This is a high-level summary of the key insights from our full paper — which digs deep into real-world approaches to AI governance, integration, and ROI in global supply chains. The full content combines research from AI governance thought leaders and firsthand stories from executives at shippers, LSPs, and supply chain software providers.
Across our sessions, four themes kept coming up:
- Excitement is high — but so is caution. Most supply chain teams see real potential in AI, but adoption is being slowed by concerns over control, risk, and unclear ROI.
- Governance is the unlock. The teams moving fastest are the ones that treat governance as a built-in function — not a barrier to overcome.
- Middleware is emerging as the control layer. Forward-looking companies are using integration platforms to enforce policy, restrict agent access, monitor AI behavior, and keep systems aligned.
- Shadow AI is already in play. Teams are experimenting informally — with chatbots, agents, and external tools. Without visibility and controls, that innovation creates risk.
What leaders are doing now:
On data governance:
- Tagging and inventorying AI-relevant data
- Implementing role-based access and data masking
- Embedding policy at the integration layer
- Treating data validation and lineage tracking as critical
On agent oversight:
- Creating inventories of AI agents and their permissions
- Assigning clear human ownership to every agent
- Using “human-on-the-loop” models for critical decisions
- Logging every action for audit and transparency
- Piloting agents with narrow, scoped autonomy
On ROI and risk:
- Defining ROI metrics up front for every AI initiative
- Regularly reviewing AI projects as a portfolio (kill the zombies)
- Watching for cost creep in API or compute usage
- Embedding compliance review into AI workflows
- Quantifying governance as a value driver, not just a risk reducer
On integration:
- Using middleware to centralize control and observability
- Enforcing policy through data pipelines — not after the fact
- Building fallback paths in case AI services fail
- Tagging and logging AI-initiated transactions
- Exploring emerging standards like MCP for scalable agent interaction
If you're nodding along to these themes, you're not alone — the questions, concerns, and wins we heard in executive rooms around the world are strikingly consistent. The full paper that follows dives deep into what’s actually working for supply chain leaders deploying AI today.
Download the Report