Who should enter the ‘Best Use of AI in DevOps‘ category?

The ‘Best Use of AI in DevOps’ category recognizes organizations applying artificial intelligence to improve the reliability, efficiency, and performance of modern software delivery and operations. This award celebrates practical AI implementations that help teams monitor complex systems, respond to incidents faster, and optimize infrastructure in real-world production environments.

Entries may demonstrate how AI is used to enhance observability through intelligent monitoring and anomaly detection, enabling teams to identify issues earlier and reduce alert fatigue. The category also covers automated incident response and root cause analysis, where AI helps correlate events, diagnose problems, and support faster resolution. Submissions may further showcase predictive capacity planning and resource optimization, using AI to forecast demand, control cloud costs, and ensure consistent performance at scale.

Judges will also consider the use of AI to improve CI/CD pipelines, including deployment risk assessment, test optimization, and failure prediction, as well as proactive approaches to reliability, security, and risk management. Successful entries will show clear operational impact, such as reduced downtime, faster release cycles, or improved system resilience.

This category is open to technology providers, in-house platform teams, and DevOps organizations delivering measurable, responsible AI innovation across the software development lifecycle.

DevOps AI Award

Example Use Cases

Applications of AI in DevOps may sit within one of these example areas, among others:

DevOps monitoring with AI

Intelligent monitoring and anomaly detection helps users by seeing problems before users do. AI systems may continuously analyze logs, metrics, traces, and events to detect anomalies, performance degradation, or emerging failures beyond static thresholds.

Typical use cases:

  • Log and metrics correlation across distributed systems
  • Early detection of abnormal behaviour or drift
  • Noise reduction and alert prioritisation
  • Observability enhancement in complex environments

You may wish to demonstrate some measurable outcomes in your awards submission, such as:

  • Reduced false positives and alert fatigue
  • Faster detection of genuine issues
  • Clear improvement over rule-based monitoring

Automated incident response with AI for DevOps

Automated incident response and root-cause analysis can use AI to resolve incidents faster and with confidence. AI applied during live incidents can correlate signals, identify likely root causes, and recommend or automate remediation actions.

Typical use cases:

  • Probable root cause identification
  • Event correlation across services and infrastructure
  • Automated runbooks and response actions
  • Post-incident analysis and learning

Consider some measurable metrics to demonstrate the success of your work:

  • Reduced mean time to resolution (MTTR)
  • Improved accuracy of incident diagnosis
  • Demonstrable operational relief for teams

Server security

AI can be used for predictive capacity planning and resource optimization to scale efficiently without guesswork. AI models may forecast demand and optimize infrastructure usage across cloud, hybrid, or on-prem environments.

Typical use cases:

  • Predictive workload and traffic forecasting
  • Intelligent auto-scaling decisions
  • Cost optimization and waste reduction
  • Performance versus cost trade-off modelling

Potential areas to consider when demonstrating the effectiveness of AI in DevOps:

  • Lower infrastructure or cloud spend
  • Improved performance consistency
  • Evidence of proactive, not reactive, scaling

AI to improve employment pipelines in DevOps

AI may be employed for intelligent CI/CD pipeline optimization. Here, AI may be used to improve build, test, and deployment pipelines by predicting failures, prioritizing tests, and assessing deployment risk.

Typical use cases:

  • Test selection and prioritization
  • Deployment risk scoring
  • Failure prediction in build pipelines
  • Continuous improvement of release workflows

Some examples of measurable outcomes you may wish to demonstrate in support of your entry:

  • Faster release cycles
  • Fewer failed or rolled-back deployments
  • Increased confidence in continuous delivery

Use of AI in DevOps to enable risk management

Proactive reliability, security and risk management uses AI to build resilience into systems by design. This covers AI systems that predict reliability, configuration, or security risks before they cause incidents or outages.

Typical use cases:

  • Configuration drift detection
  • Predictive reliability and failure modelling
  • Vulnerability and misconfiguration identification
  • Resilience and chaos engineering insights

Evidence you may consider supplying in support of your submission:

  • Fewer outages or security incidents
  • Improved system stability over time
  • Strong governance and risk management

Areas to Highlight in Your Submission

Judges score nominations across these five key areas:

  • Innovation: The features or technology that makes your DevOps solution unique - or transformed your market.

  • Impact: Evidence of the positive effect your DevOps solution has brought to your customers.

  • Scalability: How your solution grows or adapts to changing business needs, without significant upgrades or overhauls.

  • User Experience: How intuitive your DevOps solution is to use for users of varying roles or skill levels.

  • Relevance: What makes your solution a worthy winner in this particular category.

Although not formally scored, focus on these areas specific to this category, can help your nomination stand out:

Optimization

Using AI to forecast demand, optimize resource usage, and control infrastructure costs.

Monitoring

Applying AI to identify anomalies, failures, or performance degradation beyond static thresholds.

Response

Leveraging AI to support or automate incident resolution and root cause analysis.

More A.I. Awards Categories

Next Steps to Enter The A.I. Awards

To enter this AI Awards category, or any other category in The AI Awards, please follow these three simple steps:

  • Download the entry form. Open up the ‘AI Awards Simple Form’ document.

  • Complete the form. You only need to complete the form once, even if entering multiple A.I. Awards categories.

  • Submit your entry. Head to the ‘Submit Now’ section on our website, select ‘The A.I. Awards’ and the category/categories you are entering from the list, upload your form and any other materials you would like the judges to review, and process your fees.

The A.I. Awards is a program from The Cloud Awards. Since 2011, we’ve been helping organizations across the globe gain the recognition they deserve for market-leading innovation in the cloud computing and software sectors.

For a detailed breakdown of all the benefits you receive as an awards entrant as either a shortlistee, finalist or ultimate winner, please see our “Why Enter?” page. The many benefits are replicated across all international awards programs. If you have any questions about this category, please contact us.