Experiment and validate AI implementation in 2 weeks

Among the countless possibilities and stimuli that the Generative Artificial Intelligence paradigm offers, multiple paths emerge for companies. Not surprisingly, in this fast-moving technology landscape, risks exist both for those who seek to adopt it and for those still waiting for its "better" versions.

It was to break out of this potential paralysis that we developed the AI Sprint method. Designed to identify opportunities, assess needs, and anticipate risks, the method produces a proof of concept tailored to the company's reality in just a few weeks, validating generative AI solutions for enterprise.

Over the past year, our experience delivering AI implementation services has taught us that, beyond separating hype from real opportunities, knowledge exchange with the business is fundamental. The solutions delivering the most results, as studies¹ show, emerge from collaborations between enterprises willing to innovate and specialized, experienced technology partners.

How AI Sprint was developed:

• It is the result of years of design sprint practice and facilitating the adoption of emerging technologies in large enterprises;

• Of our teams' continuous curiosity in adopting new technologies and applying them to business problems;

• Of collaborations and hands-on experiences with companies such as Movida, Mars Wrigley, and Reckitt, as you can explore in this account produced by our design team.

Meet the method:

Discover the method:

Facilitating dialogue between business teams and technology specialists, the method collaboratively investigates how and where the new paradigm can deliver the most value to the company. Simplifying the complexity of the technology and fostering a valuable knowledge exchange with the business, our AI solutions are designed to scale through incremental deliveries.

Unlike broad architectures and systems, the best results come from solutions that focus on improving specific business problems. The method concludes with a validated prototype — where not only a part of the software is ready, but there is also clarity on the steps and investment needed to scale the solution.

Treating business context as fundamental to the success of AI platforms for business, our role is not only to help build them, but also to foster companies' autonomy with the technology. By empowering teams to incorporate and innovate with the technology, the method accelerates adoption in enterprises, offering a concrete path for companies to achieve results with Artificial Intelligence.

Are you ready to explore the new paradigm in your business? Schedule a conversation.

Want to learn more about how we build our solutions? Read our article on how we create multi-agent systems to accelerate results in enterprises.