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.

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.

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.

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.

Some of the best features of Digital.ai Release include release orchestration, automated workflows, and reusable pipeline templates. It also provides dashboards to track release status and identify bottlenecks.

Digital.ai Release standardizes the release process across teams. Managing multiple scripts or goals, everything can be orchestrated in a single pipeline.

Digital.ai Release coordinates the deployment and ensures consistent releases. Automation ensures that every deployment follows a standardized workflow. Digital.ai Release has improved our release reliability.

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.

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.

The features I find most valuable in Digital.ai Release are the integration with MS Teams, because we have MS Teams channels that publish or push notifications to that. When we start deployments, it sends a notification to the people that we are doing a deployment to their environment. It notifies them when the deployment is started, completed, or if attention is required.

I also appreciate the fact that it has plugins for Bamboo and I use lots of Gradle and JSON scripts, and we do SQL upgrades as well, triggering Flyway scripts via Bamboo, along with the integration with XLD and Jira; it's all Atlassian software.

Regarding environment management capabilities, Digital.ai Release is mostly useful for me, as it is more application related and that is managed via my XLD dictionary. We have one artifact that is environment agnostic, which has placeholders that correspond to my XLD keys and values, and at deployment time, it substitutes the placeholders with those environment specific values. We don't need to make a specific deployment artifact for dev, test, or production; it is all the same artifact using environment variables, ensuring what we take to production is what was tested.

The solution can apply one template across multiple applications for email approvals or orchestrations. 

The load time is good and can handle anything we are doing. 

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.

The most valuable feature of Digital.ai Release is its ability to communicate with various deployment systems, such as XLD and batch deployments, as well as integrate with tools, such as Flyway and Bamboo. We use Bamboo as our build orchestrator, and Digital.ai Release also integrates with Jira, another Atlassian solution. These capabilities make it a powerful tool for managing workflow, test automation, and other processes.

The ability for all these systems to work cohesively together is the most valuable feature.

The orchestration from our perspective was nice because right now, we're having to manually launch deployment jobs out of Jenkins. The orchestration, building the release, and then just executing it and managing that pipeline — the orchestration capabilities are great for that.

It had the built-in metrics that we did use in selling to the executives. The metrics were good. It retained build-information so we didn't have to go back through it and dig out some old records to figure out what we've done — that was a really nice feature. 

From my perspective, I don't know about the entire team, just automating the backfill off of the production deployment was one benefit.