Generative artificial intelligence is inherently disruptive, and paradoxically, its impact is largely absent from the current discourse.
Generative artificial intelligence, as one of the most transformative technologies of our time, is inherently disruptive, and paradoxically, its impact is largely absent from the current discourse. For the first time, anyone — not just data or technology experts — holds the potential of artificial intelligence in their hands, thanks to its use of natural language.
This revolutionary accessibility presents organizations with a competitive challenge that must urgently be addressed: how can they take advantage of a technology that is becoming democratized at unprecedented speed, and whose impact depends on how teams think, collaborate, experiment, and learn?
Some respond that their organization is not yet ready. That they first need to consolidate systems, handle data, or develop specific skills. And some say that a cultural transformation is needed first, to harness AI’s potential. But what if it were precisely the opposite? What if experimenting with generative AI — on a small scale and in a controlled environment — were key to catalyzing the desired transformation?
That’s what we’re seeing in the organizations that have already moved forward. A small operations team, in a pilot project limited to 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 — and gains in seconds the insights that used to take days. A marketing group uses AI to generate creative concepts and rapid prototypes — and anticipates weeks of work. These cases share two factors in common: real impact and collective learning. And studies are beginning to confirm this correlation: organizations with more innovative cultures are achieving higher returns from their application of generative AI.
These are small steps that generate momentum. And that momentum requires more than 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 departments, focused on a specific challenge, to design, test and learn at an accelerated pace. And it is this practice, materialized for instance in design sprints, that can make an organization more prepared to explore AI’s potential — in a real, tangible, and cross-functional way.
Because in the end, true intelligence is not only 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 drive it.
Intelligence may be artificial. But transformation is human and organizational. Changing the algorithm means changing the mindset.
Tiago Luís, Director of the Executive Program Disrupt through Design Sprints and AI, at CATÓLICA-LISBON