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Last Updated Nov 29, 2021 —

Increase velocity and reduce risk with AI and machine learning

Businesses have adopted Agile Data-Driven approaches that blend together with their digital transformations, but there is always room for improvement. Thus, the need for better AI and ML integration within their DevOps processes. 

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Artificial Intelligence (AI) and machine learning (ML) have proven useful in bringing new power to DevOps. This evolving technology allows DevOps to easily manage and monitor the software lifecycle while simultaneously simplifying workflows and the collaboration process. AI is extremely helpful in situations where there is an abundance of data generated or passing through a repeatable process. Machine learning in response can then identify the inherent characteristics which connect the data. 

As organizations grow and continue down the path of digital transformation, they are becoming better at applying the compiled data and analytics, resulting in added value for both the company and its customers. Throughout the years, businesses have adopted Agile Data-Driven approaches that blend together with their digital transformations, but there is always room for improvement. Thus, the need for better AI and ML integration within their DevOps processes. 

Trends in enterprise software delivery 

Despite the leaps and bounds made in change and DevOps management, trends are continually evolving in the enterprise software delivery industry.  

Technology research and consulting companies such as Gartner and Forrester have identified some of these upcoming key trends: 

  • Gartner: Unifying DevOps tools into platforms 
  • Forrester: Using data to improve business outcomes 

Gartner has recognized that in most environments today, there is a multitude of tools used throughout the entire DevOps lifecycle. As such, there is a movement among organizations to switch from disjointed toolchains to value stream delivery platforms. Currently, only about 10% of organizations today use integrated platforms, but Gartner predicts this will increase to 40% by 2023. Converting to such a platform can help solve a slew of issues, like increasing end-to-end visibility and avoiding the complications of integration. 

Forrester anticipates that adopting an integrated VSM will bring together business and development leaders to identify successful outcomes. Although many organizations have adopted Agile and/or DevOps, there is a phenomenon occurring where they are still struggling to realize the improved outcomes that these practices are supposed to achieve. Ultimately, there is a disconnect between outputs being migrated into production and the value generated. 

Change management is facing new challenges 

Some of the most common challenges organizations face are: 

  • Improving Productivity: How can we focus CAB teams on risky changes? 
  • Achieving High Reliability: What actions will mitigate the risk of a change-related outage? 
  • Automating IT Operations: Which low-risk changes can be automatically approved and deployed?  
  • Accelerating Innovation: How is change risk holding back the ability to increase change frequency? 
  • Improving Customer Experience: How can one identify change-related issues before customers find them? 

The key to addressing such challenges is to utilize the wealth of information that tends to be latent in most companies. Many businesses are sitting on mountains of data and unfortunately, a majority of them don’t use that data in a meaningful manner, thus the need for AI and machine learning. 

Approaching change and risk 

What can organizations do to improve? Assessing change management risks can help predict the likelihood of failure for a given change. Change failure prediction solutions use AI to analyze dozens of data points about historical changes in your company's service management system and various other sources. It then can identify not only the top risk factors but also which of these changes has the highest probability of failure. 

Evaluating the risk of changes all starts with the change itself. Lots of rich information regarding the change can be mined from the IT service management system regarding the change assignment group. This allows your teams to get answers to questions revolving around development, deployment, and customer experience like: 

  • How much time was involved in developing and testing the change? 
  • How much code was changed? 
  • How was the change integrated? 
  • How many bugs are identified? 

The release orchestration layer can pull all the data collected from the ML algorithms and tie it together into a unified and traceable process. With such a vast amount of information sitting dormant amongst your tools, it’s essential to utilize it properly in order to get the most value out of your DevOps and change management systems. 

Using the right change failure prediction tool 

Once the risk factors are established, Digital.ai Change Risk prediction uses AI to monitor planned changes and assign a probability of failure based on the factor’s values. From there, your teams can review planned changes and the probability of failure risk by calendar date and individual change. For each change, teams can evaluate and understand the specific risk factors that indicate a high probability of failure. 

A multitude of changes can be predicted with the ML algorithm. Each ML algorithm is different; the AI creates a unique set of rules and calculations to predict change failure based on the historical data based on a customer’s implementation. Some common ones you may see are CI alerts, CI prior changes, group failure rates, pre-product defects, and more, but could change depending on your organization’s needs. In order to get the most benefit, your organization should then be looking to embed this change risk prediction into your release process so you can see the changes flowing through the manual channels and the automated CICD pipelines. 

Get in the fast lane with AI-powered information 

With all this data at your disposal, how can you connect intelligence directly into the orchestration process for maximum velocity and minimum risk?  

Risk isn’t limited to a singular domain, incidents and outages span boundaries. By feeding the data as early as work items and value creation activities into the change risk prediction system, you can gain a holistic approach to identifying where the pockets of risk are in the process. As teams develop and make progress on work items, they are collecting data about what’s happening which will then be interpreted into the AI/ML system. 

Flow is central to the idea of value stream orchestration. But the fundamental concept of orchestration is having a templatized approach and having a defined process of how one takes the value stream creation and then connects them to end customer outcomes and production deployments. When you have a defined, concrete pattern for how your teams can accomplish these items, your organization will be able to move at a higher velocity and deliver higher quality. 

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