Artificial intelligence is often presented as a magical solution capable of solving any business problem. However, many organizations begin their AI initiatives without a clearly defined use case, reliable data, or a realistic understanding of what the technology can deliver. This talk explores two of the most common challenges in enterprise AI adoption: the gap between business expectations and technical reality, and the lack of structured, consistent, and contextualized data. It also discusses the role of technical professionals in transforming broad AI ambitions into measurable and achievable solutions. Finally, I will present a real-world predictive analytics project developed for the Brazilian consortium market. The case demonstrates how understanding the business problem, preparing the data, and selecting the right technical approach increased prediction accuracy from approximately 55% to 91.5%, turning an abstract AI initiative into a practical decision-support solution.
Thalita Regina Terpilauskas de Sa is a Business Intelligence, Data Analytics, and Automation professional with over 12 years of experience developing data-driven solutions for international organizations. Throughout her career, she has worked at leading global companies, including Louis Dreyfus Company, COFCO International, and Cargill. She is the founder and CEO of Riverstone Insights, a US-based data and AI consulting company that provides Business Intelligence, data analytics, automation, and AI solutions to clients such as HP, Italkraft, and DirectCon. Her expertise includes Power BI, SQL, Python, machine learning, predictive analytics, and business process automation. She recently completed the MIT IDSS Data Science and Machine Learning Program and focuses on transforming complex business challenges into practical, measurable technology solutions.
