Most supply chain organizations are spending real budget on AI, but few can say whether it's working. JBF Consulting surveyed 215 supply chain and logistics professionals in 2026 on how they select, fund, measure, and govern AI initiatives. We found a gap between activity and evidence that we like to call the AI Discipline Gap.
The report breaks down six findings across planning, measurement, funding, alignment, and post-launch governance, then maps each one to a specific practice that closes the gap.
What's inside:
- Why 85% of organizations select AI opportunities without a consistent process, and what that costs in prioritization
- The measurement gap behind the "15% success rate" headline, and why it likely undercounts real progress
- Why bigger AI budgets at $10B+ enterprises don't produce higher reported success
- The one implementation challenge 63% of respondents say they need outside help with (it isn't the one they blame first)
- Five management practices that move an organization from AI activity to AI results
Frequently Asked Questions
What percentage of companies are pursuing AI without a plan?
78% of the 215 supply chain leaders JBF surveyed in 2026 report pursuing AI initiatives without a documented plan, according to JBF Consulting's "AI Without a Plan" survey report.
Why do AI pilots in supply chain stall before production?
JBF's 2026 survey found 40% of AI initiatives remain stuck at prototype or pilot, often because funding models cover exploration but not the ongoing cost of implementation, operation, and scale.
Does bigger AI budget mean better AI results in supply chain?
JBF's 2026 survey of 215 supply chain leaders found reported success rates stayed roughly flat across revenue tiers — 12-17% regardless of company size — even though $10B+ enterprises reported 50% ongoing or dedicated AI funding versus 20% for companies under $1 billion. Budget scale and reported outcomes moved independently of each other.
