How Italian CIOs produce value with gen AI

“It’s more flexible and less expensive,” Ciuccarelli adds. “But it’s not certain we’ll not also use these open systems in production and for external projects. In the short term, proprietary systems are a more prudent choice and give a fast time-to-market, but I don’t rule out open models. On the contrary, I imagine a multi-model future along the lines of multicloud.”

The MLOps paradigm

Equally important for Ciuccarelli is updating the gen AI model with MLOps and LLMOps, which help AI and algorithm governance. “In fact, machine learning models and generative AI, being based on neural networks, risk greater drifts and need exact prompts,” he says. “Such new phenomena aren’t always easy to understand and govern. New components, such as vector databases or orchestrators, have also entered the architecture.”

According to analysis by the World Economic Forum, governance is one of the four pillars of gen AI implementations, along with staff training, budgeting, and team alignment. The CIO will need to be supported by the CISO so concrete business cases for gen AI are accompanied by the study of risks and KPIs that guide targeted and defined solutions. Governance will also need to have the right amount of flexibility, in that it has to manage, not prohibit, the use of gen AI products by employees.