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- AI Strategy (315)
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Conversational AI Latency and the Mechanics of Turn-Taking
A practical framework to measure voice AI latency, tune endpoint detection and barge-in, and decide when the experience is ready to launch.
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How to Build Product Verification Loops for AI Software Factories
A practical operating model for linking factory tests and AI evals to post-ship evidence, product decisions, and compounding learning.
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Designing AI Products That Learn Without Losing User Trust
A practical framework for placing AI in the workflow, capturing meaningful feedback, governing adaptation, and turning usage into safer product learning.
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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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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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Feature Management as a Product Development Discipline
A practical guide to controlled feature releases, evidence-based rollout decisions, cross-functional ownership, and the governance needed to manage complexity.
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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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Behavioral Analytics for AI Agent Activation and Retention
A practical framework for measuring an AI agent’s first useful outcome, repeat value, and behavior-triggered interventions without mistaking activity for trust.
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How I Make AI Agents Speak Like Our Team: A Conversation Design Playbook That Lifts CSAT
Your AI Agent will sound like an LLM unless you intentionally design how it communicates. I share a practical conversation design playbook covering tone, response structure,…
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AI Inference Economics: Optimize for Value, Not Cost
A practical framework for balancing AI inference cost, latency, and quality against conversion, retention, support demand, and revenue.
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A Practical Model for Amplitude Behavioral Web Intelligence
A decision model for combining Amplitude analytics, zoning, heatmaps, replay, performance signals, and experiments without overstating the evidence.
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How Agentic Analytics Reshapes Product Development Roadmaps
A practical synthesis of how product agents connect behavioral analytics, controlled experiments, roadmap decisions, and accountable automation.
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Reusable AI Agent Workflows Need Evaluation Contracts
A practical model for packaging agent skills with traces, test fixtures, guardrails, and product metrics so reuse does not weaken accountability.
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How to Build a Resilient Experimentation Program at Scale
A practical operating model for producing trustworthy decisions through layered evaluation, governed measurement, and reversible delivery at scale.
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Analytics-Led Growth Engineering: A Practical Operating Model
Build a growth engineering system that connects trusted behavioral data, focused experiments, safe releases, and durable activation and retention gains.
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Product Analytics for Retention: A Practical Operating System
Build a trustworthy retention analytics loop that connects valuable behavior, clean instrumentation, diagnosis, and controlled product experiments.
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Supercharge Core Web Vitals with Amplitude’s Global Agent: Faster Rankings, Happier Users
Core Web Vitals are a direct lever on user experience and SEO, so I use Amplitude’s Global Agent and Amplitude AI Agents to measure and improve…
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How to Validate Behavioral Heatmap Accuracy Before You Act
A practical workflow for checking screenshot fidelity, click placement, segmentation, and supporting evidence before you act on a heatmap.
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How to Prove the ROI of an AI Product Before You Scale It
A practical system for tying AI product behavior to incremental revenue, real cost savings, and risk-adjusted launch, scale, or rollback decisions.
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No More Accidental Agents: How We Engineered Global Agent’s Helpful, Curious Personality
Most AI agents inherit accidental personalities from prompts and defaults. We took a different path, treating personality as a measurable product surface and optimizing it with…
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