JBF Consulting, a leading logistics strategy advisory and technology integration firm, announced today the findings from a new survey examining how organizations are approaching AI across supply chain and logistics operations. The research reveals a significant gap between AI experimentation and the organizational disciplines needed to turn those initiatives into measurable business value.
Among more than 2,000 senior supply chain, logistics, and technology leaders that the online survey was presented to in July 2026, 78% said their organizations are pursuing AI without a formal plan, while 85% said AI opportunities are selected without consistent criteria or a formal evaluation process. At the same time, only 11% said their organizations establish a baseline and measurable success criteria before launching an AI initiative, and just 3% evaluate results against the original business case afterward.
From AI Experimentation to Execution
The survey found that 40% of respondents said their organizations are currently in a prototype or pilot stage, compared with just 23% that have AI running in the business. Funding also remains heavily weighted toward experimentation: only 16% reported ongoing funding for implementing, operating and expanding AI, while 61% rely on exploration funding, case-by-case approvals or have no dedicated funding.
The result is a significant gap between testing AI and operationalizing it. Without clear criteria for prioritization, funding and measurement, organizations risk accumulating pilots without a defined path to production or scale.
“Organizations are not necessarily struggling because AI technology isn't capable,” said Brad Forester, CEO of JBF Consulting. “The bigger challenge is establishing the discipline to identify the right problems, build a measurable business case, align the organization and determine whether an initiative actually delivered what it was supposed to deliver.”
Leadership Expectations Outpace Operational Readiness
The survey also identified a disconnect between leadership expectations and operational realities. Only 22% of respondents said leadership and operations are aligned on AI goals and timelines. 61% said leadership wants fast, visible wins while operations understand that results take longer, while 53% said leadership is focused on cost or headcount savings while operations view AI as a longer-term capability.
Governance presents another gap. Only 10% said their organizations have formal AI governance that is actively maintained. 23% reported having no formal governance, while 32% did not know whether a governance framework exists.
These gaps become particularly important as organizations move AI from isolated experimentation into operational decision-making, where clear ownership, measurement, and ongoing oversight become critical to continuity of such advanced technology.
Data and Implementation Remain the Biggest Needs
Despite the focus on AI capabilities, respondents identified execution-related needs as their greatest areas for outside support. Preparing data and technical architecture ranked first at 63%, followed by planning and managing implementation at 60%, measuring outcomes and scaling successful initiatives at 52%, and evaluating technology and vendors at 49%.
The findings also highlight a disconnect around data. While only 17% identified data readiness as the factor that has most limited AI progress, poor data quality ranked second among the implementation challenges respondents identified. This suggests organizations may underestimate data requirements when planning AI initiatives and encounter those challenges later during implementation.
“The next phase of AI adoption will be less about experimenting with what the technology can do and more about measuring against clearly defined business outcomes,” added Forester. “Organizations that can connect strategy, data, implementation and measurement will be in a much stronger position to turn AI investment into sustained operational value.”
