Adopting AI solutions can be challenging. Businesses often encounter roadblocks such as limited data, outdated infrastructure, or the difficulty of transforming ideas into tools that truly deliver results. Yet, despite these hurdles, AI holds the potential to reshape industries and tackle meaningful problems. Realizing that potential requires practical expertise, well-defined strategies, and a deep understanding of how to align AI with real-world needs.
Maryna Bautina has dedicated her career to helping businesses bridge this gap. From her early days in data science to leading AI adoption strategies for global organizations, she has built a reputation for solving complex problems with a hands-on and thoughtful approach. In this interview, Maryna shares her journey, the lessons she’s learned, and her vision for AI’s transformative potential. Her story is a powerful testament to the value of curiosity, adaptability, and a commitment to driving meaningful progress – one solution at a time.
Q: Maryna, what inspired you to pursue a career in data science and AI in the first place?
Maryna: Growing up, I always had a strong affinity for mathematics and a natural curiosity about computers. Initially, I envisioned myself as a traditional software developer, but what truly captivated me was the transformative power of data. I became fascinated by how you could use historical data to predict future outcomes. It wasn’t just about numbers; it was about unlocking insights and creating tangible value from data. That realization set me on a journey to explore this field and understand how data could be leveraged to solve real-world problems.
Q: Where did your career begin, and how did you end up working for international companies?
Maryna: My career began in the banking sector, working as a data scientist for a Ukrainian bank. There, I tackled projects like transaction analysis, fraud detection, credit risk assessments, and process optimization. These weren’t just academic exercises; they were high-stakes challenges where data-driven insights made a real difference in decision-making, often with immediate impact.
After gaining experience there, I wanted to broaden my horizons and joined a global consulting firm. That shift was a game-changer. I worked with leading companies across various industries, solving diverse and complex challenges at a much larger scale. From predictive models for supply chains to AI-powered tools for the mining industry, every project pushed boundaries. That diversity is what keeps me motivated – it’s an incredible journey.
Q: Speaking of diversity, what does your role as a lead data scientist currently involve?
Maryna: My role goes beyond just coding and building machine learning models. While I still enjoy the technical aspects, my responsibilities include developing AI adoption strategies, brainstorming innovative solutions, and turning ideas into prototypes. Once we build a working solution, I help businesses integrate and adopt these tools effectively.
It’s a multifaceted job – one day, I might lead a brainstorming session on AI innovations, and the next, I’m troubleshooting deployment challenges with a client. My technical background helps bridge the gap between business discussions and technical execution. It’s demanding, but seeing AI’s tangible impact on businesses is incredibly fulfilling.
Q: How do you help companies identify where AI can make the most significant impact in their operations?
Maryna: It begins with a deep understanding of the business – its objectives, challenges, and workflows. To ensure a clear and strategic approach, I use a methodology I developed and refined over time and call it Strategic AI Impact Blueprint (SAIB). Over the years, this methodology has proven to be quite effective in identifying and prioritizing opportunities where AI can deliver the most meaningful results. It consists of three key stages:
- Discovery and goal alignment: Collaborating with stakeholders to uncover pain points and inefficiencies while ensuring AI initiatives align with the organization’s strategic goals.
- Impact and feasibility mapping: Evaluating opportunities based on their business impact and AI feasibility. This ensures we focus on initiatives that are both meaningful and practical.
- Tailored roadmap development: Creating a detailed solution roadmap with measurable KPIs, implementation phases, and expected ROI.
The key is prioritizing problems that are both significant and solvable with AI. Not every issue lends itself to an AI solution, so part of the process involves clearly communicating AI’s limitations and recommending alternative approaches when necessary.
Q: Sometimes companies face challenges like a lack of data or infrastructure. How do you address these obstacles?
Maryna: These challenges are common, but they can be overcome. When data is scarce, I look for ways to augment it – through synthetic data generation, transfer learning, or leveraging external datasets. For infrastructure, I often recommend starting small. Cloud platforms make it easier to build scalable solutions without heavy upfront investment. The goal is to create a proof of concept and expand once the business sees value in the solution.
Q: Has your recognition as a Google Cloud Champion Innovator influenced how you address such challenges?
Maryna: Definitely. Being recognized as a Google Cloud Champion Innovator has connected me with a global network of experts and resources. Sharing ideas and staying updated on cutting-edge solutions has been invaluable in addressing challenges like data scarcity or infrastructure limitations. The recognition has also bolstered my credibility, making it easier to advocate for innovative approaches like cloud-based solutions. It’s a constant source of motivation to push the boundaries of what’s possible with AI.
Q: Your solutions clearly have a tangible impact. Can you share an example of a successful AI implementation?
Maryna: One project I’m particularly proud of involved a generative AI solution for an e-commerce company. We used advanced NLP models to analyze customer feedback, uncovering trends that powered a recommendation system and adaptive marketing strategies. The result? A 20% revenue increase in six months across 12 regional markets.
Another example is a demand forecasting tool that I led the development of for a global retailer. By integrating time-series analysis and machine learning, we reduced stock outs by 25%, improved inventory management, and even supported sustainability by cutting waste. These projects demonstrate how AI can drive both operational efficiency and business growth.
Q: Many AI projects struggle to transition from prototype to production. What’s your secret?
Maryna: The key is designing with the end goal in mind, involving stakeholders early, and making iteration effortless. A prototype isn’t truly successful if it only works under ideal conditions. That’s why I engage business leaders, IT teams, and end-users from the start. Regular feedback ensures alignment and avoids last-minute surprises. Additionally, I focus on workflows that simplify monitoring, retraining, and updates. If fixing an issue feels overly complicated, it’s a sign the planning wasn’t thorough. Iteration and adaptability are crucial to success.
Q: With such a diverse range of projects, how do you stay ahead in this fast-evolving field?
Maryna: It requires a combination of continuous learning, practical experimentation, and active engagement with the broader professional community. I dedicate time to staying updated through various channels – reading research papers, attending industry conferences, participating in webinars, and following thought leaders in my field. However, knowledge on its own isn’t sufficient. The true value lies in applying what I learn. I make it a priority to experiment with new tools, frameworks, and methodologies, whether it’s exploring algorithms or leveraging the latest cloud deployment strategies. I also believe in the power of collaboration and continuous curiosity. Engaging with professional networks and communities not only broadens my perspective but also allows me to exchange ideas and insights with peers.
Q: Just recently, you secured second place at the prestigious International LLM Agents Hackathon, hosted by Berkeley RDI. What key skills and strategies do you believe contributed to your success in the competition?
Maryna: It wasn’t just me – our success was truly a team effort between me and a close colleague. We each brought diverse experience across different industries and technologies, which gave us a unique perspective on how AI can drive real, tangible impact. Having worked closely with AI for years, we had the technical expertise to move quickly from concept to execution. One of the biggest challenges was balancing the competition with our professional and personal commitments. It took a significant amount of energy and focus to transform a simple idea into a functional prototype – one that wasn’t just technically impressive, but also had real-world applications. Given that we were competing against nearly 3,000 participants from around the world, many of whom had exceptional ideas and technical depth, we knew we had to refine our solution repeatedly to ensure it was innovative, practical, and scalable. More than anything, this experience reinforced our belief in AI’s potential to drive meaningful change – and the crucial role that collaboration plays in making that happen.
Q: Finally, how do you see AI evolving in the next decade, and what role do you hope to play?
Maryna: AI is evolving at an incredible pace, and I see it becoming even more deeply integrated into our daily lives, industries, and decision-making processes. We’re moving beyond just automating tasks – AI is becoming more autonomous, more context-aware, and increasingly capable of reasoning in complex, dynamic environments. One of the biggest shifts will be in how AI collaborates with humans. I believe the future isn’t about AI replacing people, but about augmenting human capabilities, enabling smarter decision-making, and driving innovation in ways we haven’t even imagined yet. Ethical considerations, transparency, and responsible AI development will also be critical as these systems become more powerful. As for my role, I want to be at the forefront of building AI solutions that create real, measurable impact. Whether it’s through research, product development, or shaping AI strategy, I see myself continuing to bridge the gap between cutting-edge technology and practical, scalable applications. Just like in the hackathon, I believe collaboration is key – bringing together diverse perspectives, technical expertise, and a shared vision to ensure AI is developed in a way that truly benefits society.
