Digital.ai

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.

My main use case for Digital.ai Release involves using Jenkins, XL Deploy, and XL Release. Jenkins typically handles the build, XL Deploy maintains all information related to the packages, such as their location, the latest builds, and which packages are on each server, while XL Release performs the actual release to the servers.

The best features that Digital.ai Release offers are the custom pipelines and the ease of customizing different releases in the same pipeline, especially with different user interactions. We have infrastructure team members who follow the pipeline while we, as customers, approve different roles or different responsibilities within the same pipeline, with one person doing approval and another doing the deployment part, which is valuable from my perspective.

Involving both infrastructure and application teams in the same pipeline has genuinely helped my process, as we have one specific person starting the pipeline, another approving it, and another coordinating as DevOps or monitoring all processes from the infrastructure side, providing excellent assistance because we have different and clearly separated responsibilities.

Digital.ai Release has positively impacted my organization by making the process easier than using a highly customizable Jenkins that is difficult to manage. Since everything was already in place when I started working with this company, I cannot easily articulate what the specific benefits to the company are. However, I can say that I previously worked with a fully customized Jenkins alone, and this approach is significantly easier and more maintainable.

Sometimes it can be tedious when fulfilling multiple different packages with their key and value pairs. If we make any mistakes in the second step and return to the first screen, everything is erased, so there is no way to return to the first screen and start again with the same package or make small updates or changes to the initial screen before restarting the process.

Digital.ai Release is very helpful in saving time, but I wish there was the possibility to use the API as a release manager to automate pre-filling the pipelines.

If we had an API that could be used on the user side, similar to the one in JIRA where we can create a personal token without granting full access to Digital.ai Release, I could have my script automate the process instead of fulfilling the template field by field, which would be excellent.

I chose eight out of ten because I see it is a nice tool. I do not know it in depth to give a higher or lower rating at this point, but for my use and role, I appreciate the parts of the tool I am familiar with. We could have some small enhancements, such as being able to go back to the first screen while keeping everything filled in, but beyond that, I do not have any other suggestions.

I have been using Digital.ai Release for around one year.

Digital.ai Release is very stable from my perspective.

I do not have enough information about the scalability of Digital.ai Release. I am just a user, and I do not even know how many different users are currently using the solution.

I had previously used different solutions, including GitHub, GitLab, and Jenkins, specifically its first version named Hudson. However, in this company, I have only used XL Release and XL Deploy.

My advice for others looking into using Digital.ai Release is that it is a nice solution for corporate companies. It is particularly valuable when you have different roles interacting in the pipeline, and it provides an easy way to connect with other applications such as Jenkins and servers including Linux, Control-M, and others.

As a user from a corporate company, the details of the configuration, possibilities, and features such as the mentioned AI capabilities are somewhat closed off to me. I would appreciate knowing more, and I wonder if you have any demo account or something I could use for a trial period. I also have a small company with solutions in AWS using Python and different Linux servers, Kubernetes, and similar technologies, and I would like to try to see how Digital.ai Release could be useful for my personal company.

I gave this review a rating of eight out of ten.

It helps with creating documentation, release processes, deploying to lower environments, scheduling meetings, and sending emails to stakeholders. The goal is to reduce manual work and save time.

We start by creating documentation for all these tasks. Initially, this includes all the details of the artifacts and the database. Then, the developer updates these details.

We perform the deployments using Digital.ai Release. What we do is input the artifact information, and it handles all the deployments in the lower environments, usually within a minute or up to five minutes.

This way, we avoid the need to use Jenkins to locate the artifacts and then deploy them, which would take much more time. Here, we simply input all the artifact details once, and later, we only need to update the artifact version for deployment. This significantly reduces our time.

Moreover, once the deployment is completed, Digital.ai Release automatically sends an email notification stating that the deployment is complete, and we can begin testing. So, that's how it works efficiently for us.

I like it because previously we had to manually create documentation, and deployment also would take much time.

Also, for higher environment deployments, we had to create tickets for other teams. That time is also reduced because the manual work has tremendously decreased. We just have to click one button, and it will create everything for us.

It's crucial in cases like deployment errors. Digital.ai allows rollback to previous versions, enhancing our ability to recover quickly.

For higher environments, what we do is roll back to the previous version using Jenkins if there's an issue.

There are many areas of improvement. Currently, we put artifact details manually. What we could improve, in our case, is the deployment instruction base. Developers input all the information, including which artifact and where it needs to be deployed. What Digital.ai could do is automatically go to the deployment instruction page, take those artifact details, and implement them. This way, there would be no need to manually input the details.

It is a stable product. I would rate the stability a seven out of ten because sometimes it gives errors and doesn't work properly. Whenever it happens, it gives a headache because we have to hand over the deployment processes to the other team. That time we have to do things manually.

In our current setup, there isn't an option for auto-scaling. We have a fixed capacity, so there's no need to adjust it on the fly.

We've pre-calculated the capacity needed and proceed with deployments based on that. There really isn't an option to increase capacity as needed.

Regarding Digital.ai, you have to make automation a priority. You can integrate multiple tools with it, which you can use for various automation. However, it's not easy; you can't just get the software and start using it from day one.

You have to learn how to use YAML files, how to integrate other applications, and how to create different tasks, like deployments, Jira tickets, or sending emails. There's a lot to learn, so you have to understand the process as well.

I would recommend it. Overall, I would rate the solution a seven out of ten.