Shivam.Consulting Blog
AI products, enablement & hiring
- AI Strategy (274)
- Generative AI (105)
- IT Leadership (32)
- Leadership (78)
- Product Management (324)
- Product Management Leadership (286)
- Uncategorized (32)
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How to Lead When AI Expands Roles Before Titles Change
A practical operating model for using AI across functional boundaries without blurring accountability, review, or specialist ownership.
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How to Benchmark AI Humanizers Without Gaming the Test
A practical framework for testing AI humanizers across detector disagreement, content fidelity, writing quality, latency, and workflow fit.
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Enterprise AI Ownership: What Your Company Must Control
A practical framework for deciding which AI assets, learning loops, contracts, and platform layers your enterprise must control as models change.
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China AI Safety Governance at WAIC 2026: A Product Playbook
A practical playbook for turning China’s WAIC 2026 signals on loss of control, agent boundaries, and cyber risk into product decisions.
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AI Video Generation Advances: A Product Leader’s Playbook
A practical framework for turning gains in video coherence, control, and open-source access into workflows, evaluations, and sound product bets.
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Reliable Agentic AI Architectures: A Production Blueprint
A production blueprint for agentic AI covering bounded graphs, independent verification, safe tool execution, durable state, and eval-driven rollout.
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How to Run Customer-Facing AI Implementations That Scale
A practical operating model for turning vague customer AI requests into safe production deployments without creating a permanent custom-work queue.
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How to Scale AI Pilots Into Everyday Operating Work
A practical operating model for choosing the right workflow, setting AI boundaries, measuring value, and scaling with auditable controls.
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Cost-Aware AI Model Selection: Pay for Accepted Work
A practical framework to compare AI models by accepted-result cost, test quality, choose hosting, and route production work with safe fallbacks.
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How to Design Production AI Guardrails for Sensitive Workflows
A practical control-plane design for routing sensitive data, constraining agent actions, enforcing approvals, testing failures, and preserving auditability.
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Claude Code for Non-Technical Professionals: A Safe Start
A practical, low-risk way to use Claude Code for file-based product work, with a bounded first project, review gates, and reusable prompts.
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Gamma’s AI Pivot: Why the Blank Page Mattered More Than AI
Gamma’s pivot shows how to find an AI-native wedge, prove activation, price real value, and absorb enterprise demand without losing focus.
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How to Build Trustworthy AI Diagnostics for Women’s Health
A practical framework for defining clinical scope, communicating uncertainty, preventing automation bias and governing sensitive health data.
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AI Productivity Is Not a Product Outcome: Measure What Matters
A practical operating model for linking AI-enabled speed to customer behavior, business impact, quality guardrails, and deliberate capacity choices.
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AI-Assisted Behavioral Analytics: A Product Leader’s Playbook
A practical operating model for using AI to investigate user behavior, validate product insights, build cohorts, and turn launch data into safer decisions.
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From Amplitude Adoption to Customer Value: A Leadership Model
Build a customer value model that connects Amplitude behavior data to business outcomes, credible claims, and clear leadership decisions.
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Competing on Experience: A Retail Banking Product Strategy
A practical framework for choosing the banking journeys, measures, and operating habits that turn customer experience into durable advantage.
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Operating Lessons from Plaid’s COO for Scaling Through Change
Plaid COO Eric Sager’s operating approach shows how capacity, customer ownership, decision trade-offs, delegation, and onboarding can support resilient growth.
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A Practical Framework for Choosing Product Management Tools
A workflow-first framework helps product teams select, govern, and reassess tools without mistaking a long feature list for a coherent operating system.
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A Practical Framework for Measuring New Feature Success
A practical framework for deciding whether a new feature earns adoption, improves the user experience, and contributes to a meaningful product or business outcome.
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Browse topics
- AI Strategy (274)
- Generative AI (105)
- IT Leadership (32)
- Leadership (78)
- Product Management (324)
- Product Management Leadership (286)
- Uncategorized (32)
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