Edge AI Applications As The Catalyst For AI PC Market Growth

Forbes

2 days ago

Ajith Sankaran, Executive Vice President, C5i.
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Despite all the buzz, the adoption of high-performance AI PCs with powerful neural processing units (NPUs) has been especially sluggish. Since their launch in mid-2024, these devices have captured just 5% of AI PC market sales. This can be attributed to several factors:
• AI PCs typically command a significant price premium without clearly articulated benefits. Many users remain unconvinced that these costs translate to meaningful improvements in computing experiences.
• Compatibility concerns persist, particularly with first-generation advanced RISC machine (ARM)-based systems that may not support legacy software.
• There is a scarcity of software applications that fully harness AI PC capabilities.
According to a 2024 ICD report, the global market for personal computing devices was "set to grow 3.8% in 2024, reaching 403.5 million units." However, this growth is primarily driven by a nearly double-digit growth in tablets. According to Jitesh Ubrani of IDC, 'There seems to be a big disconnect between supply and demand as PC and platform makers are gearing up for AI PCs and tablets to be the next big thing, but the lack of clear use cases and a bump in average selling prices has buyers questioning the utility.'
I believe the answer to realizing the potential of AI PCs in enterprise scenarios lies in understanding and utilizing edge AI. To understand why, let's take a closer look at how these systems operate.
Edge AI And Its Relationship With AI PCs
Edge AI represents the convergence of AI and edge computing, enabling AI algorithms to run directly on local devices rather than in remote data centers. This approach processes data where it's generated, eliminating the need to send information to the cloud for analysis and returning results almost instantaneously.
AI PCs are well-positioned to serve as powerful edge AI platforms due to their unique hardware architecture. They integrate three processing components:
• A central processing unit (CPU) for general computing tasks.
• A graphics processing unit (GPU) for parallel processing workloads.
• A neural processing unit (NPU) optimized for AI computations.
This triad of capabilities allows AI PCs to handle edge AI applications with efficiency. The performance benefits can be substantial; security company CrowdStrike reported that its software's CPU consumption dropped from 35% to 1% when running on machines equipped with Intel NPUs.
Global shipments of AI PCs are projected to reach 114 million units in 2025, accounting for 43% of all PC shipments. I believe that edge AI that incorporates the latest advances in generative AI and agentic AI could provide tangible benefits that justify the premium pricing of AI PC for consumers and enterprises. As more developers create software that leverages NPUs and other specialized AI hardware, the value proposition should become clearer, driving increased adoption across both consumer and enterprise segments.
Emerging Edge AI Applications Driving AI PC Demand
• Manufacturing Intelligence
Manufacturing environments are proving to be fertile ground for edge AI applications. AI systems running locally on AI PCs can monitor equipment health in real time, detecting anomalies and predicting potential failures before they occur. This can reduce costly downtime.
Quality control represents another application. AI-powered cameras connected to edge computing systems can inspect products for defects with precision and consistency.
• Healthcare Innovations
The healthcare sector also stands to benefit from edge AI. Portable diagnostic devices equipped with edge5 AI can analyze medical images such as X-rays, MRIs, and CT scans locally, providing rapid insights without requiring cloud connectivity. This is particularly valuable in remote areas. And wearable health devices using edge AI can analyze biometric data locally, detect anomalies and alert healthcare providers without transmitting sensitive patient information to remote servers.
• Retail Transformation
In retail, edge AI applications are revolutionizing operations and customer experiences. AI-powered cameras and sensors can track inventory levels in real time, optimizing stock replenishment. The same infrastructure can analyze customer behavior patterns, enabling retailers to deliver personalized recommendations and promotions. These capabilities require significant local processing power that can be provided by AI PCs to analyze video feeds and sensor data in real time.
• Security and Privacy Protection
Edge AI can deliver faster performance while keeping sensitive data local instead of sending it to cloud services. For example, Bufferzone NoCloud "uses local NPU resources to analyze websites for phishing scams using computer vision and natural language processing." Edge AI applications can enhance banking security by detecting unusual transactions and immediately alerting users.
Recommendations For Effective AI PC and Edge AI Adoption
1. Develop edge-native AI applications for real-time decision-making.
Prioritize building edge-native AI applications that leverage the NPUs in your organization's AI PCs to execute machine learning models locally. For example, manufacturing firms can deploy vision systems on AI PCs to perform real-time quality inspections directly on production lines, reducing defect rates while eliminating cloud dependency.
2. Deploy agentic AI systems for autonomous workflow optimization.
Agentic AI excel at autonomously managing complex, multi-step processes. In supply chain, running agentic AI systems on AI PCs can allow you to dynamically reroute shipments based on real-time traffic data processed at the edge, reducing delivery delays. Financial institutions can also combine agentic AI with edge computing to autonomously monitor transactions for fraud patterns, triggering immediate alerts while keeping sensitive financial data localized.
3. Implement privacy-centric AI architectures for regulated industries.
Consider adopting hybrid edge-cloud AI architectures to balance computational demands with regulatory compliance. For example, banks can deploy on-premise AI PC clusters to run agentic AI fraud detection systems, ensuring customer transaction data never leaves internal networks.
4. Build scalable edge AI infrastructure with modular hardware.
Invest in AI-optimized hardware ecosystems that support both current and emerging workloads. For instance, consider deploying AI PCs with dedicated NPUs for employee productivity tools and pairing them with edge servers containing GPU/TPU arrays for heavy computational tasks.
5. Integrate generative AI with edge computing for adaptive systems.
By fusing generative AI with edge computing, you can enable dynamic system adaptation within your company. For example, manufacturers can deploy small language models on AI PCs to generate equipment repair instructions tailored to real-time sensor data, reducing machine downtime.
Conclusion
While initial adoption of AI PCs has been slow due to high costs, compatibility issues and a lack of applications, the emergence of edge AI use cases is beginning to demonstrate the value of local AI processing. As developers increasingly leverage NPUs to build edge-native and agentic AI solutions, I believe the value proposition of AI PCs will become more evident, driving broader adoption across consumer and enterprise markets.
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