Executive Insights: IoT, Edge, and AI Predictions for 2024

January 12, 2024

In this blog post, ClearBlade Founder & CEO Eric Simone shares his predictions for the future of IoT, Edge, and AI in 2024.


Until now, edge computing has primarily performed simple tasks such as protocol conversion, data filtering, and real-time rule processing. With the rising interest in AI, edge computing will run AI models locally, close to where the data is being generated, providing efficiency and resiliency. The AI movement is accelerating edge computing adoption, making AI the “killer app” for edge technologies (known as edge AI). Running AI models at the edge enables businesses to leverage large amounts of data streaming from equipment, which is much more complex and expensive to accomplish with cloud computing alone. 

Take oil drilling, for example; wellsites in remote locations communicate via satellite, providing limited and expensive bandwidth; a typical large wellsite streams several TB of data daily. The AI models that guide the wellsite reside remotely in a data center, so only a fraction of the data is used to hydrate that AI model due to the limited bandwidth, making the AI model less effective. However, if you move the AI model to the edge, all the data can be used in real-time, making the AI predictions much more accurate. This pattern occurs across multiple industries, including energy, transportation, and buildings.

I also see progress in transportation: today, edge AI is already being applied to mass transportation to automate scheduling, optimize routing, and improve customer communications.

Eventually, the ever-elusive smart home will become a reality as edge AI is embedded into our appliances and smartphones, enabling them to adapt and learn to interact naturally with our everyday commands. Something depicted years ago on The Jetsons and Star Trek will finally become a reality.

Bottom line: AI will become the “killer app” for edge computing, making it the primary reason companies get serious about applying it to their businesses.


Digital twins have been a hyped technology for many years. The problem with most digital twins is that they are disconnected from actual physical devices, severely limiting their capabilities. Let’s explore a digital twin’s definition:

A digital twin is a digital model of an intended or actual real-world physical product, system, or process (a physical twin) that serves as the effectively indistinguishable digital counterpart for practical purposes, such as simulation, integration, testing, monitoring, and maintenance.

They must connect to their physical counterparts in real time to get more value. We call these operational digital twins. Here is an operational digital twin’s definition:

An operational digital twin is a digital model connected to a real-world physical product, system, or process (a physical twin) that serves as the digital counterpart for practical purposes, such as simulation, integration, testing, monitoring, and maintenance.

An operational digital twin brings a traditional digital twin out of the scientific world and into the operational world, providing more value to operators who can now monitor, track, and control their physical equipment. By using IoT technology, a simple digital twin is transformed into a much more usable operational digital twin.  

Bottom line: Operational digital twins emerge as digital twins get connected with IoT, providing more value to operations and business as a whole.


Generative AI is dominating the news right now, and rightfully so, because it is transforming virtually everything we do. By leveraging the power of generative AI to create, connect, and control operational digital twins, we will experience a massive shift in how operations experts interact with their physical equipment.

Until now, operations personnel needed to work with their technical software counterparts (IT, software engineers, etc.) to access their physical equipment remotely. This caused confusion and frustration as businesses could not promptly deliver results to the field. I have experienced hundreds of situations where operations folks gave up or accepted that progress was never coming.

Enter generative AI embedded into software and delivered to operational experts who now have the power to configure everything in their natural business language using simple text or voice commands. This advancement creates an abstraction layer that removes the programmer from the critical path of simply getting something done.

We will see companies that have digital twin and IoT software invest in adding generative AI to augment their already powerful software. The ones who do it well will accelerate past those who do not.

Bottom line: Generative AI will elevate operational digital twin software usage, enabling operational professionals to control physical equipment in their natural business language.


Over the past few years, the world has witnessed the rise of Environmental, Social, and Governance (ESG) movements. With increasing awareness and concern for the planet’s sustainability and the impact of businesses on society, ESG has become a crucial factor in decision-making processes. These movements are gaining momentum in companies worldwide, but their effectiveness has been hard to measure, and most ESG policies appear to be nothing more than glorified marketing campaigns.

2024 will see companies moving to make their ESG initiatives more accountable, specifically in energy usage, by measuring their effectiveness with IoT. Companies are rolling out technology to monitor their buildings for energy efficiency, their vehicles for fuel consumption, and their remote equipment for operational effectiveness. All this information is being logged and measured as they progress toward corporate climate goals.

Because IoT and operational digital twin technology have progressed to put the power into the hands of operational personnel, this movement has gained momentum, and we are seeing real progress now.

Bottom line: In 2024, ESG is becoming accountable through advancements in IoT and digital twin technology, enabling companies to reach their ESG program goals.


Over the past few years, cloud providers, IBM, Google, and SAP, to name just a few, have moved away from their acquired and internally developed IoT solutions. We are seeing a foundational shift in how IoT is delivered because what most have been trying to achieve over the past decade has failed.

Most IoT projects are unsuccessful because building flexible, scalable, and cost-effective IoT solutions are complicated. We will see cloud providers fully embrace third-party IoT solutions in 2024. Cloud providers are moving to a partner-led IoT strategy because they want their customers to be successful and extend their IoT solutions into the cloud providers’ data analytics and AI services.

These third-party IoT partners are committed to IoT, and their expertise is laser-focused on the complex nuances of creating secure, flexible, and profitable IoT at scale. In addition, IoT partners provide expertise in different verticals, allowing them to provide specialized offerings while leveraging the cloud providers’ core computing, data, and AI infrastructure. It’s a win-win for the entire industry.

Bottom line: Embracing proven, third-party IoT software in partnership with cloud providers significantly improves the entire IoT industry’s success.

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