Scaling Enterprise AI Beyond the POC
About this episode
The episode discusses the challenges of scaling enterprise AI beyond proof of concept, featuring insights from Shimon Ben-David of WEKA.
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…
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