AI and IoT are a pair of technologies shaping our interconnected world’s future. The result is a synergy between AI-powered IoT devices that can enhance business operations across many industries, allowing companies to make informed decisions or even prevent machinery failures. Read our article and learn about successful examples of AI Implementation in IoT projects. Is AI still in IoT, or can we already talk about Artificial Intelligence of Things?
- 1. AI-enabled IoT. Key statistics on AI implementations
- 2. Future of AI-powered IoT applications
- 3. Integration of AI and IoT for operational efficiency
- 4. Real-world examples of AI and IoT implementation
- 5. How does AI improve connectivity and decision-making in IoT systems?
- 6. Machine Learning in IoT
- 7. What are the benefits of AI and IoT?
- 8. AI in IoT or Artificial Intelligence of Things?
- 9. IoT and AI trends will become increasingly important
AI-enabled IoT. Key statistics on AI implementations
IoT – this technology is not just about smart cities, smart homes, and wearable gadgets. It is a transformative industrial advantage. In recent years, IoT has been quite hype, and we know the hype curve is high right now. IoT and AI increasingly often join forces to collect data and enable IoT devices to use their maximum potential. According to IoT Analytics Research, in 2022, only 17% of Artificial Intelligence solutions were implemented in IoT.
Future of AI-powered IoT applications
However, this state is expected to change by 2027 when nearly 50% of IoT solutions are predicted to have an AI component, with 13% being AI-based and 43% AI-augmented. That shows the real power of AI within Internet of Things solutions.
Integration of AI and IoT for operational efficiency
Why do companies decide to integrate AI into the Internet of Things?
So where does the huge popularity of combining AI and IoT take its roots?
First, it is all about convenience and seeking ways to expedite operations. Humans naturally seek efficiency and convenience, often preferring to avoid performing repetitive and laborious tasks ourselves.
Consequently, we delegate these tasks to computers, leveraging automation to handle them. By enabling automated systems to manage and control various processes, we can ensure that once we detect an issue or an opportunity, it can be addressed promptly and effectively by these systems.
Now it is time to use AI
Once we can control our connected devices, why not leave it to the computer to – using AI and Machine Learning techniques – analyze data generated by IoT devices and assess if something wrong is going on with those smart devices? This allows us to take appropriate actions. This way, IoT and AI-enabled data analysis ensure operational excellence.
Real-world examples of AI and IoT implementation
Many companies that have decided to integrate AI into their IoT environment can already boast interesting solutions. Our client is a leader in green energy and wind turbine manufacturing. They faced the challenge of operating thousands of wind turbines worldwide with only a few operators.
Their goals and challenges
- Centralized maintenance of worldwide wind fleet
- Different technologies (some 25-years old)
- Operator learning period and rotation
- Number of devices and alarms to handle
How did they prepare for their AI with IoT journey:
- Up to 300 built-in sensors
- Different communication options – wired and wireless (mostly cellular)
- Data delivery from periodical SMTP to real-time OPC-UA
Artificial Intelligence in IoT solution
For the client, the AI solution turned out to be the best one. It was based on sophisticated conditional rules working on a streaming analytics engine where the incoming data was compared with the enterprise data. This resulted in many alerts. So, to address that with a limited workforce, streaming analytics and integration with the enterprise systems (SAP and ServiceNow) were used. The simpler alerts of non-critical severity were managed automatically. By creating the proper work orders for the proper maintenance, human operators were relieved and could handle critical issues.
Once the problem is resolved in the field, the status is automatically synchronized back from SAP systems to the IoT platform and the dashboard. This way, the operators know that the problems are resolved. With an autonomous approach, we reach very high productivity of the maintenance and operations teams.
 Streamline Your IoT Operations Andrzej Gumieniak, our Head of Practice IoT, is here to help you navigate the complexities of IoT solutions. Book a consultation to discuss your case. Schedule a meeting |
How does AI improve connectivity and decision-making in IoT systems?
For that, we can take advantage of Artificial Intelligence model assistance, pre-trained for specific use cases. This way we do not need to build up sophisticated conditional rules based on many parameters inside our business logic as AI supports us with certain routine tasks. That leads to an especially important and useful application: prescriptive maintenance. This maintenance strategy takes advantage of machine data to outline maintenance-related tasks.
Also read: Predictive maintenance
Machine Learning in IoT
Machine learning (ML) is playing a significant role in the Internet of Things (IoT) ecosystem. By leveraging AI and ML algorithms, companies can extract valuable insights from the vast amount of data generated by IoT sensors. Nowadays you can use IoT platforms (e.g. Cumulocity IoT) that have built-in ML components that make Machine Learning model training and implementation easier and quicker.
AI in IoT streamlines data analytics
Using the incoming data, you can process it through analytics and predictive Machine Learning models. You no longer need to create alerts or work orders manually. Instead, you can utilize an AI advisor or a more sophisticated rules engine, taking advantage of contextual data to support issue resolution.
IoT and AI for better alarm monitoring
AI-enabled automation eliminates the need for operators to sift through thousands of alarms. Operators can then focus on the most challenging cases, while simple and obvious ones can be managed automatically by the central IoT platform.
What are the benefits of AI and IoT?
- Faster issue resolution – data collected from the IoT sensors is analyzed in the instance by AI, which allows for quicker issue detection and resolution.
- Improved security – IoT security is considered one of the biggest challenges and concerns. AI algorithms can monitor access to IoT networks and detect suspected activities.
- Predictive & prescriptive maintenance capabilities for preventing downtime – by employing cognitive algorithms used in IoT, you can prescribe actions and instruct the maintenance staff on what to do or even issue the appropriate commands to the team automatically.
Also read: The top challenges in IoT
AI in IoT or Artificial Intelligence of Things?
Artificial intelligence in itself is a powerful tool. When it becomes the heart of an organism such as a network of connected IoT devices, you get an intelligent system capable of learning and making decisions. In this context, there is increasing talk of AIoT (Artificial Intelligence of Things) technology, in which IoT devices talk to each other and act independently of human intelligence.
 DOWNLOAD OUR EBOOK! These maturity levels and how to progress to the next one are discussed in the existing ebook “IoT Maturity Levels - from Digitization to Autonomy”Download now! |
IoT and AI trends will become increasingly important
IoT applications increasingly leverage AI algorithms to analyze the vast amounts of data generated by industrial IoT devices. The combination of AI and IoT technologies allows for real-time data processing and the ability to make decisions based on the insights gathered in many industries now, including logistics, automotive, and manufacturing. Overall, the benefits of AI in IoT are vast and continue to expand as AI and IoT work together. We can expect this trend to gather pace, as the number of connected devices worldwide in 2025 is forecasted to hit 25 bn, according to IoT Analytics.
- 1. AI-enabled IoT. Key statistics on AI implementations
- 2. Future of AI-powered IoT applications
- 3. Integration of AI and IoT for operational efficiency
- 4. Real-world examples of AI and IoT implementation
- 5. How does AI improve connectivity and decision-making in IoT systems?
- 6. Machine Learning in IoT
- 7. What are the benefits of AI and IoT?
- 8. AI in IoT or Artificial Intelligence of Things?
- 9. IoT and AI trends will become increasingly important