Edge computing is already disrupting industries for the better with its ability to gather and process data right at the edge. For example, it can flag maintenance issues on a factory floor in real time, analyze a patient’s vital signs in an ambulance and give immediate feedback to paramedics.
But what happens when you add the power of AI to the edge? This opens the door to improvements in operational technology (OT) security and observability, a more robust return on investment through the more effective use of equipment and better energy management, and innovations like digital twins. It offers the promise of turning data into actionable insights in real-time.
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This is all done without having to program every scenario or use case in detail. Instead, data containing examples and other training information teaches the GenAI model about the world and what is happening.
Edge AI in practice
In manufacturing, for example, edge AI can go beyond simply using a camera to check products on a conveyor belt for obvious defects. It can be trained with information about what constitutes good and bad product units; thereafter, it can make those calls in real time by independently applying AI-driven reasoning. It can alert staff more accurately to a broader range of potential issues along the conveyor line and even proactively recommend repair procedures or estimate repair costs before equipment breaks.
Beyond predictive maintenance, edge AI in manufacturing has applications in safety, energy, quality, supply chain management and more. Powering AI at the edge can make an immediate impact on any factory automation initiative – there is no need for large, drawn-out digital transformation projects.
Connecting the silos
This is a pivotal shift at a time when global spending on edge computing is already expected to reach $232 billion in 2024, an increase of 15.4% from 2023, amid a growing number of IoT devices, IDC estimates.
It also forms part of the more significant trend of IT/OT convergence – integrating IT systems, which manage data and communications, with OT systems that control physical devices and industrial operations. This convergence is driven by the need for improved efficiency, productivity and instant decision-making in industrial environments.
By activating data streams from previously inaccessible manufacturing equipment, edge AI is setting the stage for a new digital transformation era in the industrial sector. With a constant stream of OT data from a broader range of sensors, machinery, cameras and applications, manufacturers can improve their maintenance schedules, for example. Rather than completing 1,000 scheduled maintenance jobs based on a static roster, the AI-driven analysis might show that they can do 20% less and get the same result.
Hence the reason as to why interest in edge AI solutions is growing. Companies are discovering how they can automate business processes and unlock new opportunities for innovation, while addressing concerns over latency, security and costs by bringing GenAI models to the edge.
Smaller models do better
To bring GenAI use cases to the edge, we need language models that can function independently from a cloud computing ecosystem within the IT environment – independent of cloud-based resources. So, a large language model (LLM) has to be specialized to fit on the hardware at the edge; think “small language models”. Also, because the gathered data is time-series bounded, to make AI actionable, it has to be processed locally. Speed and latency therefore become key requirements.
Instead of relying on large and complex learning models, edge AI applications use smaller, task-orientated models that need significantly fewer resources. While advanced AI systems like OpenAI’s GPT-4.0 use more than 50 billion parameters, an edge AI application might need less than a few hundred, making it ideal for real-time processing on edge devices.
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Accessible AI – for everyone
One of the standout capabilities and demands of edge AI is its ability to organize vast amounts of unstructured data into a cohesive data plane. This means AI developers can now create impactful use cases for operations that previously could not use AI technology effectively.
Factory managers and operations engineers are also now unrestricted by the need for extensive cloud infrastructure. With edge AI, factory operators can build and implement innovative AI applications directly for the factory – making AI accessible and inclusive. Let’s give the power of AI to factory workers everywhere and reskill frontline workers organically on new technology.
Additionally, edge AI use cases reduce the costs of cloud computing and data transportation and improve security by keeping sensitive operational data local.
A fully managed platform makes edge AI simple
As is often the case with trending technologies, organizations might be swayed by promised benefits but then find it hard to manage the technology – at least until the technology becomes mature and self-manageable.
Working with an expert service provider circumvents this problem, and it’s no different when it comes to edge AI. Managed service providers can revolutionize how manufacturers handle their OT data.
Edge AI deployments facilitate real-time data streaming from OT devices, effectively creating a real-time data lake of the operational environment. From there, these platforms can automatically discover, unify, contextualize and normalize data from OT devices and assets across the manufacturer’s operations. As a result, manufacturers can focus on core operations, while their managed service providers and developers can build and deploy AI applications at the edge.
A leap forward
Whether on the factory floor, in an emergency room or in a retail setting, edge AI is not just a technological advance; it’s a business revolution. By turning data into actionable insights, it allows organizations to respond more swiftly to operational challenges, allocate resources optimally and adapt to changes in real time – all of which contributes to a significant competitive advantage.
This is the perfect time to learn what edge AI can do for your business.
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