A widespread adoption gap has emerged in transportation and supply chain AI, where investment outpaces organizations' ability to deploy solutions operationally. According to a survey from Kinaxis, executives are significantly more optimistic about AI ease of use than operations and IT leaders—for example, 32% of C-Suite respondents said detecting anomalies would be difficult, compared to 64% of operations leaders (Supply Chain Dive - Technology). This disconnect leads to executives approving sweeping AI initiatives that operations teams find risky or difficult to implement in practice.
Shipwell CEO Greg Price argues that operations teams need concrete proof of AI effectiveness, not promises of efficiency gains. The crucial questions operations leaders ask include whether the tool works within their specific workflow, provides reliable results without constant human oversight, and can be trusted to act with appropriate autonomy (Supply Chain Dive - Technology). For commerce practitioners, this means AI adoption succeeds when solutions are designed around actual operational problems and validated through co-innovation with the teams who will use them daily.
Real-world validation is emerging: Shipwell's Track & Trace AI Worker automatically resolved 53% of monitored shipments that encountered exceptions, reducing manual intervention requirements (Supply Chain Dive - Technology). Success comes from starting with a specific operational problem, defining guardrails and autonomy levels upfront, proving outcomes in real workflows, and expanding adoption as trust grows rather than asking teams to adapt operations to the technology.