Mastering AI for Business Analysts (2024): Top Tools/Techniques
Arunabh Satpathy
Companies are now increasingly harnessing AI tools for business analysis. Deploying generative AI for business analysis will be the key differentiator between mediocre and high performing teams. A study shows that AI technologies enable business professionals to produce 59% more business documents in an hour than before.
For this reason, selecting the right AI tool for business analysis is crucial. In this post, you will explore how Business Analysts (BA’s) can effectively use AI tools, identify the tools that are best for business analysis, and address some commonly asked questions about AI – including whether AI will take away BA jobs (hint: it won’t).
Table of Content
1. How can a Business Analyst use AI tools?
Business analysts play a multifaceted role in today’s enterprises. They identify business needs through data collection and analysis and thus bridge the gap between IT and business. Some of their tasks include creating requirements, strategic planning, business model analysis, process design, and more.
This wide range of skills is why BAs will greatly benefit from integrating AI into their workflow.
Among the tasks mentioned above, BAs play a role in process automation, internal communications, data aggregation, and idea generation. Here are some specific ways AI tools can increase BA productivity for these (and other) tasks:
Surfacing and Explaining Findings Within Data
- Requirements Analysis: AI tools can improve the quality of requirements writing by ranking work items on writing quality scales like the 6Cs: context, content, clarity, consistency, completeness, and conclusion.
Business efficiency is greatly enhanced by the 6C’s analysis capability, which streamlines the analysis procedure. Teams can save time by doing this and concentrate on other important work. By guaranteeing that the team considers all factors, AI analysis helps users make better and more informed judgments.
- Mapping Data: BAs can map different sets of data to surface valuable information about their products. For instance, they can look at all work items (through queries) on Azure DevOps about a product’s features and map them to user requirements to find gaps in user satisfaction.
Gleaning Insights from Data
- Holistic Analysis: AI allows for comprehensive analysis beyond traditional metrics. For instance, BAs may need to find out whether a given set of features match compliance standards. For instance, a BA can compare a large set of features for a product documented in work items and ASPICE compliance regulations.
- New Insights: Machine learning helps uncover previously unconsidered factors. For example, a BA in a finance firm can use AI to reveal a correlation between complex weather patterns and clothing purchases to aid sales forecasting.
Vetting Business Impact and Project Feasibility
- AI Exploration: Analysts can use AI to identify and evaluate promising use cases. For instance, Sephora’s use case enhanced their Virtual Artist tool with AI, enabling customers to virtually test makeup and get tailored beauty suggestions.
- Strategic Decisions: In strategic decision-making, AI helps BAs evaluate the viability of future projects. For instance, a healthcare software company can crunch data to enable business analysts to find new business cases.
- Cost-Benefit Analysis: Business Analysts can assess the investment worthiness of AI initiatives before implementation. For example, some traders can use AI models to outperform the S&P 500, over a three-year analysis period.
2. Which AI Tools are Best for Business Analysis?
AI tools for business analysts differ greatly, reflecting the diverse skills and tasks BAs handle.. Here are the top AI tools for Business Analysts:
i. Copilot4DevOps Plus
- Copilot4DevOps Plus is a game-changing assistant for business analysts natively built into Azure DevOps. It helps streamline workflows, automate repetitive tasks, provide insights, improve quality and accelerate projects. With its intuitive interface, even non-technical team members can contribute effectively.
Some of its features include:
- Elicit: Generate and iterate high-quality requirements, functional and integration test cases, risks, and more from work item data. Identify missing requirements and test cases.
- Analyze: Analyze the quality of requirements and related details using various methods (MOSCOW, INVEST, PABLO, etc.).
- Transform: Create a summary (Summarize), add more detail to a work item (Elaborate), or reframe requirements (Paraphrase) in different terms.
- Convert: Convert work items into Gherkin language, use cases, and user stories.
- Translate: Translate work item data into multiple languages with high accuracy.
- Impact Analysis: Perform Impact Analysis to get a prioritized list of affected work items with the suggested changes.
- Q&A Assistant: Generate questions for requirements elicitation from prompts and get recommended answers.
- Dynamic Prompts: Create tailored prompts to act on selected or queried work items.
With a generous 30-day free trial, Copilot4DevOps Plus is the most affordable AI for business analysts.
ii. Sisense
Sisense offers both low-code and no-code options for data analysis. Its drag-and-drop interface makes it easy to create reports and dashboards. Its features provide data analysis, visualization, export, dashboards, and high-volume data handling. It offers a 30-day free trial.
iii. Akkio
Sisense offers both low-code and no-code options for data analysis. Its drag-and-drop interface makes it easy to create reports and dashboards. Its features provide data analysis, visualization, export, dashboards, and high-volume data handling. It offers a 30-day free trial.
iv. Google Looker
Sisense offers both low-code and no-code options for data analysis. Its drag-and-drop interface makes it easy to create reports and dashboards. Its features provide data analysis, visualization, export, dashboards, and high-volume data handling. It offers a 30-day free trial.
v. ChatGPT
ChatGPT is an “everything” tool that professionals can use in many different applications. However, it isn’t specifically designed for DevOps professionals like BAs. Its drawbacks include difficult prompting, workflow disconnection, and hard fine-tuning. It also poses some AI security concerns in enterprises.
vi. Tableau
For business analysts, Tableau’s strength is data visualization and dashboards. It is one of the better-known tools for BAs. However, it lacks requirements elicitation and analysis tools.
vii. Qlik Sense
Qlik Sense is designed for BAs who want data integration, visualization, and AI analysis tools. It also offers a question-based UI for data exploration.
4. Other Questions That BAs Ask About AI
- Will a Business Analyst be replaced by AI?
Business Analysts who use AI tools can analyze data and surface insights faster but are unlikely to fully replace human expertise. By leveraging continuous learning, company AI-use policies, change management, and putting human decision making at the center, companies can employ highly productive BAs. - How to leverage AI as a Business Analyst?
BAs can leverage AI through requirement creation and analysis, data analysis, pattern detection, visualization and more when making strategic decisions.
5. What Is the Future of Business Analysis With AI?
The future of business analysis is intertwined with AI tools to improve decision making and strategy. Initially, users will see the enhancement of existing features. For instance, analysis tools will incorporate the INVEST and MOSCOW analysis methods, facilitating clear, testable user stories for agile development.
Over the next few years, a new wave of upcoming AI technologies like AI agents will help BAs further delegate repetitive tasks to AI. The human-AI collaboration in business analysis is set to redefine the industry.
And cutting-edge tools like Copilot4DevOps Plus are the key to flourishing as a BA.