While generative AI and AI agents dominate the conversation, many companies remain stuck in the proof-of-concept phase. According to Roberto, the real challenge is not building something that works at 90% — it’s achieving the final 10% required for production: scalability, governance, robustness, and seamless integration with enterprise systems.
With the rapid evolution of artificial intelligence, distinguishing real business value from market noise is critical. At SDG, the approach is clear: test early, test deeply, and validate impact before scaling. Through innovation frameworks, AI observatories, and structured experimentation, the focus is on identifying technologies that deliver measurable business outcomes.
AI may feel like magic to end users — but behind the scenes, it is engineering. Roberto explains how enterprise AI agents require:
The rise of agentic AI marks a shift from simple chatbots to autonomous systems capable of executing tasks, collaborating with other agents, and driving automation at scale.
One of the most transformative impacts of AI is in business intelligence and decision-making. By connecting AI directly to enterprise data through semantic layers, organizations can move from static reports to real-time, data-driven insights.
SDG’s Insight Gen platform enables business users — not just technical teams — to interact with corporate data in natural language, accelerating decisions and improving agility.
As AI agents gain autonomy, governance becomes essential. Defining roles, access permissions, operational scope, and human oversight ensures that AI systems operate securely and responsibly within enterprise environments.
Looking ahead, SDG is expanding its AI ecosystem with new components such as:
A key trend highlighted for 2026 is the emergence of the semantic data layer — enabling AI systems to transform raw data into actionable business knowledge.