Many DevOps professionals are wondering if generative AI will be a friend or foe, even as DevOps remains at the forefront when it comes to onboarding new technologies. Our DevOps communities have been enabling and fostering the collaborative cultural shift and evolving roles and responsibilities of software professionals. In this article, we explore the impact of generative AI in key DevOps roles and explain how to prepare ourselves for the shift.

Let us start by highlighting the basic roles and their broad areas of responsibility so we begin with a common vocabulary.

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Which of the following best describes how you currently monitor and manage systems across your hybrid cloud environment?(Required)

Multiple separate tools for different environments

Multiple separate tools for different monitoring and management functions

Single consolidated dashboard

Custom built monitoring and management solution

No full monitoring and management solution

Don’t know

How valuable would it be for your monitoring and management solution to incorporate predictive analytics to help you identify and address system issues that are critical and not waste time or effort working through all the data and “noise” that is not critical?(Required)

Extremely valuable

Moderately valuable

Somewhat valuable

Slightly valuable

Not valuable

How do you most typically address potential system issues BEFORE they impact your operations?(Required)

Automated workflows and playbooks to remediate potential issues as soon as they are detected

An orchestrated response process that leverages automation, AI, and human expertise to rapidly respond to potential issues

Advanced monitoring tools such as predictive analytics and anomaly detection to proactively identify and address potential issues

Respond to issues as they arise, using manual processes and troubleshooting techniques

A combination of automated remediation, orchestrated response, and/or proactive monitoring to address potential system issues

Don’t know

Other

What methods does your organization use to control and monitor cloud/IT spending?(Required)

Centralized procurement processes

Pre-approved budget thresholds

Automated cost monitoring tools

Department-level spending controls

Regular cost audits and reviews

Cloud cost optimization platform

Approval workflows for resource provisioning

Native financial operations tools provided by the cloud provider

No formal controls in place

Don’t know

Other

Please specify:

Which methods do you rely on most to detect security vulnerabilities in your systems?(Required)

Automated scanning tools

Manual assessments

Vendor notifications

Threat modeling (for early detection)

None of these

Don’t know

Other

Please specify:

What are the TWO most common ways you prioritize vulnerabilities to address within your environment?(Required)

Based on potential impact

Based on exploitability

Regulatory compliance requirements

Based on CVSS scores

Risk-based analysis of multiple factors

First-in-first-out approach

No formal prioritization process

Don’t know

How do you typically handle system updates and security patches?(Required)

Automated updates and patches

Manually review and apply updates and patches on a regular schedule

A combination of automation and manual review

Apply updates and patches on an as-needed basis.

Outsourced update and patch management

No defined process

Don’t know

Other

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Overview of DevOps Roles

DevOps roles are evolving quickly, but PagerDuty outlined six main DevOps roles for modern organizations.

1. The DevOps Evangelist: Responsible for delivering changes, collaborating with dev and ops teams and working toward optimizing flow, enabling feedback and fostering experimentation.
2. The Code Release Manager: Responsible for creating cadence between delivery and deployment of code. This role often requires deep technical expertise to run and maintain the releases.
3. The Automation Architect: Responsible for designing and implementing automated pipelines and reducing manual toil.
4. The Experience Assurance Expert: Responsible for user experience, enabling feedback loops and ensuring superior experience, assessing feature performance and improving end-product usability.
5. The Software Developer & Tester: Developers and testers are responsible for the design and development of the software product. Primary responsibilities are writing code, testing and fixing bugs.
6. The Security and Compliance Engineer: Responsible for the overall security and compliance of the software product. Collaborating with all the above roles to ensure standards and regulations are implemented.

All roles above are expected to work across the value chain to maximize their impact. Note that there are many other roles, including leadership and executive roles, which we have not covered in this article. Next, let’s assess the impact of generative AI on some of these roles.

Adding Generative AI to Every Role

Before diving into the business value of adding generative AI to job roles, let’s build a common vocabulary, starting with defining generative AI. According to Gartner, generative AI can learn from existing artifacts to generate new, realistic artifacts (at scale) that reflect the characteristics of the training data but don’t repeat it. It can produce novel content such as images, video, music, speech, text, software code and product designs.  

Automated code generation: AI-augmented code generation tools serve as companions for developers and testers. As an example, GitHub Copilot, developed by GitHub in collaboration with OpenAI, primarily offers predictive code generation and supports multiple languages. Tabnine offers deep learning capabilities and supports around 20 programming languages. 

As these and other tools evolve, software developers’ time may be freed up to focus on higher-level, more creative, and innovative thinking and business value-driven development. Some pundits say these code generation tools may be a stopgap in the race to hire developers. It may also result in generative AI-based moderators, a new job role entirely. These moderators would help with exporting or reusing existing code rather than trying to reinvent it.

AI-based release management: Generative AI can also help with release management; from a simplified approach for generating release notes to boosting the productivity of the feature release to setting a precise release cadence, generative AI-based tools are coming to our rescue. For example, Digital.ai introduced predictive decision-making, threat insights and many more features to orchestrate releases. Another example is K8sGPT, a tool for scanning your Kubernetes clusters, diagnosing and triaging issues in simple English. More integration of generative AI is on the way for DevOps tools. Release managers’ roles are at a pivotal point and will evolve with time and new skills to manage real-time releases, integration with several channels of special edition software, classic upgrades and core software releases. They will need to address different customer segments with managed subscription-based, open source versions and take a human-centric approach to software releases.

Security and compliance engineers: DevSecOps firm Jfrog is integrating generative AI capabilities into its Xray and Artifactory solutions. It will automatically detect security flaws, license violations and more early and often. Snyk surveyed more than 400 IT professionals and found the use of artificial intelligence (AI) to write code is creating a security paradox. Generative AI also can be used to simulate malware attacks and identify vulnerabilities. However, this is a double-edged sword as it also can be used to enhance the portfolio of bad actors. That means that security and compliance engineers must increase their focus on regulatory, assessment, audits and validation of generative AI outcomes. Specialist knowledge, decision-making, and ethical evaluation are needed for a strategic approach toward next-generation cybersecurity capabilities. 

The automation architect: An automation architect is responsible for decomposing complex tasks and automating them through reusable code. These tasks become more sustainable, with less rework and delays. As an automation architect, generative AI is another tool in the toolset that helps improve efficiency and productivity. Generative AI can help automation architects to scale and allow self-service options for DevOps teams and automate redundant tasks.

Generative AI’s Impact on Key Job Roles

As discussed in the previous section, there are many jobs that will be transformed by generative AI’s influence. Many companies will view generative AI-enhanced tools as a pot of gold where most of the work that requires building templates, infrastructure provisioning, log analysis, reuse or refactoring code can be done with the help of generative AI-enabled tools.

• Short-term skills gap: Software-related skills gaps are everywhere, and organizations need to go beyond reskilling to solve the problem. The role of leaders is key for successfully transitioning into the new era of workforce modeling. DevOps professionals should also look at strategies to improve our productivity by investing in capabilities and tools to fill the skills gap.

• Mid-term talent augmentation: Role disruption is most likely to happen due to generative AI; research and a more structured approach are needed to address the upcoming change. DevOps teams must focus on communication patterns, onboarding of human-machine collaborative models and ensuring performance of the new work models. Copyright, intellectual property (IP), licensing and the use of open source technology will be essential ingredients of mid-term talent augmentation with generative AI.

• Long-term, flexible workforce: Widespread and massive onboarding of generative AI in the long term will require a pragmatic approach to flexible workforce modeling. Task-based frameworks will emerge to guide the flexible orchestration of workforce, roles or even jobs or tasks. Task-based frameworks may mature into capability-driven roles that evolve into a flexible workforce where humans and machines together will deliver the desired business outcomes. 

With rapid advancement in generative AI technology, companies will be forced to design new ways of working to keep pace with these changing dynamics. DevOps professionals should see this as an opportunity to get ahead of the curve, assess how generative AI technologies can boost productivity and enhance it with new tools and capabilities.