AI Strategy
- AI Strategy (429)
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- Leadership (81)
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- Product Management Leadership (373)
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How to Design Multi-Agent Fintech Support That Finishes Work
A field guide to dividing agent roles, controlling multi-day fintech cases, enforcing compliance, and measuring verified resolution instead of chat volume.
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AI Product Management Skills: A Practical 12-Month Roadmap
A 12-month roadmap for building AI literacy, evaluating prototypes, shipping reliable workflows, and scaling product governance.
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Context-Driven AI Product Engineering That Survives Production
Learn how to design context contracts, retrieval pipelines, evaluations, and ownership so an AI feature stays grounded and useful in production.
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2026 Support Capacity Playbook: Bold AI Automation, Smarter Staffing, Zero‑Surprise SLAs
AI is reshaping support capacity planning, and the old linear volume-to-headcount model no longer holds. I center plans on automation rate (AI Agent involvement rate ×…
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AI in Product Design: My Proven Playbook, Real Use Cases, and the Tools That Win Faster
AI in product design has moved from novelty to necessity, and the fastest-moving teams are weaving it through discovery, prototyping, and validation. I share a practical…
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From Concierge to AI Marketing Engine: Inside Mowie’s Document Hierarchy Playbook
SMBs don’t need a bigger team to market like pros—they need smarter systems. In this first-person breakdown, I unpack how Mowie evolved from a hands-on concierge…
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Automated Insights for Product Teams: Uncover Causal ‘Aha’ Moments in Minutes, Not Weeks
Automated insights change the analytics game by surfacing causal patterns and “aha” moments in minutes, not weeks. Instead of manual, exhaustive searches, I get ranked hypotheses…
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Unlock Real-Time Product Insights: Amplitude + OpenAI MCP in ChatGPT, Without BI Bottlenecks
Connecting Amplitude analytics to ChatGPT via OpenAI’s MCP brings product insights directly into the flow of work. I can ask natural-language questions about activation, retention, and…
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Operationalizing AI: A Practical System for Scalable Growth
A practical operating model for choosing AI use cases, designing controlled workflows, measuring value, governing risk, and scaling what works.
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How to Build a Self-Improving AI Support Operation
A practical operating model for turning AI support failures into owned, tested changes that improve resolution without weakening quality or trust.
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A Practical Governance Model for Enterprise AI Support Agents
A practical operating model to control AI support-agent autonomy, meet service obligations, preserve audit evidence, and manage change safely.
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Beyond Accuracy: The Trust-First Evaluation Metrics I Use to Scale High-Impact AI Products
Trust—not accuracy—determines whether AI features earn adoption, retention, and long-term impact. In this piece, I share the layered metrics I use to evaluate model quality and…
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From No-Code Hack to 10,000 Weekly Calls: Inside Perk’s Voice AI That Actually Works
A small Perk team turned a Make.com hackathon into a voice AI agent that makes 10,000+ weekly hotel verification calls—reducing failed virtual card payments and traveler…
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How Startups Earn Visibility in ChatGPT and Perplexity
A practical system for turning buyer questions, structured pages, independent proof, and repeatable evaluations into startup visibility in AI search.
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Evidence-Driven Product Analytics: From Signal to Decision
A practical operating system for turning behavioral signals into sound hypotheses, reliable experiments, and product decisions your team can defend.
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How to Run AI-Augmented Workflow Experiments That Matter
A practical playbook for testing AI-assisted workflows with clear hypotheses, full-job metrics, guardrails, and evidence-based autonomy decisions.
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How Amplitude AI Feedback Turns Noise into Product Signal You Can Ship With Confidence
Customer feedback is abundant, but reliable product signal is rare. With Amplitude AI Feedback, I turn fragmented qualitative input into evidence-backed insights tied to real user…
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AI-Ready Data Governance: A Practical Trust Framework
A practical operating model to trace AI inputs, enforce access, automate quality controls, and prove trust without slowing delivery.
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Own Your AI: 4 Essential Roles to Supercharge Support and Prevent Performance Drift by 2026
AI doesn’t fail because models are weak; it fails when no one owns performance. In this third installment of my 2026 customer service planning series, I…
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Unlock AI Product Roadmaps: Essential Tools Every PM Needs to Prioritize and Ship Faster
The AI era demands roadmaps that learn as fast as your customers do. In this first-person playbook, I share how I connect AI strategy to product…
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- AI Strategy (429)
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