistance of a cumbersome cloud server.
Healthcare is another domain that can benefit tremendously through the convergence of AI and IoT at the edge. Since even a small amount of latency can be the difference between life and death, this convergence enables organizations to provide responsive healthcare to patients – when they need it.
With high availability and resiliency of edge computing, doctors can access patient information in real-time and make well-informed medical decisions – thus improving healthcare outcomes and reducing costs.
For example, using AI algorithms, doctors can gather and analyze IoT data gathered from patients locally, and quickly identify infected users early on, so the outbreak of a disease can be controlled. This is especially beneficial for rural patients, who live far away from a care center, as they get access to critical healthcare resources whenever they need it.
The automotive industry also stands to benefit just as much from the convergence of AI and IoT at the edge. With autonomous vehicles generating significantly more data every day, processing this data at the edge makes driver safety a priority. The data gathered by sensors, when processed by neural network-based AI algorithms, decides the next action in real-time, making car driving flawless.
For example, when an autonomous vehicle needs to break in a dangerous situation, the AI application must be capable of analyzing sensor data and identifying the hazard – immediately. Edge allows the vehicle to overcome the millisecond latency and apply emergency breaking – thus greatly reducing the probability of an untoward incident, and saving lives.
The combination of AI and IoT has long been helping businesses detect anomalies, generate predictions, improve operational efficiency, reduce downtime, and enhance risk management.
Today, their convergence at the edge is enabling organizations to extract insights and make accurate predictions – in real-time – without being impacted by the latency challenge of traditional cloud computing networks.
With Gartner predicting that 75% of enterprise-generated data will be processed outside a traditional data center or cloud, it’s time to combine AI and IoT at the edge, and leverage it to drive efficiency outcomes like never before.
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