How to use Generative AI in your Business: Get Inspired by some Real World Examples

How to use Generative AI in your Business: Get Inspired by some Real World Examples


The Role of Generative AI in Maverick’s Workflow

At Maverick, GenAI is a fundamental component of our workflow. We employ it to automate various processes, enabling our developers to focus more on creative problem-solving and less on manual tasks. For instance, when confronted with a complex coding task, generative AI can provide draft code segments that can be refined and integrated into the final solution. This speeds up the coding process and reduces the risks of human error.

Generative AI also plays a critical role in increasing the efficiency of our development team. It acts like a virtual assistant, offering real-time code suggestions based on the project context. 

GenAI can learn from previous coding patterns and provide recommendations that match the style and structure of the existing codebase. This results in a more seamless and efficient development process and helps maintain code consistency throughout the project.

Transforming Repetitive Tasks

Repetitive tasks can slow development. 

Here again, GenAI comes to the rescue. It can automatically identify and complete these repetitive tasks, saving our developers valuable time. It can also detect potential issues in the code much earlier in the development process, allowing for more timely and accurate error correction. 

With digital products becoming increasingly complex, GenAI’s ability to automate tedious tasks can be a huge help for developers of all levels.

Revolutionising Code Drafting with AI Suggestions

Generative AI has redefined the drafting process of new code. It’s no longer about staring at a blank screen and tediously writing every line of code. Instead, AI-based tools can kick-start this process by offering initial code suggestions. 

These suggestions are not random; they’re based on the AI’s deep understanding of previous coding patterns and the current context of the project. This integration of AI in the drafting process is a game-changer. It’s like having a seasoned developer by your side, nudging you in the right direction as you write a new piece of code.

Efficiency in code generation is not just about speed but also about maintaining a high level of code quality. Generative AI tools are designed to learn from the best coding practices. They can provide clean, efficient code suggestions and adhere to your project’s style guide. This results in fewer revisions and a smoother transition from the draft to the final code.

Empowering Developers to Tackle Complex Challenges

Generative AI doesn’t just make coding easier; it empowers developers to tackle complex challenges head-on. With AI handling the rudimentary aspects of code drafting, developers can dedicate more time to problem-solving, design optimisation, and innovation. This results in the development of software that is not only functional but also innovative and forward-thinking.

Note that the first drafts of code provided by generative AI are not meant to be perfect. They’re meant to be a starting point, a springboard that inspires developers and guides them in a specific direction. 

These drafts, informed by AI’s interpretation of previous coding patterns, provide valuable insights into potential solutions and coding strategies. They’re a crucial tool that aids developers in tackling complex coding challenges with confidence.

Accelerating Code Updates with Generative AI

GenAI has a crucial role in updating existing code faster. It can scan through the codebase and identify areas that need optimisation or updating, making it quicker for developers to locate and address these instances.

AI-driven tools can also provide real-time suggestions for code improvements, reducing the time spent on iterative testing stages.

By leveraging generative AI, developers can benefit from real-time suggestions to enhance their existing code. AI-driven tools can detect patterns and make recommendations based on best practices and standards in the industry. This real-time feedback significantly minimises the need for extensive back-and-forths during code reviews, accelerating the overall updating process.

The Role of Generative AI in Continuous Learning

As a rich source of information, generative AI also plays an instrumental role in continuous learning for developers. It can help developers understand new languages or frameworks, draw parallels between coding paradigms, and suggest best practices. 

Incorporating AI into daily coding practices can lead to an expanded skill set and a deeper understanding of complex coding concepts and strategies.

Generative AI allows developers to learn and grow alongside their projects. As it suggests code improvements and presents alternative solutions, developers can grasp new concepts and broaden their skill set. This continuous, AI-driven learning leads to improved code quality and a more comprehensive understanding of software development.

Measuring the Impact of AI on Task Completion Rates

Generative AI–based tools greatly influence task completion rates, especially concerning intricate tasks. The ability of these tools to provide real-time suggestions and generate code significantly reduces the time spent on manual coding and iterative testing phases. 

As a result, it’s been noted that the probability of completing the tasks within the set time frames increases by approximately 25-30%. This data suggests AI’s profound impact on task efficiency, particularly in complex, time-intensive operations.

Project timelines witness a dramatic improvement with the integration of generative AI. The machine-driven assistance in coding tasks speeds up the development process, allowing developers to perform complex tasks more efficiently. 

This efficiency keeps the project on track and creates a buffer for unexpected delays or changes, bringing a certain flexibility to project timelines.


GenAI is a powerful tool for software development, significantly improving workflow and enabling developers to reach new productivity levels. By reducing the time spent on mundane tasks and optimising the process of meeting deadlines, AI-driven tools have led to more efficient software development cycles and better outcomes. 

Its power lies in its ability to provide real-time data, streamlining processes and reducing bottlenecks. As AI continues to evolve, so will its impact on software development, making it an ever-more integral part of the process. With AI, developers can expect greater accuracy, faster turnaround times, and increased productivity.

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