AI at the edge is reshaping the way businesses handle data. Here’s how this emerging trend operates, why it’s gaining momentum over traditional cloud-based AI, and which industries are poised to benefit the most.
Artificial intelligence has been in development for decades, but it wasn’t until tools like ChatGPT became widely accessible that AI entered the mainstream. These cloud-native platforms helped organizations and individuals fully realize AI’s capabilities, sparking rapid adoption across sectors.
According to a McKinsey study from 2024, 72% of enterprises had already implemented AI. That number is expected to rise significantly, with 92% of organizations planning to increase AI investments over the next three years—largely in pursuit of the trillions in productivity gains AI promises to deliver. Yet, even with widespread adoption, challenges persist: concerns over security, vendor dependence, data transfer delays, and limited infrastructure control remain key obstacles.
Edge AI tackles these issues by bringing processing closer to where data is created—instead of relying solely on distant cloud data centers. This proximity alters not just the logistics of data handling but also how and where AI can be applied.
So, how does this model change AI deployment, and who stands to gain the most? And what role will compact AI models—like small language models (SLMs)—play at the edge? Let’s explore the core advantages and some of the limitations of edge AI, along with trends shaping its future.
Why Edge AI Offers a Strong Value Proposition
When AI workloads are moved from centralized data centers to the edge, four key advantages emerge: stronger connectivity, faster response times, more efficient data usage, and heightened data protection.
1. Stronger and More Reliable Connectivity
By keeping data processing local, edge AI minimizes dependency on internet bandwidth and limits the need for consistent cloud connectivity. This ensures that applications remain responsive, even in areas with unreliable connections.
2. Faster Response Times
Low latency is critical in environments where real-time insights impact performance or safety. Edge AI processes information on-site, drastically reducing the delay in decision-making.
3. Smarter Data Management
For companies generating massive amounts of data, edge processing streamlines operations by analyzing information locally and forwarding only actionable insights to central systems. This reduces network congestion and cloud storage costs while improving throughput.
4. Better Security and Data Sovereignty
Cloud breaches can expose sensitive data, as seen in the 2019 Capital One incident involving AWS. Edge computing can mitigate such risks by enabling air-gapped operations—networks completely disconnected from the internet—which limits external attack surfaces. This is especially useful in regulated industries like energy and manufacturing.
Turning Potential into Practice with Edge AI
Platforms like ZEDEDA make it easier for businesses to adopt edge AI technologies. I spoke with Padraig Stapleton, SVP of product and engineering at ZEDEDA, who explained how their SaaS-based orchestration platform supports clients across industries like oil and gas, helping them deploy AI applications directly on edge hardware.
These applications often include machine learning models that interpret data collected on-site—such as from solar farms or drilling platforms—delivering insights far faster than if the data had to travel back to centralized data centers.
Stapleton emphasized that security is a top priority: “Many clients can’t risk sensitive data being transmitted over the internet. For them, everything from inference to processing must happen locally.”
He also pointed to latency as a key driver: “Immediate decisions can’t wait for round trips to the cloud. Local processing enables that speed.”
Challenges of AI at the Edge
Edge AI isn’t without limitations. Devices on the edge typically have fewer resources than cloud servers. This means processing-intensive models may run slower, and updating them can be more complicated.
As Stapleton explained, “The cloud offers broader access, easier updates, and better tools for model development and retraining. That flexibility is harder to replicate at the edge.”
Setting up edge AI also tends to involve more manual work, which adds complexity. A hybrid strategy—developing AI models in the cloud and deploying them at the edge—is one way to balance performance and agility, but it’s not always straightforward.
What the Future Holds for Edge AI
By 2028, it’s expected that half of all AI workloads will happen at the edge—up dramatically from 5% in 2023. The hyperscale edge computing market is also projected to hit nearly $20 billion by 2029.
This growth is supported by advances in hardware. NVIDIA’s Jetson chips, for example, bring significant computing power to compact edge devices. Emerging technologies like neuromorphic processors, silicon photonics, and FPGAs are also expanding edge capabilities.
Stapleton noted particular excitement in manufacturing, oil, and gas, where automation is driving demand. For instance, edge-based apps are increasingly used to inspect products on production lines, evaluating them for defects in real time.
ZEDEDA clients also use edge AI for safety monitoring. Using computer vision, their systems can verify if workers are complying with safety protocols—like wearing proper gear—and trigger alerts instantly if violations are detected.
Two Edge AI Deployment Models Gaining Traction
Looking ahead, two primary ecosystems are shaping the future of edge-based AI.
The first is a hybrid approach: AI models are developed and trained in the cloud but pushed to the edge for inference. This offers flexibility—business logic can stay consistent while AI models evolve independently.
Stapleton said clients are increasingly seeking to decouple applications from models to update each independently and streamline workflows.
The second approach is more common in sectors like energy, where data security is paramount. In these scenarios, companies opt for fully isolated (air-gapped) edge environments where model training, deployment, and monitoring all occur on-premises. This model suits organizations unwilling or unable to connect their infrastructure to the internet.
How SLMs Will Influence Edge Applications
Small language models are expected to play a key role in edge deployments, particularly in sectors like automotive, oil and gas, and industrial automation. These compact models, adapted from larger LLMs, can provide specialized knowledge and insights directly at the edge.
Many of these industries currently rely on machine learning for basic tasks like predictive maintenance or image recognition. But SLMs could change that by bringing AI-driven expertise to areas with minimal human oversight.
Stapleton believes these models will help fill workforce gaps, especially in remote or hazardous environments: “SLMs can assist teams with task execution or decision-making, even where skilled personnel aren’t available.”
Starting Small to Build Big Impact
Every decade seems to bring a major tech shift—first the internet, then the cloud, and now AI. However, hype often clouds reality. The best way forward, according to Stapleton, is to begin with focused, manageable projects.
At ZEDEDA, they’ve followed this philosophy by integrating the Llama 3.1 8B model into an internal knowledge-sharing tool. That initial step is now informing broader efforts to scale AI solutions across their ecosystem.
Solve Real-World Problems at the Edge
Edge AI marks a pivotal change in how data is processed, offering significant gains in speed, privacy, and efficiency. Still, managing distributed systems and dealing with limited hardware remain real challenges.
Organizations like ZEDEDA help businesses bridge this gap by simplifying deployment, reducing operational overhead, and enhancing security—all while providing centralized oversight across sprawling edge networks.
Their goal: bring cloud-like functionality to edge environments, enabling businesses to deploy AI solutions faster, maintain devices more effectively, and secure operations using zero-trust models. With the right approach, edge AI can unlock powerful new capabilities across nearly every industry.
