What is AIOps?
AIOps, or AI operations, is the practice of managing AI systems throughout their life cycle, from development and evaluation through deployment, monitoring, governance and ongoing improvement. It brings together the capabilities needed to reliably operate predictive models, large language models (LLMs) and AI agents in production.
Unlike IT operations AIOps, which applies AI to IT operations, AI operations focuses on managing AI systems themselves – from traditional machine learning models to LLM-powered applications and AI agents.
Building AI is only the beginning
AI systems don't stop changing when they reach production.
Data changes, model performance can decline, prompts evolve, knowledge sources change and AI agents interact with increasingly dynamic environments. Teams need visibility into how AI systems perform after deployment, not just whether they successfully launched. AI operations connects development and production so teams can detect changes, evaluate performance, manage deployments and continuously improve AI systems.
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Detect changes
Identify quality, performance and behavioral regressions before they affect users.
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Maintain consistency
Apply repeatable operational practices across models, LLMs and AI agents.
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Scale AI operations
Extend governance, evaluation and monitoring across AI systems as your portfolio grows.
04
Automate responsibly
Automate operational tasks while maintaining appropriate human oversight.
Manage the AI life cycle with SAS AIOps
SAS AIOps brings together five connected capabilities – Ground, Align, Evaluate, Operate and Oversee – to manage AI from development through production and continuous improvement. Governance applies throughout the AI life cycle, while evaluation and operational feedback help teams identify and address changes over time.
Explore SAS AIOps capabilities
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Ground AI in trusted context
Connect AI systems to enterprise knowledge, governed data and business logic so they have the context they need to produce relevant, reliable results. Grounding can include enterprise knowledge sources, embeddings, retrieval-augmented generation (RAG), context and interaction management, sensitive data identification, and data preparation and governance.
Outcome: More relevant, reliable and enterprise-aware AI behavior.
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Align AI behavior with business expectations
Guide how models, LLMs and AI agents behave using prompts, policies, guardrails and human expectations established during development. Capabilities can include prompt engineering and management, behavioral guardrails, fine-tuning, toxicity and bias detection, PII detection, human review and reusable prompt and policy repositories.
Outcome: Promote responsible AI behavior and reduce compliance risk before deployment.
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Evaluate AI continuously
Evaluate AI systems before deployment and continuously in production to measure quality, safety, performance and business outcomes. Evaluate models, LLMs, agents and RAG pipelines using online and offline testing, response-quality and grounding assessments, toxicity and bias testing, version comparisons, regression detection and business outcome validation.
Outcome: Identify quality changes and unexpected behavior before they affect users.
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Operate AI at scale
Deploy, run, monitor and orchestrate predictive models and LLMs across enterprise environments with consistent operational practices. Manage AI assets through registration, orchestration, workflow coordination, version management, monitoring, retraining and flexible deployment across cloud, on-premises and other supported environments.
Outcome: Repeatable, scalable production operations for enterprise AI assets.
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Oversee AI throughout the life cycle
Maintain visibility, accountability and control over AI systems throughout their life cycle. Apply policies and controls, maintain end-to-end traceability and auditable records, monitor model and agent behavior, and support human oversight, intervention and escalation. These capabilities also support controlled decommissioning when AI systems are no longer needed.
Outcome: Keep AI systems explainable, accountable and aligned with organizational requirements.
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Gain enterprisewide visibility into AI
View AI use cases, models and agents in a centralized dashboard, with alerts that help identify governance gaps and priority risks.
Outcome: Greater visibility, accountability and oversight across enterprise AI assets.
How does SAS AIOps support AI governance?
AI governance requires continuous visibility and control over how models, LLMs and agents are developed, deployed and used. SAS AIOps makes governance operational by embedding oversight, evaluation and traceability throughout the AI life cycle.
Apply governance controls
Apply policies, controls and approval workflows throughout the AI life cycle.
Maintain visibility and traceability
Monitor AI performance and behavior while preserving lineage, documentation and auditable records.
Identify risks earlier
Identify privacy, bias and other compliance-related risks as AI systems are developed and operated.
Maintain human oversight
Support review, intervention and escalation when human involvement is required.
One operational framework for every kind of AI
Different AI systems introduce different operational requirements. AIOps provides a common foundation for managing predictive models, generative AI and agentic AI while supporting the evaluation, monitoring, orchestration and governance needs of each. AIOps builds on established operational practices such as MLOps and LLMOps while extending them across the broader AI life cycle.
Predictive AI
Register, validate, deploy, monitor and retrain models as data and performance change.
Generative AI
Ground LLMs, manage prompts, evaluate responses and monitor quality and safety.
Agentic AI
Coordinate agents and tools, observe actions, evaluate behavior and maintain appropriate human oversight.
Continuously improve AI in production
AIOps creates a continuous feedback loop between development and production. Teams can use evaluation results, operational telemetry and business outcomes to identify changes and improve AI systems over time.
Why SAS for AI operations?
Within the broader SAS® Viya® data and AI life cycle, AIOps focuses on operationalizing AI systems from development through production and continuous improvement. You can manage predictive models, large language models (LLMs), AI agents and decision workflows while maintaining visibility, oversight and control throughout the AI life cycle.
One connected AI life cycle
Manage data, models, LLMs, agents and decisions through a connected environment, helping teams move from development and evaluation to production and ongoing improvement.
Open by design
Support SAS, open source and third-party models and LLMs so organizations can use the AI technologies that fit their business needs.
Continuous evaluation & oversight
Evaluate AI before deployment and monitor behavior in production. Apply policies, traceability and human oversight as AI systems operate and evolve.
Enterprise-scale operations
Deploy and operate AI across cloud, on-premises, databases and streaming environments, with consistent operational practices across heterogeneous AI assets.
Built for human accountability
Keep people involved in consequential decisions and autonomous workflows, with operational controls that support oversight and intervention throughout the AI life cycle.
See AI operations in action
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Accelerate regulatory analysis with generative AI
Southern States Energy Board needed a faster way to analyze thousands of public comments on complex regulatory issues while maintaining accuracy and human oversight. Using SAS Viya, SAS Visual Text Analytics and an LLM, the organization combines traditional and generative AI to categorize and summarize feedback, with human reviewers refining the results. A process that previously took months can now be completed with significantly greater speed while providing transparent, traceable insights.
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Operationalizing 1,900+ models at scale
Georgia-Pacific needed to operationalize analytics on large volumes of real-time data, but model deployment was taking more than three months, and integrating Python and R models into production workflows was difficult. With SAS Viya, Georgia-Pacific operationalized more than 1,900 SAS and open source models running multiple times per second and reduced model development and deployment time by up to 70%.
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Accelerate AI from prototype to production
Dompé farmaceutici needed to industrialize how predictive models were developed, validated and deployed across its drug discovery platform while maintaining trust and governance. With SAS Viya, Dompé created an AI factory that automatically generates and validates models and delivers production-ready analytics. The company reduced the time to move from initial prototype through model development and validation to full production deployment from weeks or months to hours or days.
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Standardize and automate model operations
S-Bank lacked a structured, scalable process for model development and monitoring, which limited visibility and created silos across the analytics life cycle. With SAS Viya on Microsoft Azure, S-Bank standardized and automated analytics processes, enabled automated model monitoring and real-time decisioning, and accelerated more accurate loan processing.
Related products & solutions
- SAS® AI NavigatorGet a unified view of AI across your organization. SAS AI Navigator helps govern models, agents and use cases with centralized visibility and policy alignment.
- SAS® Model ManagerSimplify the creation and management of your model collection with a web-based interface that easily automates the model management process.
SAS AIOps frequently asked questions
What is AIOps?
AIOps, or AI operations, is the practice of managing and improving AI systems throughout their life cycle, from design and development through evaluation, deployment, monitoring, governance and ongoing improvement. It brings together the capabilities needed to reliably operate predictive models, large language models (LLMs) and AI agents in production.
Why is AIOps important for enterprise AI?
AI systems can behave differently after deployment as data changes, model performance declines, prompts evolve and agents interact with changing environments. AIOps helps teams detect changes earlier, maintain consistent operational practices, improve visibility and continuously improve AI systems while maintaining appropriate oversight and governance.
How is AIOps different from MLOps and ModelOps?
MLOps and ModelOps provide established practices for managing machine learning models throughout development and production. AIOps builds on these practices and extends the operational framework to include newer forms of AI, including LLMs and AI agents, with capabilities for evaluation, observability, orchestration and governance across the broader AI life cycle.
What is the difference between AIOps and LLMOps?
LLMOps focuses on operationalizing large language models and the applications built with them. AIOps takes a broader approach, providing an operational framework for different types of AI – including predictive models, LLMs and AI agents – with consistent evaluation, monitoring, orchestration and governance across the AI life cycle.
How does AIOps support generative AI and AI agents?
AIOps provides operational capabilities for generative and agentic AI, including grounding and context management, prompt and policy controls, evaluation, monitoring, orchestration and governance. This helps teams address the distinct operational requirements of LLMs and AI agents while maintaining consistent practices across the AI life cycle.
How does AIOps support AI governance?
AI governance establishes the policies, controls and accountability organizations need to manage AI responsibly. AIOps helps put those requirements into practice through operational visibility, traceability, life cycle policies, audit records, monitoring, human oversight and controls throughout the AI life cycle.
How does AIOps improve AI reliability and observability?
AIOps provides visibility into AI systems throughout development and production. Continuous evaluation and monitoring can help teams identify changes in quality, performance and behavior, including model drift, unexpected LLM outputs and changes in agent behavior. Teams can then use these insights to respond and improve AI systems over time.
Can AIOps support SAS, open-source and third-party models?
Yes. SAS Viya supports SAS, open source and third-party models and LLMs, allowing organizations to use the AI technologies that best fit their business needs while managing them within a unified operational framework.
How does AIOps help organizations operate AI at scale?
AIOps provides a consistent operational framework for managing models, LLMs and AI agents across enterprise environments. Capabilities such as deployment, orchestration, monitoring, version management, retraining and governance help organizations manage heterogeneous AI assets while maintaining consistent operational practices.
