Giulia Venturi

No ratings yet.

When I started my bachelor’s degree a few years ago, “AI tools” basically meant Grammarly or, for the more daring, QuillBot. Their role was simple – help polish grammar or rephrase sentences from academic papers. Fast forward two years, and I’ve just started my master’s degree. Despite the education system being the same and in the same country, lots around it feels different. Suddenly, AI is everywhere. From ChatGPT to Lovable, from AI note-takers to slide generators. It feels like every academic task has its own intelligent assistant.

The difference in how we learn and create is noticeable. During my bachelor’s, brainstorming meant long group discussions and staring at blank pages. Today, I can have an AI tool challenge my ideas, summarize research papers, or even predict what questions might come up in my exam. At the same time, discussions among peers and students have also changed. We don’t exchange only ideas anymore, but we mainly discuss prompts and compare AI outputs. I personally believe this has raised the overall quality of student work that is handed in, not because AI does the thinking for us, but because it makes high-level thinking more accessible. However, this shift also comes with new responsibilities. Using AI tools effectively requires critical thinking, self-awareness, and still knowledge on the topic addressed. I’ve realized that AI works best when it’s a partner, not a substitute. The ideas still have to come from us and AI just helps us shape and express them quicker.

In a way, AI has democratized creativity in academia. Surely, it helps students to reach new standards in structure, language, confidence, ideas, and much more. Still, I sometimes wonder: has AI truly democratized creativity, or is it slowly making us lazier by letting algorithms do the thinking we used to do ourselves?

5/5 (1)

What do some of the best-known tech companies have in common? Surely, they all are incredibly successful. But they all are also platform-based organizations that through the agility with which they are able to innovate and scale, have redefined entire industries by creating ecosystems and connecting users, data, and services in ways traditional firms could never match (Blumberg et al., 2020). Over the past decade, platform business models have revolutionized entire industries. Airbnb reshaped hospitality without owning a single hotel. Uber transformed urban transport without a fleet of cars. Spotify turned music into a service rather than a product. Yet, while these consumer-facing industries have been transformed almost overnight, others — like procurement, manufacturing, or healthcare — have been far slower to “platformize.”

The difference lies in how easily digital platforms can generate network effects. Platforms thrive when they connect fragmented markets, reduce transaction costs, and create standardized interactions between users (Madanaguli et al., 2023). If you think of a ride, a song, or a stay, transactions here are relatively simple. In sectors such as hospitality or entertainment, data is abundant, trust can be built through ratings, and users switch platforms easily.

In contrast, industries like B2B procurement involve complex relationships built on trust, quality assurance, and long-term contracts (Beard, 2025). Transactions are high-value, and negotiations are delicate and require time. As a result, even if the benefits of digital matching are clear – transparency, efficiency, and data insights – adoption takes time. Even emerging B2B platforms such as Torg or Wonnda, which connect food and beverage buyers with suppliers, must first overcome structural and cultural barriers before achieving the same level of network-driven momentum as Uber or Airbnb. Still, things are changing. Advances in AI and automation are reducing friction in traditionally “offline” sectors. As information flows become more standardized, we can expect a new wave of platforms to emerge — this time not for your next ride or vacation, but for sourcing ingredients, manufacturing components, or managing supply chains.

The question is no longer if platforms will disrupt every industry, but how fast and who will lead the change.

References

Beard, N. V. (2025, September 8). Optimising B2B procurement for the future (2025). BigCommerce. https://www.bigcommerce.co.uk/articles/b2b-ecommerce/b2b-procurement/

Blumberg, S., Kürtz, K., Bossert, O., & Richter, G. (2020, March 12). The power of platforms to reshape the business. McKinsey & Company. Retrieved October 5, 2025, from https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/tech-forward/the-power-of-platforms-to-reshape-the-business

Madanaguli, A., Parida, V., Sjödin, D., & Oghazi, P. (2023). Literature review on industrial digital platforms: A business model perspective and suggestions for future research. Technological Forecasting and Social Change, 194, 122606. https://doi.org/10.1016/j.techfore.2023.122606

Soares, I., & Nieto-Mengotti, M. (2024). Network effects on platform markets. Revisiting the theoretical literature. Scientific Annals of Economics and Business, 71(4), 605–623. https://doi.org/10.47743/saeb-2024-0029

From Grammarly to GPT: How AI Changed My University Experience

9

Please rate this

Platformization at Different Speeds: Why Some Industries Lag Behind

8

Please rate this