If you’re actively exploring the use of AI in your operations, you’re not alone. In a recent study of over 8,000 organizations conducted by Cisco, 97% reported that AI deployment is an urgent business requirement. More than half (61%) believe their competitiveness will be seriously impacted if they don’t get AI up and running in the next year.
Despite these concerns, 92% of companies aren’t actually ready to deploy AI, for one simple reason: The foundational data needed by AI isn’t centralized and accessible. Instead, it’s fragmented, disconnected, and siloed across various systems.
These findings mirror Redwood’s own recent survey of our Customer Advisory Board (CAB) members — which is summarized in our new AI in Logistics Report. When we asked our CAB members about their biggest challenges in implementing AI, 35% cited data quality and availability as the single largest barrier to AI value. Integration gaps across TMS, ERP, and other planning solutions were named as the biggest challenge by 28% of respondents.
That means 60% of Redwood customers surveyed aren’t struggling with the AI itself — they’re challenged to create the right foundation for AI.
For logistics teams, data accessibility and tech integration challenges aren’t new. In fact, in Redwood’s hundreds of customer engagements spanning two decades, we’ve helped many companies optimize their data and systems for increased real-time visibility and responsiveness, as well as cost and service improvements.
What’s new today is the enormous impact disconnected data and systems are having as “AI blockers.” AI has tremendous potential to ingest volumes of data, apply analytics, make an optimal decision, and even autonomously pull an execution lever. But the outputs of AI are only as good as its inputs.
Provide AI engines with clean, up-to-date, formatted, and standardized data, and you’ve got a supply chain that’s automatically on track for cost and service excellence — one you can confidently rely on. But an AI engine that’s ingesting faulty, incomplete, outdated, or siloed data will never make the best enterprise-wide decisions. If your systems aren’t communicating and sharing data, then AI will essentially be flying blind when you deploy it in your supply chain.
The negative impacts of bad data are far-reaching and potentially disastrous, ranging from faulty predictions and biased recommendations to operational breakdowns, legal penalties, and lasting reputational damage.
Optimized data begins with integrated systems.
At Redwood, we believe there’s a simple solution to create enterprise-wide data that’s clean, reliable, real-time, and AI-ready. That solution is systems integration.
Every modern logistics team is both empowered and challenged by a tech architecture that’s grown organically over time. It’s smart to keep adding new apps, upgrading legacy tools, and embracing innovation. But, as the IT landscape evolves and systems proliferate, major disconnects occur. Solutions like the ERP, WMS, and TMS don’t share the same data, speak the same language, or focus on the same objectives.
Redwood views data readiness as an architectural mandate. For AI to make and execute optimal decisions for the end-to-end supply chain, it needs reliable, real-time data from the end-to-end supply chain. That means systems must be connected, integrated, and aligned.
That mandate might sound daunting, especially in a world where 60% of companies already feel like they’re losing competitive ground without AI. And, using traditional approaches, it would typically take up to 18 months to perform this type of enterprise-wide integration.
The good news you need right now? Working with a modern 4PL like Redwood compresses this timeline significantly.
Integrated systems begin with Redwood.
With our purpose-built RedwoodConnect integration layer, proven templates and APIs, and established best practices, Redwood’s integration infrastructure is ready to launch in your supply chain, right now. We can quickly connect your disparate WMS and TMS platforms, ERP systems, and carrier networks into a single, trusted AI-ready architecture.
With these systems connected and sharing centralized data, AI gains real-time access to integrated carrier performance data, rate fluctuations, impending missed deliveries, and other fast-changing supply chain facts. That means AI can make informed decisions, grounded in an up-to-date, holistic view of the end-to-end supply chain. This kind of consolidated database would take years to build independently, but Redwood creates it in weeks or months via systems integration.
With centralized, clean data, the real potential of AI is unleashed — and decision-making and execution become truly autonomous. From orders and inventory levels to freight spend benchmarks and other KPIs, AI automatically optimizes daily operations, guided by your own business rules and pre-defined guardrails.
When a new event emerges at one node — whether an upstream supply shortage or a downstream blocked route — AI recognizes that event in real time, then determines the impact for every other node. It makes the right choice for all functions and partners. And, as it learns over time, AI can also surface new opportunities and drive process innovation across functions and trading partners.
Imagine a connected, always-on supply chain.
What does your future supply chain look like when it’s enabled by AI?
With connected technology and accessible, real-time data, AI agents can autonomously flag and resolve disruptions, predict and manage tightening capacity, anticipate and avoid weather events, and collaborate with partners to protect service levels. In the “human in the loop” environment of the future, your managers are elevated to strategic roles where they define the rules for AI, monitor its outputs, and intervene only when necessary.
This future state is within your reach when you partner with Redwood on data and systems integration. Why not contact us today? Let’s start creating the foundation for AI success in your supply chain.
This blog is the third in a five-part series from Redwood. Over our next two blogs, we’ll explore two additional pillars of AI readiness. Up next: Create an optimal AI architecture that works with your integrated technology stack.