Many organizations have embraced the hybrid cloud for its flexibility, scalability and capacity to help accelerate market deployment for goods and services. Hybrid cloud helps businesses worldwide 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 as well as the sheer number of applications in enterprise portfolios today.

Our experts believe that a hybrid cloud management platform that prioritizes generative AI-infused automation could help you advance transformation successfully. An integrated hybrid cloud can equip organizations with operational agility to capitalize on emerging technologies and new global markets, and has already helped some organizations realize cost savings and efficiencies, improve IT performance and deliver and scale new services more quickly.

Start with a platform-centric approach

Standardization is crucial for organizations looking to automate and modernize. In a hybrid cloud environment, standardization addresses 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 should 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 helps ensure that technology implementation remains focused, and it underscores the importance of a top-down approach to an organization’s generative AI strategy. Through alignment with business priorities, engineers can effectively determine the necessity of generative 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 generative AI implementation.

An autonomous IT management system is designed to simplify technological operations, key business processes and design systems. It pulls from possibly disparate data sources with integrated data, promoting faster and more informed decision making.

Generative AI technology is a leap ahead and can simplify application development by helping engineers automate code and document generation. By drawing from various foundation models, generative AI uses powerful transformers to generate content from unstructured information. Today, we see our clients are using or considering using automation capabilities to automate IT operations, IT asset management and asset utilization.

With generative AI, organizations can automate tasks and enhance customer service and sales functions, to improve the efficiency of these processes. Existing sales and service engineers can use language-based generative AI to augment their skills and find contextual or industrial knowledge to help them deliver better customer experiences or solve problems faster.

Generative AI offers a host of potential 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 positions 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.

Scalability and security

When discussing automating IT Operations, organizations should note the importance of managing your organization’s security operations (SecOps) with generative AI technology. IBM has found that by integrating generative AI into SecOps, organizations can efficiently identify and address security 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 can vary across industries. Our experts believe generative AI can be useful for identifying anomalies within data and associating them with various sources of information (such as raw code and past business failures).

For instance, organizations can use generative AI tools to support audit compliance activities and documentation according to relevant audit standards. Once the evaluation is complete, these generative AI tools are designed to flag inappropriate words or phrases for human agents to assess.

Achieving modernization with generative AI

A robust, AI-driven, automation-first platform for managing hybrid cloud workloads can help modernize and accelerate the hybrid cloud transformation and journey for clients. Organizations can leverage tactics like code generation to automate IT processes and modernize legacy applications for increased organizational agility.

Utilizing code generators, engineers are positioned to draft prompts that guide generative AI to create code that the engineers can review, modify and deploy. This can significantly accelerate the development of applications and services.

For example, generative AI can make it easier for developers and IT operators to write code with AI-generated recommendations based on natural language inputs.