01
The edge is now: how enterprises are future-proofing with AI
Infrastructure intelligence: scaling enterprise AI at the edge
Sponsored by
Commercial Feature
The edge is now: how enterprises are future-proofing with AI
Rather than accepting the limitations of cloud approaches to AI, forward-thinking enterprises are bringing intelligence right to the network edge
Duncan Jefferies
In association with
Duncan Jefferies
In association with
Like a genie in a bottle, AI is often confined by the cloud, unable to work its magic where it's needed most: in-store, on the factory floor, and across other sites at the network edge.
Despite this fact, many enterprises still default to cloud approaches to AI. This creates a range of challenges, including increased costs, latency and bandwidth constraints that slow down decision-making, and outage and security issues. Soon, the truth becomes clear: not all applications can or should be run from the cloud.
That includes many AI applications that rely on computer vision or enable smart manufacturing to deliver results. Because no matter whether enterprises are trying to detect quality issues on a production line or deploy an advanced loss prevention solution, every round-trip data makes to the cloud is a drag on performance.
Putting AI at the network edge alleviates this issue. AI applications running outside of the cloud or data centre can respond at the speed needed for rapid decision-making and seamless customer experiences. In addition, the cost of embracing AI applications across multiple sites and lines of business is greatly reduced.
Jeff Ready, CEO and co-founder of Scale Computing, draws a comparison to playing a resource-intensive video game on the public cloud 24/7 for a year. He calculates that this could cost around $33,000 due to data movement and processing fees. “Any modern game is going to max out the GPU pretty much the entire time you're playing, and it’s going to hammer the CPU as well,” he says. “That's not unlike an AI application.”
Playing the same game remotely on a local network on local hardware, such as a MacBook and PS5, which is essentially “an AMD computer with AMD CPU and GPU”, only requires the player to pay once for their hardware to play as much as they like.
In effect, the same principle applies when deploying and running AI applications: once you have the right infrastructure at the network edge, you’re no longer beholden to cloud data processing and storage costs.
Transforming industries
When data is processed and stored at the network edge, organisations gain faster insights, reduce latency, and improve reliability for critical applications, enabling real-time decision-making and innovation closer to where services are delivered.
It enables retailers to take full advantage of cutting-edge technologies like AI promotions, self-service kiosks, and smart shelves. The latter uses computer vision and weight sensor data to automatically track inventory, sending real-time alerts when products need replenishing: the kind of innovative solution that could offer a critical advantage in today's fast-paced retail environment.
In fact, automated and integrated edge infrastructure supports a range of high-volume, latency-sensitive AI workloads. The combination of computer vision and cameras on manufacturing assembly lines enables the detection of defective products. With edge infrastructure, there’s no need to send video feeds to the cloud for processing. Instead, everything happens locally.
One of the biggest advantages of modern edge platforms is their ability to run traditional enterprise applications alongside AI workloads without creating operational complexity.
The option to deploy AI applications on the same edge infrastructure that supports point-of-sale systems, inventory management, or manufacturing control systems means that organisations can easily experiment with AI and scale initiatives incrementally.
In other words, there’s no need to extensively overhaul or adapt existing infrastructure to take advantage of the technology.
Scaling without complexity
To fully unleash AI at the edge, enterprises must consider not just where data is processed, but how apps are deployed and managed at scale.
In fact, AI innovations are unlikely to deliver the desired results without a comprehensive approach to infrastructure and app management – one that encompasses everything from physical hardware like servers, GPUs, and storage, to system health, application delivery, and updates.
Traditional approaches that require manual infrastructure installations at every site can lead to delays in deploying AI, while also making operational issues more complex to address.
Scale Computing achieves this by bringing together virtualisation, servers, storage and backup/disaster recovery into a single solution, and packaging it with a powerful fleet management tool for monitoring and managing clusters. The result is a highly efficient edge infrastructure that’s also easy to install and maintain.
SC//Fleet Manager’s zero-touch provisioning automates the setup process. Instead of sending technicians to each location, enterprises can ship pre-configured hardware that automatically connects to the centralised management.
However, “with zero-touch deployment of infrastructure and the ability to preconfigure everything in SC//Fleet Manager, Scale Computing’s tool for monitoring, managing, and maintaining edge infrastructure, you radically lower the cost of getting that infrastructure out there.” This effectively bridges the gap between AI applications and the expertise needed to get them up and running at the network edge.
This effectively bridges the gap between AI applications and the expertise needed to get them up and running at the network edge.
“If you've got a thousand stores and you find a new application that needs infrastructure, you might spin up a project and send out guys in white panel vans to install servers with GPUs in them a thousand times,” says Ready.
“There's going to be BIOS updates and security patches for the OS, which you’re also going to have to do thousands of times, and so on and so forth. So the cost of getting that application out there and maintaining it is extremely high.”
Deploying applications at the edge ultimately unlocks all kinds of new opportunities for using AI. Organisations can get new applications up and running in hours rather than weeks. Lower operating costs make niche but potentially powerful AI applications economically viable for the first time.
“The cloud was a different place to build and integrate applications – and you could do it much faster,” says Ready. “[In fact], 95% of the apps in the cloud today weren't migrated there — they were built in the cloud to begin with. And this is exactly what we see happening at the edge.”
In other words, when it comes to unleashing the power of AI, the edge is where the magic happens.
Four ways to unlock the 'art of the possible’ for edge AI applications
With the right infrastructure, enterprises can effortlessly deploy, monitor, and scale AI applications at the network edge
AI offers enterprises countless ways to improve operations and gain a competitive advantage. However, when AI applications are deployed in the cloud, cost, speed, and scalability issues often reduce their effectiveness.
The solution lies in moving beyond traditional cloud-based approaches to embrace self-healing, fully integrated edge infrastructure. This enables fast, flexible, and cost-effective deployment of critical AI apps right where they are needed most: in-store, in warehouses, on the factory floor, and across a wide range of other critical sites.
Here are three key benefits that illustrate the power of this approach:
01
Deploy new applications in hours, not weeks
With traditional approaches to edge infrastructure, hundreds or even thousands of site visits may be needed to deploy cloud-based AI apps at scale. Consider a retail chain that wants to use computer vision for loss prevention across 1,000 stores as an example.
Historically, this would involve manual hardware installations, individual application setups, and extensive testing at each location. Deploying a single app across every site could therefore take weeks, months, or even years.
Deployment on a fully integrated and supported edge platform completely disrupts this timeline, however. Pre-configured hardware is shipped directly to locations and store operators simply need to plug it in to get everything up and running.
Scale Computing’s new Zero-Touch Application Deployment release will enable IT teams to effortlessly deploy AI applications to one, some, or all of these sites with the click of a button.
Zero-touch Application Deployment also helps developers to write apps faster, and allows people who would normally never be involved in coding to contribute to them. This creates the conditions for an explosion of AI innovation and creativity across the enterprise.
The most successful experiments can be deployed across multiple sites immediately, matching the speed of business change rather than traditional app deployment timelines.
As Jeff Ready, CEO and co-founder of Scale Computing, says: “The same ease of use, the same ease of deployment that Scale Computing offers in terms of deploying infrastructure, we are now bringing to actual applications and application management.”
02
Run legacy and next-generation workloads side-by-side
One of the most significant barriers to AI adoption is the fear that it will disrupt existing mission-critical applications. Enterprises are wary of the challenges involved in integrating AI into complex and somewhat creaky legacy systems, as well as the business risks associated with running essential software alongside data-intensive AI workloads.
Fully-integrated edge platforms address these concerns by supporting diverse workload types on a unified infrastructure at the network edge.
Virtualisation means that the same hardware that runs point-of-sale systems, inventory management, or manufacturing controls can simultaneously support computer vision, predictive analytics, and other AI applications, all without disruption or excessive complexity.
In other words, enterprises can mix and match old and new applications on the same infrastructure, creating a future-proof environment that can be scaled up or down as needed.
This unlocks a huge range of possibilities. For example, a hospitality company could deploy facial recognition for contactless check-in across hundreds of hotel locations in next to no time, and run it alongside existing property management systems and guest services applications, without the need for specialised installation expertise.
03
Benefiting from AI without scaling IT teams
A unified platform with zero-touch provisioning (ZTP) allows enterprises to amplify their AI capabilities without big increases in staff overheads. It eliminates the need for IT staff to be present at edge installations, which significantly reduces the costs and lead time for adding or replacing hardware at the edge.
There’s also no need to have IT personnel on-site to run sophisticated AI applications at the network edge, as teams can remotely automate the deployment, monitoring, and maintenance of AI apps across hundreds or even thousands of sites. In fact, a self-healing platform like Scale Computing’s can monitor and automatically fix certain issues before they become problematic, reducing the need for IT intervention.
One retailer initially deployed Scale Computing’s infrastructure to enable a loss prevention application, but the platform’s flexibility and ease-of-use enabled rapid expansion of AI at the network edge. Within nine months, they had deployed 31 new applications, as employees discovered further opportunities for AI-driven improvements in operations as employees discovered further opportunities for AI-driven improvements in operations
“They embraced AI-assisted application development and found people in the organisation—store managers, operations people, logistics—who understood ways to make the store more efficient, sell more product, make it more profitable,” says Ready. “In a few years, they could have hundreds or thousands of applications deployed at the edge, each one incrementally making a store better.”
04
Unlocking the art of the possible
Unlocking the art of the possible with edge AI isn’t just about deploying artificial intelligence: it’s about reimagining what is achievable when infrastructure enables rather than constrains innovation.
When app deployment happens in hours rather than weeks, when legacy systems and AI workloads play well together, and scaling doesn’t require proportional increases in complexity or staff overheads, organisations can gain maximum impact from AI with minimal hassle and discover a new world of possibilities for this game-changing technology.
Scale smarter: the cost-effective path to edge AI
To scale edge AI without spiralling costs, enterprises must unify infrastructure, automate management, and streamline operations.
For enterprise firms exploring AI at the edge, scale and cost are two sides of the same coin.
Adding more infrastructure to every site isn’t sustainable, especially with cloud budgets ballooning and IT teams already stretched.
IDC analysis indicates that while edge computing can reduce data transmission and storage costs, the scale and scope of edge projects can quickly escalate expenses.
Factors contributing to these costs include setting up infrastructure, integrating edge deployments, and managing distributed systems without centralised IT support.
A unified platform can help reduce total cost of ownership (TCO) while delivering reliable performance and simple, remote manageability.
From the ability to run multiple workloads on a single system to automated updates and predictive auto-scaling, organisations can do more with less – and do it smarter.
“It’s important to ensure the best value from both an economic and environmental perspective,” says Mike Hoy, CTO at UK edge data centre firm, Pulsant.
“If every organisation went alone, hardware, people, skills, carbon all come at a high price. Utilising shared or unified platforms relieve that burden and typically provide higher performance or efficiency than a mid-size organisation can achieve alone.”
Consolidating systems and processes
Unified systems can reduce TCO by minimising hardware and manual oversight by removing duplicate hardware silos and the manual effort associated with maintaining them.
“Instead of buying, racking, and patching separate boxes for logs, metrics, voice, video, or AI, you now have a single unified compute platform which manages telemetry collection, pre-processing, model inference, and analytics. Organisations no longer need multiple specialised appliances,” explains Patrick Ehlen, chief scientist at Voice Brain, which uses AI at the edge to analyse group communications using voice data for the emergency services, customer service, and healthcare industries.
Ehlen highlights that SC//Platform offers a unified software system that collapses multiple toolchains into a single behavioural-intelligence engine. This engine ingests telemetry at 5G scale and performs real-time analytics, anomaly detection, and decision tracing — all within one storage pool and license.
A flexible API layer then permits integration of the same intelligence service into any application, agent, or workflow without custom connectors.
Because SC//Platform operates as a managed service within your private cloud, it auto-scales and self-heals automatically, removing the need for manual capacity planning around GPU nodes and pipeline bottlenecks.
“By consolidating hardware, software, and operational workflows into a single platform, organisations can operate with fewer devices and reduced staffing, alongside fewer integration projects and predictable billing, delivering a noticeably lower total cost of ownership.”
Streamlining costs
Similarly, automation and remote management can also eliminate expensive site visits. The platform’s built-in health checks and automated scaling keep system performance running without anyone being involved, while telemetry-driven anomaly alerts only flag genuine issues, so engineers are dispatched solely when it matters.
“People efficiency is one of the biggest drivers for automation, with the added benefit of reducing human error. Look at what happened when we created the automated production line,” explains Hoy.
“Not only does it improve efficiency – and sustainability – by avoiding costly travel, but it also means multiple people can work on multiple systems, pooling capability and providing a better service,” he says. “Also, more automation means more devices and more data. Having a mindset geared towards making site visits simply doesn’t scale.”
Moving beyond cloud computing
At the same time, edge AI is being pitched as a viable alternative to the cloud, through its ability to lower costs and improve performance.
“Edge AI complements cloud computing by executing inference tasks directly where data is generated, reducing latency and avoiding excessive cloud ingress and egress fees,” says Nick Ewing, managing director of Efficiency IT, a specialist in data center, consultancy, and engineering.
“This hybrid approach not only enhances performance but also offers greater cost control, leading many enterprises to repatriate workloads from cloud-only environments.
Additionally, in low-connectivity environments, edge nodes continue running full AI pipelines locally. This avoids costly failover, mitigating both operational risk and expense.
Finally, unified tooling consolidates telemetry collection, analytics, compliance and remote management into one integrated stack.
Organisations no longer require multiple cloud services, each with separate licences, dashboards, and support teams, thereby simplifying administration and reducing overhead.
“Collectively, these factors enable edge AI to match or exceed the real-world performance of pure-cloud models whilst delivering tighter cost control and a significantly lower total cost of ownership,” he says.
For enterprise firms exploring AI at the edge, a unified edge AI platform can offer a smarter path forward by lowering TCO while ensuring consistent performance and simplified remote management.
With capabilities like multi-workload support, automated updates, and predictive auto-scaling, organisations can maximise resources, minimise complexity, and scale intelligently – arguably doing more with less, and doing it better.
The edge AI setup checklist
Edge AI promises huge gains — but without the right foundations, deployments fail. This checklist helps CIOs and CTOs build scalable, secure, and future-ready solutions.
Edge AI holds massive potential for enterprises, especially across sectors such as retail, healthcare, maritime, manufacturing, and hospitality.
This is because it can enable real-time insights, reduce latency, cut bandwidth costs, and support privacy by keeping data local.
But without the right foundations, deployments can stall or even fail outright. These include robust infrastructure, a secure and scalable deployment process, model governance, and integration with other enterprise systems.
If these are missing, edge AI can fail to deliver its promised value, and enterprises may end up with a fragmented, costly, and underperforming solution.
CIOs and CTOs must therefore assess their organisation’s readiness and prioritise what’s needed to make AI at the edge a scalable, safe, and cost-effective success.
Define business objectives
Firstly, it’s critical you identify specific business outcomes for edge AI – such as real-time analytics for manufacturing, predictive maintenance, or autonomous retail.
“Ensure alignment with organisational goals by documenting two or three high- impact use cases with measurable KPIs – for example, a 20% reduction in downtime, or 15% cost savings,” suggests Ron Westfall, practice lead at analyst firm, HyperFRAME Research.
Engage stakeholders
Once you have identified those business goals, involve business units, IT, and operations teams to validate use cases and ensure cross-functional buy-in. This can include scheduling workshops and forums with key stakeholders to align on priorities and objectives.
Transparent and predictable costs
One mistake enterprises can make is focusing on AI software, but neglecting costs like hardware, maintenance, connectivity, and edge-specific support. Projects can then become unsustainable or cost-prohibitive over time, even if technically successful.
Martin Butler, professor of digital transformation at Vlerick Business School, says you should consider: “Have you modelled the total cost of ownership (TCO), including hardware, energy, maintenance, interfaces, security, privacy, and scale, and is the cost structure sustainable in the long term?”
At the same time, he adds: “Can your infrastructure run traditional workloads and AI models side-by-side and scale without requiring significant IT investments or new headcount?”
Power and space constraints
Next, confirm edge locations have adequate power, cooling, and physical space for hardware. Here you can audit edge sites for environmental suitability and plan upgrades if needed.
Also, do you have tools to remotely deploy, monitor, and manage infrastructure across all locations from a single interface, even across hundreds or thousands of sites?
Built-in resilience and autonomy
If edge systems fail to operate reliably during network outages, the consequences can be serious, costly, and even dangerous, depending on the industry. Therefore, you must ensure your edge systems can operate during outages or without on-site support and can recover from hardware or software failures automatically.
Security and compliance at the edge
Edge devices often collect and process sensitive, real-time, mission-critical information, like patient records, financial transactions, operational telemetry, or video surveillance. If not properly secured, data can be intercepted, manipulated, or stolen.
Is your edge infrastructure equipped to handle data securely, comply with relevant regulations, and protect against emerging cyber threats to the same extent as centralised processing?
“This is especially relevant in edge AI due to data being processed outside the data centre where governance is usually well managed, says Butler.
Achieving and maintaining compliance
Edge AI deployments must meet Industry-specific standards.
“Here, you should consult legal or compliance teams to map requirements and complete a compliance gap analysis,” says Westfall.
Similarly, establish policies for data collection, storage, and processing at the edge to comply with regulations (for example, HIPAA). Compose a data governance framework, including retention and anonymisation policies.
Data availability and quality
The effectiveness of AI, both during training and inference, depends directly on the quality of the data it’s exposed to. So, ensuring edge devices generate sufficient, high-quality data for AI model training and inference is critical.
“Profile edge data sources for volume, variety, and veracity; address gaps with synthetic data or additional sensors,” suggests Westfall.
Performance monitoring and management
Performance monitoring and management are crucial for edge AI systems because they ensure that the systems operate reliably, efficiently, and safely in real-world, distributed environments.
Here, you can use telemetry to track latency, accuracy, and resource usage during the pilot. Assess and implement monitoring tools and analyse pilot data weekly, says Westfall.
Compatibility and interoperability
If edge AI operates in isolation, without integration into ERP, EHR, supply chain, or other core systems, valuable insights can get trapped at the edge and deliver no value to the broader business.
“Is your platform designed to work across diverse hardware architectures, sensor types, and software ecosystems, and is it flexible enough to evolve with emerging AI standards?” says Butler.
Similarly, he adds: “It’s early days for AI standards. Consider the issues of compatibility and interoperability. Edge AI often requires specialised hardware (for example, GPUs, TPUs, or FPGAs), along with software, sensors, APIs… [they] need to be compatible,” says Butler.
This 10-point checklist will help ensure that your platform is a solid foundation that AI needs to thrive, without compromising uptime, flexibility or future potential.