AI Strategy
- AI Strategy (429)
- Generative AI (113)
- IT Leadership (38)
- Leadership (81)
- Product Management (394)
- Product Management Leadership (373)
- Uncategorized (32)
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International Governance for a Verifiable Frontier AI Slowdown
A practical framework for defining, verifying, and enforcing a reciprocal frontier AI slowdown, plus the controls product leaders can build now.
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How Product Leaders Should Evaluate AI Model and Robotics Releases
A practical framework for deciding which frontier AI and robotics releases deserve evaluation, investment, monitoring, or a deliberate no.
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AI-Enhanced Threat Intelligence: From Feeds to Decisions
A practical operating model for turning threat intelligence and internal exposure data into faster, evidence-backed security decisions with controlled AI autonomy.
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A Practical Playbook for AI in Back-Office Operations
A practical framework for choosing back-office AI workflows, setting safe autonomy, and proving improvements in cost, quality, and resilience.
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A Practical Audit for Removing Unnecessary LLM Calls
A practical framework for tracing LLM calls, testing simpler replacements, and cutting cost and latency without damaging product outcomes.
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How to Turn AI Spending Into Measurable Business Value
A practical model for linking AI costs to completed work, customer outcomes, released capacity, and auditable financial results without overstating savings.
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How to Plan for Slower Frontier AI and Persistent Risk
A practical operating model for investing in AI while controlling permissions, blast radius, detection, containment, and recovery.
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The Real Cost of AI-Assisted Software Product Delivery
A practical way to price AI-assisted delivery across prototyping, production engineering, reliability, launch, operations, and maintenance.
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Apple AI Pricing: What Product Leaders Should Plan For
A practical framework for interpreting Apple’s AI paywall and pricing AI around customer value, usage costs, context, and completed work.
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A Practical System for Personal Multi-Model AI Workflows
Build a portable AI workflow that routes work by job, tests models fairly, and turns repeated corrections into reusable skills without vendor lock-in.
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AI Agent Swarms: An Operating Model for Product Leaders
A practical framework for deciding when agent swarms earn their complexity, building the control plane, and proving value before rollout.
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How to Build an AI Agent Business Case That Survives Scrutiny
A practical framework for proving AI agent ROI through workflow baselines, realized benefits, full costs, production pilots, and scale gates.
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How Autonomous AI Changes the Work of Product Leaders
A practical operating model for delegating ambitious work to autonomous AI while preserving human judgment, decision rights, security, and accountability.
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Why AI Adoption Stalls After the Pilot—and How to Fix It
A practical guide to finding the data, integration, authority, context, and ownership failures that keep promising AI pilots from delivering.
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How to Evaluate Corporate Data for AI Training Deals
A practical framework for testing enterprise data rights, quality, privacy, model fit, and economic value before committing to an AI training deal.
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Designing AI Products for Chip and Platform Volatility
A practical framework for choosing AI infrastructure, testing provider claims, and building exit paths before platform shifts reach customers.
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AI Churn Prediction: From Risk Scores to Retention Action
A practical operating model for defining churn risk, routing interventions, choosing whether to build or buy, and proving retention impact.
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Measuring Enterprise AI Value: A Practical Adoption System
A practical system for connecting enterprise AI usage to repeat behavior, workflow outcomes, quality guardrails, and defensible financial impact.
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How to Build Governance Infrastructure for Autonomous AI Agents
A practical control-plane blueprint for identifying agents, limiting delegated authority, monitoring behavior, and preserving recourse.
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How AI Engineering Leaders Should Run Competing Bets
A practical model for running parallel AI engineering bets, comparing them fairly, choosing on evidence, and converging without political fallout.
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Browse topics
- AI Strategy (429)
- Generative AI (113)
- IT Leadership (38)
- Leadership (81)
- Product Management (394)
- Product Management Leadership (373)
- Uncategorized (32)
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