How to implement AI into cloud management and operations

Phase 1. Perform an assessment

First, evaluate the challenges your team is trying to solve. You need to determine if AI can even help overcome the issues, as well as if it's time to augment existing processes or replace them entirely.

Make informed decisions about whether your current infrastructure can accommodate the growing demands of AI services and applications. Factor scalability, reliability and performance into the assessment. You must also review data management practices to ensure a seamless integration of AI technologies into cloud infrastructure. These practices include the following:

• Data backup.
• Disaster recovery.
• Data encryption.

Additionally, review the current state of your data governance frameworks, including data privacy policies and procedures. Such an extended, detailed assessment secures your organization's and customers' information with proper compliance standards.

AI initiatives need clear goals and measurable metrics to define success.

Phase 2. Define objectives and KPIs

AI initiatives need clear goals and measurable metrics to define success. One way to prove new AI tools and practices are working efficiently is to measure KPIs. Common KPIs for cloud management focus on system performance, security and cost optimization. Be sure to take the time to review your existing data about speed, scalability and reliability derived from your current methods.

Moving to AI for cloud management permits additional data and insights to improve efficiency and effectiveness. By extension, the predictive capabilities of AI enable you to anticipate future cloud needs and adjust resources accordingly.

Cost optimization is a growing use case for AI to help reduce cloud spending. By predicting cloud usage patterns and automating resource allocation, AI eliminates waste and ensures that organizations maximize their cloud spend.

AI, as part of cloud management, can offer more granular controls and aggregation of data via automation.

Phase 3. Select the right services and tools

Tool selection should not be overlooked, especially if teams upgrade to an AI-enabled cloud management or cost optimization tool. Take extra steps to conduct a pilot project or proof of concept to ensure the tool meets requirements. Involve business stakeholders who may need to consume cloud-related data to ensure AI delivers data and reporting requirements.

AI, as part of cloud management, can offer more granular controls and aggregation of data via automation. This opens more opportunities to integrate with other back-end systems beyond a cloud management platform. Easing deployment and cloud integration issues depends on whether you implement a third-party AI tool into your cloud management stack or an AI service from your cloud provider. Most of today's third-party cloud management tools work in hybrid and multi-cloud environments.

Cloud teams need to learn about the benefits and potential challenges of the implementation, plus how the AI-enabled cloud management platform changes their work. For example, if you implement Cast AI, ProsperOps or a similar cost optimization tool, your team needs to know about additional reporting options that are available. It also takes time to train users to maximize AI for reporting.

Phase 4. Monitor and refine processes

Bringing AI into cloud management practices doesn't save time on monitoring, continuous improvement and refinement. The increased access to back-end data equates to more work to ensure that your organization takes full advantage of AI.

AI can increase monitoring options for cloud teams because it can analyze large volumes of data from cloud resources. Such a gain in analysis improves anomaly detection and enables predictive analysis. Factor time into your project schedules for your team to refine their cloud management practices, especially reporting and alerting.