Changing the Algorithm Means Changing the Mindset
Generative artificial intelligence has a disruptive quality whose impact is, paradoxically, largely absent from the current discourse.
As one of the most transformative technologies of our time, generative AI carries a disruptive potential that, paradoxically, is often overlooked in the broader conversation. For the first time, anyone — not just data or tech specialists — has access to the power of artificial intelligence, thanks to its use of natural language.
This revolutionary accessibility presents organizations with a pressing competitive challenge: how can they harness a technology that is being democratized at an unprecedented pace, and whose impact depends on how teams think, collaborate, experiment, and learn?
Some respond by saying their company is not yet ready — that systems need to be consolidated, data cleaned, or specific skills developed first. Others argue that a cultural transformation is needed before AI’s potential can be realized. But what if it’s exactly the opposite? What if experimenting with generative AI — on a small scale and in a controlled environment — is actually the key to catalyzing the transformation they seek?
This is what we’re seeing in organizations that have already taken the leap. A small operations team, through a pilot project within one department, experiments with AI to reduce response times to internal requests — and discovers a new way of collaborating across departments. A commercial unit uses AI to prepare for client meetings — gaining in seconds insights that used to take days. A marketing group uses AI to generate creative concepts and rapid prototypes — saving weeks of work. These cases share two things in common: real impact and collective learning. And studies are starting to confirm this correlation: organizations with more innovative cultures are seeing greater returns from the use of generative AI.
These are small steps that create momentum. And that momentum requires more than just technology. It demands a new way of working — more agile, more empathetic, more focused on experimentation. It requires, for example, the ability to bring together people from different areas, focused on a specific challenge, to design, test, and learn at speed. And it’s this practice — embodied in tools like design sprints — that can make an organization better equipped to explore the potential of AI in a real, tangible, and cross-functional way.
Because in the end, true intelligence isn’t just in the algorithm. It’s in the organization that knows how to learn from it. It’s in the culture that values experimentation. It’s in the leadership that challenges inertia. It’s in the teams that dare to test, to iterate, to do things differently. And above all, it’s in the people who recognize that this revolution concerns them — and who, therefore, choose to be a part of it.
Intelligence may be artificial. But transformation is human and organizational.
Changing the algorithm means changing the mindset.
Tiago Luís Professor at CATÓLICA-LISBON | Executive Education