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Saket Kumar

Saket Kumar

VP of Consumer Analytics, Citi · Brand-side

Topics Saket shaped

AI & technology, Researcher craft & identity, Insights function & business

Episodes

What Saket said — indexed claims

  • Four techniques together control LLM hallucination risk in enterprise products: rule-based hybrid checkpoints, traceability/explainability of each step, human-in-the-loop review, and grounding through fine-tuning or retrieval-augmented generation. — Ep. 4 · 18:05 · cl-saket-kumar-007
  • AI governance is not optional; zero-trust architecture with role-based access control is essential because a single model holding broad context creates real information-leakage risk. — Ep. 4 · 27:00 · cl-saket-kumar-010
  • There must be a balance between a generalized AI that handles broad tasks and a fine-tuned AI grounded in the organization's specific context; tilting too far toward bespoke tuning yields a hard-coded system, while pure generality misses business-specific KPIs. — Ep. 4 · 40:25 · cl-saket-kumar-019
  • Per-token computational cost makes large-scale LLM deployment expensive: every employee query carries cost, and scaling to thousands of users requires significant investment in digital infrastructure, servers, and memory. — Ep. 4 · 35:50 · cl-saket-kumar-017
  • Differential privacy is a critical architectural pattern for the AI era: organizations can still understand consumers without ingesting sensitive personal data, and governments should push for its adoption. — Ep. 4 · 32:45 · cl-saket-kumar-014
  • When data is highly sensitive — as in finance — organizations should not move infrastructure entirely to the cloud; on-premise solutions remain necessary to limit external leak and threat exposure. — Ep. 4 · 30:00 · cl-saket-kumar-012
  • Personal sensitive information should always be removed before fine-tuning or training large language models; PII should not be part of what an LLM learns from. — Ep. 4 · 28:50 · cl-saket-kumar-011
  • Data scientists and analysts traditionally spent about 60% of their time pulling data and only 20% generating insights; AI flips that ratio, transforming the role from data puller to strategic adviser — an 'insight architect' who designs AI-driven workflows and refines model output. — Ep. 4 · 23:40 · cl-saket-kumar-009
  • Generative AI is mature for descriptive analytics but limited for predictive and prescriptive analytics — not because of model inefficiency but because LLMs cannot understand a specific organization's strategy, KPIs, and objectives without targeted training on metadata and unstructured business context. — Ep. 4 · 37:35 · cl-saket-kumar-018
  • Enterprise Gen AI adoption should follow three sequential phases: workforce training, copilot projects that work alongside employees, and only then product engineering — skipping straight to product design wastes money on proofs of concept that don't serve real needs. — Ep. 4 · 12:10 · cl-saket-kumar-004

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