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5 Predictions for Edge Computing and Virtualization in 2025

It’s a new year, and with it, we’ve brought together some of our resident product experts and technical evangelists here at Scale Computing to share their insights and offer their forecasts on what trends might shape the edge computing, hyperconverged infrastructure (HCI), and virtualization market in the year ahead.

1. Frustrations with VMware Mount, Pushing IT Leaders to Seek Modern Alternatives

2024 proved to be a turbulent time for IT as VMware customers faced mounting pressures from licensing complexities, escalating costs, and restrictive product bundles. Broadcom’s acquisition-driven focus on short-term profitability – raising prices, cutting R&D, and changing products to focus on profit and not customer needs – continues to alienate long-time customers and partners. These factors forced some organizations to make hasty decisions to mitigate immediate financial and operational burdens, while others, constrained by resource or timing limitations, began planning longer-term strategies to transition away from VMware.

This dissatisfaction represents more than a momentary shift; it marks the early stages of a broader exodus. Broadcom’s attempts to repair relationships by recalibrating its approach may come too late, as the IT community has grown skeptical of its intentions and more confident in pursuing alternative solutions that emphasize flexibility, innovation, and customer-first priorities.

The growing discontent has not only opened the door for VMware competitors but also stimulated the search for disruptive technologies and operational models. These shifts are likely to drive long-term changes in virtualization, including greater adoption of open-source hypervisors, edge-optimized platforms, and hybrid-cloud solutions.

2. Edge Computing Will Fuel the Next Generation of AI Innovation in the Enterprise

In 2025, edge computing will become a foundational element of AI, shifting how companies collect, process, and analyze data. As AI applications grow more sophisticated and data-intensive, relying solely on cloud-based architectures will prove cost-prohibitive for many organizations. Likewise, the rapid scaling of data from AI applications requires immense computational resources, leading companies to turn to multi-layered edge infrastructures where data processing and storage can occur closer to the point of collection. For industries such as retail, healthcare, manufacturing, and transportation, this edge-centric approach addresses both financial and operational challenges associated with cloud usage, as companies need to ensure predictable costs while enhancing AI performance.

The unpredictability of cloud costs will continue to be a significant driver in the shift toward edge-based AI processing. Cloud providers often charge based on data transfer, storage, and compute time, all of which scale dramatically with AI’s high CPU and GPU demands. By processing data locally at the edge, organizations can mitigate the volatility of fluctuating costs and reduce their dependency on long-term cloud storage and computation. Furthermore, edge processing enables real-time data analysis, which is becoming increasingly critical in industries like healthcare, manufacturing, and retail.

In retail, for instance, edge computing allows stores to leverage AI for real-time inventory management, customer behavior analysis, and even security monitoring without incurring excessive cloud costs. Similarly, in manufacturing, where IoT devices and sensors monitor equipment health, edge-based processing can enable predictive maintenance while reducing latency and dependence on the cloud.

Looking ahead, 2025 will likely see increased investment in multi-layered edge networks that can dynamically support AI workloads. With edge computing addressing both the operational and financial demands of AI applications, a broad cross-section of industries is poised to adopt more robust edge solutions, transforming edge-based infrastructure into a critical enabler of AI-driven innovation.

3. Scalable Solutions at the Edge: ROBOs Will Embrace Containerized Deployments in 2025

In 2025, containerized solutions will become indispensable for remote office/branch office (ROBO) deployments as edge computing takes on a more prominent role in the distributed enterprise. As organizations seek scalable, agile application deployment across numerous locations, containers will provide the ideal lightweight, portable execution environment needed to meet these goals. By encapsulating applications and their dependencies into self-contained units, containerization allows developers and IT teams to create consistent, reliable deployment packages that work seamlessly at the edge. This approach is particularly valuable for distributed enterprises that require rapid deployment, simplified maintenance, and minimal resource overhead across multiple sites.

The success of containerization at the edge parallels that of the public cloud model, which popularized streamlined, application-first approaches to software deployment. Concepts like continuous integration and continuous deployment (CI/CD) and DevOps practices shifted focus from infrastructure management to application-centric development. By enabling developers to package applications in universal formats that operate the same way across development, testing, and production environments, containerization simplifies the otherwise complex deployment process.

Industries are already seeing these benefits play out in their edge deployments. In retail, containers allow stores to run localized applications for inventory tracking, customer insights, and real-time analytics, helping reduce reliance on cloud connectivity. Similarly, in healthcare, containerized applications are being deployed in clinical environments to support diagnostics and data processing directly at the point of care. Manufacturing plants, meanwhile, are using containers to standardize and automate processes across facilities, enabling quick updates and reducing the need for specialized infrastructure in each location.

Additionally, containerized solutions provide a universal framework that mitigates the complexities of managing edge sites with varying architectures. Edge operators can build multi-architecture packages that support a variety of CPU architectures, reducing compatibility issues and allowing organizations to optimize deployments for cost and performance. Furthermore, the layered nature of container images minimizes data transfer needs, as only updated layers are downloaded during deployment, addressing the challenges of limited or costly connectivity often faced at the edge.

With standardized observability and monitoring capabilities, containers offer ROBO environments a robust mechanism for maintaining application health and functionality across all sites and 2025 will see organizations continue to embrace containerized solutions as they look to improve the resilience and agility of their edge environments.

4. IaC and Kubernetes Bring Cloud-Like Simplicity to the Edge in 2025

In 2025, edge computing will mature to offer the same simplicity, flexibility, and agility traditionally associated with today’s popular public cloud platforms, transforming how organizations deploy low-latency, localized applications. With advancements in edge infrastructure and management practices, developers will increasingly be able to build and manage edge applications as easily as they do in the public cloud, paving the way for innovative services in industries that demand real-time data processing and minimal latency.

A critical enabler of this transformation is the adoption of Infrastructure as Code (IaC) principles at the edge. By extending IaC practices to edge environments, DevOps teams can leverage automated, version-controlled deployments for remote infrastructure, streamlining configuration and reducing deployment times. This approach enhances consistency, simplifies management, and allows for rapid scaling, even in highly distributed environments. As a result, edge computing is evolving to mirror the programmability and efficiency that made cloud platforms so successful, empowering organizations to maintain consistency across diverse environments without introducing further complexity.

Kubernetes is also gaining traction as a key technology that enables edge orchestration. As more enterprises seek to deploy applications at the edge, Kubernetes provides the scalability, reliability, and orchestration capabilities needed to manage distributed workloads. While challenging to implement, Kubernetes at the edge offers a unified platform for containerized applications, allowing organizations to standardize deployments and streamline management across both cloud and edge infrastructures.

Together, the principles of IaC and Kubernetes orchestration are set to transform the edge from a fragmented set of isolated nodes into a cohesive, cloud-like environment. By combining automation with robust orchestration, edge computing will support a growing range of applications that demand low latency, including IoT, augmented reality, and autonomous systems.

5. Edge Computing Fuels the Expansion of Computer Vision Beyond Retail in 2025

In 2025, edge computing will usher in a variety of innovative use cases for computer vision, driving adoption across industries beyond its retail origins. The ability to process visual data locally at the edge enables real-time decision-making and enhances operational efficiency in scenarios where latency and bandwidth constraints would otherwise hinder AI applications. While 2024 marked a turning point for computer vision in retail – powering innovations like automated checkout, personalized shopping experiences, and loss prevention – we believe 2025 will see this technology flourish in sectors such as healthcare, logistics, and manufacturing, where the demand for real-time insights is accelerating.

Healthcare providers are poised to leverage computer vision at the edge for applications like diagnostic imaging, patient monitoring, and surgical assistance. By processing visual data locally, hospitals and clinics can make faster, data-driven decisions, enhancing patient outcomes while maintaining compliance with stringent data privacy regulations. Similarly, logistics companies will deploy edge-enabled computer vision to optimize warehouse operations, track inventory in real time, and improve last-mile delivery efficiency. These capabilities reduce delays and errors, ensuring smoother supply chain operations in a world increasingly reliant on rapid, reliable deliveries.

Manufacturing will also benefit from the marriage of edge computing and computer vision, enabling advancements in quality control, predictive maintenance, and worker safety. Smart cameras integrated with edge infrastructure can improve the identification of defects on production lines, monitor equipment health, and detect potential hazards, all in real time. Across all these sectors, the shift to edge-powered computer vision reduces reliance on cloud infrastructure, making the technology more cost-effective and accessible for enterprises.

About Scale Computing 
Scale Computing is a leader in edge computing, virtualization, and hyperconverged solutions. Scale Computing HC3 software eliminates the need for traditional virtualization software, disaster recovery software, servers, and shared storage, replacing these with a fully integrated, highly available system for running applications. Using patented HyperCore™ technology, the HC3 self-healing platform automatically identifies, mitigates, and corrects infrastructure problems in real-time, enabling applications to achieve maximum uptime. When ease-of-use, high availability, and TCO matter, Scale Computing HC3 is the ideal infrastructure platform. Read what our customers have to say on Gartner Peer Insights, Spiceworks, TechValidate and TrustRadius.

About Version 2 Digital

Version 2 Digital is one of the most dynamic IT companies in Asia. The company distributes a wide range of IT products across various areas including cyber security, cloud, data protection, end points, infrastructures, system monitoring, storage, networking, business productivity and communication products.

Through an extensive network of channels, point of sales, resellers, and partnership companies, Version 2 offers quality products and services which are highly acclaimed in the market. Its customers cover a wide spectrum which include Global 1000 enterprises, regional listed companies, different vertical industries, public utilities, Government, a vast number of successful SMEs, and consumers in various Asian cities.

How Does UL 4600 Keep Autonomous Trucking Systems Safe?

The third edition of UL 4600 was released in 2023 to add specific requirements for the use case of autonomous trucking and to address changing industry trends.

Here, we explain what’s happening in the autonomous trucking industry, why UL 4600 is important for autonomous trucking specifically, and how static analysis helps to overcome challenges for this evolving technology.

Read on or jump ahead to the section that interests you most:

➡️ start Your Free Static Analysis Trial

While some just dream of fleets of autonomous trucks efficiently delivering goods across the country, others are already at work to ensure they can do so safely and resiliently. With the update to ANSI/UL 4600, the Standards for Safety for the Evaluation of Autonomous Products, Edition 3, in play, embedded software teams now have better safety guidance just as self-driving technology is ramping up to make shipping faster, more cost-effective, and more efficient in the face of driver shortages and rising transportation costs.

Autonomous trucking software is on the cusp of widespread adoption. To prepare for the inevitable transition from closed-course testing to widespread deployment, OEMs need to understand the current state of trucking automation and how to effectively implement software safety practices.

What Is Autonomous Trucking?

Autonomous or driverless trucks operate with minimal to no human input. Instead, autonomous trucks or autonomous tractor-trailers rely on sensors — usually combinations of cameras, LiDAR, and radar — to feed environmental data into algorithms and actuators that control the vehicle.

The Society of Automotive Engineers (SAE) have defined levels of driving automation that are adopted across the industry, which range from Level 0 (completely manual) to Level 5 (completely autonomous). While the lower levels with an automated driving system (ADAS) are already in play for autonomous trucks, software development teams will need to ensure functional safety for the higher autonomy levels, ideally Levels 4 (High Automation) and 5 (Full Automation) for the freight industry to benefit from autonomous trucking.

Autonomous Trucking Development Today

Trucking is the dominant mode of inland freight transportation in many countries, with several trucking companies competing to be the first to operationalize autonomous driving. Driverless technologies offer significant potential value for fleet operators: They can reduce operating costs, overcome driver shortages, and improve efficiency.

For example, driverless semi trucks (or lorries) can employ truck platooning more effectively than human drivers, where vehicles follow each other at the same speed to improve fuel economy and reduce their impact on traffic.

While there have been a few setbacks in recent years — for example, TuSimple, Navistar and UPS shut down their “Driver-out” self-driving truck system in 2023 and Waymo, Embark and Locomation are no longer actively developing autonomous trucks — there are many more new entrants working toward wide-scale deployment:

Traditional OEMs are nearing full operationalization, with trucks already being tested in North America. In partnership with Aurora, Volvo revealed its first production-ready autonomous truck in May, and Daimler Truck reported that its Freightliner Cascadia semi-trucks are meeting closed-course acceptance tests in October of 2024.

These announcements indicate that the automotive industry is driving autonomous trucking plans forward — so software development teams in automotive manufacturing will need to get familiar with the challenges, solutions, best practices, and compliance with UL 4600 to ensure they are prepared for these exciting advancements.

Trucking Automation Software Challenges

There are five main challenges for teams developing autonomous trucking software:

It’s hard to reuse autonomous software built for cars.

The development and testing of self-driving cars focus on short, low-speed routes with stop-and-go traffic. In contrast, autonomous trucks will operate on long-haul highway routes at higher speeds and will encounter less traffic, more variable terrain, and transitions between urban and rural roads.

More critically, a Class 8 driverless semi-truck has a gross vehicle weight eight times that of the average passenger vehicle — before loading its cargo. This means autonomous driving software has to account for a larger turn radius, longer stopping distances, and the presence of a trailer that can weigh as much as 14,000 U.S. pounds unloaded.

Software has to handle many use cases.

Trucking automation software must accommodate different vehicle classes, cargo conditions, and route types. While some fleets deliver consumer packaged goods within cities, most autonomous trucks are particularly good for long distances, which will need to be accounted for in the software. Still others transport hazardous, refrigerated, or liquid materials across international borders.

There is also the scalability factor: To build and test multiple branches of software to handle various scenarios at scale, developers should design systems to accommodate a wide range of input and control conditions before deploying them into real-world trucking environments.

Verification and validation require long highway driving.

Once acceptance tests are performed on closed-loop circuits, autonomous trucks must conduct road tests on highways, ideally for hundreds of miles across the country. That way, developers can ensure their autonomous trucks can cover the distances, runtime, and road conditions necessary for the long haul. The U.S. Department of Energy reports that the average semi-truck travels over 62,000 miles annually.

Security must be a top priority.

Similar to their automotive counterparts, driverless trucks must address the following security concerns:

  • Protect connected infrastructure and endpoints such that there is acommon baseline of trust between nodes.
  • Track and adapt to vulnerabilities that will continue to grow as malicious actors realize new opportunities to attack and destabilize critical trucking networks.
  • Secure the manufacturing supply chain with vendors who may not have had to deal with software security before.
  • Comply with cybersecurity regulations and best practices, such as ISO/SAE 21434.

Safety compliance must be accounted for.

Safety will be the key differentiator between manufacturers that make it to market and those stuck in verification and validation activities. Fully unmanned trucks operating at highway speeds present real concerns, including difficulty in handling unexpected situations and decision-making capabilities, in addition to more typical concerns about undefined behaviors and software malfunctions.

The challenges of developing safe trucking automation systems lie in their components. Everything from sensors to decision-making algorithms to vehicle motion control must be scrutinized. Given this complexity, manufacturers will find themselves relying on automated tools, like Perforce Static Analysis, to help with UL 4600 compliance.

Understanding UL 4600: Safety Principles and Processes for Autonomous Trucking

UL 4600 is the first safety standard designed specifically for autonomous and connected vehicles. Unlike a traditional UL satefy standard, UL 4600 takes a “safety case” approach with real-world applications in a specific environment — and the inclusion of autonomous trucks in UL 4600 Edition 3 includes trucking-specific examples. The standard helps developers build a safety case for carious aspects of system development and maintenance:

“It offers framework that leads designers of autonomous systems through the required thought process to ensure all possible complications have been considered. What are the safety questions that need to be considered in design? How do you think beyond design and for the lifecycle of the vehicle? Can quality and consistency be assured across manufacturers?

— Dr. David Steel, Executive Director of UL Standards & Engagement, ULSE, Inc.

The UL 4600 Edition 3 standard requires developers to follow a three-step approach for assessing and validating driverless truck safety:

  1. Make a measurable safety claim, where developers state how the autonomous truck should operate.
  2. Make an argument that proves the claim is true by describing the perception technologies and the systems that are triggered by them.
  3. Provide evidence that the system will perform as expected by providing simulation results, road test outcomes, and other proof that the autonomous truck will perform as stated.

The end result is a safety case arguing that an exceptionally robust combination of analysis, simulation, closed course testing, and public road testing have been performed — with evidence given — to ensure an appropriate level of system safety.

How Static Analysis Helps Achieve Autonomous Truck Safety

There is a specific requirement in UL 4600 for coding standard compliance, as the development process should be similar to that of IEC 61508 or ISO 26262, so developers should use static analysis to some degree and produce the results of source code analysis. Static code analyzers — like Perforce Helix QAC and Klocwork — support these goals by ensuring comprehensive code coverage and sufficient supporting evidence in these areas:

Amid the pressure of getting driverless trucks to market, these tools enable developers to focus on feature development rather than compliance activities.

About Perforce
The best run DevOps teams in the world choose Perforce. Perforce products are purpose-built to develop, build and maintain high-stakes applications. Companies can finally manage complexity, achieve speed without compromise, improve security and compliance, and run their DevOps toolchains with full integrity. With a global footprint spanning more than 80 countries and including over 75% of the Fortune 100, Perforce is trusted by the world’s leading brands to deliver solutions to even the toughest challenges. Accelerate technology delivery, with no shortcuts.

About Version 2 Digital

Version 2 Digital is one of the most dynamic IT companies in Asia. The company distributes a wide range of IT products across various areas including cyber security, cloud, data protection, end points, infrastructures, system monitoring, storage, networking, business productivity and communication products.

Through an extensive network of channels, point of sales, resellers, and partnership companies, Version 2 offers quality products and services which are highly acclaimed in the market. Its customers cover a wide spectrum which include Global 1000 enterprises, regional listed companies, different vertical industries, public utilities, Government, a vast number of successful SMEs, and consumers in various Asian cities.

GLPI: IT service management and its integration with Pandora FMS

GLPI is a free IT Service Management (ITSM) solution that allows you to manage assets, incidents and requests within an organization. It works as an incident tracking and service desk system, optimizing technical support and technological resources.

It also includes hardware and software inventory, contracts and licenses, offering a centralized view of the whole infrastructure. Its intuitive web interface and customization options ensure flexibility and scalability for businesses of any size.

Content:

 

What does it bring to your company?

  • IT asset management.
  • Follow-up and troubleshooting.
  • Technical support optimization.
  • Scalability: adaptable to companies of any size.

GLPI is ideal for organizations looking to improve the management of their technology resources, automate processes and optimize IT service management.

 

All the advantages of GLPI together with Pandora FMS

GLPI has an integration in Pandora FMS that some customers are already enjoying.

With it you may automate ticket creation, for which you may use a plugin that you may find in the library . This plugin allows integrating Pandora alerting of ticket creation in your GLPI environment through the rest API available to the service.

Each time an alert is executed and triggers the plugin, it opens a ticket in GLPI with information about the module that triggered the alert: agent, module data, IP address, timestamp and description of the module, with a title for the ticket, category, assignment group and priority, which may vary depending on the alert action.

Running the plugin with its parameters, in a configurable time interval, allows you to automate the whole process of creating a ticket that would normally be performed by a user. It is necessary to use credentials to authenticate with your environment (username and password or a token, which must be generated beforehand). The plugin configuration allows you to specify a title, description, priority, category, group, type of query. In addition, it will check whether there is already a ticket created with these features so that in case it is already created, it is only necessary to add its corresponding follow-up.

The plugin makes use of a parameter called “–recovery” that sets two different paths in its execution. If used, the plugin will check the status of the specified ticket and if it is not closed add a comment on it, if it is closed, it will not do anything else. If it is not used, the performance will be the same, but it will change if the ticket is closed or does not exist. If closed, it will create a new ticket. If it does not exist, it will create the ticket if there is a computer with the same name as the agent specified with the “–agent_name” parameter.

The plugin does not need additional dependencies for its use, since these are already incorporated. But it is necessary for in the GLPI environment to have rest api enabled, since the plugin makes use of it for ticket creation.

For that, access, in your GLPI environment, Setup → General

The “Enable Rest API” option must be enabled.
From that menu you may also enable whether you want to be able to authenticate with credentials, with tokens or both.

Once done, it will be possible to use the plugin, for that, it will be necessary to configure an alert command. By creating alert commands, you may specify and automate ticket creation.

This can be done from the alerts menu, in commands:

Enter a name, group, and the command, using as values for the parameters the macro _fieldx _, where x is the number of the parameter (they do not distinguish any order, each macro just needs to have a different number).

Once configured, you may configure the macro value below in the description fields.

 

About Version 2 Digital

Version 2 Digital is one of the most dynamic IT companies in Asia. The company distributes a wide range of IT products across various areas including cyber security, cloud, data protection, end points, infrastructures, system monitoring, storage, networking, business productivity and communication products.

Through an extensive network of channels, point of sales, resellers, and partnership companies, Version 2 offers quality products and services which are highly acclaimed in the market. Its customers cover a wide spectrum which include Global 1000 enterprises, regional listed companies, different vertical industries, public utilities, Government, a vast number of successful SMEs, and consumers in various Asian cities.

About PandoraFMS
Pandora FMS is a flexible monitoring system, capable of monitoring devices, infrastructures, applications, services and business processes.
Of course, one of the things that Pandora FMS can control is the hard disks of your computers.

Perforce Introduces AI Validation: Adaptive, Intelligent AI Testing for Enterprise Teams

Perfecto’s new AI Validation moves autonomous testing closer to reality through context-aware testing.

 

MINNEAPOLIS, Jan. 28, 2025 — Perforce Software, a DevOps company for global teams requiring speed, quality, security, and compliance at scale along the development life cycle, announces the launch of AI Validation, a new capability within its Perfecto continuous testing platform for web and mobile applications.  

Perfecto’s AI Validation completely changes the way organizations experience testing. Instead of creating multiple scripts and frameworks, which are cumbersome and do not scale on multiple platforms that require consistent digital experiences, AI Validation uses advanced artificial intelligence to validate applications visually and contextually and dynamically adapts to application changes without human intervention. Designed to address the increasing complexity of modern applications, this innovation empowers teams to deliver software faster while maintaining the highest levels of quality. 

While many testing solutions rely on AI co-pilots to simply create more scripts, Perforce’s AI uses natural language prompts and does not rely on objects or code, instead creating durable tests that work across platforms. This user-centric approach eliminates the need for specialized scripting knowledge, allowing anyone—regardless of skill set—to adopt and scale test automation quickly. By removing co-pilot complexity, Perforce moves towards autonomous testing, an AI-driven approach to testing that eliminates the need for human intervention.  

“The success that our early adopters have already experienced with AI Validation is a huge validator to our approach with testing,” said Stephen Feloney, Vice President, Product Management at Perforce. “Creating more frameworks and more code in co-pilot does not help testers do what they have always wanted: validate exactly what appears on the screen. This is what AI Validation provides them. Our early customers are already experiencing a reduction in their time by around 20%, but we anticipate this will be closer to 50% or more as we continue to innovate in this area.”

Perfecto client, Midwest Tape, a physical and digital media distributor, has already incorporated AI Validation into their testing strategy, reducing their overall testing time by 20%.

“AI Validation has proven to be extremely beneficial and critical for our testing processes, as it eliminates the dependency on traditional [object] locators,” said a QA Automation Lead at Midwest Tape. “Given that our application relies heavily on [object] locators, which can often be unreliable and prone to flakiness, the use of AI-driven validation significantly enhances stability and efficiency.”

Another client, Servus Credit Union, has also utilized AI Validation in their testing and looks forward to the growth potential it provides for their organization. “We are excited about where this can go,” said Byron Chan, Digital Delivery Quality Assurance Lead at Servus. “I see tremendous potential because eventually you could come up with test cases in this prompt format before development even starts, and then once developed/deployed, you could potentially avoid manual testing and automation test development because it’s already done.”

Perfecto’s AI Validation is tailored for enterprise teams navigating the complexities of multi-platform testing. Its seamless integration into CI/CD workflows enables continuous and scalable testing that evolves with the dynamic demands of agile and DevOps practices. By simplifying processes and enhancing adaptability, it empowers teams to maintain quality and speed across the development life cycles.

Whether validating a complex trending graph, bar chart, or a dynamic calendar view—across Android, iOS, and varied screen resolutions—AI Validation significantly improves quality, lowers maintenance, and reduces costs while fundamentally changing how testing is done.

KEY FEATURES OF AI VALIDATION

  • Dynamic Adaptability: Manually updating scripts or object locators whenever an application changes leads to frequent test failures and costly maintenance. Perfecto’s AI Validation inherently avoids this issue by eliminating reliance on fragile locators and script updates—so there is no need for continuous adjustments when the UI evolves. This ensures uninterrupted testing and significantly lowers costs.
  • Contextual Test Coverage: Unlike basic OCR-based solutions, Perfecto’s AI-driven approach verifies the meaning behind dynamic elements—charts, dashboards, or graphics—to ensure user experiences reflect the intended content. This deep level of coverage ensures thorough visual validation across all application layers.
  • Efficiency At Scale: Slow feedback loops and fragmented processes bog down agile and DevOps teams. AI Validation seamlessly integrates into CI/CD pipelines, accelerating releases and allowing teams to adapt quickly to any change in their development cycle with extensible SDKs.
  • Anyone Can Test: Test creation and maintenance demands specialized scripting skills, limiting participation to a few technical experts. AI Validation’s natural language prompts open testing to everyone on the team, expanding coverage while freeing specialists to tackle more complex challenges.

AI Validation represents a paradigm shift in testing, marking a new era of seamless innovation—and this milestone is just the beginning. Over the coming months, Perforce will unveil a series of transformative releases that promise to redefine industry standards and push the boundaries of continuous testing. Some of these capabilities will include autonomous testing, the simplification of test creation through low-code workflows and AI-guided suggestions, the ability to automatically adapt to real-time changes across platforms, AI-driven dashboards that pinpoint root causes for faster resolution, and the ability to continuously adapt to UI, data, or logic changes in real time, eliminating manual updates and ensuring your testing remains resilient and future-ready.

Visit www.perfecto.io to discover how AI Validation can simplify testing, enhance coverage, and accelerate delivery timelines. While there, request a custom demo to see the feature in action and experience the future of testing firsthand. Current Perfecto customers can unlock the power of AI Validation by contacting their system administrator to enable the feature for their workplace instance.

Additional Resources:

About Perforce
The best run DevOps teams in the world choose Perforce. Perforce products are purpose-built to develop, build and maintain high-stakes applications. Companies can finally manage complexity, achieve speed without compromise, improve security and compliance, and run their DevOps toolchains with full integrity. With a global footprint spanning more than 80 countries and including over 75% of the Fortune 100, Perforce is trusted by the world’s leading brands to deliver solutions to even the toughest challenges. Accelerate technology delivery, with no shortcuts.

About Version 2 Digital

Version 2 Digital is one of the most dynamic IT companies in Asia. The company distributes a wide range of IT products across various areas including cyber security, cloud, data protection, end points, infrastructures, system monitoring, storage, networking, business productivity and communication products.

Through an extensive network of channels, point of sales, resellers, and partnership companies, Version 2 offers quality products and services which are highly acclaimed in the market. Its customers cover a wide spectrum which include Global 1000 enterprises, regional listed companies, different vertical industries, public utilities, Government, a vast number of successful SMEs, and consumers in various Asian cities.

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