Edge AI Applications As The Catalyst For AI PC Market Growth
Ajith Sankaran, Executive Vice President, C5i.
getty
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.
Forbes Business Council is the foremost growth and networking organization for business owners and leaders. Do I qualify?
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Forbes
37 minutes ago
- Forbes
Healthcare's Denial Crisis Is Getting Worse—Unless Clinics Get Smarter With AI
Sally Ragab, Founder & CEO @ Neunetix. In early 2024, one of our clients, a specialty clinic in the Midwest, discovered it had unknowingly failed to submit over 6,000 claims due to a silent software integration failure. The result: nearly $200,000 in lost revenue, most of it unrecoverable due to expired filing windows. The culprit wasn't negligence or bad billing—it was fragmentation. It can create blind spots—leading to claim submission failures, missed deadlines, and preventable denials. Denied claims siphoned an estimated $260 billion from U.S. healthcare providers in 2024 alone. Specialty clinics—oncology centers, orthopedic practices, infusion providers—are hit hardest. Their margins are tight, their billing is complex and each denial cuts twice: once financially, and again in delayed or denied patient care. That's why the next wave of financial recovery in healthcare isn't coming from new payer contracts—it's coming from smarter tech. And AI, if used efficiently, is leading the way. The Real Cost Of Denials—And The Role Of AI Most providers still rely on a patchwork of systems to track, submit and appeal claims. When those systems don't integrate—or worse, when no one notices—they bleed revenue. AI can't fix everything. But it can predict denials before submission, help identify errors and even generate appeal language. When trained on the right data, it works fast, consistently and at scale. Our examination of 27 peer-reviewed and industry studies published between 2020 and 2025 found that models using advanced algorithms achieved AUROC scores between 0.82 and 0.92, which means it has an excellent predictive ability. Clinics that deployed those tools saw denial rates drop by up to 45%, shaving a median nine days off accounts receivable cycles and preserving roughly $1.4 million in annual savings per 100-bed equivalent each year. In short, today's off-the-shelf machine-learning tools already catch about nine out of 10 risky claims; exotic deep-learning models can do slightly better, but only if you feed them a million-plus past claims—far beyond what a typical clinic keeps on file. Why Specialty Clinics Feel The Pain First Specialty clinics face a perfect storm of challenges: high-cost drugs, evolving payer policies and thousands of diagnosis and procedure codes that need to match up precisely. One denial can equal a $10,000 loss. If one ICD-10 or HCPCS code is wrong—or out of date—the claim can be denied. And those codes change every year. When those denials stack up, clinics risk closing programs or closing entirely Adding to the chaos is the fact that insurers now use their own AI models to reject claims—models that providers often can't inspect. That creates an urgent need for transparency and speed on the provider side. AI won't eliminate denials entirely. But it can help level the playing field. A Five-Step Framework For AI Denial Management To help providers operationalize AI safely and effectively, I recommend this implementation roadmap: 1. Readiness Audit: Start by cataloging your denial categories, A/R metrics and critical claim data (CPT, HCPCS, ICD-10, prior-auth status). Dirty or missing 837/835 data accounts for up to 40% of predictive model errors. 2. Model Selection: Start simple. Boosted-tree models offer strong early performance and are interpretable—so your billing staff can see why a claim is flagged. Avoid 'black box' AI in healthcare. 3. Pilot And Back-Testing: Test the model in 'shadow mode' on a subset of claims for at least 90 days. Target a ≥20% precision lift before considering deployment. 4. Workflow Integration: Embed denial predictions directly into your EHR or billing software. Don't send billers hunting. Route high-risk claims to seasoned billers for pre-submission scrub or immediate appeal. 5. Continuous Governance: Retrain models quarterly. Audit for bias. Document your oversight. States like Connecticut are already drafting legislation that could require human review of AI-influenced decisions. Be ready. Ethics, Transparency And The Provider's Role Let me be clear: AI should not be used to deny care. Our mission at Neunetix is to protect access to care by helping providers recover revenue they've rightfully earned. That means appealing denials—not rubber-stamping them. This distinction matters. A 2025 AMA survey found that 60% of physicians worry AI could be used to systematically deny needed treatment. Transparency, override logs and clinician review should be standard practice. Done right, AI isn't a barrier to care—it's a lifeline for providers. The Bottom Line Specialty clinics don't have time to waste. With denial rates rising and payer rules getting harder to track, it's no longer optional to adopt smarter tools. It's essential. The clinics that embrace AI now—not as a magic fix, but as a smart assistant—will recover faster, protect more patient care and outperform peers still stuck in reactive mode. Knowing why you're getting denied isn't enough if you can't act on it quickly. And if your systems aren't talking to each other, neither are your dollars. Forbes Business Council is the foremost growth and networking organization for business owners and leaders. Do I qualify?
TechCrunch
43 minutes ago
- TechCrunch
In just 3 months, CoreWeave CEO, once a crypto-mining bro, becomes a deca-billionaire
CoreWeave co-founder and CEO Michael Intrator's net worth has skyrocketed to about $10 billion in the three months since the AI firm went public, Bloomberg reports. His company's debut was both the biggest tech IPO so far of 2025 – raising $1.5 billion – and also somewhat of a clunker: its founders had reportedly hoped to raise a lot more – up to $4 billion – and had to skinny their ambitions. CoreWeave still feels a bit like both a success and a house of cards. It offers AI training and inference cloud services built upon a growing stockpile of Nvidia GPUs. One of its investors is Nvidia, which helps it obtain the precious, short-in-supply chips. CoreWeave has both Microsoft and OpenAI as customers – the latter signed a deal to buy $12 billion worth of services and still has about $11 billion worth to buy. And Nvidia increased its stake after the IPO, the company disclosed. But CoreWeave borrows money against the GPUs to pay for them – and its IPO wasn't big enough to get it out of that cycle. It's got about $8.8 billion worth of debt as of March, it disclosed, with interest rates as high as 15%. Even though it brought in almost $1 billion in revenue in Q1 alone ($985 million), it recorded a net loss of about $315 million. That has not scared away investors who remain eager for ways to make money on AI. CoreWeave's stock has soared almost 300% since its March IPO, raising Intrator's net worth to above $10 billion, Bloomberg calculates. But the wildest part of Intrator's history, as well as that of his co-founders Brian Venturo and Brannin McBee, is that the whole thing started out as a make-money-quick, crypto mining enterprise when their previous company, a hedge fund, failed. Techcrunch event Save $200+ on your TechCrunch All Stage pass Build smarter. Scale faster. Connect deeper. Join visionaries from Precursor Ventures, NEA, Index Ventures, Underscore VC, and beyond for a day packed with strategies, workshops, and meaningful connections. Save $200+ on your TechCrunch All Stage pass Build smarter. Scale faster. Connect deeper. Join visionaries from Precursor Ventures, NEA, Index Ventures, Underscore VC, and beyond for a day packed with strategies, workshops, and meaningful connections. Boston, MA | REGISTER NOW The business partners went from a closet full of GPUs to thousands of them in a New Jersey warehouse, to an AI training experiment with an open source LLM group, EleutherAI, Venturo previously told TechCrunch. Today, the company is servicing the biggest LLM players on the planet, reportedly seeking to buy its competitor Core Scientific, and the founders are billionaires. And, as we previously reported, it's not all paper money. All three founders pocketed over $150 million apiece by cashing out of shares ahead of the IPO. CoreWeave remains a symbol of the AI industry in 2025: Massive, fast-growing revenue, investor enthusiasm built on an insatiable need for more resources. Coreweave declined additional comment.
Entrepreneur
44 minutes ago
- Entrepreneur
What Every B2B Brand Should Be Doing to Earn Trust in 2025
These four strategies help brands show up more authentically and earn buyer confidence where it matters most. Opinions expressed by Entrepreneur contributors are their own. Your average decision-maker's inbox is overflowing with so-called "thought leadership." In fact, a recent analysis of over 8,000 long-form LinkedIn posts revealed that more than half were likely written by AI. Buyers are becoming adept at spotting this; separate research shows that 50% of them will stop reading the moment a post feels machine-generated. This has created a market where getting noticed is easy, but earning trust is incredibly difficult. For businesses with complex sales cycles involving legal, finance and IT departments, genuine trust is the ultimate competitive advantage. Related: 5 Ways to Build Brand Customer Trust (and Why It Matters More Than Ever Before) Let AI handle speed, not strategy AI helps us move fast. It flags competitor news before it trends and gives us testing data while most people are still on their first coffee. But speed is only helpful if you know when to slow down. AI can't tell you when your messaging no longer hits or when your audience is losing interest. That takes human judgment and experience. While adopting AI is necessary for any business to remain competitive, this same technology is not equipped to handle the nuanced decisions that require genuine human experience and understanding. AI won't catch when your message stops landing or when your market starts to shift under you. That's your job as a leader. These decisions fall to those whose insights are shaped by firsthand experience from taking late-night support calls and persuading skeptical investors. When automation is used correctly (to increase efficiency and manage repetitive work), it provides the speed businesses need while leaving the vital work of creating a persuasive narrative to talented humans. Lead with the founder's voice Big contracts may pass successfully through legal and finance, but they still begin with a gut-level "yes" from one real person. Buyers need to see a face they can call when, say, the integration freezes at 2:00 a.m. or a new privacy law turns the plan upside down. They won't get that reassurance from glossy pitch decks alone. It comes through small, personal signals like a founder who writes (or edits) her own LinkedIn posts instead of farming it out, records a quick 5-minute voice note for an industry podcast because the topic can't wait or hops into a conversation thread to admit a misstep and explain the fix. Those moments prove there's a human who will stay accountable even after the contract is signed. When leaders show up in their own words, they clear the unspoken bar every buying committee sets: Can we trust these people when things get messy? If you're able to clear that bar early, the rest of the approval chain starts moving a lot faster. Related: What Do Modern B2B Customers Want? It's More Complex Than You Think Earned media builds trust and appears where it counts Being featured in a respected publication is a powerful shortcut to building credibility. It works on three levels: journalists vet your claims for prospects, search engines amplify your reach, and decision-makers view the coverage as a stamp of approval. Recent research further confirms this, with 67% of B2B leaders stating that features in trade media directly shape their brand's reputation. My own agency experienced this firsthand. After an article about us was published, demo requests doubled. The bigger shift, however, was tonal. The majority of our leads began conversations with questions about implementation and internal team integration, and they never doubted our viability because a trusted source had already vouched for us. Tell one story, everywhere When people bump into your brand, they should feel as if they're hearing the same voice finish the same sentence. That kind of repetition is what branding is all about. The more often a buyer sees the same core promise in different places, the faster "Who are these folks?" turns into "I know these folks" and, eventually, "I trust these people." That same powerful story must present what problems you solve, why you matter in your vertical and how you prove results. Then, apply that message consistently everywhere. For example, a LinkedIn post might carry the conversational version, a press quote can feature a compelling statistic and your website can support it with case studies or "As Seen In" logos. While tailoring the delivery for the audience is important, the central theme must never shift. When all public touchpoints reinforce the same idea, visibility solidifies into credibility, and prospects are not left wondering if they're seeing three different companies in three different places. Related: How to Maintain Brand Authenticity in an Increasingly Skeptical World As marketing tools like language models get slicker and growth hacks continue to promise the world, it's crucial to remember that slow-earned trust that convinces a skeptical buyer to say yes can never be replicated nor fabricated. In a year's time, the noise will have faded. But the gap between being seen and being trusted is where the next generation of market leaders will find their edge.
