In today’s rapidly evolving technological landscape, banks and financial institutions are seeking ways to modernize and streamline their operations. FlowX.AI offers an open, flexible, and secure platform enhanced by Artificial Intelligence to meet such needs.
One of the key advancements driving this transformation is the use of Large Language Models (LLMs). These models are changing the way developers and information workers perform their tasks, significantly boosting productivity and efficiency. In this blog article, we focus on understanding how LLMs are used to improve productivity across various sectors.
What LLMs are all about
Large Language Models, such as GPT-4, Claude-3.5, Mistral are advanced AI systems capable of understanding and generating human-like text based on the data they have been trained on. These models can perform a variety of tasks, including text completion, translation, summarization, and even code generation. Their ability to comprehend context and generate coherent responses makes them invaluable tools in numerous applications, particularly in software development and enterprise operations.
Enhancing developer productivity with LLMs
Generative AI tools, powered by LLMs, are making a notable impact on software development. Recent research indicates that these tools can substantially decrease the time needed for various coding tasks.
For example, they can cut the time to create code documentation in half, reduce the time to write new code by nearly half, and optimize existing code in about two-thirds of the usual time as shown in a recent study from McKinsey and GitHub.
Chart re-created based on data from: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/unleashing-developer-productivity-with-generative-ai#/
As expected, the greatest productivity gains are seen in simpler tasks, whereas the boost remains for more complex tasks, though it is less significant. Interestingly, however, using LLMs to assist with these complex tasks still speeds up their completion. As you can see below, developers that used generative AI to assist with complex tasks were more likely to complete the tasks in a given timeframe:
Chart re-created based on data from https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/unleashing-developer-productivity-with-generative-ai#/
The study defines the following areas as the ones where LLMs can be the most impactful.
- Expediting manual and repetitive workLLMs can handle routine tasks such as auto-filling standard functions, completing coding statements, and documenting code functionality based on developer prompts. This allows developers to focus on solving complex business challenges and developing new software capabilities.
- Jump-starting the first draft of new codeWhen faced with a blank screen, developers can use LLMs to generate initial code suggestions, helping them overcome writer’s block and get started more quickly.
- Accelerating updates to existing codeLLMs can make changes to existing code faster with effective prompting, reducing the time spent adapting code from online libraries and improving prewritten code.
- Increasing ability to tackle new challengesLLMs help developers rapidly brush up on unfamiliar code bases, languages, or frameworks, providing the kind of support they might seek from an experienced colleague.
Enabling information workers to get more efficient
Beyond software development, LLMs are also enhancing productivity for enterprise information workers. In another study [2], Microsoft’s research on their Copilot tools demonstrates substantial productivity gains across common tasks such as email retrieval, content creation, and meeting summarization.
These tools typically increase speed without sacrificing quality, providing meaningful time savings and improving the overall work experience. The key takeaways from this study show:
- Increased speed
- Tasks performed with LLM-based tools were completed in significantly less time compared to traditional methods. For instance, workers using Copilot tools reported substantial reductions in the time required to complete routine tasks, often completing them in just 26% to 73% of the time it would typically take.
- Maintained qualityThe quality of work produced using LLM-based tools remained high. Studies showed that task accuracy was generally unaffected, even as the speed of task completion increased.
- Reduced effort and increased job satisfactionWorkers using Copilot tools found tasks less draining and reported higher levels of job satisfaction. This reduction in perceived effort and increase in satisfaction can help companies retain and motivate their talent.
Chart re-created based on data from https://www.microsoft.com/en-us/research/uploads/prod/2023/12/AI-and-Productivity-Report-First-Edition.pdf
These results are further validated by a comprehensive literature review by Al Naqbi et al. (2024), that shows that Generative AI has significant impacts across various professional sectors, including academia, technology, communications, agriculture, government, and business.
Apart from the already discussed items, they show that generative AI can help in improving decision making where AI tools assist in data analysis and interpretation, providing insights that support better decision-making processes. This is particularly useful in fields like healthcare and finance, where large datasets require thorough analysis. Generative AI can also be used to provide better personalized experiences in sectors such as education and tourism, where it provides personalized experiences by tailoring content and recommendations to individual users’ needs and preferences.
How FlowX.AI is enhancing banking productivity using LLMs
One of our primary goals at FlowX.AI is to integrate Large Language Models into the banking sector to improve productivity in developing banking applications. By leveraging LLMs, we are helping our clients achieve greater efficiency and innovation in their development processes. We believe this aligns with the broader trends identified in these studies where AI can be used to significantly enhance the performance and work productivity.
To achieve this, we employ a modular approach that breaks down tasks into smaller, manageable segments, and each task is handled by specialized AI agents tailored for specific functions. This strategy enables developers to define and create processes on our platform faster and with greater accuracy. Each AI agent is designed to assist with distinct aspects of the development lifecycle, ensuring a seamless and efficient workflow.
We are committed to continuously exploring new ways to enhance productivity, ensuring our clients stay up-to-date with technological advancements and operational improvements in the financial industry. Stay tuned for more details!
References
- Unleashing developer productivity with generative AI.
- Cambon, Alexia, et al. "Early LLM-based Tools for Enterprise Information Workers Likely Provide Meaningful Boosts to Productivity." Microsoft Research. MSR-TR-2023-43 (2023).
- Research: quantifying GitHub Copilot’s impact on developer productivity and happiness
- Al Naqbi, Humaid, Zied Bahroun, and Vian Ahmed. "Enhancing Work Productivity through Generative Artificial Intelligence: A Comprehensive Literature Review." Sustainability 16.3 (2024): 1166.
In today’s rapidly evolving technological landscape, banks and financial institutions are seeking ways to modernize and streamline their operations. FlowX.AI offers an open, flexible, and secure platform enhanced by Artificial Intelligence to meet such needs.
One of the key advancements driving this transformation is the use of Large Language Models (LLMs). These models are changing the way developers and information workers perform their tasks, significantly boosting productivity and efficiency. In this blog article, we focus on understanding how LLMs are used to improve productivity across various sectors.
What LLMs are all about
Large Language Models, such as GPT-4, Claude-3.5, Mistral are advanced AI systems capable of understanding and generating human-like text based on the data they have been trained on. These models can perform a variety of tasks, including text completion, translation, summarization, and even code generation. Their ability to comprehend context and generate coherent responses makes them invaluable tools in numerous applications, particularly in software development and enterprise operations.
Enhancing developer productivity with LLMs
Generative AI tools, powered by LLMs, are making a notable impact on software development. Recent research indicates that these tools can substantially decrease the time needed for various coding tasks.
For example, they can cut the time to create code documentation in half, reduce the time to write new code by nearly half, and optimize existing code in about two-thirds of the usual time as shown in a recent study from McKinsey and GitHub.
Chart re-created based on data from: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/unleashing-developer-productivity-with-generative-ai#/
As expected, the greatest productivity gains are seen in simpler tasks, whereas the boost remains for more complex tasks, though it is less significant. Interestingly, however, using LLMs to assist with these complex tasks still speeds up their completion. As you can see below, developers that used generative AI to assist with complex tasks were more likely to complete the tasks in a given timeframe:
Chart re-created based on data from https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/unleashing-developer-productivity-with-generative-ai#/
The study defines the following areas as the ones where LLMs can be the most impactful.
- Expediting manual and repetitive workLLMs can handle routine tasks such as auto-filling standard functions, completing coding statements, and documenting code functionality based on developer prompts. This allows developers to focus on solving complex business challenges and developing new software capabilities.
- Jump-starting the first draft of new codeWhen faced with a blank screen, developers can use LLMs to generate initial code suggestions, helping them overcome writer’s block and get started more quickly.
- Accelerating updates to existing codeLLMs can make changes to existing code faster with effective prompting, reducing the time spent adapting code from online libraries and improving prewritten code.
- Increasing ability to tackle new challengesLLMs help developers rapidly brush up on unfamiliar code bases, languages, or frameworks, providing the kind of support they might seek from an experienced colleague.
Enabling information workers to get more efficient
Beyond software development, LLMs are also enhancing productivity for enterprise information workers. In another study [2], Microsoft’s research on their Copilot tools demonstrates substantial productivity gains across common tasks such as email retrieval, content creation, and meeting summarization.
These tools typically increase speed without sacrificing quality, providing meaningful time savings and improving the overall work experience. The key takeaways from this study show:
- Increased speed
- Tasks performed with LLM-based tools were completed in significantly less time compared to traditional methods. For instance, workers using Copilot tools reported substantial reductions in the time required to complete routine tasks, often completing them in just 26% to 73% of the time it would typically take.
- Maintained qualityThe quality of work produced using LLM-based tools remained high. Studies showed that task accuracy was generally unaffected, even as the speed of task completion increased.
- Reduced effort and increased job satisfactionWorkers using Copilot tools found tasks less draining and reported higher levels of job satisfaction. This reduction in perceived effort and increase in satisfaction can help companies retain and motivate their talent.
Chart re-created based on data from https://www.microsoft.com/en-us/research/uploads/prod/2023/12/AI-and-Productivity-Report-First-Edition.pdf
These results are further validated by a comprehensive literature review by Al Naqbi et al. (2024), that shows that Generative AI has significant impacts across various professional sectors, including academia, technology, communications, agriculture, government, and business.
Apart from the already discussed items, they show that generative AI can help in improving decision making where AI tools assist in data analysis and interpretation, providing insights that support better decision-making processes. This is particularly useful in fields like healthcare and finance, where large datasets require thorough analysis. Generative AI can also be used to provide better personalized experiences in sectors such as education and tourism, where it provides personalized experiences by tailoring content and recommendations to individual users’ needs and preferences.
How FlowX.AI is enhancing banking productivity using LLMs
One of our primary goals at FlowX.AI is to integrate Large Language Models into the banking sector to improve productivity in developing banking applications. By leveraging LLMs, we are helping our clients achieve greater efficiency and innovation in their development processes. We believe this aligns with the broader trends identified in these studies where AI can be used to significantly enhance the performance and work productivity.
To achieve this, we employ a modular approach that breaks down tasks into smaller, manageable segments, and each task is handled by specialized AI agents tailored for specific functions. This strategy enables developers to define and create processes on our platform faster and with greater accuracy. Each AI agent is designed to assist with distinct aspects of the development lifecycle, ensuring a seamless and efficient workflow.
We are committed to continuously exploring new ways to enhance productivity, ensuring our clients stay up-to-date with technological advancements and operational improvements in the financial industry. Stay tuned for more details!
References
- Unleashing developer productivity with generative AI.
- Cambon, Alexia, et al. "Early LLM-based Tools for Enterprise Information Workers Likely Provide Meaningful Boosts to Productivity." Microsoft Research. MSR-TR-2023-43 (2023).
- Research: quantifying GitHub Copilot’s impact on developer productivity and happiness
- Al Naqbi, Humaid, Zied Bahroun, and Vian Ahmed. "Enhancing Work Productivity through Generative Artificial Intelligence: A Comprehensive Literature Review." Sustainability 16.3 (2024): 1166.
