CloudBees announced the integration of CloudBees’ continuous delivery and release orchestration solution, CloudBees CD/RO, with Argo Rollouts.
Today, DevOps teams and organizations are increasingly looking to implement tools that can streamline various processes to run more efficiently with less error. Of these tools rising in popularity, artificial intelligence (AI) and automation are two that continue to see implementation. In 2022, 95% of DevOps teams have already implemented, or have plans to implement AI into their DevOps while 97% of organizations believe that business process automation is crucial for digital transformation. It is easy to see why these numbers are so high - AI and automation can take on tasks that may be tedious and time consuming for humans. By implementing these tools into existing DevOps features, DevOps teams and organizations can effectively achieve more while operating with less, allowing employees to use their bandwidth in furthering strategic and innovative business goals. The implementation of these tools into DevOps processes is not quick or mindless but does feature a simple approach that is classified through four phases and three core principles. After all, when adopting these features into DevOps, you are not sprinting, you are running a marathon, so prepare and act accordingly.
Each of these four phases, discover, plan, execute and scale, marks an important step on the road to successful AI and automation adoption. While each DevOps team and organization is unique, the four phases lay a foundation that any team can use as benchmarks on this journey of digital transformation.
Phase 1: Discover Where You Stand
Prior to digging into the technical nit and grit of the AI and automation implementation into DevOps processes, DevOps teams and organizations must first understand the larger picture of their current process and technology standing.
Where are we today?
What are our goals?
Where in the organization has the highest potential for impact by implementing these features?
Analyzing these questions will help teams compile a baseline of the areas that can benefit the most from the AI and automation integration. These areas often include testing, monitoring, and deployment, which, by nature, are business-critical tasks that have traditionally been fulfilled manually, using up valuable time and resources. By identifying these areas, DevOps teams can focus their efforts and resources on the initiatives that will have the greatest overall impact.
Finally, teams should build a framework that defines how AI and automation can be implemented across projects, so processes do not have to be reinvented along the way.
Phase 2: Planning for Adoption
Once you have a clear understanding of the current state of your DevOps processes and goals for implementation, the next phase in AI and automation adoption is developing a plan to reach that goal. During this step, DevOps teams should determine aspects of the project, such as a designated timeframe and the personnel that will take part. Additionally, questions should be asked about what success will look like and what metrics or key performance indicators (KPI) will be in use. This step in the process is when executive buy-in should come so they can ensure the whole organization gets behind the plan.
Phase 3: Execute on Your Plan
Upon determining a path forward for adoption, teams can begin phase three: execution. Teams should identify and select the first high-potential project that will feature AI and automation integration, such as testing, for example. For many years, companies relied on employees to configure and run manual tests, a tedious and long-winded task. Naturally, AI and automation can greatly improve this process.
The next step of execution is to identify and create roles for the people working in the project. For example, manual testers' jobs may change to fit alongside automated testing. Once completed, it is time to start testing the new methods to see what works and what does not based on the plans initially created. It is important to keep in mind that this is a process of experimentation and iteration, meaning teams will likely encounter setbacks or challenges along the path to success.
Phase 4: Scale from Your Proven Model
After iterating the process and finding success with AI and automation adoption, the final step of scaling comes into play. DevOps organizations can now take this proven model and scale it to the rest of their processes, or where applicable within different departments. With the right team and operation in place, businesses should be able to replicate the process faster and easier.
3 Core Principles: Measure, Collaborate, Optimize
Throughout the entire process, it is important to abide by the three core principles of measuring, collaborating and optimizing. When the process begins, it is crucial to constantly measure what you do and learn from it as you go. That's the only way you can refine your plans and improve upon them.
Additionally, it is important to not operate in a silo. Involving others in your organization and making sure to communicate the progress, including your successes and failures, allows for easier optimization and understanding in the long run.
Adopting AI and automation into your DevOps processes is no small feat and should not be treated as such. Taking the time to evaluate and plan will set your teams up for success once implementation occurs. After all, it's a marathon, so act accordingly.
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