The impact of Artificial Intelligence on the work of a Business Analyst – opportunities and threats

In recent years, solutions using Artificial Intelligence (AI) have rapidly developed. Nearly every industry is now striving to incorporate AI to achieve its goals more quickly and efficiently. AI is no longer just a fantasy from science fiction movies – it has become a reality transforming nearly every aspect of our lives and work.

One of the most exciting areas where AI has, or will have, a significant impact is business analysis. In the near future, we can expect the development of AI to not only change the tools and techniques used in business analysis but also alter the entire way we think about managing a company.

This article explores exactly what changes AI can bring to business analysis and the opportunities and threats of this breakthrough. 

New possibilities in Business Analysis thanks to AI

Automation of data analysis processes 

One of the most obvious ways AI influences business analysis is by automating data analysis processes. Using advanced machine learning algorithms and deep learning, AI systems can process large datasets much faster and more accurately than human analysts. This enables faster detection of trends, patterns, and anomalies in data, which in turn allows companies to make better-informed business decisions.

Personalization of analysis and recommendations

Thanks to AI, Business Analysis can become more personalized for specific clients and markets. AI systems can analyze data on client behavior and preferences, enabling the delivery of personalized recommendations for products or services. This, in turn, can lead to increased client engagement and an enhanced shopping experience.

Forecasting and modeling future trends

AI facilitates the development of advanced forecasting models, which can predict future market trends, changes to client preferences, and other factors that impact business operations. This allows companies to better prepare for upcoming changes and adjust their business strategies to maximize profits and minimize risk.

Business process optimization

AI can also optimize business processes by identifying areas for change or improvement. For example, companies can use data analysis and optimization algorithms to optimize supply chains, inventory management, production planning, and other key operational processes.

The benefits of using AI in Business Analysis

What benefits can the skillful use of AI bring to the work of a business analyst? Let’s take a look at the most important ones:

1. Increased efficiency and productivity – automating data analysis processes with AI can significantly increase the efficiency and productivity of a business, allowing for faster decision-making and better resource utilization. 
2. Improved quality of business decisions – with more accurate data analysis and forecasts of future trends, businesses can make better-informed business decisions, leading to increased competitiveness and profitability.
3. Quick response to market changes – thanks to AI’s forecasting capabilities, businesses can respond swiftly to market changes and adjust their strategies to keep their competitive edge.
4. Improved customer experience – personalizing data analysis through AI allows for more tailored products, services, and client experiences, fostering greater client loyalty and engagement.

Challenges of using AI in Business Analysis

The main challenges of applying AI to Business Analytics include:

1. Data privacy – using client data for AI business analysis presents numerous challenges related to data security and privacy. It is essential to ensure adequate data security measures and respect clients’ right to privacy.
2. Lack of understanding of the technology – some businesses may struggle to understand and effectively use advanced AI technologies for Business Analysis. Adequate training and education for staff on the benefits and opportunities of AI are crucial. 
3. Risk of data misinterpretation – Despite AI’s advanced capabilities, there is a risk of data misinterpretation, leading to poor business decisions. It is necessary to exercise caution and maintain a critical approach when interpreting the results of data analysis.

AI-related tools useful in Business Analysis

Various AI tools can be used in Business Analysis to assist with data collection, processing, and analysis. They can radically improve analytical processes’ effectiveness, accuracy, and speed, helping businesses make better, data-based decisions. Here are a few examples:

1. Data analysis and visualization
   • Tableau, Power BI – data visualization tools that can be enhanced with AI to automate analyses and detect patterns.
   • Google Data Studio – allows for creating interactive reports and dashboards using data.
2. Process Automation
   • UiPath, BluePrism – Robotic Process Automation (RPA) tools are used to automate routine business processes, making analyses more effective.
3. Intelligent Chatbots
   • ChatGPT, Dialogflow – enable conversations with clients, collect feedback, and analyze their needs.
4. Project Management
   • Monday.com, Asana with AI – project management tools that can leverage AI to predict delays or identify risks in projects.
5. Business Intelligence
   • Qlik Sense, Domo – BI platforms using AI to analyze data in real time and generate reports.
6. Process optimization
   • Simul8, AnyLogic – process simulation tools, which help identify bottlenecks and optimization opportunities.

AI tools in business analysis can significantly improve the effectiveness, accuracy, and speed of analytical processes, enabling businesses to make better, data-driven decisions.

Examples of AI use in Business Analysis

Let’s look at an example of how AI can support a business analyst. For this, we will use a popular chatbot called ChatGPT. Using this tool, we will attempt to create a user story for the introduction of a new ERP system.

To do so, go to  https://chatgpt.com/ and log in. In the prompt window, type: ‘Write a user story for implementing a new ERP system” and press Enter. In response, you will get the following result:

ChatGPT response

Title: The Implementation of a New ERP System for the Optimization of Business Processes.

Description: As [a user role], I want [description of need/action], so that {goal/benefit].

User Stories:

1. User Story 1: Order Management
   • As a Sales Manager
   • I want to be able to browse, create, and edit client orders in the new ERP system
   • So that I can improve the effectiveness of sales processes and track order status in real time.
2. User Story 2: Inventory Management 
   • As a Warehouse Manager
   • I want to have access to accurate and up-to-date stock information
   • So that I can optimize stock management processes, avoiding both surpluses and shortages.
3. User Story 3: Invoicing and Payments 
   • As an Accountant
   • I want the ERP system to automatically generate invoices based on orders and track payments
   • So that invoicing can be streamlined, mistakes – minimized and financial flow tracked.
4. User Story 4: Business Data Analysis 
   • As a Financial Director
   • I want access to advanced reports and financial analyses in real time 
   • So that I can make well-informed strategic decisions based on up-to-date data.  
5. User story 5: Client Relationship Management 
   • As a Customer Service Specialist
   • I want access to the complete history of client interactions, including orders, complaints and correspondence 
   • So that I can provide more personalized and effective customer service. 
6. User Story 6: Project Management
   • As a Project Manager 
   • I want to monitor project progress, resource allocation and cost in the ERP system 
   • So that I can better manage projects and ensure their timely completion within budget. 
7. User story 7: Integration with External Systems
   • As an IT Administrator 
   • I want the new ERP system to integrate with existing company systems, such as payroll and e-commerce systems 
   • So that I can ensure smooth data flow between systems to avoid manual data entry.

Acceptance Criteria:

1. The ERP system must allow for the creation and editing of orders and tracking their status.
2. Information on stock levels must be updated in real time. 
3. The system should automatically generate invoices based on orders and track payments. 
4. Access to reports and financial analyses must be swift and intuitive.
5. Client interaction history should be easily accessible and complete.
6. The project management module must enable monitoring of progress, resources and cost.
7. The system has to integrate with existing external systems.

My comment

As shown in the example above, Chat GPT had no problem creating basic user stories for this case. However, I believe that while it is certainly a valuable tool for the analyst, who can use AI as a source of inspiration or ideas, it will not replace the analyst completely. AI generated only a few examples, while a comprehensive list of user stories for such a complex project would be much longer. The acceptance criteria generated by AI could also prove to be an interesting addition, supporting the analyst in their daily tasks and highlighting significant issues.

Risk analysis

Another interesting example I would like to present is the preparation of risk analysis for introducing a new ERP system. While it may not be strictly related to business analysis, analysts often support project managers in preparing such assessments.

In this case, let’s provide the following command: “Prepare a risk analysis for the implementation of a new ERP system.” The results are shown below:

ChatGPT response

Here is an example of a risk analysis for a project introducing a new ERP system:

1. Risk identification

2. Risk Assassment

Each risk was evaluated based on the probability of occurrence and its potential impact on the project. Risks with the highest score (above 15) should be prioritized.

3. Risk mitigation strategies
   • Training and Communication: Regular training for employees and transparent communication about the purpose of the implementation.
   • Requirements Analysis: A thorough analysis of business needs before the rollout to avoid functionality mismatches.
   • Testing and Monitoring: Introduction of a test plan and monitoring progress to swiftly identify and address issues.
   • Change Management: Establishing clear rules and procedures for introducing changes during the project.
   • Executive Support: Regular meetings with management to secure their engagement and support.

My comment

The above example shows how easily a basic risk list can be created for a project. However, these risks are a bit “textbook,” focusing mainly on human resources, which surely is not sufficient for a project of this scale.

What is interesting is that AI created columns with probability values and impacts on the project and offered mitigation strategies for each risk. Just like in the first example, it can be a simple and valuable tool for an analyst or project manager who is starting to create such an analysis. However, for an actual project, greater accuracy and precision are required.

Conclusions

In the near future, Artificial Intelligence will play an increasingly important role in business analysis, changing not only tools and techniques used in study but also the way we think about managing a business. While the challenges associated with AI adoption are real, the benefits this technology offers are immense and could lead to a significant increase in effectiveness, efficiency, and competitiveness for companies. The key challenge is to prepare for this future through education, invest in the right technologies, and maintain awareness of data security risks and requirements. 

Therefore, one may ask: Will AI be able to replace the Business Analyst role in the near future? AI is continuously evolving, and we must acknowledge that there is a risk of this happening. The role of the Business Analyst may be less necessary for projects or may get significantly reduced. However, such solutions will still require people who can skillfully utilize or maintain AI tools for project purposes. Therefore, it is quite likely that the scope of future Business Analysts’ duties will change, making their job more efficient and pleasant 😊 

Sources

• ChatGPT
• Wpływ sztucznej inteligencji na przyszłość pracy – nowe perspektywy i wyzwania
• Najpopularniejsze narzędzia AI
• Hironori Takeuchi, Shuichiro Yamamoto, Business Analysis Method for Constructing Business–AI Alignment Model, 24th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems
• Ida Merete Enholm, Emmanouil Papagiannidis, Patrick Mikalef & John Krogstie, Artificial Intelligence and Business Value: a Literature Review, Information Systems Frontiers (2022) 24:1709–1734

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You can also find out more about the use of AI in the IT industry in other articles by our experts.