Scaling Enterprise AI Beyond the POC

Enterprise AI is easy to demonstrate. The real test begins when a promising POC meets production costs, security requirements, data movement, latency, and internal adoption.Shimon Ben-David, CTO at WEKA, joins Amir to discuss the gap between experimenting with generative AI and operating it at scale. They explore how classical AI differs from generative AI, why production exposes problems that demos hide, and how companies with limited AI maturity can start building useful internal capability.Practical Takeaways• A successful POC proves that an outcome is possible. It does not prove that the system will be affordable, secure, reliable, or fast at scale.• Enterprise AI adoption reaches across infrastructure, engineering, data, security, and business teams. It cannot be owned by one group in isolation.• Adding more GPUs will not fix slow data access, poor utilization, weak pipelines, or an experience users do not want to use.• External support can help, but the person or firm involved needs to stay through implementation and production, not stop at recommendations.• Companies that are behind should begin with proven use cases, build internal experience, and quickly stop experiments that fail to show value.Key Moments00:00 Why moving enterprise AI into production remains difficult01:55 The difference between classical AI and generative AI adoption07:05 How companies can use AI without having a formal AI strategy11:35 Why successful POCs often struggle when they reach production17:35 Competitive pressure, AI FOMO, and the need to calculate real ROI22:00 Why AI adoption requires cross organizational change33:10 Where a company with limited AI maturity should beginOne Line That Stuck“The promise is there. It is possible. You just need to do it properly.”Subscribe to The Tech Trek for more conversations about how technical teams are building, operating, and adapting around AI, data, product, platform, and engineering execution.