The DevOps playbook is rapidly transforming because of recent advancements in artificial intelligence (AI). DevOps teams now have access to technology capable of improving automation and optimization in practically everything they do — from integration to delivery to infrastructure and more. AI tools also promote the ‘shift-left’ model, prompting cost-saving early bug detection and resolution. With AI having such wide-ranging positive effects, it is little wonder that organizations that quickly embraced it report experiencing improved customer satisfaction and increased service productivity.
AI has made a powerful impact on traditionally inefficient DevOps areas, such as anomaly detection, root cause analysis, automation of repetitive tasks, security enhancements, performance optimization and capacity planning. That does not mean it is not without its challenges. A host of potential problems can prevent organizations from experiencing maximum AI benefits. The good news is that companies that take the time to understand and address these challenges will quickly improve the speed and agility of their DevOps teams to build, test, release and maintain software applications.
How AI Revolutionizes DevOps
The impact of AI can be seen in five key areas, or ‘enablers’ (Figure 1). In DevOps, enablers refer to tasks that help Agile teams work more efficiently and deliver more value to end users. They make up the core of the DevOps playbook. Here is how AI transforms these enablers:
- Culture and Skills: AI drives ‘shift left’ programming, breaks down silos and encourages continuous learning — all of which help staff members take maximum advantage of new technology.
- Architecture: Significant AI initiatives in this area include platform-based architecture, microservices, loose coupling, containers and serverless computing to boost operational efficiency and agility.
- Methodology: AI initiates time- and labor-saving automation across the DevOps landscape in compliance/IT controls and security, testing, service ownership and development policies.
- Tooling: Thanks to AI, the variety of DevOps tools, along with their effectiveness and ease of use, has soared. For example, DevOps teams can now take advantage of several tools, including GitHub Copilot, an automatic programming tool; Dynatrace, which offers AI-powered monitoring and application performance management and Moogsoft, an AIOps platform that uses AI to correlate events and reduce noise in alerts and much more.
- Infrastructure: AI introduces cloud-first strategies to improve efficiency and infrastructure-as-code (IaC) to replace cumbersome manual processes. It is also used in public-private partnership (PPP) frameworks to create more effective IT management structures.
Figure 1: Five Key Enablers for AI in DevOps
By utilizing AI in these enablers, companies can significantly improve the performance of IT services across core metrics focused on business value by measuring throughput (velocity) and stability (quality). For instance, Netflix’s decision to use AI-powered Chaos Monkey for performance monitoring in its DevOps pipeline reduced unexpected outages worldwide by 23%. At the same time, Google’s use of tools like TensorFlow Extended (TFX) in its continuous integration and continuous delivery (CI/CD) processes cut its time lost to unnecessary shutdowns by 35%. Unfortunately, companies often need to overcome frequent obstacles to unlock major benefits like these from their AI implementations.
Primary Challenges of Implementing AI in DevOps
Obstacles that prevent companies from realizing the full benefits of AI include data quality and availability, legacy systems, lack of expertise, security and compliance risks and model drift and maintenance. Overcoming these challenges requires a well-thought-out, strategic approach. For instance, to eliminate the data quality and availability challenge, organizations could set stricter data collection rules and logging mechanisms. They could also use centralized data storage and implement regular data cleansing and preprocessing pipelines to filter out noise.
General Electric (GE) is a notable example of what happens when companies pay attention to data quality. The company recently implemented a comprehensive data governance and quality management strategy within its Predix platform. Additionally, it began using automated tools to cleanse, validate and monitor the data generated by its industrial equipment. As a result, GE produced accurate, real-time insights that it then used to boost decision-making. Tips for prevailing over other common challenges include:
- Legacy Systems: Choose tools that support common CI/CD platforms, use application programming interfaces (APIs) and connectors to integrate AI with monitoring tools such as Prometheus, ELK Stack and Datadog and start with small integrations before scaling up.
- Lack of Expertise: Provide regular AI and machine learning (ML) training for DevOps teams, encourage collaboration between team members and AI experts and take advantage of low-code platforms and pre-built AI models to reduce the learning curve.
- Security and Compliance Risks: Apply role-based access control (RBAC) and encryption for AI-accessed data. Also, only use AI tools that comply with industry standards and regularly audit AI models for security vulnerabilities.
- Model Drift and Maintenance: Establish automated retraining pipelines based on new data, continuously monitor AI performance and rely on drift detection tools to identify when models need to be updated.
In addition to being prepared to address these challenges, companies can increase their chances for success by being aware of other potential issues, such as AI explainability and trust, cost and resource constraints, resistance to change, false positives and noise and scalability and performance optimization.
AI DevOps Trends and Innovations
AI technology develops rapidly, with new trends and innovations constantly appearing. As a result, companies that fail to stay current risk falling behind. Today, this requires implementing strategies where natural language processing (NLP) is used to improve collaboration between developers and operations. It also means projecting how new encryption algorithms, intrusion detection systems and other technologies are best used to optimize DevOps workflows.
By staying up to date and leveraging AI in DevOps, teams can deliver better software to their customers more quickly due to improved operational efficiency and agility. A significant benefit of AI is its ‘shift left’ nature, which allows it to bring operations closer to development. It is possible to see how an application will behave in the real world earlier in the development lifecycle with AI, and fixing any issues is cheaper. The result? Organizations that take proper advantage of AI speed up software production, cut costs and produce software programs that generate higher customer satisfaction rates. That is an exciting win-win-win scenario for any business in today’s competitive marketplace.