IT work is particularly stressful work, even in the world of DevOps. Despite planning work in manageable sprints or cycles that can bring some level of consistency and predictability, many teams can still end up scrambling to put out fires. Or they may end up with significant “crunch” time spent hurrying to complete in-progress work before a looming deadline.

Factors like these can make the IT field especially prone to burnout. According to the 2022 State of Burnout in Tech Report by Yerbo, about 41% of tech professionals working with a high risk of burnout, which is why the need for AI predictive analytics is greater than ever.

What Is Burnout and How Does Unpredictability Affect It?

The WHO categorizes burnout as an occupational phenomenon resulting from “chronic workplace stress that has not been successfully managed” that is categorized by 4 major states: exhaustion, self-inefficacy, cynicism, and depersonalization.

Unpredictability can be one of the biggest contributors to work-week stress and overall tech professional burnout. Teams may end up thinking they have a smooth schedule ahead of them, only to be hit with deployment issues or escaped defects, leading to those common feelings of self-inefficacy or cynicism.

The good news is that cutting-edge AI predictive analytics can now anticipate these types of issues. They can then be addressed long before they’re able to have an impact that leads to unplanned work. With less unplanned work and a sense of greater control over their schedule, tech professionals can enjoy higher levels of job satisfaction, likely leading in turn to improved product quality and employee productivity.

DevOps Stress Often Results From Hectic Scheduling

Using techniques such as Kanban and scrum planning, software development leaders can add a level of predictability to daily IT work. However, deadlines and priorities can lead to the creation of planned cycles that are less-than-perfect when it comes to accommodating human needs. For example: if multiple changes are requested in a row, these changes may be scheduled in such a way that engineers will feel the need to rush to complete them all. Any unplanned outages or escaped defects can cause the entire development lifecycle to fall apart, resulting in delays and work-item logjams.

Teams may also encounter the opposite problem: long periods of lulls without a clear direction for how to fill the open timeslots. Going from a “hurry up and wait” to an “all hands on deck” mindset can generate lots of unpredictability and stress for DevOps employees.

This problem isn’t a new one either. In a 2019 article, software engineer Stefan Bradstreet cautions, “Working in such a way keeps a team from finding consistency and momentum.”

To solve this issue, Bradstreet recommended that DevOps leaders try to raise questions on what it is you need to deliver, and what value they are bringing to their customers. This information should then be used to drive priorities for your team’s work using a project plan that breaks down deliverables clearly. This should help your team achieve more balance in their story assignments and control your scrum velocity.

Even with the best efforts of DevOps leaders, a lack of visibility into factors that lead to unplanned work and dense (or start-and-stop) schedules can cause scheduling controls to break down. Unrealistic deadlines may then be set.

What workers need is a way to better predict which work items currently in the pipeline have the highest capacity to cause trouble. They can accomplish this by using predictive AI risk management in combination with informative dashboards and a best-of-breed release orchestration tool, all well-integrated with one another.

AI Predictive Analytics Anticipate Risk Factors to Proactively Address Them

Using’s Intelligence – Change Risk Prediction solution, DevOps organizations can use their own historical data to model which factors contribute the most to undesirable outcomes like deployment delays or defect leakage. The model is different for every organization, based on correlations between factors that have the highest predictive potential. This avoids production environment failures by increasing visibility on change risk scores and patterns in key risk factors that lead to change failure.

Certain changes may be at particular risk because of a history of failures in their change category, such as changes involving a specific database call. Other times, factors outside individual changes can be the driving factor, such as a high volume of changes that do not require downtime scheduled on a single day.

The AI risk management solution is for understanding where you have risks for production deployments and defect leakage across applications, teams, and business dimensions. It is a proactive approach as we know this beforehand and can get in front of potential quality issues and defect leakage.

While continuous delivery accelerates application delivery, major application disruptions can generate significant negative business impacts, especially for enterprises with complex environments.

Ultimately, by flagging these issues proactively, team leaders and product owners will have a more realistic understanding of what actions are needed and how to prioritize them. This results in less unplanned work and better insight into where to apply limited time and effort.

Thanks to informative dashboards, we can also see which upcoming workdays or weeks may be more likely to end up with a packed schedule. We can then proactively include work items in lulls in the schedule, leading to smoother and more predictable workdays.

Gain Control Over DevOps Risks to Gain Control Over Work Schedules

Lack of insights and visibility is the number one culprit behind stressful work weeks for many clients. Implementing solutions like Change Risk Prediction and Flow Acceleration gives DevOps leaders the power to not only obtain visibility but also take action.

The most immediate effect is that upcoming risks will be known, allowing teams to begin working ahead of the problems or preparing for worst-case scenarios. Over time, DevOps teams can avoid getting seasick on waves of extreme highs or lows in their schedule.

Implementing these solutions can also proactively avoid situations where an unrealistic amount of work is expected, which leads to crunch or work item delays. Using Release allows leaders to proactively smooth out the schedule or re-assign tasks directly from the dashboard view.

In the end, it is all about simplifying the control of DevOps cycles while empowering teams with the information they need to succeed. Although this cannot eliminate workplace stressors all at once, it can make stressful days spent rushing to meet deadlines or putting out fires much rarer.


To learn more about how to get visibility into your entire DevOps pipeline, check out our Intelligence DORA Metrics product brief.

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