A wearable heart rate monitor. A “smart” shopping cart. A quality control mechanism. These are all valuable IoT devices – but to be truly effective, they must do more than gather data. They must be able to process that data in real time.
That’s where Edge AI comes in.
By distributing AI infrastructure and deploying AI “at the edge,” organizations can gain real-time insights from IoT devices while cutting costs and improving security. It’s a massive breakthrough – and one that Sales Partners should be discussing with customers.
In this article, we’ll describe how Edge AI can transform an organization – and guide you through the typical Edge AI conversation.
What is Edge AI?
“Edge AI,” also called “AI at the Edge,” refers to the deployment of distributed artificial intelligence infrastructure to local “edge devices,” including Internet of Things (IoT) sensors. This distinguishes it from traditional AI, which is processed in a centralized data center or in the cloud.
In essence, Edge AI combines the advantages of edge computing and AI, allowing for real-time data analysis at the location where the data is produced. This arrangement eliminates the reliance on cloud computing and often streamlines AI-powered operations.
In some cases, AI applications require more compute power than Edge AI solutions can handle, which is resolved by integrating Edge AI with cloud computing or physical dedicated AI infrastructure.
Why Edge AI, Why Now?
The Edge AI market is expected to grow from $27 billion in 2024 to $270 billion in 2032. Why the explosive growth? Because recent trends in AI and mobility make Edge AI an optimal technology for organizations across industries.
Here are some specific trends that explain why Edge AI is so appealing:
- The Rise of Generative AI at the Edge. When generative AI is deployed on edge devices, it can create, summarize, and analyze content – even without an internet connection.
- The Explosion of IoT Devices. By the end of 2024, there were 18.8 billion IoT devices collecting data from around the world. Sending all that data to the cloud would be slow, expensive, and inefficient – making Edge AI an attractive alternative.
- The 5G Rollout. 5G networks offer high bandwidth and low latency, which makes them a perfect match for Edge AI solutions.
- Demand for Real-Time Insights. In fast-moving industries, businesses need to know what’s happening now – and Edge AI provides near-instant data analysis.
- Data Privacy and Security Concerns. In a world where 81% of organizations have experienced at least one cloud security incident in the past 12 months, security is understandably a major concern – and it’s one that Edge AI addresses.
Outcomes Spotlight: The ROI of Edge AI
When implemented correctly, Edge AI can have a transformative effect on an organization’s operations, turning their IoT deployments into engines for efficiency and growth.
Here are some specific benefits you should discuss with prospects and customers:
- Reduced Latency. With Edge AI, data doesn’t need to travel to a distant server, resulting in rapid response intervals.
- Improved Data Privacy & Security. Edge AI solutions keep sensitive data on an organization’s local device, eliminating risky exposure to third-party servers.
- Lower Bandwidth Costs. By processing data locally instead of sending it over the internet, Edge AI preserves bandwidth and lowers costs.
- Enhanced Reliability. Distributed edge AI solutions don’t rely on the cloud or centralized infrastructure to operate, and they’re increasingly incorporating hardware redundancy and software fault tolerance to limit downtime.
- Increased Efficiency & Automation. Edge AI’s capacity for real-time data analysis is a game-changer for many organizations, allowing them to automate more of their operations while significantly increasing efficiency.
Distributed Infrastructure is the Foundation for Edge AI
Edge AI success depends on having the right infrastructure in the right places. That means relying on the unique strengths of public clouds, neo clouds, specialized GPUaaS providers, enterprise data centers, and strategic inference locations at the digital edge through privacy-preserving interconnections. In other words, high-performance AI-ready infrastructure positioned close to users and devices, with optimized connectivity back to core and cloud environments. This distributed design optimally positions AI workloads where they deliver maximum value: training where compute is abundant, inference where data is generated, and sensitive processing where privacy is protected.
That’s why Intelisys partners with Equinix—a global provider of dedicated high-performance data centers purpose-built for AI workloads with high-speed, low-latency direct connections to all major cloud, data and AI providers. Together, Intelisys and Equinix offer Sales Partners easy access to the computing power, edge processing, and interconnection capabilities that traditional data centers can’t match, giving your customers the foundation they need to fully capture Edge AI opportunity. Learn how distributed infrastructure helps unlock the power of Edge AI.
Edge AI Use Cases (by Industry)
Real-time insights from Edge AI can prove valuable in practically any industry. That said, Sales Partners should be aware of especially prominent use cases in three main verticals: manufacturing, healthcare, and retail.
Manufacturing
The ability to monitor large-scale operations in real time makes AI at the Edge a hugely valuable resource for manufacturing companies. IoT sensors integrated with AI can alert operators to any present or predicted failures, while machine learning and predictive analytics combine to make quality control more efficient.
Healthcare
By employing Edge AI in wearable devices and other medical equipment, healthcare facilities can improve real-time patient monitoring. AI embedded in medical imaging equipment can also conduct immediate, accurate analyses. Together, these capabilities allow medical professionals to gain the insights they need faster – often to lifesaving effect.
Retail
Edge AI unlocks a world of futuristic possibilities for retailers – including “smart stores” where shopping carts are embedded with IoT sensors. The goal? Personalize customer experience (CX) in a way that boosts efficiency while increasing customer satisfaction.
Discussing Edge AI with Customers/Prospects
Ready to guide customers and prospects through the brave new world of Edge AI? Start by asking the right discovery questions – and then make sure you’re providing helpful insights on solution fit and implementation.
Edge AI Discover Questions
Here are some expert-engineered questions for kickstarting the Edge AI conversation:
- “What are some of the biggest operational challenges you’re facing right now?”
- “Where are the bottlenecks in your current processes?”
- “Are there any areas of your business where you need to make faster decisions?”
- “How are you currently using the data you collect from your IoT devices?”
- “What are your biggest concerns around data privacy and security?”
- “Are there any tasks that are currently being done manually that you would like to automate?”
By asking these questions, you’ll position yourself as a trusted partner while learning more about how the specific organization could implement Edge AI.
Why Data Matters (Training vs Inference)
When discussing Edge AI with customers, make sure they appreciate the importance of data for training AI models.
Depending on stakeholders’ level of AI knowledge, you might have to remind them of the two stages of AI implementation:
- Training. During this stage, the AI model is exposed to available data while it learns about desired inputs and outputs.
- Inference. This is the stage where the AI model actually makes predictions, conducts analysis, and produces outputs.
The higher the data quality, the more effective the AI training will be – and the better the results of AI inference.
The “Solution” vs the “Technology”
To ensure clarity when discussing Edge AI, try to distinguish between “solutions” and “technologies.” The “solution” is the specific AI model that a customer might employ. This solution is enabled by “technologies” like machine learning, predictive analytics, and natural language processing.
Edge AI itself is a technology, and the Intelisys catalog includes multiple solutions that incorporate it.
Cross-Selling Opportunities
Organizations often implement Edge AI alongside other mobility and edge computing technologies – which means Sales Partners have plenty of cross-selling opportunities. For example, customers might pair an Edge AI solution with updated connectivity technology like 5G or SD-WAN, a combination that would decrease latency and boost efficiency. They might also invest in hardware like edge infrastructure, IoT sensors, and IoT devices.
Since Edge AI is partly a security-enhancing technology, you might cross-sell endpoint security and compliance services – giving organizations a way to comprehensively secure their data while leveraging AI.
And remember, Edge AI can’t always handle all of the data an AI application needs. That means some organizations will need enhanced compute and storage capabilities alongside their Edge AI deployments.
The Future of AI at the Edge
Edge AI is still a relatively new technology, meaning the future will likely bring more exciting developments. The edge devices themselves, from cameras to medical devices, will likely grow even more sophisticated, while Edge AI software will become more robust and easier to employ. These advancements will lead to even more Edge AI adoption.
Eventually, these new technologies could usher in a whole new era – with trillions of “smart” devices collecting and analyzing data in real time. The effect this could have on productivity and efficiency is almost unimaginable.
By discussing Edge AI with your prospects and customers, you’re leading them towards the forefront of this new technological transformation.
Learn More About Edge AI
To learn more about AI at the Edge, reach out to your regional Intelisys Sales Engineer. They’ll be happy to discuss the exact Edge AI solutions that are right for your customers.
Also, check out more Intelisys resources on leveraging artificial intelligence: