Recently Redwood surveyed members of our Customer Advisory Board (CAB) to gain insights into one of the most talked-about topics in logistics today: AI. Our study looked at where customers are in their AI journey, the challenges they’re facing, and the factors driving success for some organizations. The result is our inaugural AI in Logistics Report.
The top-level results paint a clear picture of the state of AI adoption in the logistics industry. While 37% of our respondents have identified AI and predictive decision support as a top investment priority, only 13% of companies have achieved a quantifiable return on that investment. Nearly half (40%) of participants have yet to launch a pilot of AI in their operations.
While AI is being rapidly adopted in many sectors, logistics teams have obvious hesitations. A new study by TechCo reveals that only 11% of fleet professionals have a positive outlook on the future of AI in logistics — a steep drop from September 2025, when 30% reported feeling optimistic. About two-thirds (65%) of fleet professionals today report they’re not exposed to AI at all in their daily work.
In implementing AI, logistics teams face these common challenges
What’s behind our industry’s reluctance to embrace AI? We asked our CAB members specifically about their biggest challenges in implementing AI. The answers won’t surprise you if you’ve already piloted AI in your own operations:
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35% cite data quality and availability as the single largest barrier to AI value
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28% point to integration gaps across TMS, ERP, and other planning tools
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15% say their internal processes simply aren’t designed for AI-driven decision making
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11% noted the importance of organizational change management in increasing AI adoption
These findings echo what Redwood has witnessed in our own customer engagements, where we routinely identify and master AI-adoption obstacles. The simple truth is that you can’t capitalize fully on the promise of AI unless you’ve intentionally created the right foundation for AI. That foundation includes structured and accessible data, an integrated digital ecosystem, AI-friendly workflows, and an informed, enthusiastic team that understands the benefits.
When logistics teams accelerate directly to achieving long-term outcomes from AI, without completing the foundational work, they underperform on their operational and financial objectives. Why? They’re trying to apply AI in an organization infrastructure that simply isn’t ready for AI.
The solution? Redwood’s four pillars of AI readiness
Because successfully leveraging AI requires coordination across data, technology, processes, and workforce enablement, Redwood has created four pillars of success for logistics teams. Organizations that want to realize the full potential of AI must have a firm foundation across all these dimensions.
Pillar 1: Organizational Structure and Operating Model Readiness
Most logistics organizations are built for manual intervention over autonomous, AI-enabled data ingestion and decision-making. In fact, supply chain professionals spend about two workdays out of every week manually tracking data. When teams try to adopt AI without first addressing this structural problem, AI becomes a standalone analytics tool used by humans, instead of unleashing its full power to act autonomously.
Pillar 2: Data Foundation and Integration Resources
Data and integration challenges aren’t new. For decades, we’ve helped customers overcome them to achieve better financial and service results. What’s new is that they have now become “AI-blockers.” Without connected, coherent data streams and an integrated technology stack, AI decisions can’t be made, communicated, executed, or orchestrated across the supply chain.
Pillar 3: Technology and AI Architecture Readiness
Too many logistics teams select and configure standalone AI tools for deployment in their existing technology environment — which, as we’ve already observed, isn’t integrated. That means AI agents, which are designed for autonomous execution, can’t be deployed across carrier networks, TMS and WMS platforms, and freight audit workflows. AI agents can add tremendous value by automating high-volume, manually intensive workflows — but not unless the right tech architecture is there.
Pillar 4: Change Management, Governance, and Workforce Readiness
In our view, change management is the obstacle that’s most underestimated by organizations attempting an AI-enabled transformation. Too often, the logic behind AI decisions isn’t visible or explainable — so planners fail to trust and execute them. Planners conduct redundant analysis or use their intuition, instead of leveraging AI. Special effort is needed to get the entire team educated and on board with AI.
AI isn’t a tech investment. It’s an operational transformation.
There’s a lot of hype around AI in logistics, and most of it is justified. We’ve seen AI, deployed on the right foundation, truly transform our customers’ operations — from booking carriers and planning loads to mastering disruptions and minimizing inefficiencies. The technology is there, and it’s proven.
How can organizations establish the four pillars and realize the benefits of AI, quickly and confidently? As a modern 4PL, Redwood goes beyond traditional execution capabilities, to fully orchestrate technology and logistics execution in light of AI and other advancements.
Redwood experts routinely work across the four pillars, aligning data, systems, processes, and people to optimize customer supply chains. While building an AI foundation sounds time-consuming, resource-intensive, and intimidating, Redwood knows how to get you there quickly. We don’t rip and replace. We connect and integrate, working with your existing assets, proven templates, and our proprietary RedwoodConnect platform.
You’ll be surprised by how quickly Redwood can get your organization AI-ready. Download our AI in Logistics Report or contact us today to learn more.
This blog is the first in a five-part series from Redwood. Over our next four blogs, we’ll take an in-depth look at each of the four pillars of AI readiness. Up next: Optimize your organizational structure and operating model for AI.