Why B2B distributors are challenged to scale AI, and the four capabilities that fix it.
Adoption is effectively universal. Returns are not. In a 2026 benchmark, only 17% of B2B AI deployments reported a significant return, and MIT found roughly 95% of enterprise generative-AI pilots delivered no measurable business result. The cause was not the models. It was brittle workflows and a commerce layer that was never ready.
This report shows where operational AI actually stalls in distribution, and gives your team a practical path to close the gap in 90 to 180 days, organized around four capabilities and a value scorecard.
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Distributors promoting AI to investors, versus those who have built it into how customers find, evaluate, and buy.
Enterprise generative-AI pilots that returned no measurable business value, per MIT. The problem was workflow fit, not the model.
B2B buyers who now prefer a rep-free buying experience, up from 33% in 2020.
Sources: 2026 AI-Guided Buying Maturity Study (distributor AI investment and AI-guided buying figures); MIT research on enterprise generative-AI pilots; published B2B buyer-preference research. Full citations appear in the report.
Why "AI tourism" is the predictable result of buying tools without changing the infrastructure underneath.
The hidden bottleneck: your commerce layer, and why 62% of distributors have solved transactions but only 14% have solved AI-guided buying.
The four capabilities that separate commerce AI operators from commerce AI tourists.
What a realistic 90 to 180 day commerce AI sprint looks like, with explicit off-ramps.
A commerce AI scorecard that ties every use case to a metric your operators already recognize.
VP and C-suite leaders at mid-market B2B distributors and manufacturers ($300M–$5B in revenue) who are deciding where the next AI dollar goes and want to know it will move margin, capacity, or retention before they spend it.
The McFadyen AI Commerce Readiness Audit is a diagnostic, not a sales call. It shows where your commerce layer is ready, where it is weak, and which use cases are most practical to pursue first. The first step costs nothing and commits you to nothing.
