Why did I feel it was interesting to interview Aleksandra? Because she is a prominent academic and AI expert with a deep passion for the intersection of technology, business, and society. She currently serves as Vice-Rector for Innovation and AI and Associate Professor at Kozminski University. Alongside her academic work, Aleksandra is a Senior Research Associate at the Center for Labour and Just Economy at Harvard University and a former Visiting Fellow at MIT. Her extensive experience in AI and its applications spans across education, business, and research, and she is also an advisor at Campus AI Poland, where she plays a key role in shaping AI development in the region. Through her career, she has become a respected voice in the AI community, frequently delivering keynote speeches and advising companies on how to leverage AI for growth.
This interview offers valuable insights into her work and perspective on the evolving role of AI in both business and everyday life. Her unique blend of academic expertise and practical experience provides a compelling narrative about the promises and challenges of AI. In discussing misconceptions about AI, the future of work, and its ethical implications, she highlights critical issues that businesses and society must address as AI continues to transform industries. Aleksandra’s emphasis on AI as a tool for collaboration, empowerment, and creativity makes this interview a fascinating exploration of how we can shape the future of AI to better serve humanity.
Here’s our exchange:
Where are you from originally, and how was your childhood there?
I’m originally from Szczecin, a city in Poland near the German border. I grew up there and went to school right after the end of communism, during a time of rapid transformation and new opportunities. My childhood was shaped by a mix of curiosity, technology, and philosophy—I was always fascinated by the intersection of the human and digital worlds. Growing up, I was drawn to books, ideas, and the evolving role of technology in our lives. Szczecin, with its rich history and unique position as a border city, influenced my perspective on collaboration, transformation, and the broader impact of technology on society.
How was your family composed, and what were the family dynamics like?
I was raised by my mother, who is a highly accomplished judge at the NSA, Poland’s Supreme Administrative Court. Her intellect, discipline, and dedication to justice had a profound influence on me, shaping my perspective on responsibility and critical thinking. My father passed away when I was nine, which was a defining moment in my childhood. As an only child, I had a lot of independence but also a close bond with my mother. Our family dynamic was one of strong values, intellectual curiosity, and resilience. And of course, there was Maciek, our beloved dachshund, who was a huge part of our lives. He brought warmth and energy to our home, making it feel a little less serious at times.
Could you say that there is someone in your family who inspired you to be who you are today?
My mother played a fundamental role in shaping who I am today. As a judge, she exemplifies not only deep legal expertise but also an incredible sense of fairness, responsibility, and intellectual rigor. Watching her work with such precision and dedication gave me a firsthand understanding of the importance of structure, analytical thinking, and persistence in any field. Beyond her career, she was a role model in resilience. Raising me as a single mother after my father’s passing when I was nine was no easy task, yet she did so with strength and grace. She never imposed limits on my curiosity—whether I was drawn to philosophy, technology, or AI, she encouraged exploration and critical discussion rather than rigid rules. This openness to ideas, combined with her strong ethical foundation, shaped how I approach my own work.
Her ability to balance a demanding career with raising a child taught me the importance of discipline and self-reliance. At the same time, she always emphasized the value of fairness and empathy—qualities I see as essential in the world of AI and ethics today. While my interests diverged from law, the principles she instilled in me—intellectual curiosity, analytical depth, and a commitment to fairness—are still at the core of how I think and operate.
How did your experiences at MIT and Harvard shape your approach to AI and innovation?
My experiences at MIT and Harvard were certainly transformative in shaping my approach to AI, innovation, and the future of work. At MIT, particularly at the Center for Collective Intelligence, I was immersed in a research culture that focused on collaborative AI—not just how machines can replace human labor, but how they can augment and enhance human capabilities. This perspective aligned closely with my belief that AI should be a tool for empowerment rather than replacement. The interdisciplinary nature of MIT—where business, engineering, and social sciences intersect—deepened my understanding of how AI can drive organizational and societal change.
Harvard provided another crucial dimension, especially through my work at the Labor & Worklife Center. There, I explored the intersection of AI, labor markets, and ethics, further solidifying my focus on human-centered AI. Harvard’s emphasis on policy, regulation, and the societal implications of AI helped me think beyond just technological capabilities—considering governance, fairness, and long-term societal impact. Together, these experiences reinforced my commitment to collaborative AI, ethical design, and responsible innovation. They shaped my perspective that AI’s role is not just about automation but about augmentation—ensuring it works with people rather than against them, and that we build AI-driven ecosystems that are inclusive, transparent, and aligned with human values.
What motivated your passion for promoting the use of AI in business and everyday life?
During my time at MIT and Harvard, I saw incredible AI advancements, but I also noticed a gap between research and real-world applications. That motivated me to focus on bridging that divide, helping businesses and individuals integrate AI in ways that are both practical and ethical. I’ve always been drawn to the idea that AI should be transparent, explainable, and accessible—not just a tool for big corporations or highly technical users, but something that can support decision-making and innovation across different fields.
I must say I also see AI as a key factor in the future of work. While many fear that it will replace jobs, I believe its real power lies in augmenting human capabilities. That’s why I’ve focused so much on collaborative AI—how machines and people can work together, rather than against each other. AI has the potential to make our work smarter, more efficient, and even more creative if we use it correctly. At the core of it all, I see AI as more than just a set of algorithms. It represents a cultural and societal shift, and how we choose to implement it will shape the future. I want to ensure that the future is one where AI supports human potential rather than limits it.
As an advisor at Campus AI Poland, what are your priorities for fostering AI development and adoption in the region?
Campus AI Poland is certainly an ambitious initiative that aims to create a dynamic ecosystem for AI education, research, and application. It’s not just another AI hub—it’s a place where academia, business, and technology intersect to drive responsible and human-centered AI development. One of its core missions is AI literacy and accessibility. AI should not be limited to engineers and data scientists; it should be a tool that anyone—educators, business leaders, policymakers, and creatives—can understand and use effectively. That’s why Campus AI focuses on education and upskilling, offering workshops, training programs, and interdisciplinary projects that demystify AI for a wider audience.
Beyond education, Campus AI is also about bridging research and real-world applications. Poland has a strong AI research community, but too often, innovations stay within academic walls instead of being implemented in business and society. Campus AI creates a space where startups, corporations, and researchers can collaborate on AI-driven projects that solve real-world problems, from automating routine tasks to enhancing decision-making and creativity.
Campus AI is also an experimental playground—a place where people can explore new AI applications, test out cutting-edge tools, and push the boundaries of what’s possible with generative AI, collaborative AI, and AI-driven learning environments. Whether through hackathons, research collaborations, or interactive AI-driven projects like our Bookbot for the book we published together with Tamila Triantoro entitled Converging Minds, where you can talk about the book, have it summarize it for you, or even write new chapters—the goal is to make AI tangible, engaging, and meaningful.
What are the key challenges businesses face when integrating AI into their operations, and how can they overcome them?
Businesses integrating AI into their operations face several key challenges, but with the right approach, these hurdles can be turned into opportunities.
One of the biggest issues, as I mentioned earlier, is the lack of AI literacy. Many decision-makers still see AI as either a magic solution or an overly complex technology they don’t fully understand. This leads to unrealistic expectations or, conversely, hesitation to implement AI at all. The best way to overcome this is through education and strategic upskilling. Businesses should invest in AI training, not just for tech teams but across departments, so employees understand how AI can complement their work rather than replace it.
Another major challenge is data quality and accessibility. AI systems are only as good as the data they are trained on, and many companies struggle with fragmented, biased, or insufficient datasets. Overcoming this requires a strong data governance strategy to ensure that data is clean, structured, and representative. Companies should also prioritize ethical AI practices, particularly when it comes to bias and fairness in decision-making.
Then there’s the issue of AI implementation and scalability. Many businesses experiment with AI in isolated pilot projects but struggle to integrate it across their entire organization. To scale AI effectively, companies need clear AI roadmaps, executive buy-in, and a focus on collaborative AI—where AI tools work alongside employees rather than creating unnecessary complexity. Successful AI adoption isn’t about replacing human workers but enhancing productivity, creativity, and decision-making.
Could you share an example of a company or industry that has successfully leveraged AI for transformative growth?
A great example of AI-driven transformation is Roche, particularly in how it has leveraged AI for drug discovery, diagnostics, and personalized healthcare. Roche has embraced AI not just as a tool for automation, but as a way to accelerate medical breakthroughs and improve patient outcomes.
One of its biggest innovations comes from its AI-powered pathology and diagnostics. Through its subsidiary Genentech, Roche has been using AI to analyze massive datasets from clinical trials and medical imaging. AI-driven tools help detect diseases earlier and more accurately, particularly in oncology, where precision medicine is crucial. Roche’s AI-based algorithms assist pathologists in identifying cancer markers faster, reducing the time it takes to make critical diagnoses.
Also, Moderna is another great example of how AI can drive transformative growth, particularly in drug development and mRNA technology. Unlike traditional pharmaceutical companies, Moderna was built from the ground up as a data-driven biotech firm, using AI and machine learning to accelerate vaccine and drug discovery. One of the most remarkable AI applications at Moderna was in the development of its COVID-19 vaccine. AI helped design and optimize mRNA sequences at an unprecedented speed. While traditional vaccine development can take years, Moderna’s AI-driven approach allowed it to generate a vaccine candidate within days of sequencing the virus genome.
What are the most promising trends in AI innovation that businesses should be paying attention to in the next few years?
The next few years will bring major shifts in AI that businesses should be paying close attention to. One of the most significant trends is the move toward smaller, more specialized AI models. Instead of relying on massive, general-purpose systems, companies are starting to adopt more efficient, domain-specific AI that requires less computational power and offers more targeted insights. This shift makes AI more practical for businesses that don’t have the resources to run large-scale models but still want to benefit from automation and intelligence.
Generative AI is also evolving beyond text-based chatbots. We’re seeing AI-generated video, synthetic voice, and multimodal models that blend text, images, and sound in entirely new ways. This will transform marketing, entertainment, and customer engagement, making hyper-personalized content creation easier than ever. Businesses that integrate these tools early will gain a serious competitive edge.
Another area gaining momentum is AI for decision intelligence. Instead of just analyzing data, AI is increasingly being used to support strategic decision-making. Companies are using AI-driven analytics not just to predict trends but also to recommend actions in real-time. This is becoming especially important in industries like finance, logistics, and healthcare, where rapid, data-driven decisions can make all the difference.
How do you see the role of AI evolving in everyday life over the next decade?
Over the next decade, AI will become so seamlessly integrated into everyday life that we’ll stop thinking of it as “AI” and simply see it as part of how we live, work, and interact with the world. It won’t just be about automation or digital assistants—it will be embedded in ways that enhance our decision-making, creativity, health, and even our relationships.
One of the most visible shifts will be in how we interact with technology. AI-powered interfaces will move beyond screens and keyboards to become more natural and intuitive. Voice, gesture, and even brain-computer interfaces could replace traditional input methods, making interactions feel more fluid and personalized. Imagine an AI that anticipates your needs—whether it’s adjusting your home environment based on your mood, proactively helping with scheduling, or even assisting in real-time creative work.
In education and work, AI will act more as a collaborator than just a tool. Instead of simply automating tasks, it will enhance human skills, providing real-time feedback, assisting in complex decision-making, and even fostering creativity. Learning will become hyper-personalized, with AI-driven tutors adapting not just to what you need to learn, but how you learn best. This aligns with the vision behind EUonAIR, where we are building an AI-enabled virtual campus designed to support both students and academic staff. By integrating AI into learning management systems and digital classrooms, EUonAIR aims to create a more adaptive, interactive, and inclusive educational environment.
In your work at Harvard’s Center for Labour and Just Economy, how do you approach the ethical challenges associated with AI in the workplace?
At the Center for Labour and Just Economy, my approach to the ethical challenges of AI in the workplace is grounded in the belief that AI should enhance human work, not replace it. AI has the potential to reshape jobs, but the key question is how—whether it will lead to greater inequality or create opportunities for more meaningful and equitable work. One of the biggest ethical concerns is AI-driven job displacement. While automation is inevitable in some areas, the focus should be on reskilling and upskilling workers, ensuring that AI augments human capabilities rather than making entire professions obsolete. I advocate for policies and frameworks that support worker transitions, investing in training programs that prepare employees for new, AI-enhanced roles rather than leaving them behind.
Wage inequality is also a growing concern. AI-driven productivity gains often benefit corporate profits rather than workers. I advocate for models where AI-generated efficiency translates into better wages, reduced working hours, and improved job quality, rather than simply driving down labor costs. AI should be a tool for shared prosperity, not just efficiency at the expense of workers. Worker autonomy is another key focus. With AI being used for employee monitoring and algorithmic management, there’s a fine line between optimization and invasive surveillance. I push for AI governance models that respect privacy, dignity, and worker agency, ensuring that AI is used to support, not control, employees.
What are the most common misconceptions about AI that you encounter during your keynote speeches?
One of the biggest misconceptions I encounter is the overhype around AGI (Artificial General Intelligence) and superintelligence. Many people assume that we are on the brink of AI surpassing human intelligence in a broad, autonomous way, but in reality, today’s AI is narrow, task-specific, and fundamentally reliant on human-designed data and algorithms.
The idea that AI will soon become self-aware, make independent decisions, or outthink humans across all domains is more science fiction than science. AI models, no matter how advanced, lack common sense reasoning, genuine understanding, or true autonomy. They operate based on statistical patterns, not deep comprehension. Even the most powerful language models can’t reason beyond the data they’ve been trained on—they can predict, but not truly think.
Superintelligence is also often misrepresented as an inevitable endpoint when, in reality, the real challenges of AI today are much more grounded: bias, explainability, data quality, and ethical governance. Instead of worrying about AI overthrowing humanity, we should be focusing on how we design AI to be transparent, fair, and useful for society.
I always emphasize that the real future of AI isn’t about machines becoming human—it’s about how we shape AI to complement human intelligence, enhancing creativity, decision-making, and collaboration.