AI is becoming a game-changer for cloud management and operations, however, there is no immediate satisfaction when it comes to AI and cloud computing, and businesses need a strategy in place to cut the hype and truly benefit from this emerging technology.
If you're interested in adopting AI to improve your cloud management practices, review the following four phases in more detail:
1.Conduct an assessment 2Define objectives and KPIs3Choose the right services and tools4Monitor and improve processesPhase 1Perform an assessmentFirst, assess the challenges your team is grappling with. You need to determine whether AI can help overcome these issues and whether it's time to enhance existing processes or replace them entirely. To make informed decisions about whether your current infrastructure can meet the growing demand for AI services and applications, factoring scalability, reliability, and performance into the assessment, you must also review your data management practices to ensure that AI technologies are seamlessly integrated into your cloud infrastructure, including: Data Backup, Disaster Recovery, Data Encryption In addition, review the current state of your data governance framework, including data privacy policies and procedures, such an extended, Detailed assessments protect your business and customers' information with appropriate compliance standards. Phase 2Define objectives and key performance indicatorsAI initiatives require clear goals and measurable metrics to define success, and one way to demonstrate that new AI tools and practices are working effectively is to measure KPIs. Common KPIs for cloud management focus on system performance, security, and cost optimization, and be sure to take the time to examine your existing data on speed, scalability, and reliability derived from your current approach. Turning to AI for cloud management gives you more data and insights to improve efficiency and effectiveness, and by scaling, AI's capabilities enable you to future cloud needs and adjust resources accordingly. Cost optimization is a growing use case for AI to help reduce cloud spending, and by using cloud usage patterns and automating resource allocation, AI eliminates waste and ensures organizations maximize their cloud spending. Stage 3Choose the right services and toolsTool selection should not be overlooked, especially as teams upgrade to AI-enabled cloud management or cost optimization tools, taking the extra step of a pilot project or proof of concept to ensure the tools meet the requirements and engage business stakeholders who may need to use cloud-related data to ensure AI delivers data and reporting requirements. AI, as part of cloud management, can provide more granular control and data aggregation through automation, which opens up more opportunities to integrate with other back-end systems beyond cloud management platforms. Mitigating deployment and cloud integration issues depends on whether you implement third-party AI tools in your cloud management stack or AI services from a cloud provider. Most of today's third-party cloud management tools work in both hybrid and multi-cloud environments. Cloud teams need to understand the benefits and potential challenges of implementation, and how AI-enabled cloud management platforms can change their work, for example, if you implement Cast AI, Properops, or similar cost optimization tools, your team needs to be aware of the other reporting options available, and it will take time to train users to maximize AI for reporting. Stage 4Monitor and improve processesIntroducing AI into cloud management practices doesn't save time on monitoring, continuous improvement, and granularity. Increased access to back-end data means more work is needed to ensure your business gets the most out of AI. AI can increase monitoring options for cloud teams because it can analyze large amounts of data from cloud resources, and this benefit in analytics improves anomaly detection and enables the best analytics, incorporating time factors into your project plan so that your team can improve their cloud management practices, especially reporting and alerting.