Interview: Marcos M. Orteu - Navigating the Impact of Generative AI on Digital Markets and Organizations
With one foot in academia and the other in the consulting world
Generative AI is transforming markets, and who better to discuss its impact than my friend Marcos M. Orteu? As Managing Partner at D⅄N⛛MICS, Marcos is deeply immersed in digital markets, regulation, and strategy. He’s worked with private, public, and nonprofit clients, giving him a broad perspective on how AI is shaking things up.
Beyond consulting, Marcos is also a Full-Time Professor at UTDT Business School in Argentina, where he teaches about digital platforms, industrial organization, and applied microeconomics. With one foot in academia and the other in the business world, he offers a unique take on the promises—and challenges—of integrating AI into real-world decision-making.
How did you first become interested in generative AI, and what role does it currently play in your work?
My interest in generative AI emerged naturally through my work in digital markets. For years, machine learning was just one of many algorithmic tools available to solve problems. It wasn’t a distinct field of focus—it was just part of the digital toolbox.
But that changed a few months ago with the explosion of tools like ChatGPT. Suddenly, AI moved from being a technical, back-end solution to a consumer-facing product. That visibility created an unprecedented shift—people began interacting directly with the technology, experiencing its power and limitations firsthand. It quickly became clear that this wasn’t just another trend; it was a game-changer for digital markets and beyond.
What are some challenges you see organizations facing when adopting generative AI?
One major challenge is that organizations struggle to change their behavior and processes, even when presented with incredible tools. Generative AI has vast potential, but the gap between the technology’s capabilities and an organization’s ability to adapt and implement it effectively is huge.
This is not just a technical or resource issue—it’s cultural. For instance, many organizations lack the expertise to assess whether the outputs they receive from generative AI are accurate or valuable. As a result, there’s a risk of blindly trusting the technology without understanding its limitations.
But there’s hope. We’re starting to see specific solutions emerge that are so compelling they drive adoption naturally. Once people see tangible results—automated workflows, better insights—they begin to change how they operate. The key is creating tools and processes that bridge this gap and make the transition seamless.
You touched on the democratization of AI. What implications does this have for businesses and consumers?
The democratization of AI has made powerful tools accessible to millions, which is both exciting and challenging. Tools like ChatGPT give individuals and organizations the ability to harness AI for a wide range of tasks, from automating routine processes to generating content.
However, this accessibility also raises important questions about trust and oversight. Some people might not have the skills to critically evaluate AI outputs. For businesses, this means investing in training and creating guardrails to ensure that the technology is used effectively and ethically.
What about consulting? How is generative AI changing the way consultants work?
In my experience, generative AI is a useful brainstorming and research tool, but it’s not a magic solution. It helps structure ideas, generate different perspectives, and automate some of the heavy lifting in research. However, I’ve also seen how its lack of precision can be a challenge—sometimes it provides plausible but incorrect information, so you always need a critical filter.
A big shift I’ve noticed is how clients perceive AI. Many assume it can replace traditional consulting work, but in reality, it’s more of an assistant than an expert. The real value comes when consultants combine AI’s speed with human judgment—using it to test hypotheses, challenge assumptions, and refine strategies. The best results come when AI and human expertise work together, not when one replaces the other.
And education? How do you see generative AI impacting students and professors? Do you use it in your own teaching?
Right now, generative AI can be a helpful learning aid. For students, it works well as a scalable tutor, providing quick explanations, summarizing concepts, and helping structure arguments. It’s particularly useful for handling basic, repetitive questions, freeing up time for deeper learning. However, its biggest limitation is that it often lacks depth and accuracy, so students still need to develop strong critical thinking skills to assess and refine AI-generated outputs.
For professors, AI has potential, but today, it’s still quite limited. It can help with content organization, suggesting alternative explanations or generating case studies, but teaching at a high level still requires deep, specialized expertise. You can’t rely on AI to provide cutting-edge insights or nuanced academic discussions—professors still need to master their fields thoroughly. In the future, we might see AI becoming a more powerful co-pilot in education, but for now, it’s more of a light support tool rather than a transformational force in higher education.
Any closing thoughts on the broader implications of generative AI?
Generative AI is forcing us to rethink how we work and innovate. It has incredible potential, but success will depend on our ability to adapt. Whether it’s in corporate settings, academia, or personal life, the real challenge lies in bridging the gap between what’s possible and what’s practical.
At the same time, generative AI is just a tool. The creativity, decision-making, and judgment still come from us. It’s up to organizations and individuals to find ways to integrate these technologies thoughtfully, balancing efficiency with human insight.