Massimo, thank you for being here today. Let’s talk about artificial intelligence, a topic that is being discussed across all sectors and at every level within organizations. AI is often associated with algorithms that promise to help us, but for these algorithms to truly help, they must be actionable. Your work and your company focus heavily on actionability within organizations. Could you explain why this is so strategic?
Thank you, Raffaele. It’s a pleasure to be here and to discuss such a critical topic. Actionability in AI is essential because it’s what transforms insights into tangible outcomes. My perspective on this stems from how I see the world—I tend to notice signals everywhere. These signals, when processed or transformed, result in outputs that often become new signals themselves. But for these outputs to have real value, they need to be actionable.
Actionability comes from the ability of either humans or machines to interact effectively with these signals. Many actions can now be fully automated, thanks to advancements in AI. Historically, automation was rule-based, as seen in robotic process automation (RPA). However, today, AI adds cognitive capabilities to these processes, enabling a deeper, more nuanced level of automation.
This creates a collaborative environment where humans and machines work together in a symbiotic relationship. Machines handle repetitive, data-intensive tasks, while humans focus on strategic decision-making and creative problem-solving. This balance is the cornerstone of actionability in modern organizations.
That’s an insightful perspective. Shifting gears slightly, I know you are a strong advocate for open-source technology. Why do you believe open source is so important, and what role do you see it playing in the future of AI?
My belief in open source comes from observing its historical success. If we look at the evolution of technology, open-source solutions have consistently outperformed their closed -source counterparts in certain critical areas. Take, for example, the internet—it runs largely on Apache servers, which are open source. Before this, the space was dominated by closed-source solutions. This success is rooted in collaboration. Open source allows multiple entities, often with diverse goals and approaches, to work together and create something far greater than what could be achieved in isolation. It may appear disorganized on the surface, but the collective effort generates immense power and innovation.
In the context of AI, we’re already seeing a significant move toward open source. Many AI tools and frameworks being developed today are open source, making them accessible to a global audience. This trend is accelerating, and I expect it to shape the future of AI development.
Regulation will also play a role in this evolution. While I believe there should be oversight, it should focus more on the applications of AI rather than restricting the underlying science, mathematics, or models. Open-source innovation, coupled with thoughtful regulation, will drive progress in a responsible and inclusive manner.
Let’s return to actionability. In your experience, are there specific markets or sectors that are more mature and better prepared to adopt these technologies?
That’s an excellent question. Compared to previous technological trends, such as social networking or blockchain, AI has scaled much faster and reached a broader audience. This rapid adoption has been driven by advancements in computing power and the increasing availability of AI tools.
What’s remarkable is how AI has permeated virtually every industry—small businesses, large corporations, and everything in between. There’s no longer a clear divide between “mature” and “immature” markets. Everyone, regardless of scale or sector, is exploring how AI can improve their processes.
However, some industries, such as finance and healthcare, have been quicker to adopt AI due to the immediate and measurable benefits it offers. For instance, AI’s ability to process large datasets, detect patterns, and automate decision-making is transforming how these industries operate. That said, the speed and breadth of AI adoption mean that even unexpected sectors are now exploring its potential.
To conclude, I’d like to hear your thoughts on startups versus large enterprises in the AI space. What is the value of a startup compared to a multinational corporation when it comes to delivering solutions?
That’s a fascinating comparison. Larger organizations tend to be more structured and resource-rich, which gives them a certain stability. However, this structure also makes them slower to adapt to change. For example, implementing a new AI-driven process in a multinational corporation can take significant time due to the layers of approval and integration required.
Startups, on the other hand, are inherently agile. Their lean structure allows them to respond quickly to market changes and pivot when necessary. This adaptiveness is a double-edged sword—it’s a strength in fast-moving markets but can be a challenge in highly regulated or resource-intensive industries.
What makes startups particularly valuable in the AI space is their ability to innovate without being constrained by legacy systems or bureaucratic processes. They can experiment, fail fast, and iterate, which is crucial when dealing with cutting-edge technologies like AI.
In situations where rapid adaptation is essential—such as responding to emerging trends or shifting customer demands—startups often have the upper hand. Their ability to pivot and deliver tailored solutions makes them invaluable players in the AI ecosystem.
