Artificial Intelligence as a Driver of Change
Artificial Intelligence (AI) is reshaping how entire industries operate, becoming central to the next wave of digital change. Companies are investing millions in AI-driven solutions to boost operational efficiency, improve customer experience, and drive revenue growth. Among the first to embrace these advances is the fintech sector, where competition drives rapid experimentation. According to a recent McKinsey report, generative AI has the potential to increase banking productivity by 5% and reduce global costs by as much as $300 billion.
For many organizations, adopting AI is no longer an experiment but a strategic necessity to stay competitive. AI is already being used to enhance customer service, analyze large datasets, detect fraud, and manage risk. Real-world examples demonstrate AI’s vast potential across industries—from finance and healthcare to retail and transportation.
Generative AI represents a major breakthrough, enabling the creation of intelligent systems capable of generating text, images, code, and insights. As computational power and model accessibility continue to grow, the number of AI-driven solutions will expand exponentially—limited only by the imagination of developers and the strategic vision of organizations.
Why Companies Organize AI Hackathons
Modern corporations understand that to remain leaders, it is no longer enough to simply implement ready-made solutions. It is essential to cultivate an internal culture of innovation, where experimentation with AI becomes part of the organization’s DNA. That is why an increasing number of companies are hosting internal hackathons to build prototypes and explore new technological opportunities.
Hackathons give employees a chance to test ideas beyond their daily responsibilities, bringing together people from different departments and encouraging cross-functional collaboration. These events create a space for free creativity and experimentation, where business analysts, architects, developers, data engineers, and product specialists work side by side to generate new insights and solutions.
AI-focused hackathons are particularly valuable in the era of generative intelligence. They allow teams to test hypotheses, train models on real corporate data, and develop intelligent assistants, forecasting tools, and automation systems that enhance decision-making and efficiency.
Before the Hackathon: From Doubt to Discovery
Before applying for the hackathon, I hesitated. I wasn’t entirely sure how a business analyst could contribute in a competition that seemed focused on coding and model development. AI, I thought, was mainly about algorithms and technical implementation.
But I am genuinely glad that I decided to join the team. From the very first brainstorming session, it became clear that the analyst’s role is not peripheral at all — it is an essential part of the bigger picture. Translating business needs into AI logic, shaping use cases, and connecting technology with real-world impact turned out to be the very foundation of our success.
An Interdisciplinary Team as the Foundation of Success
Our hackathon became a vivid example of how the diversity of roles and expertise can transform an idea into a complete AI solution. The team included developers, business analysts, and data analysts—professionals who complemented one another at every stage of the project.
Developers designed the system architecture, ensured stability and technical implementation, built API integrations, and created a user interface prototype.
Data analysts prepared, cleaned, and analyzed data, selected appropriate machine learning models, tested hypotheses, and optimized parameters for better accuracy.
As a business analyst, I was responsible for defining the project goals, outlining possible AI use cases, structuring the requirements, and ensuring clear communication and transparency among all team members.
Each of us became not just an executor but a co-author of the idea, recognizing the value of AI not only in its technology but in the meaning it brings to the business.
The Stages of Our Work at the Hackathon
The hackathon process resembled an accelerated version of a full-scale AI integration project, with each phase following a clear structure:
- Problem Definition: We began by identifying real business challenges—how AI could reduce manual operations, improve analytical accuracy, and optimize decision-making.
- Data Analysis: Data analysts performed initial data cleansing, identified key patterns, and uncovered potential insights.
- Concept Development: Business analysts translated requirements into technical scenarios, defined success metrics, and designed the logic of the solution.
- Prototype Creation: Developers built a minimum viable product (MVP), demonstrating the model in action.
Solution Presentation: The final stage included presenting the prototype, data architecture, and business value analysis.
The result was a functional AI product that improved analytical efficiency and reduced operational risk—a clear demonstration of how interdisciplinary collaboration can turn ideas into tangible, value-driven innovation.
The Contribution of Each Role
Hackathons are often associated with speed. But in reality, their true purpose is not just to “build fast” — it is to learn how to work in sync. Developers give ideas form. Data analysts turn data into solutions. Business analysts define direction and meaning.
This synergy produces projects that are not only innovative but deliver real business value, balancing creativity, data-driven insight, and strategic focus to turn experimentation into measurable results.
Ethical Principles and Responsibility
During the hackathon, we also focused on the ethical dimension of AI. When designing solutions that handle sensitive data, we followed the principles of transparency and explainability. Every algorithm had to be auditable — capable of clearly explaining why it made a particular decision.
Ethical standards are now an essential part of AI design. They include:
- protecting personal data;
- preventing discrimination and algorithmic bias;
- ensuring transparency and human oversight;
- complying with regulatory requirements across jurisdictions.
As a result, the hackathon became not just a competition of ideas, but a laboratory of responsible AI—a space where every step was guided by awareness of technology’s social impact and the need for accountability in innovation.
From Idea to Recognition
At the end of the hackathon, our solution was highly praised by the expert jury, and the team advanced to the semifinals. This achievement recognized not only the technical quality of the project but also teamwork, creativity, and systems thinking.
For all participants, it was more than just a competition — it was an inspiring experience, a chance to see how AI brings together people with different perspectives, teaching them to collaborate, balance speed with quality, and believe that collective effort can transform ideas into real solutions.
What Hackathons Teach
Looking back, I realize that the hackathon became one of the most powerful learning experiences of my career. Those few intense days taught me more about collaboration, adaptability, and the real meaning of innovation than any formal training could.
A hackathon is essentially an accelerated learning cycle. In just a few days, you move from concept to working prototype, compressing what would normally take months into hours of focused teamwork. You learn to:
- prototype ideas quickly, without overanalyzing;
- embrace uncertainty, because not everything goes as planned;
- communicate across disciplines, finding a shared language between code, data, and business logic;
- and think critically and adapt constantly, as new insights appear every hour.
For me, as a business analyst, this experience was especially valuable. It showed that our role goes far beyond documentation or requirement gathering. We are the ones who connect meaning to technology, who ensure that innovation has direction and purpose. The hackathon reminded me that a true business analyst is not on the sidelines of AI development — we are at its heart, helping translate creativity into solutions that genuinely move the business forward.
The Role of the Business Analyst in AI Projects
Given the interdisciplinary nature of AI initiatives, the traditional role of the business analyst (BA) has evolved dramatically. Today, the analyst is no longer just a liaison between business and IT but an architect of digital transformation, ensuring alignment between corporate strategy and technological capabilities.
With the advent of artificial intelligence, this role has expanded even further. A modern business analyst must now develop a deep, practical understanding of technology—immersing themselves in the technical context alongside developers and data analysts. It is no longer sufficient to translate business needs into requirements; analysts must grasp how AI models function, what data they require, how they are trained, and how their outcomes can be interpreted and integrated into business processes.
This new reality demands continuous learning and adaptability. AI introduces a broad new layer of concepts, tools, and methodologies that analysts must master quickly to remain effective. They need to understand both the possibilities and the limitations of emerging technologies, anticipate risks, and identify how AI capabilities can be leveraged to deliver measurable business value.
AI has also blurred the traditional boundaries between business and technology. The business analyst now operates at this intersection—bridging strategy, data, and innovation. Those who can fluently speak the language of both business and AI are becoming indispensable in shaping the next generation of intelligent, data-driven organizations.
Core Business Analyst’s Competencies in AI Integration
Business analysts in AI projects are responsible for aligning goals, strategies, and actions across all stakeholders—from data specialists and developers to managers and decision-makers.
Based on hands-on experience in AI integration, several key factors determine the success of such initiatives:
- Requirements Gathering and Analysis. No AI project can begin without clear objectives. We define the business problem, success criteria, and translate them into actionable technical requirements that form the foundation for model design and training. At the same time, introducing AI should never be done for its own sake — the need must be well-justified, carefully analyzed, and clearly linked to measurable business value. Our role is to ensure that every AI initiative addresses real challenges, aligns with strategic goals, and delivers tangible results beyond pure experimentation.
- Data Analysis. The quality of data lies at the heart of every AI system. Analysts manage data collection and organization, ensuring accuracy, completeness, and relevance. This includes overseeing ETL (Extract, Transform, Load) pipelines and preparing datasets for model training. However, this is not about replacing the work of data analysts. Instead, the business analyst must understand what types of data are needed and how they can serve business objectives. Acting as the voice of the business, the BA helps identify relevant data sources, define their context, and articulate business meaning for data teams.
- Change Management and Process Adaptation. Implementing AI frequently requires both procedural and cultural change. The BA supports this transition by fostering communication, designing training programs, and ensuring smooth adoption of new systems and workflows. After implementation, it may seem that intelligent systems will take over most tasks while people simply enjoy the results—but this is far from the truth. AI solutions still demand a careful and informed approach. Users must understand how to apply AI effectively, maintain it, and adapt its configuration or logic as business conditions evolve. This introduces a new layer of continuous learning. Business analysts, being closest to both business processes and technical implementation, often take the lead in this area—guiding teams, educating users, and ensuring that AI becomes a sustainable, value-adding part of daily work.
- Model Validation and Performance Evaluation. Once an AI-driven product is launched, the analyst’s work continues through constant evaluation and improvement. As the product evolves, it may drift from its original purpose or react differently to new data, requiring timely recalibration. Business analysts facilitate feedback between users, data scientists, and developers, identifying when adjustments are needed. They help keep AI solutions transparent, explainable, and relevant, making ongoing validation a key element of responsible product management.
- Ethical and Legal Considerations. The ethical dimension of AI and data use is critical. Every AI system relies on data, and how that data is collected, processed, and interpreted directly impacts individuals and organizations. Analysts ensure compliance with privacy, fairness, and security standards, helping to mitigate bias, promote transparency, and protect user information. Beyond compliance, they advocate for responsible AI by fostering accountability and awareness of automated decisions. By connecting technical, legal, and business perspectives, analysts help create a governance culture where innovation and responsibility coexist.
Thus, the business analyst becomes a strategic partner, capable of uniting technical expertise, data understanding, and business vision.
How Business Analysis and AI Intersect in the Future
As AI becomes embedded in daily business operations, the demand for analysts who understand both data and technology will continue to grow.
Analysts will need not only a deep grasp of data architecture but also hands-on familiarity with machine-learning models and the ability to translate their outputs into business action.
Business analysts are becoming not just intermediaries but catalysts of innovation, unlocking the potential of technology and shaping strategies for sustainable digital growth.
Looking Ahead
The intersection of business analysis and artificial intelligence opens unique opportunities for organizational transformation. Successful AI implementation is impossible without an interdisciplinary approach and mutual trust among professionals.
Hackathons provide the ideal environment for this collaboration: developers and analysts learn to understand each other, data experts and managers find common ground, and companies gain real prototypes ready for implementation.
The future belongs to teams that experiment and learn together. AI will never (I believe so) substitute human judgment; instead, it extends what we can achieve. And when technology meets awareness, creativity, and expertise, the results exceed all expectations.
Conclusion
Our experience in the AI hackathon confirmed one essential truth: genuine innovation emerges at the intersection of knowledge. When developers, business analysts, and data specialists unite around a shared goal, they create solutions that transform businesses and improve lives.
Artificial intelligence is becoming not only a tool but also a catalyst for growth, collaboration, and creativity.
And hackathons — these vibrant arenas of teamwork and experimentation — allow us to experience the future of technology today.
