‘AI Best Practices for Commerce’ is a structured guide to applying artificial intelligence in commerce operations.
Tom McFadyen, CEO of McFadyen Digital, with over 20 years of experience in digital commerce and marketplace projects, leads as the author.
Editors and contributors include Devon Hillard, Don Davis, Mark Brohan, and others from the McFadyen Digital team, drawing from their work on enterprise platforms and e-commerce implementations.
The 524-page hardcover, released January 12, 2026, organizes use cases into the commerce value chain: Market, Sell, Fulfill, Support, plus the software delivery lifecycle.
The content focuses on proven applications from industry leaders. You find details on how AI addresses specific business challenges, the exact solutions deployed, and measurable outcomes.
It covers personalization of customer journeys, better forecasting and inventory planning, automation of customer support through conversational AI, and acceleration of software development with generative AI tools.
What You Will Learn
You will learn to map AI opportunities directly to commerce processes and prioritize them for maximum impact.
The book provides an adoption playbook to move from experimental pilots to scaled implementations, along with ROI frameworks and technology overviews, including agentic AI.
Each use case specifies the challenge, the AI approach used in practice, and typical results, so you can evaluate fit for your team.
Early readers rate it 5.0 out of 5 stars based on two Amazon reviews as of January 2026. Feedback highlights its clear, business-focused structure and actionable depth for teams ready to deploy AI at scale.
Our Key Takeaways:
- This book delivers over 125 documented AI use cases organized by the commerce value chain to help you apply artificial intelligence effectively in real operations.
- Commerce and technology leaders at companies building or optimizing e-commerce, marketplaces, or enterprise platforms benefit most from its practical focus.
- You read it to identify high-impact AI opportunities, understand expected business results, and follow proven paths to scale adoption beyond initial tests.
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