The secret of businesses that scale with AI (from "recreational" to mission-critical)

The buzz about artificial intelligence is constant, but what remains concrete when the hype dust settles?

In the first episode of the new season of Genius Talks, Alcebíades Araújo, AI Specialist at Squadra, hosted Tatiana Oliveira, CEO of AI Brasil, and Romulo Cioffi, CIOO and Chief AI Officer at Squadra, for a frank discussion on why some companies are already reaping billions in results with AI, while others are still stumbling over unrealistic expectations.

Want to watch the full episode? Available on major platforms: YouTube, Spotify, and Apple Podcasts.

The fall of expectations and the silent journey

Many organizations are facing what we call the "expectation crash." The common mistake was trying to apply generative AI to unstructured processes or the core of the business without proper preparation.

"So, what happened with the hype? They tried to apply generative AI to processes that were not structured."

Companies that are now success stories, like iFood, didn't start yesterday. They undertook a journey of data structuring and machine learning that lasted years before generative AI gained the spotlight.

"The ideal is to have a framework, called decision intelligence, where the core is with machine learning."

The 3 pillars of AI beyond the hype

According to Romulo Cioffi, the secret is to put value ahead of technology: Value First. That is, technology must serve the business, not the other way around. During the debate, three fundamental pillars for moving beyond the hype were highlighted:

• Context is king: AI needs context to avoid hallucinations and generate real value; this context comes from the stakeholders' experience and the semantic organization of business data;

"To work with that model, for it to deliver value, you have to structure your information in such a way."

• Learning culture: the biggest impediment to AI scaling today is people, and to reverse this scenario, c-levels need humility to admit what they don't know and create an experimental environment where mistakes are accepted and are part of the process;

"For the culture to be a strong culture, you need to have a learning community, where mistakes are part of it."

• End of silos: a holistic view is essential; bringing data scientists into business areas ensures that technology solves real problems, not just isolated technical issues.

"Data professionals included, so they can look at the business, understand the concept, the flow."

Our role is to be AI tutors

We are changing the way we work.

With tools that accelerate development (like Cursor), the role of the technical professional shifts to curation and AI tutoring. The competitive advantage will lie in the ability to model knowledge and business rules, ensuring that the machine delivers the expected results.

Do you agree? Want to watch the full episode? Available on major platforms: YouTube, Spotify, and Apple Podcasts.