AI-Powered Business Analysis Solution
Overview
In the era of AI-driven productivity, we are committed to integrating generative AI into business analysis processes. This innovation aims to automate the generation of user personas, customer journeys, and development story cards. AI solutions can swiftly and accurately complete complex tasks, significantly enhancing analytical efficiency and accuracy.
Challenges
Traditional business analysis processes rely on manual operations, which are complex and time-consuming. Business analysts need to spend a significant amount of time identifying target user groups from business requirements, creating user personas, mapping customer journeys, and transforming them into development story cards. Manual operations are not only prone to errors but also affect the efficiency of the entire product development process, resulting in inconsistent quality of requirement information received by developers. The challenge we face is how to solve these problems through AI automation while ensuring smooth connections between various stages.
Research Findings
Through in-depth interviews and workflow observations with business analysts, product managers, developers and QA，we found:
• Business analysts are often bogged down by repetitive, low-value tasks during the analysis process, affecting their focus on high-level decision-making.
• Product managers need a global perspective to control the progress and direction of business analysis, but are often unable to quickly adjust strategies due to time delays caused by manual operations.
• Developers heavily rely on the quality of business analysis outputs. When requirement descriptions are unclear or inaccurate, development efficiency significantly decreases, leading to rework and schedule delays.
• Quality Assurances should have sufficient business context and are involved throughout the entire process to guarantee that all key requirements are covered, avoiding omissions or misunderstandings.
Personas
Emily Johnson (Product Business Analyst)
Emily's core goal is to improve the efficiency and quality of business analysis work. She aims to accurately gather business requirements, ensuring software projects align with business objectives and meet user expectations. Her challenges lie in information acquisition and priority setting, requiring close collaboration with multiple departments to ensure comprehensive and accurate requirements. Additionally, technical limitations affect her balance between business needs and technical feasibility. The opportunity for AI tools lies in automating and simplifying the process of extracting complex business requirements, helping her work more effectively.
Alexandra Chen (Product Technical Architect)
Alexandra's main expectation is to quickly understand complex business requirements and transform them into high-quality technical solutions. Her challenges lie in the complexity of business requirements and communication barriers between technology and business, especially in ensuring technical feasibility while meeting business needs. Through AI-generated business analysis documents, technical solutions can be clearer and more precise, thereby improving development efficiency and product quality.
Nathan Patel (Product Quality Assurance)
Nathan focuses on generating more comprehensive and detailed business-based test cases to ensure product quality. His challenges mainly manifest in time pressure and the balance between quality and development speed. He needs to complete testing tasks within a limited time while ensuring the product is released on schedule and meets user requirements. AI can help Nathan understand business requirements faster and more accurately, thereby improving testing efficiency and coverage.
Each role faces unique challenges, and the opportunity for AI solutions lies in helping them achieve their goals more efficiently by simplifying processes, improving information accuracy, and promoting cross-departmental collaboration.
Product business analyst's workflow
Discover stage: Business analysts are responsible for discovering business requirements and opportunities, and clarifying requirements (the yellow part in the diagram is marked as one of the opportunities where AI can improve).
Define stage: Analysts generate user journeys based on priorities and create user story maps (also marked as an opportunity for AI improvement).
Delivery stage: Automatically generate user story cards, perform knowledge management and feedback, and ultimately deliver business value effectively.
This AI tool helps business analysts complete tasks more efficiently by automating user journey generation and knowledge management, and provides high-quality user story cards for product development, driving collaboration and optimization throughout the entire process.
Solution
Our AI solution helps business analysts significantly reduce the workload of repetitive tasks by automatically generating user personas, customer journeys, and development story cards. Product managers can view the progress and content of business analysis in real-time through the system, and provide suggestions and adjustments. Developers benefit from more accurate and high-quality story cards, ensuring clear communication of development requirements. The entire product not only improves analytical efficiency but also enhances development quality, ultimately achieving efficient collaboration throughout the product development process.
Design
We aim to provide users with a global overview through a data visualization dashboard, helping people understand the business analysis situation across the entire organization. This includes displaying the stages of different business analyses, areas that have recently received attention or high traffic, and quick access to business modules that users have recently visited.
In the initial stage, we can see a list of all business analysis units, making it convenient for people to filter and find the content they want, and clearly see the stage each business analysis unit is in.
In the requirements communication stage, we interact with an AI chatbot to confirm and focus on requirements, while preparing for the next step of user classification.
AI assists in generating user personas and creates initial user journeys based on scenarios and user characteristics from business requirements. Business analysts can clearly view these contents through visual presentations and make modifications and adjustments when necessary.
