How Red Hat Ansible Automation Platform works with artificial intelligence
In this blog, we will learn how Red Hat Ansible Automation Platform works with artificial intelligence.
Managing IT Operations in an AI-Driven World
Imagine you’re leading a busy IT department. Each day, your team tackles repetitive but essential tasks—software installations, scaling systems, monitoring health checks, and rolling out security updates. These are routine operations, yet they consume valuable time and resources. Now, imagine your CTO tasks you with transitioning an experimental AI project into a fully operational, enterprise-grade solution—without adding staff or expanding your infrastructure budget. To make things even more challenging, this new solution must work seamlessly with existing business-critical systems.
Bringing Automation Into the Picture
This is where automation steps in. When enterprise IT practices are built on standardization, automation becomes a powerful enabler, especially for AI infrastructure. If you’re currently utilizing Red Hat Ansible Automation Platform, you can effectively apply your automation expertise to manage AI workloads.
If you’re just starting out with automation, getting up to speed is easier than you might think. Getting started with Ansible Automation Platform is straightforward, and features like Red Hat Ansible Lightspeed, powered by generative AI, can significantly shorten the learning curve.
Whether you’re a seasoned automation expert or just beginning, automating your IT operations—including AI initiatives—can reduce operational costs and help you realize value from AI investments faster.
Laying the Groundwork: Start with Automation
One of the most effective ways to move AI from pilot to production is by automating its deployment. Red Hat refers to this method as “AI infrastructure automation,” which offers tangible business advantages like saving time, minimizing configuration mistakes, and improving system stability.
Implementing AI Infrastructure Automation
Ansible Automation Platform helps automate the installation, configuration, and ongoing management of Red Hat OpenShift AI and Red Hat Enterprise Linux AI (RHEL AI). This enables consistent, repeatable setups for predictive and generative AI workloads, minimizing manual intervention.
Even though AI infrastructure provides immense value, much of the setup and management remains manual. Here are some automations that can ease deployment:
- Establishing Secure Connectivity: Automate secure data transfers between systems, including edge environments.
- Consistent Deployments: Leverage Ansible Playbooks to maintain uniformity in RHEL AI and OpenShift AI implementations.
- Automating Security Controls: Manage user access with role-based policies to protect sensitive data and systems.
Automation also benefits the broader AI ecosystem, such as:
- Networking Hardware: Automate configuration of switches, routers, and related components.
- Databases: Deploy and manage vector databases for retrieval-augmented generation (RAG) use cases.
- Load Balancing: Set up infrastructure to serve AI models via HTTP and APIs like OpenAI or Llama Stack.
- Storage and Connectivity: Manage systems required for training data and alignment pipelines.
There are many more components in typical AI architectures that are ideal candidates for automation. These span across networks, cloud services, virtualization, OS layers, app delivery platforms, and AIOps tooling, with Red Hat’s partner ecosystem supporting each layer.
Event-Driven Automation for AIOps
Once AI infrastructure is automated, what’s next? The automation expertise you’ve already built can be applied to the applications and services running on this infrastructure. But what about scaling automation efforts or closing skills gaps? That’s where AI-assisted development can help.
Accelerate Automation with AI-Powered Tools
Red Hat Ansible Lightspeed combines generative AI and reliable automation features by integrating IBM Watson Code Assistant, enhancing productivity and effectiveness. It helps developers create and deploy automation content faster and more accurately, accelerating the journey toward fully automated AI infrastructure.
If you’re already using OpenShift AI, you’re also working within the OpenShift ecosystem, which can run diverse workloads—including virtual machines—further expanding your automation reach using Ansible Lightspeed.
Importantly, Red Hat has embedded AI into Ansible workflows in a way that feels natural. Developers may not even realize AI is helping them write code faster and with fewer errors, thanks to this seamless integration.
Going a Step Further: Fully Automated AIOps
So far, the focus has been on human-assisted automation. Can automation independently respond to events as they occur within a system? Yes, it can.
With event-driven automation, systems can automatically respond to changes in the environment without human intervention. This is a core element of modern AIOps, and it provides benefits such as:
Faster Issue Resolution: Reduces downtime by reacting quickly to problems, shortening mean time to resolution (MTTR).
Self-Healing Systems: Enables infrastructure to recover from failures on its own.
Data Utilization: Helps clean, analyze, and act on big data more effectively.
Improved Efficiency: Increases productivity by using insights to optimize operations at scale.
Simplified IT Management: Reduces repetitive tasks and operational noise.
Real-Time Decision Making: Uses automated reasoning to take immediate action based on incoming data.
AIOps not only scales data analysis far beyond human capabilities—it also improves the overall quality of IT service management.
Building Trust and Guardrails for AI Automation
After deploying and automating your AI infrastructure and establishing autonomous AIOps workflows, the next step is ensuring AI operates within your organization’s guardrails. The ability to automate a task doesn’t always mean it should be done, particularly without appropriate governance and review.
For automation to be truly effective and responsible, it must align with your organization’s security standards and compliance requirements. Policy enforcement is essential to make sure AI operates in a way that’s trustworthy, reliable, and accountable, just like any other part of your business.
