IT automation is mission-critical for organizations to maintain a competitive edge. Red Hat Ansible Automation Platform is a comprehensive solution that not only addresses the immediate needs of IT operations, but also lays the groundwork for future advancements, including artificial intelligence (AI). In this post, you’ll learn why an automation platform is critical for an AI strategy and how MAPFRE, a global insurance company headquartered in Madrid, is taking advantage of Ansible Automation Platform to do just that. Let’s explore 4 key aspects that make Ansible Automation Platform indispensable for modern enterprises like MAPFRE.
Automation is mission-critical
Automation is no longer a luxury; it’s a necessity for innovation. With the increasing complexity of IT environments, manual processes are prone to errors and inefficiencies. Ansible Automation Platform enables organizations to automate repetitive tasks, streamline workflows and provide consistency across their IT infrastructure. These benefits not only boost productivity, but also enable IT teams to focus on strategic initiatives that increase innovation and business growth. As AI solutions become part of everyday business processes, it's more important than ever for IT infrastructure to maintain high resiliency and reliability. For example, if AI assistant bots are part of the support lifecycle, and are managing tier 1 and tier 2 support cases, what would happen if there was an IT outage? Can an enterprise afford not to have automated solutions to help improve mean time to resolution and quickly apply solutions?
Automation as the foundation for AI
Ansible Automation Platform can amplify the impact of AI by orchestrating the tools in your stack to get the most from AI capabilities. This includes use cases like coordinating infrastructure and services, and optimizing your infrastructure by integrating AI to make it more intelligent and resilient for tasks like optimizing storage and compute. AI workloads can burden IT teams responsible for constructing, securing, scaling and maintaining infrastructure. As AI models grow more intricate and data volumes surge, the demand for AI-optimized cloud infrastructure increases. With the rising need for custom model training, businesses require enhanced computing power. New systems must be deployed and maintained to ensure accessibility for AI/ML engineers. As organizations look to integrate AI into their operations, a robust automation foundation is essential. Ansible Automation Platform provides this foundation by standardizing and automating IT processes, making it easier to implement AI-driven solutions. This synergy between automation and AI accelerates innovation and the value these tools deliver. Consider using AI solutions for IT infrastructure, as well. Ideally, AI solutions would help to proactively identify issues, re-reroute network traffic and increase storage capacity. However, without an automation platform to communicate with all the different disparate IT infrastructure, AI won’t be able to actually execute these scenarios.
Security-hardened and reliable automation
Security and reliability are paramount in any IT environment. Ansible Automation Platform is designed with these principles at its core. It offers a security-hardened enterprise platform that enforces consistent security policies and configurations. Automated security monitoring and response capabilities help quickly identify and mitigate threats, reducing the risk of security breaches. With Ansible Automation Platform, organizations can trust that their automation processes are both safe and dependable. Ansible Automation Platform provides access to signed content from Red Hat, with the ability to sign your own content and allow only signed content to be executed. This enables an end-to-end automation pipeline that allows you to execute only security-hardened automation into your environment.
Automated policy as code
Managing policies manually can be cumbersome and error-prone. Ansible Automation Platform introduced automated policy as code, enabling organizations to define and enforce policies through code. This approach applies policies consistently across all environments and can be easily audited and updated. By treating policies as code, businesses can achieve greater compliance and governance, reducing the risk of policy violations and enhancing overall IT governance. Automated policy as code also allows an organization to put guard rails on generative AI solutions. For example, if you were using Red Hat Ansible Lightspeed to autogenerate Ansible Playbooks, you could use automated policy as code to help ensure your playbooks are only using modules and best practices that have been approved by your organization for production.
To see these principles in action, watch a video featuring MAPFRE, who discusses their experience with Ansible Automation Platform. MAPFRE highlights how they use Ansible Automation Platform to enhance their automation capabilities, improve security and lay the groundwork for AI integration. Their journey underscores the transformative impact of Ansible Automation Platform on modern IT operations.
Want to learn more? Check out this webinar covering automation and AI or read a blog on the subject here.
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저자 소개
Sean is a Principal Technical Marketing Manager, Ansible, where he brings over 10 years of experience building and automating computer networks. Sean previously worked for both Cumulus Networks (acquired by Nvidia) and Cisco Systems where he helped customers deploy, manage and automate their network infrastructure. He resides in Chapel Hill, NC with his wife and children and tweets from @IPvSean.
Matthew Packer is a Principal Product Marketing Manager for Ansible Automation Platform and is responsible for cloud automation. Prior to joining Red Hat, he worked in product marketing specializing in retail payment technology at Vontier and product management at Cisco in cloud-based networking. Matthew also worked as a consultant at Honeywell in the manufacturing and utilities industries with a focus on the Internet of Things (IoT) and predictive analytics space.
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