By: Joseph Cooper

With over 8 years of experience in the IT industry, Anna Korobka has built a strong reputation as a Senior Principal Business Analyst, working with some of the influential companies in the field.  She combines deep knowledge of business analysis with expertise in AI-driven development, shaping the future of IT and business processes. In this interview, Anna shares her insights into how AI is elevating business analysis and its growing impact.

Q. AI and machine learning are transforming industries and automating tasks once requiring human expertise. As a business analyst, do you think AI can replace your work?

Anna Korobka: While AI and ML tools are powerful for specific tasks, they can’t replace the core work of a business analyst. Business analysis involves more than data processing; it’s about understanding the broader context, making strategic decisions, and tailoring solutions for stakeholders. AI tools assist with certain tasks, but they lack the human intuition needed for comprehensive analysis. For instance, one of my first AI projects eight years ago predicted products at risk of selling out using retail images. While it boosted revenue, human input remained critical in assessing real-world factors like lighting and stakeholder collaboration, underscoring the essential role of human expertise in AI-driven solutions.

Q. Why is customization so important in business analysis?

Anna Korobka: Customization is key to effective business analysis. Information needs to be presented in ways that are meaningful and actionable for each stakeholder group, whether technical teams, business leaders, or clients. Interpersonal relationships are also crucial. By observing contributors’ traits—like whether they prefer fast-paced or more measured discussions—I can adjust my communication style and refine software requirements accordingly.

Q. Data quality is essential for machine learning. Can you explain why?

Anna Korobka: Data quality is crucial for accurate predictions. Flawed data—whether incomplete, biased, or inconsistent—leads to unreliable results. In business analysis, ensuring data quality and contextual accuracy is essential. The Standish Group’s CHAOS Report shows that only one-third of IT projects meet deadlines and budgets, highlighting the challenges of success. Training AI on poor data limits its decision-making reliability.

Q. How do you address privacy and data security concerns when using AI tools?

Anna Korobka: Privacy and data security are paramount when using AI, as models require data that could expose sensitive information. I focus on regulatory compliance, use secure AI tools, and implement strong data governance. This includes anonymizing data, encryption, and monitoring systems. Consulting legal and security experts ensures compliance and protects data. In the companies I’ve worked with, I strictly follow policies on third-party AI tools to safeguard proprietary information.

Q. It sounds like the human touch is irreplaceable in business analysis. Can you explain why?

Anna Korobka: One of the greatest strengths of business analysis lies in the diversity of perspectives within a team. Effective teams bring together individuals with contrasting viewpoints, leading to creative and innovative solutions. AI, however, lacks flexibility and often sticks to predefined patterns. While it can analyze data, it can’t replicate the depth of collaboration, critical thinking, and creativity human teams bring. This diversity of thought is vital for solving complex business challenges.

Q. Do you see a role for business analysts in the future, given the rise of AI?

Anna Korobka: Absolutely. The role of business analysts is evolving, not diminishing. AI enhances our capabilities by automating repetitive tasks, but analysts remain essential for handling complex challenges. As IT products evolve, business analysts’ roles are transforming. For example, many companies, including the one I work with, are embedding generative AI (GenAI) into their cloud services. As a result, business analysts need to understand core AI concepts to support product development effectively, creating demand for analysts with AI expertise.

In agile projects, business analysts contribute to tasks like technical design, prototyping, or light coding. I have leveraged Oracle APEX, a low-code platform, to streamline development and integrate AI features. The “APEX AI Assistant” enables the automatic generation of SQL queries and application blueprints through natural language prompts, reducing development time and simplifying workflows. This has optimized project outcomes, demonstrating the value of AI in business analysis.

Additionally, I was honored to serve as an executive judge at an international IT competition, tailoring AI-focused challenges for participants. This initiative not only enriched the competition but also helped nurture junior business analysts, contributing to a stronger talent pipeline for the industry.

Q. Can you share more about one of your previous projects?

Anna Korobka: One project I led focused on comparing healthcare expenses. We built an application that generated reports comparing healthcare costs to factors like cost of living and income. The goal was to help individuals with low incomes find affordable healthcare options, solving a crucial social problem. By providing data on cost comparisons, the project helped people make informed decisions about healthcare.

In healthcare, interoperability is key for securely sharing patient data. Initiatives like the Trusted Exchange Framework and Common Agreement (TEFCA) aim to improve data exchange, enhancing patient care and reducing inefficiencies. This aligns with AI in healthcare, as AI tools can optimize data management and workflows. I’m proud to contribute to these efforts.

Currently, I’m collaborating with a university professor to implement an AI assistant to process participant forms in clinical trials. We anticipate reducing processing time by 45%, improving research workflow efficiency, and benefiting both academic and commercial applications.

Q. So, AI can enhance the work of business analysts but won’t replace them?

Anna Korobka: Exactly. AI is a powerful tool, but it can’t replace the nuanced problem-solving that business analysts provide. While we must embrace AI to stay competitive, we’re the ones making strategic decisions and understanding the full business context. In the future, more business analysts will be equipped with AI expertise, further strengthening our ability to drive innovation.

Summary

AI and machine learning offer immense potential for automating tasks and solving specific challenges, but they cannot replace the creativity, judgment, and contextual understanding of business analysts. By integrating AI into their work, analysts can amplify their capabilities while remaining indispensable in solving complex business problems. As Anna Korobka puts it, “AI is a tool to enhance our work, not a substitute for the human element that defines effective business analysis.”

Published by Jeremy S.