With artificial intelligence dominating the headlines and conversations, it seems like every company is announcing AI-related features, solutions, or initiatives for their business. That’s not a mistake. New research from McKinsey Global shows that organizations are most commonly using generative AI (gen AI). In fact, 40% of companies reporting AI adoption in their organizations say they expect their company to invest more in AI, and 28% say the use of Gen AI is already on the board’s agenda. It states that
That’s because the business disruption and benefits expected from generative AI are significant. At VMware, for example, we conduct internal AI experiments with various departments, including engineering, marketing, and customer success, to assess the impact of generative AI on our operations. Specifically, we are looking at whether AI can improve employee productivity and job satisfaction.
But as technologies like Genetic AI and ChatGPT tangibly change the way developers and people work, it’s understandable that long-standing concerns about AI’s potential to replace human tasks and jobs. is occurring. However, most technologists believe that generative AI is designed as an assistant, not a replacement.
We’re already optimizing business workflows, including employee experience and software development. And as software agility becomes increasingly correlated with business success, deploying AI to accelerate app development and improve developer velocity is critical for modern businesses.
How AI will impact software development
Software development takes a lot of time. Developers are leveraging the latest API specifications and more to write code for apps that require different user provisioning and resource configurations. The application of AI in software development can accelerate these areas, for example, by quickly retrieving information from different sources or integrating document sources.
Similarly, developers often experience writer’s block when writing code. AI-driven tools can help overcome these creative obstacles by suggesting code patterns, providing autocomplete suggestions, and generating sections of code. AI unlocks developer productivity by enabling teams to bridge knowledge, change context to obtain new information, and solve problems faster.
From a DevOps perspective, operators face some of the same issues as developers when accessing the right information. In addition to summarizing information and returning answers based on existing data, AI can take a multi-step approach by generating queries against data stores containing operational information about the environment and synthesizing that information. Tools like VMware Intelligent Assist allow teams to solve problems by asking questions about the environment they manage and generating queries about what to prioritize first.
AI app accelerator in action
App accelerators primarily help developers build intelligent assistance, whether it’s customer support or a specific product. They serve as a comprehensive guide to help developers understand the basic building blocks needed for these intelligent systems. App accelerators enable summarization services through chatbots and help teams understand what is needed to build these solutions.
Accelerators help teams understand patterns and assemble puzzle pieces faster.
For example, engineers can use embedded models and similarity analysis to greatly simplify data models and retrieve important details more quickly. First, developers need to identify relevant documentation, often in the form of PDFs or app catalogs. Additionally, implementing external model tracking and built-in model processing is important as it allows the AI system to efficiently process large amounts of documents using its LLM (Large-Scale Language Model).
Today, DevOps teams are overwhelmed by complex technology stacks and ultimately need a way to interact with their systems in more natural language. The real power of AI accelerators is their ability to help teams have more natural conversations. For example, rather than looking inside her Kubernetes environment or operator service endpoints, a developer can use AI accelerators to ask which applications are having problems.
AI app accelerators usher in a new era of AI-driven intelligent assistance. They serve as an important roadmap for developers and enterprises to navigate the complexities of building AI solutions. While AI can simplify processes and accelerate software development cycles, anything produced by AI still requires human oversight to ensure it is accurate and applicable.
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