One of the most interesting parts of the session was the GenAI adoption path, a progression many companies are currently working through. It often starts with simple prompting techniques (zero-shot and few-shot), then moves toward more sophisticated architectures: retrieval-augmented systems, tool-calling agents and eventually multi-agent environments designed to interact with structured and unstructured data at the same time.
One case discussed during the session involved a Financial Analytics ChatBot designed for business users who do not write SQL. The solution uses a text-to-SQL GenAI approach to translate natural language questions into database queries. The prototype achieved 99% retrieval accuracy, more than 83% SQL generation accuracy and response times under 10 seconds, showing how conversational interfaces can dramatically simplify access to complex datasets.
Another example focused on Next Best Offer systems enhanced with GenAI. By combining classic machine learning with contextual reasoning from language models, organizations were able to significantly improve commercial performance, increasing additional sales by around 15%, boosting revenue by 5% and reducing service costs by roughly 20%.
Beyond these specific cases, the broader takeaway is that GenAI is becoming more about architectures, governance and integration with enterprise data ecosystems. Agents, retrieval systems, evaluation frameworks and model observability are increasingly part of the same operational stack, enabling organizations to build AI systems that are scalable, auditable and production-ready.
For businesses it means AI today is designing systems that deliver reliable, contextual intelligence within the complexity of modern enterprises.
Special thanks to Vitalie Tataru and AC Technologies LTD for invitation and partnership.
