486. Managing AI Costs while Scaling Agentic Workflows with Small Language Models
From Transform NOW by SSNC Blue Prism
May 26, 2026 · 38 min
About this episode
Rob May discusses practical AI deployment and cost optimization using small language models in this episode of Transform NOW.
In this episode of the Transform NOW podcast, host Michael Marchuk hosts Rob May, CEO of Neurometric and co-host of the AI in NYC podcast, discussing practical AI deployment beyond hype. Rob explains Neurometric’s focus on small language models and inference optimization, helping companies move high-volume tasks off frontier models to task-specific endpoints to reduce latency and cost. He explores whether enterprises should stay model-agnostic versus aligning with a vendor for guardrails, culture, and data considerations, and how agentic workflows may soon choose models dynamically, with risks around constraints, availability, and stability. Rob covers model deprecation, data/model drift, and why legacy models may persist via third-party hosting. Key AI KPIs include accuracy, latency, and cost; token-usage metrics are criticized. For regulated workflows like KYC and fraud, he stresses human oversight and alerting. They also discuss governance tradeoffs, startup vs incumbent advantages, quantization and other inference cost tactics, and ongoing chip innovation driving edge AI and potential data center opportunities. -Small Language Models -Model Moats and Vendor Choice -Agents…
People in this episode
Host: Michael Marchuk
Guest: Rob May
Topics covered
- AI deployment
- small language models
- cost optimization
- agentic workflows
- model governance
- regulated workflows
- chip innovation
Keywords
- AI costs
- small language models
- inference optimization
- model governance
- KYC
- fraud
- latency
- data drift
- edge AI
Mentioned in this episode
Organizations: Neurometric, AI in NYC, SS&C Blue Prism
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