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The Internet of Things, or IoT, may become just another example of an overused term. This is especially true with the introduction of new words like deep learning, genetic algorithms, machine learning, and more.
Artificial intelligence (AI) frequently slips into the same trap.
This blog aims to provide technologists with an overview of how artificial intelligence could aid in the facilitation and understanding of the multitude of connected devices that will emerge in the next ten years, as well as to elaborate on the real-world applications of combining AI and IoT that are currently being used in industry.
Revolutionizing Industries: The Global Business Landscape As AI And IoT Emerge
Thanks to artificial intelligence (AI) and the Internet of Things (IoT), industries worldwide are undergoing radical change.
The way organizations run has changed due to these disruptive technologies, which have raised productivity, profitability, and efficiency. Businesses must grasp AI and IoT technologies' enormous potential and use them to gain a competitive edge as they become more and more common.
The Growth Of IoT And AI
AI is the process of building intelligent computers capable of carrying out tasks that generally call for human intelligence.
Conversely, the Internet of Things (IoT) is a network of connected gadgets that may exchange data and communicate with one another. Businesses may gather vast data, analyze it, and derive insightful knowledge by fusing AI with IoT.
IoT and AI technology use is still expanding quickly. A McKinsey analysis states that the manufacturing, healthcare, retail, and agricultural sectors stand to gain the most from these technologies.
By 2025, the Internet of Things (IoT) sector is expected to be worth $1.7 trillion, and artificial intelligence (AI) will likely generate over $13 trillion in value worldwide.
Optimizing Processes And Increasing Productivity
The potential of AI and IoT to improve efficiency and streamline processes is one of its main benefits. Routine procedures can be automated to reduce errors and increase productivity for businesses.
Artificial intelligence (AI) powered robots can perform complex manufacturing operations accurately and consistently, reducing costs and raising product quality.
Key Takeaways:
- Automated processes reduce errors and increase productivity.
- Lower costs and better quality products are the outcomes of accuracy and uniformity.
- IoT facilitates real time monitoring, which increases operational efficiency.
- Data analytics provides insights for proactive decision making.
According to a Deloitte analysis, implementing AI in the manufacturing sector can boost productivity by up to 20%.
Another benefit of IoT devices is that real time monitoring helps businesses manage inventories, optimize supply chains, and spot issues or mistakes before they get out of hand. Because of this connectivity, organizations can use the real time data they receive to make proactive decisions.
Improving Personalized Experiences For Customers
AI and IoT technology may provide businesses with valuable customer insights, enhancing customer experiences and customization.
By examining customer data, companies can gain additional insight into their customers' preferences, actions, and patterns. This enables them to tailor their products, services, and marketing strategies to specific customers' requirements.
Key Takeaways:
- AI-powered chatbots provide quick, personalized customer support.
- Data analytics enables targeted marketing campaigns and customized counsel.
- Consumer interactions are made easy and convenient by IoT devices.
Chatbots powered by artificial intelligence (AI) provide prompt, individualized support, increase customer satisfaction and expedite response times.
Businesses can use data analytics to target their marketing campaigns and provide customized recommendations, making their consumers' experiences more engaging.
Reducing Downtime And Implementing Predictive Maintenance
When it comes to maintenance and downtime reduction, IoT and AI have a lot to offer. Businesses can predict when machinery or equipment will malfunction by analyzing data in real time from connected devices.
Because of proactive maintenance, there is consequently less downtime and a lower need for costly repairs or replacements.
Key Takeaways:
- By putting predictive maintenance in IoT applications into practice, unnecessary downtime is avoided and costs are decreased.
- IoT sensors offer analyzable real time machine performance data.
- Efficiency is increased when expensive repairs and replacements are avoided.
A Forbes research claims that predictive maintenance techniques can save machine downtime by as much as 50%. Businesses can detect irregularities or patterns that point to possible malfunctions by utilizing Internet of Things sensors that gather data on machine performance in real time.
Companies can increase operational efficiency and prevent costly unplanned downtime by taking this proactive strategy.
Read More: IoT vs IoE: What's the Difference? Discover the Surprising Impact on Your Business!
Three Real-World Use Cases Of AI And The Internet Of Things
All the example applications we've included below are in operation and were picked to illustrate a broader range of applications.
We tried to stay away from highly specialized IoT apps (such as the ‘connected pacifier’ or the ‘tray that alerts you when you're out of eggs’) as well as IoT applications that don't use artificial intelligence at all. The following combinations of AI and IoT are helpful illustrations of how these two broad ideas overlap.
It's crucial to remember that many supposedly ‘IoT’ gadgets are not on this list. A device isn't considered ‘smart’ by the criteria we choose (connected devices that use artificial intelligence) just because it can be operated using an iPhone app.
Here are a few helpful examples:
Roomba By iRobot, Automated Vacuum Cleaning Solution
With the release of its first widely used automated hoover in 2002, iRobot established a new benchmark. The business, which was founded by a roboticist from MIT, has created technology that enables its puck-shaped vacuum robots to map and ‘remember’ the layout of their homes, adjust to new surfaces and objects, clean rooms using the most influential movement pattern, and dock themselves to recharge their batteries.
Although the Roomba's AI features aren't as well known as more widely recognized consumer AI innovations like Apple's Siri or Facebook's facial recognition, the Roomba is still the industry leader in its class and a perfect example of AI ‘embodied’ in a robot (which you can now control on your app, see Roomba's latest promotional video for the 980 model).
Nest Labs, Exemplifies Smart Thermostat Systems
While most people's lives haven't exactly been revolutionized by the 'smart home’, certain businesses are fervently working to change that.
One example is Nest, which Google reportedly paid $3.2 billion to acquire.
The Nest gadget is a prime example of smart thermostat IoT technology, valued for its simplified digital interface that takes the place of conventional dials.
It demonstrates how Internet of Things technology may be integrated for more convenience by enabling customers to monitor and control temperatures remotely through seamless smartphone communication. In theory, this is ‘IoT’, but many argue that Nest's design, feel, and interface made the device more approachable and easy to use (perhaps because Nest's founders were prominent Apple workers who worked on the iPod and iPad development).
Regarding artificial intelligence applications, Nest's equipment ‘learns’ customers' frequent temperature preferences and adjusts its energy use to fit users' work schedules.
Although this AI application is undoubtedly new, its practical benefits (home comfort, potentially significant energy savings) and successful marketing could be attributed to the majority of its success in the market.
Self-Driving Vehicles, Exemplified By Tesla Motors
Automobiles are ‘things’, and since we're drawn to ‘things’ with vital artificial intelligence, automotive technology is cutting edge (pun intended, I assume).
The jury is still out on how long it will take to have driverless highways, so it's not necessarily because autonomous vehicles will be the most straightforward Internet of Things innovation to implement. Still, it has momentum because almost all big automakers are investing billions of dollars in the technology (pun intended).
We must comprehend the inner workings of Tesla's autonomous vehicle technology to apply it as an example. Elon Musk, the CEO of Tesla, responded as follows when asked what makes the company's self-driving cars unique, according to a Fortune article: Every Tesla vehicle is part of a network.
Every car picks up on something that one learns. That goes above and beyond what other automakers are doing.
It's interesting to note that Google's approach to self-driving cars isn't all that different from others; it uses machine learning and hundreds of thousands of miles of road of test data to forecast how automobiles and pedestrians will behave under different conditions.
Future Applications For AI-Powered IoT Devices
The IoT applications of today help identify trends because they show the areas where traction has been demonstrated and the paths that venture capital and large corporations are already taking.
Still, automobiles and vacuums represent just the proverbial tip of the iceberg in terms of possible IoT and AI applications:
Security And Access Devices
Companies like ACT (Access Control Technologies) are already advancing the use of crucial fob technologies for equipment usage and door unlocking in terms of solely IoT applications.
Artificial intelligence could identify regular access patterns of various employees or roles and tiers of employees, even in companies with fewer than a thousand employees. This could provide insight into future office layouts and potentially detect suspicious activity (using the same kind of technology that modern cybersecurity uses in detecting outliers).
As for technology and adoption improvement, this area may be rife with security insight, even though we could not locate key fob/access key technologies integrating artificial intelligence or predictive analytics (particularly for more prominent firms assessing data across many locations).
Emotional Analysis, Facial Recognition
It is reasonable to argue that facial recognition technology has not yet reached its full potential, given the tremendous advancements it has made in the previous five years alone in both marketing and surveillance.
Businesses like Kairos have already focused on marketing applications and showcased high-profile clients like Nike and IMB on their home pages.
Gathering data from consumer responses to products and marketing has never been simpler, as almost every computer and smartphone built today has a camera.
Most people will be familiar with Facebook's auto-tagging feature, but further business models and applications are still being developed. Numerous newspapers, including The Washington Post, have written about the possible ethical and social ramifications of widely used facial recognition technology.
Conclusion
The seamless integration of AI and IoT is causing a shift in global enterprises.
Predictive maintenance, real-time data analytics, and automated operations are examples of how this synergy improves operational efficiency. Notable uses of AI include chatbots that utilize AI to provide customer service, smart thermostats that learn user preferences, and self-driving cars, which are prime examples of the fusion of AI and IoT.
As more companies adopt these innovations, future developments could bring about improved facial recognition software, emotional analysis, and security measures.
The convergence of AI and IoT is more than just a catchphrase. It's a revolutionary force propelling productivity, customization, and creativity in various industries.
