AI Inferencing at the Speed of Real Life - Six Five On The Road
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Scott Tease of Lenovo joins Patrick Moorhead and Daniel Newman to discuss why inferencing has become the moment where AI shifts from experimentation to real-time execution.
Inferencing is where AI proves it can act, not just think.
Patrick Moorhead and Daniel Newman sit down with Scott Tease, Vice President, Product Group, at Lenovo ISG, during Lenovo Tech World in Las Vegas, to focus on the moment AI leaves experimentation and enters live operations. As models mature, AI inference has become the layer that decides whether AI delivers real value or remains stuck in demos and pilots.
Their discussion shifts from training to inferencing and into the realities of running AI at business speed, where latency, data movement, infrastructure readiness, and energy efficiency quickly surface as limiting factors. Bringing AI closer to where data is created is increasingly unavoidable, but inference across edge, data center, and cloud raises new demands for consistency, security, and governance. As inferencing becomes the dominant AI workload, organizations that build this capability early gain not just performance, but operational leverage.
Key Takeaways:
🔷 Inferencing is where AI becomes operational: AI only creates impact when models can act reliably in real time, not just generate outputs in isolation.
🔷 Latency and data movement are the real bottlenecks: Once AI leaves centralized systems, response time and data flow define success more than model accuracy.
🔷 Edge inferencing is accelerating adoption: Bringing AI closer to data reduces delay but increases architectural and operational complexity.
🔷 Governance must scale with deployment: Security, consistency, and control become harder as inference spans environments.
🔷 Operational readiness determines winners: Enterprises that invest in inferencing infrastructure now are better positioned as AI becomes embedded everywhere.
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