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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.

Basis ActiveControl's best features are the audibility and the ability to control deployment. The governance control and compliance enforcement of Basis ActiveControl are important. We will not execute the transport management ourselves, as it is a key feature that helps us migrate between systems. It prevents missing or out-of-sequence transports, which is a beautiful feature we have used.

Basis ActiveControl is suited for our SAP Basis administration, SAP release managers, the DevOps team, SAP change managers, and SAP project managers. Basis ActiveControl is particularly useful for organizations with multiple SAP teams, systems, and frequent transportation activity.

Basis ActiveControl is a SaaS platform and a modern, cloud-enabled solution offering a marketplace that helps us with transportation management, such as moving transports safely and preventing missing or out-of-sequence transports. It also helps with release management, coordinating large SAP releases, and automating approvals. It assists with governance, quality, S/4HANA migration, multi-system management, and real-life scenarios.

In my day-to-day work with Basis ActiveControl, it helps with deployment and has governance that we use before deploying to a higher environment.

Basis ActiveControl has positively impacted my organization as there is no data loss. We can conduct our transportation quickly and effectively without the loss of any data packets since using Basis ActiveControl.

Basis ActiveControl is performing well across all environments. More DevOps integrations could be added.

We have been using Basis ActiveControl for the last six months.

Basis ActiveControl is stable.

Basis ActiveControl's scalability is quite good. It did not show any lag in terms of scaling.

Customer support for Basis ActiveControl is good. I would rate the customer support of Basis ActiveControl a ten.

We have not used any other solution before Basis ActiveControl.

There is a customization requirement for Basis ActiveControl. A complete enterprise may require tailoring workflows to match the internal governance model.

There is a great return on investment from Basis ActiveControl. We can save time in transportation, which is seamless. It is also integrating well, which helps it sync across all places.

The integrations with the SAP landscape require planning and licensing costs for Basis ActiveControl. It is generally more suitable for medium to large enterprises, not small SAP environments.