Digital.ai

Digital.ai Release is used primarily to manage and automate the release process across software environments. Instead of having developers, testers, and operation teams coordinating every deployment manually, the platform brings those activities together and makes the release process more organized. When a new application version is ready, a release is created in Digital.ai Release to identify the different steps that need to happen before it reaches production. That can include getting approvals, running tests, deploying to a development or staging environment, and checking that everything is working properly before moving the release to production.

Digital.ai Release serves as the release pipelines and deployment automation platform, which ensures that some steps occur consistently each time. This is useful because team members should not follow different processes from another team, especially when dealing with critical applications in banks. An important part is the risk registration where Digital.ai Release coordinates activities across different tools and teams so we can see where areas are, what has already been completed, and whether something is holding it up. For example, if an approval is still pending or a deployment step fails, we can identify that without having to contact different teams for updates.

Using Digital.ai Release to automate and organize the release process has changed the team's day-to-day work significantly. Initially, there were no release pipelines or software to provide that capability. Now the different stages of release can be identified and ensured to happen in the right order, from testing and approvals through a planned deployment.

The best features of Digital.ai Release include release registration, whereby it brings activities from different teams and tools into one release process. Instead of developers, testers, and operations working separately, we can see how everything fits together and where it is currently sitting in one place. Another valuable feature is deployment automation. There has been a reduction in the amount of manual work involved in deployment. Once the required conditions are met, deployment steps can be triggered automatically, which makes the releases faster and more consistent. Another feature is approval and governance control. Approval steps can be built into the release workflow, so important changes do not reach production without the right people reviewing them.

Deployment automation has made the biggest difference for the team and has positively impacted us because it reduces the amount of manual work involved in deployments. Once we meet the conditions, deployment steps can be triggered automatically.

Digital.ai Release has positively impacted the organization mainly by making the release process more organized and reducing the amount of manual coordination needed. Before, releases could involve a lot of back-and-forth between development, testing, operations, and people responsible for approvals. Now the workflow is clearly defined in the platform so everyone can see what needs to happen and where the release stands. It has also helped make the deployments more consistent, and the same steps can be followed each time, which reduces the chance of someone missing an important step or making a manual mistake.

A few challenges have been encountered, and from my experience, Digital.ai Release should improve the user interface. It has a lot of functionality, but some areas can be a little too complicated compared to others, especially when setting up more advanced release flows. A cleaner and more intuitive interface would make it easier to get started and begin using it. I would also like to see stronger reporting and analytics to help track releases, progress, and have ready-to-use reports around deployment success rates. Another area is integration, as Digital.ai Release connects with many DevOps tools, but some integrations can require configurations and maintenance, making the work hectic. If those connections could be made more straightforward, it would save time.

Digital.ai Release has been used for one year.

Digital.ai Release is stable from my experience.

Digital.ai Release's scalability works well because of the number of users on the application. As the team grows, we continue to manage them through Digital.ai Release. We do not have to create a completely different process when adding another application or team. We can create separate workflows for different applications and environments while still keeping the overall process consistent.

The experience with customer support for Digital.ai Release has been generally good. I have interacted with technical support, and when we have issues around resource workflows or deployment configurations, the support team usually helps us understand what is causing the problem and how to fix it.

I did not previously use a different solution before Digital.ai Release.

Other options were evaluated before choosing Digital.ai Release. It was compared with tools like Jenkins, GitLab, and Octopus Deploy. We wanted release registration, automation, integration, and approved workflows. Some other tools were good, but we looked for something that could handle the broader release process across our teams, not just run deployment jobs. Digital.ai Release stood out because of its release registration, approval controls, integrations, and ability to coordinate comprehensive releases.

The impact of adopting Digital.ai Release has been quite measurable. The average time spent coordinating releases has been reduced by about seventy percent, mainly because approvals, deployment steps, and status tracking are handled through the workflow instead of manual follow-ups. Deployment time has also come down by roughly thirty percent, resulting in about a twenty-five percent reduction in deployment-related errors because the same automated steps are performed consistently.

A return on investment has been seen. It is estimated that we save around four to six hours of manual coordination per release. We also saw a roughly thirty percent reduction in deployment time and about a twenty-five percent reduction in deployment-related errors. This has helped us allocate less time to fixing deployment problems and more on development and other important tasks.

In terms of pricing, Digital.ai Release is more of an enterprise-level investment. It is not a low-cost tool, and the actual cost depends on factors such as the number of users, deployment needs, and whether we want to integrate other Digital.ai products. For us, the important aspect is not just the license price but what we get back from it. We have seen a good return on investment in terms of managing a lot of business automation, reducing our manual work and deployment efforts while providing good value for the money we pay.

When using Digital.ai Release, I would rate it a nine out of ten overall.

I chose nine out of ten for Digital.ai Release because if they can improve its downsides, it takes some time to learn. There are a lot of configuration options, and setting up release workflows can be challenging, especially for someone new to the platform. If the user interface could also be simpler, particularly when managing approvals and complex release pipelines, it would be a ten out of ten.

Regarding Digital.ai Release's AI capabilities, I rate its governance and security ten out of ten. Digital.ai Release is useful because security can be built into the release process instead of being treated as something we check at the end. We can include security scans, testing, and comprehensive checks as part of the release workflow before it goes into production. On the AI side, it helps us identify patterns and potential problems in the release processes, such as repeated deployment failures. I see any issue more as decision support to make important production decisions.

Based on my experience, the accuracy and reliability of Digital.ai Release's output are quite high. The value gained is mainly in helping us spot patterns, identify possible issues, and make better decisions around the release. For instance, if the system sees repeated deployment failures or unusual patterns, it can help us focus on areas that need attention. However, we still have to validate important findings before taking action, especially for production releases, as there can be false alerts that could introduce risk, but it remains reliable.

My advice for others looking into using Digital.ai Release is first to look at the current release process and identify where the most time is being lost. Do not just buy the platform because you want automation. Rather, it is important to know which approvals, testing steps, or handoffs you want to improve. I also recommend starting with one or two pilot applications instead of trying to move every resource onto the platform at once. This approach gives the team time to build workflows and test integrations before making adjustments. My overall rating for Digital.ai Release is nine out of ten.

My primary use case for Digital.ai Release at the bank is to support our production deployments, and I find it an easy tool to use for release and orchestration.

A specific example of how I use Digital.ai Release in a deployment scenario is that we are using it to deploy our applications on production, and one of its strengths is the ability to integrate with other tools, such as Jenkins and Argo CD.

Another aspect of my main use case for Digital.ai Release day-to-day is the ability to use the go or no-go process through the tool with ServiceNow, which allows us to verify the change request and ensure all required approvals are in place, which is important in a bank because our ecosystem is critical.

The best features Digital.ai Release offers in my experience include using it alongside Jenkins, Argo CD, and ServiceNow.

The integration with ServiceNow works well because you cannot deploy some applications on production without a change request in ServiceNow, and Digital.ai Release integrates with ServiceNow to verify all these points for us.

Digital.ai Release has made my work easier and simpler by automating many actions, so I do not have to verify too many points; the tool does it automatically.

Digital.ai Release can be improved by reducing the number of tasks that need to be completed before starting a release, as there are too many tasks to get done.

The interface of Digital.ai Release can be improved as well because on first use, you will feel a bit lost, so we could work on the interface to make it easier to navigate.

I started using Digital.ai Release in my first month, so I have been using it for two years.

In terms of Digital.ai Release's accuracy and reliability of output, I find it all to be good.

Digital.ai Release is deployed in our organization as both public and private, depending on the application.

When I started working at BNP, they had already been using Digital.ai Release, so I do not know how it was before, but it has made our work easier and more simple.

I chose a rating of 8 out of 10 because I tried another tool, and to be honest, I think that tool is easier.

My advice for others looking into using Digital.ai Release is that you need to do some research about the tool to understand what you can do with it and what you cannot do with it, as well as to see some reviews on the internet. I would rate this product an 8 out of 10.

My main use case for Digital.ai Release is test management and automated testing solution. A specific example of how I use Digital.ai Release for test management and automated testing is that we have three brands which have their own websites and mobile apps, Android and iOS. We currently use Digital.ai's mobile manual testing capability to perform testing on our devices and for our applications across all three brands. We also conduct cross-browser, accessibility, and analytics testing using Digital.ai. This is the core of my main use case.

There are other solutions being experimented with at the moment in other departments like SaaS platforms and data center solutions, but my main use case is centered on testing and automation.

Since we started using the manual testing module from Digital.ai, I have seen a positive impact on my organization as our test management has improved significantly. We are now able to gather all of our test cases in a single repository and run our test cases across multiple teams through the same tool. We are also able to generate standard reports which anyone can easily access, giving us a better view of what we are testing and a better view of our code and quality coverage.

The improvements in Digital.ai Release have definitely affected my team's efficiency and productivity, with time saving being a major impact we have seen as part of this implementation of the tool. Previously, we used Confluence and Excel spreadsheets for our test case management and tried a handful of commercial tools, but they did not work for us. With the introduction of Digital.ai manual testing, we are now able to seamlessly integrate our existing automated test pipelines and our existing build pipelines, as well as our documentation tools, so that the data from Digital.ai manual testing tool seamlessly flows through other tools and we can see the coverage of our code and our test cases. The entire team is now using the tool, collaborating through the same repository, and we are creating our test cases and getting our test case coverage increased faster than previously. Standard test cases are available, and people are simply executing them and sharing links for the execution rather than downloading reports and creating PDF files to send across, resulting in significant time saved in planning, managing, and executing test cases. The major advantage for us is the visibility of our code test coverage and the reusability of our test cases.

In my experience, the best features Digital.ai Release offers are that the automated testing capability for Digital.ai is limited, but the manual testing capability is very good. What makes the manual testing capability stand out for me is that with Digital.ai's manual testing, we get access to a bunch of virtual devices hosted across various regions, allowing us to easily conduct cross-browser, cross-platform testing. They are readily available for us, and all those mobile devices more or less work like physical devices for us with capabilities of testing hybrid applications, native applications, or web view applications. They have facilities like biometrics and sensors, and we also have the capability of Apple Pay and Google Pay testing. We also have access to logs so we can look at network logs and digital logs to see what is happening on those devices. When we test it manually, everything seems to work seamlessly, but with automated testing, it is hit and miss.

Digital.ai Release definitely has good automation capabilities, but as I mentioned, it is hit and miss. It has CI/CD integrations, but the major part which is missing is that the AI capabilities are not that mature yet, which is the reason we only use it for manual testing at the moment. Although we do have capability and teams available to experiment with Digital.ai, we are leaning towards in-house open source tools which we have implemented to conduct mobile automated testing.

Digital.ai Release could be improved, particularly because the automated test capabilities are not that strong compared to some major tools like UIPath, Tosca, or SmartBear TestComplete. Finding elements, the list of assertions, or the usage of AI to automatically generate test cases based on requirements did not work entirely flawlessly for us, and we have found issues with these features.

Regarding Digital.ai Release's AI capabilities, we have not explored a lot regarding governance and security, as we have only explored mobile manual test management and automated test management. There are definitely other areas where Digital.ai Release could improve, such as workflow management, which UIPath has very good capability for. There is also support for desktop test automation, which I think can definitely be improved. Speed-wise, if you compare Digital.ai's automated testing solution with Cypress or Playwright on web applications, they are very fast and run cross-browser with parallel test execution. I think those aspects can be improved in Digital.ai test automation, along with better integration with tools. I have not seen Jira integration, but other commercial tools have extensive integration with CI/CD pipelines, documentation tools, defect management tools, and test management tools. There are also integrations with Slack, Google Chat, and emails—areas where Digital.ai can also improve.

I have been using Digital.ai Release for the last six to eight months.

Digital.ai Release is stable, but there are definitely improvements we can make.

Digital.ai Release's scalability is very good, as we can add any number of users and expand it organization-wide or to a handful of teams. I have not seen any problems with scaling out the manual testing solution, but with automated testing, we might have limitations when we run with a lot of parallel pipelines, although we have not explored that yet.

The customer support for Digital.ai Release has been good. Last time, our finance team and our infrastructure team reached out to their team members, and they responded within a few hours. Even though we are based in Australia and there might be delays because of time zone differences, we have had good responses. For the few instances we did reach out, I think we were responded to quickly.

Previously, we used a test management tool called Testmo, which was not a market leader but definitely a commercial tool. It did not help us due to its lack of AI capabilities, poor integration with Jira, and clumsy execution capabilities. Sharing test cases and results was also cumbersome. We were looking for a solution that could seamlessly integrate with the various tools we were using and make it easy to share reports with our stakeholders, which is why we switched to Digital.ai Release.

I have seen a return on investment with regard to the mobile testing capability we have used. For every release, our QAs used to spend around four to five hours on test management, reporting, execution, and release. Now that we have introduced this tool, we are saving at least one or two hours for each release. This means four to five hours saved for one QA on each release, and with multiple QAs doing multiple releases across our three or four different brands, we are saving days within a week.

The pricing, setup cost, and licensing for Digital.ai Release are a little expensive when I look at it, especially the enterprise-level licenses. However, since I have not worked with licensing directly and our finance team has handled that, I cannot comment a lot on that aspect.

Before choosing Digital.ai Release, we evaluated Qtest for test management and also looked at Zephyr and Xray. However, those tools were purely test management tools. Digital.ai was the only available option that combined manual testing and automated testing capabilities together, but as I mentioned, the automated testing solution did not work fully for us as we expected, which is why we currently only use it for manual testing.

The mobile manual testing capabilities are very good, while the test automation capabilities can be improved. My advice to others looking into using Digital.ai Release is to not expect a lot of new features in the tools. Digital.ai Release can be comparable with other commercial tools available in the market, but since it is a new tool, it might not have all the important and exhaustive capabilities that other tools have. Start with an open mind, assuming that it is a new tool in the market with minimum capabilities for typical manual testing and automated testing for mobile devices. However, I do think you can expect the tool to work well from the start, at least for manual testing. It is good that you are taking surveys from customers, as more surveys could lead to more capabilities and feedback, which could improve your tool. On a scale of one to ten, I would rate Digital.ai Release overall around six.

I use Digital.ai Release for one of my clients to run Ansible templates in multiple workflows, so we create release automation and use the release script for any Azure resources management.

For one of my clients, I use Digital.ai Release to create an Azure Landing Zone, resource group, and default subnets for multiple layers such as app, data, private endpoint, and presentation, by creating separate templates in Digital.ai that allow us to create the resource group as one template and different resources like app services and Azure SQL databases with multiple templates. We create a release to run those templates by providing input values, and Digital.ai Release is able to integrate multiple pieces, especially with Azure, Ansible, Jira, and other tools within the organization.

Digital.ai Release is primarily used for this purpose and some additional functions related to managing Azure through Ansible.

Digital.ai Release offers the best features such as good integration between multiple tools and flexible options for usage; while I have not configured the template myself, I use existing templates and find that anything can be accomplished using the workflows and integrations.

The integrations I have seen currently include Ansible, Jira, email communication, and ServiceNow, which all really help to complete the full workflow.

Digital.ai Release has positively impacted the organization I currently work for at a very high level, mainly because it has allowed us to automate many tasks that previously required manual intervention; for example, creating a resource group and all these subnets manually would have taken one to two days, whereas now it is completed within a minute using the automated processes.

As I do not work much on the configuration of the templates because I am just a consumer, I do not see much improvement required, as it looks good to me.

I am not certain about further improvements since I have not used the template settings; however, I feel that it could be more user-friendly, as the UI feels a bit old and not very appealing. Additionally, to see the logs is challenging because we need to open multiple windows, and it does not display in full screen, which could definitely be improved.

I have been using Digital.ai Release for more than a year.

Digital.ai Release looks good and is suitable for anyone who wants automation.

Regarding Digital.ai Release's AI capabilities, I find its governance and security to be good and really helpful for multiple integrations, as all OAuth is handled through SSO and connects to ServiceNow and Jira in an authenticated way; I believe it looks good and I do not see any issues with the capabilities related to governance and security.

In terms of Digital.ai Release's AI capabilities, I find its accuracy and reliability of output to be good and reliable, and I do not see any issues.

I rate Digital.ai Release seven out of ten, considering the fact that the user interface could be improved.

My main use case for Digital.ai Release is to release Azure-related cloud resources like Azure Key Vault and Application Insights to support any cloud integration on the Azure side.

A specific example of how I use Digital.ai Release for one of those Azure resources is that we normally do an annual release to update the certificate for the Azure Key Vault because the certificate expires every year, so we use Digital.ai Release in combination with Jenkins and Terraform to release the new certificate.

In addition to that, I use Digital.ai Release for most Azure resources we use to support our API like Azure Key Vault and Application Insights, application registration, and Redis Cache.

Digital.ai Release has impacted my organization positively because almost all the teams that handle cloud resources related to Azure are using Digital.ai Release in combination with Jenkins. It has become a standard way for us to release cloud-related resources, although we also use Microsoft Azure DevOps for other code releases. For Azure-related resources, this has become the standardized way.

The best features that Digital.ai Release offers are that through the template, I can view the different phases of my release, so everything is streamlined when I use Digital.ai Release, and the integration with Jenkins is very good.

The integration between Digital.ai Release and Jenkins is seamless. If there are any issues and anything goes wrong for a particular environment, I will see a red flag from Digital.ai Release. From there, I am able to have a link which leads me to the log file of Jenkins to view the details about the release, which is very convenient.

I appreciate the way I can create the template using standard artifacts. I have a section for Terraform and a section to define my release using the YAML file, and it is standardized.

Since using Digital.ai Release, one of the benefits is standardizing the way I release to my Azure environment. I could manually do everything, but that is very error-prone, and everybody might do it differently. By following Digital.ai Release, I am following the naming convention already by using a certain configuration file with variables. The best part is standardizing things, which in the long term will help me reduce costs and improve efficiency.

To improve Digital.ai Release, I think the user interface could be improved. For example, I have a plan phase before my build phase, and sometimes the toggle button is hidden. I have to toggle it before the step can be executed, or it will be skipped. Many people who did not use Digital.ai Release before do not even know there is a toggle button, and the first time when they run into that phase, they will definitely skip that step.

Regarding needed improvements, I did not do extensive reading on documentation or training material directly from Digital.ai Release. My knowledge comes from the team who has been using it. However, I would appreciate standardized training material that would give me hands-on experience.

I have been using Digital.ai Release for four to five years.

Digital.ai Release's scalability seems to be adequate, but I do not think we have done anything challenging in terms of capacity for the framework since we are only releasing a few cloud resources at a time, so we might never run into a bottleneck.

Customer support is good, and we did not run into any issues directly with Digital.ai Release's customer support because we have a release team to help us with Digital.ai Release. If we have any issues, we work with that team directly.

Before choosing Digital.ai Release, we changed many different vendors for release management over the years, but Digital.ai Release is definitely the choice for releasing cloud-related resources.

I did not think we used anything else before Digital.ai Release because this has been the standard way of releasing cloud resources from the beginning, especially since we have team members who had this experience to help us establish the framework.

Since using Digital.ai Release, one of the benefits is standardizing the way I release to my Azure environment. I could manually do everything, but that is very error-prone, and everybody might do it differently. By following Digital.ai Release, I am following the naming convention already by using a certain configuration file with variables. The best part is standardizing things, which in the long term will help me reduce costs and improve efficiency.

My advice to others looking into using Digital.ai Release is that it seems very flexible. I understand we are using Digital.ai Release's Jenkins integration, and for the Jenkins component, potentially I could switch to other solutions. It seems to me it is a flexible framework to release cloud-based resources, so it is a good option for this purpose.

I would like to see more AI capability in Digital.ai Release because AI has improved our productivity in different areas of our daily working environment. When we do development using Copilot, I see improvements when we use the cloud to help us in our development. However, in Digital.ai Release, since I am not a frequent user, I do not see much integration with AI yet, and that is an area where I would like to see further development.

I would rate this review as a nine out of ten.