Artificial intelligence (AI) is transforming the process of developing software as a whole by enhancing the quality of the code being produced and reducing the amount of time spent on repetitive chores such as testing and debugging. Two of the ways in which artificial intelligence is anticipated to have a far-reaching impact on whole industries are through the disruption of value creation and its influence on business structures and organizations. AI-driven products such as ChatGPT, Copilot, and TabNine have the potential to alter software development processes. ChatGPT is a chatbot powered by a complex language model; Copilot is a pair programmer; and TabNine is a code completion assistant, both powered by artificial intelligence. Even if artificial intelligence has the potential to drastically change the requirements engineering process, it will still be utilized in conjunction with the experience and judgment of humans in order to get the best possible outcomes. This article investigates the potential implications of AI on business analysis as well as the opportunities it brings for the field’s potential future.
1 Introduction
As a general-use technology, artificial intelligence (AI) is being likened to the steam engine and its effects on the economy in the 17th century. As a result, it is generally agreed that AI and its applications have the potential to significantly alter entire industries through the disruption of value creation and the impact of business models and organizations through a number of mechanisms, such as the redistribution of decision-making authority. Artificial intelligence is changing the nature of work and the workplace as a whole through its integration into processes and activities. Developing a more in-depth comprehension of the consequences and implications for future companies and professions is essential for a new technology like AI. AI is transforming the way we produce software in general, from enhancing code quality to shortening the time required for repetitive chores like debugging and testing. The many advantages brought to the table by artificial intelligence would help engineers become more productive and efficient, thereby revolutionizing the software development process. It is relevant and important to not only investigate how AI might improve process productivity and efficiency but also to take the first steps toward understanding how to adopt a technology that is here to stay. Some of the potential AIs tools we may consider are ChatGPT, Copilot, and TabNine. ChatGPT is a chatbot powered by a big language model. It makes use of an AI designed to have in-depth conversations with humans in order to provide solutions to complicated problems. Its capacity to have natural-sounding conversations and respond in ways that make it seem human is quite astonishing. Copilot or “AI pair programmer” is a language-agnostic AI that can provide coding ideas through natural language questions, having been trained on billions of lines of code. TabNine is an artificial intelligence code completion helper to get extended snippet ideas and focused line code completions. TabNine reuses an organization’s common coding patterns to reduce unnecessary developer toil.
2 Business Analysis and AI
Business analysis is an evolving practice; however, requirements engineering remains its “bread and butter.” AI may revolutionize the way requirements engineering is done, making the process more efficient, effective, and productive. However, it is crucial to highlight that AI is not a panacea and will be utilized in conjunction with human skills and judgment to get the best outcomes. There are a myriad of potential implications for adoption. Below, I illustrate some examples:
- Requirements Elicitation: The employment of AI can facilitate the automation of the requirements elicitation process. Intelligent chatbots, which have been trained using large language models (LLMs), have the potential to engage with stakeholders and acquire information pertaining to their needs. This new practice could be used to complement traditional elicitation techniques, such as workshops.
- Requirements Analysis: Trained AI can analyze requirements and detect trends, contradictions, and conflicts. This may aid in ensuring that the requirements are complete, consistent, and unambiguous. Although such tools are not yet widely available, technology has previously proven analogous applications, and certain pioneer tools are now available. IBM, for example, has introduced “IBM Requirements Quality Assistant (RQA),” a knowledge-driven requirements process that analyzes data to extract essential insights, expediting data management and enhancing requirements quality via the use of AI. Watson’s Natural Language Processing (NLP) capabilities serve as the foundation for the product [1]. Based on the INCOSE Guidelines for developing excellent requirements, the AI-powered tool has been trained to identify a variety of quality concerns. RQA, according to IBM, enhances the completeness, consistency, and correctness of requirements as they are developed, removing ambiguity and avoiding expensive mistakes, as well as identifying issues and providing expert help for addressing them [1]. Although requirements analysis, as we know it traditionally, is more than a quality control exercise, we should not shy away from these opportunities to enhance our practice of business analysis.
- Generating requirements: Based on the inputs supplied by stakeholders, AI may be utilized to generate requirements. Natural language processing (NLP) is an area of artificial intelligence that focuses on the interaction between computers and human language. NLP algorithms may be used to extract requirements from textual data, such as user comments or customer feedback, and develop new requirements based on the data’s insights. Assume a company is creating a new e-commerce website and wants to produce requirements based on customer input. They might examine customer feedback using an NLP algorithm to extract important terms and ideas linked to the website’s operation, such as “additional payment options,” “improving usability,” and “demo video of the product to support product descriptions.” The algorithm could then use these key phrases and concepts to generate new requirements like “the website must have additional payment options during the checkout process,” “the website must be easy to navigate and intuitive to use,” and “the product descriptions must be supported by a video demoing the product.” This method of gathering requirements makes use of real-world feedback from a large amount of unstructured data gathered from social media platforms or other online consumer review channels. It is vital to stress that this AI capacity is not intended to replace human analysis. It may give useful insights, but business analysts must analyze the results and make decisions based on their skills, experience, and knowledge of the business. The concept of employing NLP in requirements analysis is not entirely novel. In the late 1990s, NASA developed and tested such a tool. Their program was dubbed Automated Requirements Measurement, or ARM. QVscribe is an example of this new family of tools. QVscribe is a software application that analyzes and improves the stated requirements in software development projects. It uses natural language processing methods to detect mistakes and inconsistencies in requirements papers and make recommendations for improvement. QVscribe additionally contains a best practices and standards library. Notably, this example does not yet fully use the possibilities of NLP. However, research in this field is mounting, and it is only a matter of time until strong tools leveraging NLP for requirements gathering become available.
- Recommending requirements: AI may be used to provide requirements based on previous projects or industry best practices. This may aid in ensuring that the requirements are in line with the project’s aims and suit the demands of the stakeholders. This technique often entails training an AI model on a dataset of previous projects, which contains information about the requirements that were employed, the project’s objectives, and the project’s result. This information is then used by the AI model to find patterns and links between the requirements and the project results, which it may subsequently utilize to make suggestions for future initiatives. For example, if the AI model discovers that a certain set of criteria was used on numerous successful projects with aims comparable to the present project, it may suggest that those requirements be used again. Alternatively, if the model discovers that specific criteria were regularly altered or created problems in previous projects, it may propose that they be amended or cancelled. However, for such capacity to materialize, AI models must be trained on a significant quantity of data. Companies will need to gather and maintain historical requirements and other project data. Still, this may not be enough, and businesses may need to cooperate on sharing essential data in order to fulfill the demands of AI models for enormous datasets.
3 Opportunities for growth
These advances in artificial intelligence have exciting prospects for the field of business analysis. They present opportunities to develop the practice of business analysis. One opportunity is to make business analysis more data-driven. AI can assist business analysts in rapidly and correctly analyzing massive volumes of data, allowing them to make more educated decisions. AI can also detect patterns and trends that human analysts may miss, delivering deeper insights into corporate processes. This will enhance and has potential to transform requirements engineering by providing deeper insights, faster analysis, and more accurate predictions. Access to automated AI-powered data analysis and untapped sources of data can also help identify hidden opportunities and identify requirements that would be hard to grasp using traditional methods of requirements gathering.
Undoubtedly, AI improves productivity. Future AI-powered tools dedicated to business analysis will make business analysts more productive. For example, an AI tool may be used to transcribe extensive audio workshops in minutes, and then an NLP-based AI tool could be used to create the first cut of requirements from the transcripts in minutes. This increase in productivity may allow analysts to concentrate on higher-level strategic responsibilities, such as finding growth and innovation prospects, which require human insight and creativity.
Business analysts may become more versatile skilled professionals. For example, ScopeMaster (an AI- powered tool), “the intelligent requirements analyser” [2] offers an array of features to support business analysis tasks, including requirements quality checks, and auto-generated functional tests. This tool, as well as future improved versions or comparable technologies, might provide business analysts with the capacity to participate in project testing stages.
AI may have a significant impact on business analysis by enabling faster, more accurate data analysis, automating certain tasks, and providing predictive insights. As businesses continue to adopt AI-powered tools, the role of the business analyst is likely to evolve to incorporate more AI-related skills and knowledge. AI will transform business analysis by allowing faster, more accurate data analysis, automating certain processes, and delivering predictive insights. As more companies use AI-powered solutions, the position of the business analyst is expected to grow to include more AI-related skills and expertise. AI is an opportunity for business analysis, and it is here to stay!
Author: Adam Alami
Adam Alami is an assistant professor with Aalborg University, Denmark. He has broad experience in information technology practices. His career began in software development, before progressing to include business analysis and project management. Involvement in major IT transformation projects has for twenty years been the mainstay of his work. His chosen fields of research fit within the broad topic of cooperative, social, and human aspects of software engineering. He has a keen interest in business analysis and contemporary software development practices. He holds a PhD degree in Computer Science from the IT University of Copenhagen, Denmark, a Master degree in Computer Science from the University of Technology (UTS), Sydney, and a Bachelor degree in Software Engineering from the Université du Québec à Montréal. Email: [email protected]. Twitter: @AdamAlamiDK.
References/footnotes:
- IBM. Ai driven requirements management — ibm watson iot. https://www.ibm.com/ internet-of-things/learn/requirements-management-ai/index.html?chapter-05. (Accessed on 04/24/2023).
- ScopeMaster. Software requirements analysis, qa and sizing - automated. https://www.scopemaster. com/. (Accessed on 04/22/2023).