The Impact of Generative AI on Enterprise Software Development

The idea of generative AI is not novel; it was first introduced in the late 1950s by generating new sequences of data based on input text. However, it became realistically available and technically feasible after Generative AI techniques like the Generative Adversarial Networks (GAN) was released in 2014. GAN can generate images, videos, and audio. In 2017, the transformer architecture allowed us to process and understand longer sequences of text, enabling the neural networks to capture the larger context and relationships between the documents more effectively. It led to the rise of large language models. Starting in 2018, the first launch of BERT followed by GPT-2 marked a significant progress in LLM capabilities and its use, and we are seeing tremendous growth and changes since then. Generative AI has emerged as a game-changing technology that will transform many sectors. Generative AI in Enterprise Software Development One of the specialized areas in Software engineering is Enterprise software development, where Generative AI has great potential to transform it. Companies have realized the huge potential of using AI in their Enterprise software development. The rise of Gen-AI has attracted significant attention in software development, but very few studies have examined its potential impact on this field. This article will discuss how Generative AI has the potential to transform enterprise product development and its impact on businesses and industries. Let's consider an example: a tech company where software developers are tasked with building a new cloud resource management system. This system can manage everything from spending on cloud resources to delivering the best value to customers at the lowest price point. Usually, this would take months to build, including developing the code, testing the software, and debugging. However, with the introduction of AI and ML, things have changed. Developers can now get help from AI-based tools that do most of the groundwork required to develop a software product. Developers can feed the AI with a list of prerequisites, high-level requirements, specifications to be included, required verifications and validations, types of users to support, and business rules. AI tools then analyze and process this information, helping developers generate business logic, create user interfaces, build dashboards, train data models, and execute workflows. AI tools can provide suggestions or recommendations, optimize code, streamline workflows or processes, generate documentation, and automate testing. As the team works, the AI tools can assist and augment developers to deliver continuous delivery by learning and adapting to rapid changes, detecting and mitigating potential bugs, and optimizing software performance before issues arise. Using Generative AI, the project could take weeks instead of months, with higher performance and fewer errors. Generative AI-driven systems can be developed as intelligent software systems that utilize the capabilities of large language models (LLMs) and retrieval-augmented generation (RAG) to process enterprise data. The ability to process enterprise data often requires specific training or fine-tuning on that data. RAG can improve the capabilities of LLMs without explicitly training the model or fine-tuning the model on this data. Implementing Agentic generative AI systems can automate and orchestrate complex workflows, multithread and parallel process workloads among multiple agents, and interact with internal and external systems to apply logic and evaluate responses in a self-directed manner. Generative AI agents can accelerate the development of AI applications and enhance customer service, streamline business operations, and improve user experience. Gen AI agents can be used in enterprises for different use cases, such as scheduling agents to define production schedules to maximize throughput and reduce the changeover costs or performing online marketing campaigns where agents can define, launch, and run a marketing campaign by using different software tools and trigger multiple workflows for performing subtasks. Even though these systems are highly autonomous as they operate within defined parameters, they still require human oversight for critical decisions. How enterprises can use Generative AI Synthetic Data Synthetic data is valuable when sufficient, high quality raw data is unavailable for machine learning model training. In some cases, actual data is restricted due to security and privacy concerns, or sufficient high-quality data is not available to train machine learning models. In such situation we can utilize generative AI to artificially generate the data in a way that it mimics the actual data. Generative AI is a powerful tool for creating synthetic data because it can identify patterns and relationships from actual data and create synthetic data for analysis and experimentation. For example, in healthcare, where patient data is critical and confidential, AI tools can generate synthetic patient data for training healthcare AI systems. As per a study conducted by Synthesis AI in collaboration with Vanson Bourne, 89% of technology leaders think synthetic data is a path to lead innovation in AI. Coding Efficiency Enhancing coding efficiency is another use case for generative AI in enterprise domain, AI code completion tools can support and recommend multiple programming languages, enabling developers to work seamlessly across various projects. Modern AI tools can streamline coding processes, allow developers to focus on the creative side of software development, and reduce the total time to deliver the final product. AI tools have the ability to identify potential semantic, syntactic, and logical bugs as developers write their code. It can also review large code as the context windows of these under large language models have grown tremendously in the last two years. For example, Google Gemini 1.5 Pro's large language model can support up to 2 million context windows (This is a recent development, and context window size may vary based on the LLMs). AI code completion tools are powered by neural networks, transformers, and deep learning models. These models learn code patterns and structures from large codebases to generate accurate predictions and auto-completion. It can also understand the context and semantics of the code being written and offers appropriate suggestions. In GitHub's study, it was found that developers who used GitHub Copilot completed the task 55% faster than developers who didn't use AI-assisted code development software. Personalized Experience Generative AI can offer a personalized user experience where it can respond to users' queries by analyzing additional context such as the User's history, preferences, emotional state, the urgency of the query. This can lead to an increase in customer satisfaction, sales, and revenue, higher customer retention, and lower operational and marketing costs. For example, Amazon chat support or US Bank Smart Assistant empowers their business and makes them data-driven and efficient with the use of Generative AI-powered functionality. Code quality improvements and software testing Generative AI tools can revolutionize code development and testing by automatically running workflows to minimize errors and improve precision. It can perpetually detect bugs and optimize the code quality to reduce downtime and improve resource efficiency when running the software. It can also identify corner cases where human testers might miss and build strong test cases for complex parts of the code. Overall, it helps to reduce the time required to build code through the software development lifecycle. As per Gitlab's Survey, more than 30 percent of teams are using AI-powered tools for code reviews, such as Amazon's Code Guru, GitHub CoPilot, and PullRequest. Challenges and Considerations of Generative AI in Enterprise software Development While the benefits of generative AI are clear, it's not without challenges. First, AI-generated code still needs human oversight. While the AI can write functional code, it may not always follow the best practices for security, scalability, or performance optimization. Developers must review and refine the code to ensure it meets enterprise standards. There's also the risk of over-reliance on AI. Developers and teams still need to deeply understand the systems they're building and maintain their problem-solving skills. Generative AI is a tool that enhances human capabilities, but it's not a substitute for expertise. Another concern is how AI will handle complex, creative problem-solving. Generative AI is excellent at automating repetitive tasks, but when it comes to developing entirely new concepts or addressing nuanced business challenges, human insight is still indispensable. Privacy and compliance concerns Generative AI models can leak private personal information (PPI) if the models are not anonymized properly. Re-identification is a serious risk in sensitive fields such as healthcare, government, and security. Imagine the healthcare facility using generative AI to create synthetic data for research purposes. This synthetic data is kept anonymized to protect patient privacy and excludes details such as name and address; this way, researchers can use data without knowing who the actual person is. Now, let's say a patient with a rare health condition received specific treatment in that healthcare facility. Even though the patient's identity is not included, the rate combination of disease and treatment makes the data unique. Researchers or third-party users with access to the healthcare facility database can cross reference these details and recognize actual patient names because no other patient has an exact combination of factors. This is a re-identification risk when anonymous data can still be traced back to real people because of unique details. Fairness in offering solutions using Generative AI Most of the Large Language Models are trained on English and can create and perpetuate biases as the data used to train the AI model is not diverse or representative. For example, if a company uses AI tools that are trained for who native English speaker, then it will have poor performance for non-English language users as the models have not been trained for non-English languages. This could create inequity and limit access to AI tools for non-English languages. Validity and Reliability of AI systems AI models are trained on large datasets, and these datasets can become outdated over time. This can degrade the performance of the AI model in terms of generative relevant content, and output becomes non-deterministic and inconsistent. Imagine a navigation app powered by an AI model on a static dataset of city maps from a few years ago when the city's traffic patterns and highways, roads, bicycle lanes, or pedestrian zones were different. Now, the map has been changed due to construction and the city's growth. Because the AI model is trained on outdated data, the model's recommendations and suggestions may show inefficient routes and may direct drivers to roads or freeways that no longer exist. Over time, app users will notice this, which causes frustration and reduces trustworthiness. In this case, models would need to be trained with regularly updated data, the latest data from the city civil department, and traffic data to prevent performance degradation and keep the AI model's recommendations relevant. Industry Best Practices AI is an emerging technology and is rapidly evolving with new innovations and developments. Enterprises need to analyze the upcoming trends and determine how they can fit ethically within their business models. Enterprises will need to understand how they will use customer data and how it will get processed through AI models that are trained with these latest innovations. While adopting speed and efficiency in software development, they should maintain security, privacy, best practices, guidelines for ethical AI development, compliance, and control while adopting these new AI-powered tools. Here are some ways enterprises can leverage AI to ensure the organization keeps up. AI is not a passing trend AI has already shown its impact across the industry and will be part of the journey. Typically, humans are resistant to change, but, in this case, we need to be extra agile to embrace this change quickly as it will change our day-to-day work style. We should not think that AI will replace humans. Instead, we need to think of pairing AI with human intuition, thought processes, and problem-solving skills. The opportunities are limitless for businesses and enterprises willing to embrace generative AI. AI tools like GitHub CoPilot and AWS CodeWhisperer boost productivity and help developers develop, test, and debug code faster so they can focus on actual business logic and complexity. However, developing new concepts, creative problem-solving, or handling complex business challenges will still require human involvement. Think of AI tools as assistants or helpers that will help or improve efficiency, not replace the workforce. References Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial networks. arXiv. https://doi.org/10.48550/arXiv.1406.2661 Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems (pp. 5998-6008). Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv. https://doi.org/10.48550/arXiv.1810.04805 Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI Blog, 1(8), 9. GitHub. (2022). Research: Quantifying GitHub Copilot's impact on developer productivity and happiness. GitHub Blog. https://github.blog/2022-09-07-research-quantifying-github-copilots-impact-on-developer-productivity-and-happiness/ GitLab. (2022). 2022 Global DevSecOps Survey. GitLab. https://about.gitlab.com/developer-survey/ Synthesis AI & Vanson Bourne. (2022). The state of synthetic data for AI. Synthesis AI. Google. (2024). Introducing Gemini 1.5: Our most capable AI model. Google AI Blog. https://blog.google/technology/ai/gemini-1-5-google-ai/ U.S. Bank Smart Assistant. U.S. Bank. https://www.usbank.com/digital-innovation/smart-assistant.html Amazon CodeGuru – Automate code reviews and optimize application performance. Amazon Web Services. https://aws.amazon.com/codeguru/ GitHub Copilot · Your AI pair programmer. GitHub. https://github.com/features/copilot Code review as a service. PullRequest. https://www.pullrequest.com/ The idea of generative AI is not novel; it was first introduced in the late 1950s by generating new sequences of data based on input text. However, it became realistically available and technically feasible after Generative AI techniques like the Generative Adversarial Networks (GAN) was released in 2014. GAN can generate images, videos, and audio. In 2017, the transformer architecture allowed us to process and understand longer sequences of text, enabling the neural networks to capture the larger context and relationships between the documents more effectively. It led to the rise of large language models. Starting in 2018, the first launch of BERT followed by GPT-2 marked a significant progress in LLM capabilities and its use, and we are seeing tremendous growth and changes since then. Generative AI has emerged as a game-changing technology that will transform many sectors. Generative AI in Enterprise Software Development One of the specialized areas in Software engineering is Enterprise software development, where Generative AI has great potential to transform it. Companies have realized the huge potential of using AI in their Enterprise software development. The rise of Gen-AI has attracted significant attention in software development, but very few studies have examined its potential impact on this field. This article will discuss how Generative AI has the potential to transform enterprise product development and its impact on businesses and industries. Let's consider an example: a tech company where software developers are tasked with building a new cloud resource management system. This system can manage everything from spending on cloud resources to delivering the best value to customers at the lowest price point. Usually, this would take months to build, including developing the code, testing the software, and debugging. However, with the introduction of AI and ML, things have changed. Developers can now get help from AI-based tools that do most of the groundwork required to develop a software product. Developers can feed the AI with a list of prerequisites, high-level requirements, specifications to be included, required verifications and validations, types of users to support, and business rules. AI tools then analyze and process this information, helping developers generate business logic, create user interfaces, build dashboards, train data models, and execute workflows. AI tools can provide suggestions or recommendations, optimize code, streamline workflows or processes, generate documentation, and automate testing. As the team works, the AI tools can assist and augment developers to deliver continuous delivery by learning and adapting to rapid changes, detecting and mitigating potential bugs, and optimizing software performance before issues arise. Using Generative AI, the project could take weeks instead of months, with higher performance and fewer errors. Generative AI-driven systems can be developed as intelligent software systems that utilize the capabilities of large language models (LLMs) and retrieval-augmented generation (RAG) to process enterprise data. The ability to process enterprise data often requires specific training or fine-tuning on that data. RAG can improve the capabilities of LLMs without explicitly training the model or fine-tuning the model on this data. Implementing Agentic generative AI systems can automate and orchestrate complex workflows, multithread and parallel process workloads among multiple agents, and interact with internal and external systems to apply logic and evaluate responses in a self-directed manner. Generative AI agents can accelerate the development of AI applications and enhance customer service, streamline business operations, and improve user experience. Gen AI agents can be used in enterprises for different use cases, such as scheduling agents to define production schedules to maximize throughput and reduce the changeover costs or performing online marketing campaigns where agents can define, launch, and run a marketing campaign by using different software tools and trigger multiple workflows for performing subtasks. Even though these systems are highly autonomous as they operate within defined parameters, they still require human oversight for critical decisions. How enterprises can use Generative AI Synthetic Data Synthetic data is valuable when sufficient, high quality raw data is unavailable for machine learning model training. In some cases, actual data is restricted due to security and privacy concerns, or sufficient high-quality data is not available to train machine learning models. In such situation we can utilize generative AI to artificially generate the data in a way that it mimics the actual data. Generative AI is a powerful tool for creating synthetic data because it can identify patterns and relationships from actual data and create synthetic data for analysis and experimentation. For example, in healthcare, where patient data is critical and confidential, AI tools can generate synthetic patient data for training healthcare AI systems. As per a study conducted by Synthesis AI in collaboration with Vanson Bourne, 89% of technology leaders think synthetic data is a path to lead innovation in AI. Coding Efficiency Enhancing coding efficiency is another use case for generative AI in enterprise domain, AI code completion tools can support and recommend multiple programming languages, enabling developers to work seamlessly across various projects. Modern AI tools can streamline coding processes, allow developers to focus on the creative side of software development, and reduce the total time to deliver the final product. AI tools have the ability to identify potential semantic, syntactic, and logical bugs as developers write their code. It can also review large code as the context windows of these under large language models have grown tremendously in the last two years. For example, Google Gemini 1.5 Pro's large language model can support up to 2 million context windows (This is a recent development, and context window size may vary based on the LLMs). AI code completion tools are powered by neural networks, transformers, and deep learning models. These models learn code patterns and structures from large codebases to generate accurate predictions and auto-completion. It can also understand the context and semantics of the code being written and offers appropriate suggestions. In GitHub's study, it was found that developers who used GitHub Copilot completed the task 55% faster than developers who didn't use AI-assisted code development software. Personalized Experience Generative AI can offer a personalized user experience where it can respond to users' queries by analyzing additional context such as the User's history, preferences, emotional state, the urgency of the query. This can lead to an increase in customer satisfaction, sales, and revenue, higher customer retention, and lower operational and marketing costs. For example, Amazon chat support or US Bank Smart Assistant empowers their business and makes them data-driven and efficient with the use of Generative AI-powered functionality. Code quality improvements and software testing Generative AI tools can revolutionize code development and testing by automatically running workflows to minimize errors and improve precision. It can perpetually detect bugs and optimize the code quality to reduce downtime and improve resource efficiency when running the software. It can also identify corner cases where human testers might miss and build strong test cases for complex parts of the code. Overall, it helps to reduce the time required to build code through the software development lifecycle. As per Gitlab's Survey, more than 30 percent of teams are using AI-powered tools for code reviews, such as Amazon's Code Guru, GitHub CoPilot, and PullRequest. Challenges and Considerations of Generative AI in Enterprise software Development While the benefits of generative AI are clear, it's not without challenges. First, AI-generated code still needs human oversight. While the AI can write functional code, it may not always follow the best practices for security, scalability, or performance optimization. Developers must review and refine the code to ensure it meets enterprise standards. There's also the risk of over-reliance on AI. Developers and teams still need to deeply understand the systems they're building and maintain their problem-solving skills. Generative AI is a tool that enhances human capabilities, but it's not a substitute for expertise. Another concern is how AI will handle complex, creative problem-solving. Generative AI is excellent at automating repetitive tasks, but when it comes to developing entirely new concepts or addressing nuanced business challenges, human insight is still indispensable. Privacy and compliance concerns Generative AI models can leak private personal information (PPI) if the models are not anonymized properly. Re-identification is a serious risk in sensitive fields such as healthcare, government, and security. Imagine the healthcare facility using generative AI to create synthetic data for research purposes. This synthetic data is kept anonymized to protect patient privacy and excludes details such as name and address; this way, researchers can use data without knowing who the actual person is. Now, let's say a patient with a rare health condition received specific treatment in that healthcare facility. Even though the patient's identity is not included, the rate combination of disease and treatment makes the data unique. Researchers or third-party users with access to the healthcare facility database can cross reference these details and recognize actual patient names because no other patient has an exact combination of factors. This is a re-identification risk when anonymous data can still be traced back to real people because of unique details. Fairness in offering solutions using Generative AI Most of the Large Language Models are trained on English and can create and perpetuate biases as the data used to train the AI model is not diverse or representative. For example, if a company uses AI tools that are trained for who native English speaker, then it will have poor performance for non-English language users as the models have not been trained for non-English languages. This could create inequity and limit access to AI tools for non-English languages. Validity and Reliability of AI systems AI models are trained on large datasets, and these datasets can become outdated over time. This can degrade the performance of the AI model in terms of generative relevant content, and output becomes non-deterministic and inconsistent. Imagine a navigation app powered by an AI model on a static dataset of city maps from a few years ago when the city's traffic patterns and highways, roads, bicycle lanes, or pedestrian zones were different. Now, the map has been changed due to construction and the city's growth. Because the AI model is trained on outdated data, the model's recommendations and suggestions may show inefficient routes and may direct drivers to roads or freeways that no longer exist. Over time, app users will notice this, which causes frustration and reduces trustworthiness. In this case, models would need to be trained with regularly updated data, the latest data from the city civil department, and traffic data to prevent performance degradation and keep the AI model's recommendations relevant. Industry Best Practices AI is an emerging technology and is rapidly evolving with new innovations and developments. Enterprises need to analyze the upcoming trends and determine how they can fit ethically within their business models. Enterprises will need to understand how they will use customer data and how it will get processed through AI models that are trained with these latest innovations. While adopting speed and efficiency in software development, they should maintain security, privacy, best practices, guidelines for ethical AI development, compliance, and control while adopting these new AI-powered tools. Here are some ways enterprises can leverage AI to ensure the organization keeps up. AI is not a passing trend AI has already shown its impact across the industry and will be part of the journey. Typically, humans are resistant to change, but, in this case, we need to be extra agile to embrace this change quickly as it will change our day-to-day work style. We should not think that AI will replace humans. Instead, we need to think of pairing AI with human intuition, thought processes, and problem-solving skills. The opportunities are limitless for businesses and enterprises willing to embrace generative AI. AI tools like GitHub CoPilot and AWS CodeWhisperer boost productivity and help developers develop, test, and debug code faster so they can focus on actual business logic and complexity. However, developing new concepts, creative problem-solving, or handling complex business challenges will still require human involvement. Think of AI tools as assistants or helpers that will help or improve efficiency, not replace the workforce. References Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial networks. arXiv. https://doi.org/10.48550/arXiv.1406.2661 Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems (pp. 5998-6008). Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv. https://doi.org/10.48550/arXiv.1810.04805 Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI Blog, 1(8), 9. GitHub. (2022). Research: Quantifying GitHub Copilot's impact on developer productivity and happiness. GitHub Blog. https://github.blog/2022-09-07-research-quantifying-github-copilots-impact-on-developer-productivity-and-happiness/ GitLab. (2022). 2022 Global DevSecOps Survey. GitLab. https://about.gitlab.com/developer-survey/ Synthesis AI & Vanson Bourne. (2022). The state of synthetic data for AI. Synthesis AI. Google. (2024). Introducing Gemini 1.5: Our most capable AI model. Google AI Blog. https://blog.google/technology/ai/gemini-1-5-google-ai/ U.S. Bank Smart Assistant. U.S. Bank. https://www.usbank.com/digital-innovation/smart-assistant.html Amazon CodeGuru – Automate code reviews and optimize application performance. Amazon Web Services. https://aws.amazon.com/codeguru/ GitHub Copilot · Your AI pair programmer. GitHub. https://github.com/features/copilot Code review as a service. PullRequest. https://www.pullrequest.com/ Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial networks. arXiv. https://doi.org/10.48550/arXiv.1406.2661 https://doi.org/10.48550/arXiv.1406.2661 Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems (pp. 5998-6008). Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv. https://doi.org/10.48550/arXiv.1810.04805 https://doi.org/10.48550/arXiv.1810.04805 Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI Blog, 1(8), 9. GitHub. (2022). Research: Quantifying GitHub Copilot's impact on developer productivity and happiness. GitHub Blog. https://github.blog/2022-09-07-research-quantifying-github-copilots-impact-on-developer-productivity-and-happiness/ https://github.blog/2022-09-07-research-quantifying-github-copilots-impact-on-developer-productivity-and-happiness/ GitLab. (2022). 2022 Global DevSecOps Survey. GitLab. https://about.gitlab.com/developer-survey/ https://about.gitlab.com/developer-survey/ Synthesis AI & Vanson Bourne. (2022). The state of synthetic data for AI. Synthesis AI. Google. (2024). Introducing Gemini 1.5: Our most capable AI model. Google AI Blog. https://blog.google/technology/ai/gemini-1-5-google-ai/ https://blog.google/technology/ai/gemini-1-5-google-ai/ U.S. Bank Smart Assistant. U.S. Bank. https://www.usbank.com/digital-innovation/smart-assistant.html https://www.usbank.com/digital-innovation/smart-assistant.html Amazon CodeGuru – Automate code reviews and optimize application performance. Amazon Web Services. https://aws.amazon.com/codeguru/ https://aws.amazon.com/codeguru/ GitHub Copilot · Your AI pair programmer. GitHub. https://github.com/features/copilot https://github.com/features/copilot Code review as a service. PullRequest. https://www.pullrequest.com/ https://www.pullrequest.com/