IoT was never important - AIoT is

For the past decade, I've met with clients or led teams that were trying to solve for IoT. I heard from one major telco that they "needed an IoT strategy". I also spoke to a mayor of a medium-sized European city that he wanted to "solve problems using cameras and sensors". My response has always been, "what are you trying to accomplish with technology?" or "what is your overall strategy (even before they start thinking about digital or technology)?". The idea is that you first must lay out what your goals and KPIs are, how they will be measured and for whom they are designed. Then start working your way backward, determining what systems and data are required to deliver the right information to accomplish these goals or meet the KPIs. Only THEN do you need to start to think about connected products and IoT and only if there is a need to involve reatime data capture at the device, edge or person layer. None of these systems were impactful if they didn't do anything with the data. I spoke to one auto maker who had collected over 10 petabytes of data from the telemetry on their vehicles and were doing absolutely nothing with it. It was just sitting in AWS Glacier, racking up a ton of cloud costs. Obviously, this doesn't add value to the business. However, with the proliferation of large language models and other AI advances, the concept of connected devices, infrastructure and people becomes super exciting. So exciting that I see it transforming everything we do.

Over the past decade, the advent of the Internet of Things (IoT) brought with it an exciting proliferation of smart, interconnected devices that are changing the way we live and work. However, if we want to unlock the full potential of this innovative technology, it is Artificial Intelligence (AI) that proves itself indispensable. AI has become the intelligence behind IoT, allowing for advanced decision-making, predictive analytics, and complex problem-solving. Without AI, the myriad of data collected by IoT devices could not be used to its full potential, reducing these devices to little more than expensive, connected novelties. Let's delve deeper into this fascinating intersection of technologies and explore how AI is integral to IoT.

#ai at the Heart of IoT - becoming #aiot

The combination of AI and IoT, often referred to as the artificial intelligence of things (AIoT), is a powerful alliance that is bringing about transformation across various industries. Let's consider some prominent use cases:

#computervision in Manufacturing

In the world of manufacturing, AI-powered computer vision is making waves. IoT sensors and cameras are deployed across manufacturing plants, collecting high volumes of data. However, it's AI that turns this data into actionable insights. By employing machine learning algorithms, these systems can identify patterns, anomalies, and provide real-time feedback.

For instance, AI can process images captured by IoT devices to identify product defects that might be invisible to the human eye, reducing faulty products and enhancing quality control. Moreover, predictive maintenance powered by AI can anticipate equipment failures before they occur, based on the patterns identified in the data. This application not only minimizes downtime but also extends the life of machinery, leading to cost savings and increased efficiency.

#connectedhealth - using AI in Healthcare

Having a wife that is a surgeon, I hear first-hand stories of how backward the healthcare and insurance industry is, with regard to automation and leveraging data for insights. Although they have made strides in the past two decades by implementing EHR systems such as Epic, this industry is still far behind in leveraging technology to help the healthcare practitioners and staff. In healthcare, AI and IoT can revolutionize patient care and health management. IoT devices, such as ingestibles or wearable health monitors, collect extensive patient data, including heart rate, blood pressure, glucose levels, etc. AI steps in to analyze this data, predict health trends, and even alert medical professionals to potential health risks.

For instance, AI algorithms can predict potential heart attacks based on irregularities in heart rhythm data collected by IoT devices, potentially saving lives. In hospitals, AI and IoT together can monitor patient vitals and notify healthcare professionals if immediate intervention is needed. These applications allow for continuous, proactive patient care that can significantly improve health outcomes.

Smart Spaces - leveraging AI in public and private sector spaces

In smart space (e.g., #smartcities , #smartbuildings , #smartcampus ), AI and IoT together provide solutions that enhance the quality of life for residents while improving sustainability. IoT devices, such as sensors and cameras, collect data on everything from traffic patterns to air quality. AI processes this data to optimize city services.

For example, AI can analyze traffic data to adjust the timing of traffic lights to reduce congestion or suggest optimal routes for emergency services. Furthermore, smart waste management systems are predicting when waste bins will be full and optimize collection routes, improving efficiency and reducing carbon emissions.

Data: The Key to Better Decision-Making

While the term 'IoT' often takes center stage, it's the underpinning use of data and turning data into insights that truly matters. Sensors, actuators and IoT helps fill out the data picture by providing real-time view into physical assets, people and infrastructure. This vast amount of data, when processed and analyzed by AI, forms the basis for smarter, faster decision-making, both at the network edge and in the cloud.

AI at the #edge

Edge computing (#edgecomputing) refers to the practice of processing data near its source, or 'the edge' of the network, instead of sending it to a centralized cloud-based location. AI plays a critical role here, allowing for real-time data analysis and immediate action. This immediacy is vital in scenarios where a delay could have serious implications, such as autonomous vehicles making split-second decisions or health monitors detecting critical changes in a patient's condition.

AI in the Cloud

On the other hand, AI in the cloud is crucial for handling large-scale data processing tasks and complex modeling. The computational power of cloud-based AI can process enormous datasets collected from multiple IoT devices, identify trends, and make long-term predictions. Such capabilities are integral in various sectors, including weather prediction, business analytics, and even the training of AI models themselves. Additionally, combining IoT-generated data with existing systems of record or traditional data warehouses unlocks even deeper insights that can be used to optimize or automate the business.

Conclusion

Without AI, IoT alone lacks the capability to transform the raw data it collects into actionable intelligence. It's the fusion of these technologies that enables intelligent decision-making, optimizing processes, and enhancing the way we live and work. Whether it's manufacturing, healthcare, or other applications, AI is not just a component of IoT – it's an essential driver, making IoT a truly complete solution. The real importance isn't just in the 'Internet of Things', but in the intelligence we glean from these 'things' and the transformative actions we can take as a result. It's clear that as we move further into this exciting digital age, AI will continue to be at the heart of IoT, helping us unlock the full potential of our connected world.