These days, it takes just a few hours to come up with an idea for using AI. It’s much harder to build a solution that’s secure, scalable, accepted by users, and actually put to use within an organization.
We will devote this panel to the practical aspects of AI implementation. We will discuss the experiences of companies that have progressed from experiments and proof-of-concepts to production-ready solutions, as well as
the reasons why many projects never move beyond the pilot phase. It’s not just the successes that matter, but also the mistakes, failed experiments, and lessons learned from
real-world implementations.
Topics of discussion include:
Why does a PoC work, but production deployment doesn’t?
What mistakes do companies make most often, and what would they not do again today?
How to identify areas with the greatest potential for AI adoption,
How to move from individual experiments to AI operating at an organizational scale,
In-house models, open-source solutions, or off-the-shelf platforms?
how to prepare data, processes, and infrastructure,
how to assess the quality and reliability of AI-based solutions, who should own AI implementations: business, IT, or a dedicated AI team?
what really determines whether an AI project moves beyond the pilot phase and begins to create value across the entire organization