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An Executive Guide to AI Governance
AI governance provides the guardrails that ensure ethical, transparent, and accountable use of artificial intelligence. From small startups to global enterprises, organizations must align AI systems with privacy laws, fairness standards, and oversight requirements. This guide explains how governance frameworks scale by business size, what core principles to follow, and how to prepare for regional regulations like GDPR and the upcoming EU AI Act.
4 min read
AI Consumption-Based Pricing Models
Understanding consumption-based pricing is key to managing the cost of AI services. Unlike flat subscriptions or per-seat licenses, this model ties costs directly to usage—such as tokens, API calls, or compute time. This guide explains how it works, compares it to traditional pricing models, and offers practical strategies for forecasting, tracking, and optimizing AI expenses. Ideal for procurement teams and business leaders adopting AI at scale.
3 min read
Building Technical Training with AI: A Step-by-Step Guide
Creating technical training can be time-consuming, but AI makes it faster and more effective. This guide walks you through using AI to gather real-world input, draft course content, design assessments, and structure learning for deployment. Whether you’re training new hires or building product education, AI helps you deliver accurate, engaging, and scalable content aligned to real team needs.
3 min read
Measuring AI Product Adoption
Adopting AI tools isn’t just about deployment—it’s about consistent, valuable use. This guide explores proven frameworks like TAM, UTAUT, and HEART, and outlines key metrics for tracking adoption, including activation, engagement, retention, and satisfaction. Learn how to measure both user- and organization-level success, apply industry benchmarks, and use data to drive adoption. With the right metrics, you can turn AI from pilot to productivity powerhouse.
4 min read
Estimating AI Indexing Needs
As organizations scale their AI capabilities, the ability to index and analyze high-volume data—like financial records, market activity, or operational metrics—is critical. Just like a stock ticker constantly updates with new information, AI systems rely on indexing to stay current, accurate, and actionable. Understanding how to manage indexing limits and prioritize key data sources helps ensure performance, relevance, and value at every stage of AI adoption.
4 min read
Prompting AI: A Practical Guide
Learn the difference between prompts and agent instructions—two essential tools for working with AI. Prompts are real-time inputs that guide what AI does on the spot, while agent instructions define how AI behaves long term. This guide covers best practices for both, offering clear strategies by role, task, and use case to help your team get better, more reliable results from AI—whether you're generating content, automating tasks, or extracting insights.
5 min read
Why AI Adoption Fails (and What to Do Instead)
AI adoption doesn’t fail because of bad tools—it fails when people and systems aren’t ready. Learn the five reasons AI efforts stall and how the Unified AI Adoption Model helps you scale responsibly, with clarity and confidence.
4 min read
Get to Know Make AI Work
Skip the hype—Make AI Work gives you the frameworks, use cases, and worksheets you need to adopt AI with clarity and confidence. Whether you're leading a team or just getting started, this book brings the Unified AI Adoption Model (PAAF + SAAF) to life in an 8-hour narrative packed with real-world examples and practical tools.
Available in paperback, e-reader and audiobook formats, it's your field guide to turning AI from buzzword into business value.
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