Many organizations have embraced the hybrid cloud for its flexibility, scalability and capacity to accelerate market deployment. Hybrid cloud enables businesses worldwide to promote data security and accessibility for various projects and analysis. However, managing multiple hybrid clouds can be a complex endeavor, especially considering the evolving nature of enterprise requirements and the sheer number of applications in enterprise portfolios today. IDC reports that 39% of organizations have 500 or more applications in their portfolios.
A mix of institutional knowledge, legacy applications, data and analytics form the backbone of many organizations’ IT operations, however when a single component falls out of harmony, the entire system can fail. Research shows that 60%-90% of outage incidents are caused by changes in an organization’s IT environment.
Economic pressures are driving enterprises to minimize costs as they transition from traditional to more innovative operations. The abundance of data within IT Operations (including tickets, events, logs and metrics) serves as a crucial resource for any organization aiming to cut operational costs. By leveraging artificial intelligence (AI), they can extract valuable insights to achieve this goal.
However, to achieve this transformation successfully, it is crucial to incorporate a hybrid cloud management platform that prioritizes AI-infused automation. An integrated hybrid cloud equips organizations with the operational agility necessary to capitalize on emerging technologies and new global markets—and to ensure accelerated time to market for goods and services.
Start with a platform-centric approach
Standardization is crucial for organizations looking to automate and modernize. In a hybrid cloud environment, standardization helps eliminate inconsistencies, errors and discrepancies that may emerge from a complex mix of people, technology and processes working together.
Proper standardization can be challenging to achieve. Your organization must adopt a platform-centric approach to establish a foundation that promotes standardized practices, shared resources, open communication and streamlined processes.
By adopting a cloud platform as a central foundation, organizations can establish standardized practices for infrastructure provisioning, deployment, scaling, monitoring and security, while aligning with business goals. This ensures that technology implementation remains focused, and it underscores the importance of a top-down approach to an organization’s AI strategy. Through alignment with business priorities, engineers can effectively determine the necessity of AI (or assess whether a more straightforward, rules-based solution would suffice).
From complexity to simplicity
Developing a comprehensive, top-down strategy that aligns development goals with business goals enables organizations to quickly identify suitable sources and implement a well-structured and governed AI implementation.
An autonomous IT management system simplifies technological operations, key business processes and design systems. It quickly pulls from disparate data sources with integrated data, enabling faster and more informed decision making.
Generative AI technology is a leap ahead and can simplify application development by enabling engineers to automate code and document generation. By drawing from various foundation models, generative AI uses powerful transformers to generate content from unstructured information. Generative AI technology can be used to accelerate application development, reduce manual effort, and create accurate and compliant documents quickly.
With generative AI, organizations can automate tasks and enhance customer service and sales functions, improving the efficiency of these processes. Existing sales and service engineers can use language-based generative AI to augment their skills and easily find contextual or industrial knowledge to help them deliver better customer experiences or solve problems faster.
Generative AI offers a host of business benefits, including improved issue classification, code generation for issue resolution, enhanced auto-healing systems, context-sensitive automation, faster code debugging, best practice suggestions, better documentation generation, reverse engineering capabilities, and code refactoring—to name just a few possibilities.
Enhancing observability through autonomous IT operations enables system engineers to move beyond conventional IT health metrics. Instead, they can focus on more insightful “golden signals,” which include system latency, network traffic metrics, network saturation and errors.
Ensuring scalability and security
When discussing automating IT Operations, it’s critical to note the importance of managing your organization’s security operations (SecOps) with AI technology. By integrating AI into SecOps, organizations can efficiently identify, predict, and address security and compliance anomalies, as well as detect and mitigate potential threats. The goal is to leverage AI-driven automation to enhance an organization’s overall security and compliance posture.
Security and compliance are broad domains that vary across industries. Generative AI is useful for identifying anomalies within data and associating them with various sources of information (such as raw code, platform health and past business failures).
For instance, organizations can use AI tools to audit compliance documentation according to relevant audit standards. These AI tools then flag inappropriate words or phrases for human agents to assess.
Achieving modernization with AI
A robust, AI-driven, automation-first platform for managing hybrid cloud workloads will help modernize and accelerate the hybrid cloud transformation and journey for clients. Organizations can now leverage tactics like code generation to automate IT processes and modernize legacy applications for increased organizational agility.
Besides their conventional programming skills, engineers can now add “prompt engineer” to their skill set. Utilizing code generators, they can draft prompts that guide generative AI to create code that the engineers can review, modify and deploy. This significantly accelerates the development of applications and services.
For example, IBM Watson Code Assistant leverages the power of AI to make it easier for developers and IT operators to write code with AI-generated recommendations based on natural language inputs.
Enabling the ideal IT state with AI
The rise of hybrid cloud architecture has made AI implementation more accessible. By combining private and public cloud environments, organizations can leverage the infrastructure offered by hyperscalers like AWS, Azure and GCP. Training AI models closer to the data itself improves efficiency and reduces costs (compared to dedicated GPU infrastructure).
AI platforms, like watsonx, and open-source technologies, like OpenShift, further enhance AI implementation. They provide the necessary components, flexibility and scalability to support multiple AI models and applications.
As AI continues to advance and becomes more accessible, organizations can harness its potential for fostering innovation, efficiency and decision making. While there is no “single” solution for implementation, IBM Consulting can help address the complexity of innovative transformation by leveraging our technology, expertise and point of view to ensure your organization establishes a robust hybrid cloud strategy. IBM’s unique approach, which incorporates design thinking workshops, can be a valuable tool for navigating the complexities of AI implementation.
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