1. Connectivity
Are all devices, machines, sensors and platforms connected and detectable for AI control across sites?
Will AI and digitization evolve your warehouse operations? Absolutely. Will these transformations guarantee better efficiency, accuracy and customer outcomes for post/parcel and third-party logistics providers (3PLs)? Not without a strategic and holistic approach to data and operations in place first.
AI in warehouse management is already reshaping how forward-thinking operations leaders make decisions, allocate resources and respond to volatility. But AI’s success depends almost entirely on fundamentals that rarely make headlines: network infrastructure, data strategy and a willingness to rethink your operations.
Before you can count results from AI in warehouse management, your operations have to be ready to connect, contextualize and act on data. And your data must be complete and correct.
It starts with having the right digital foundation in place.
AI strategy must come before anything else
Many post/parcel providers and 3PLs realize their network isn’t AI-ready only after a deployment stalls or fails altogether: automated guided vehicles (AGVs) lose connectivity, data siloes persist or analytics lag behind real-time needs.
Choosing an AI tool and designing your network to fulfill that specific tool’s requirements is risky. AI tools evolve at breakneck speed. What’s cutting-edge today could be considered legacy next year, leaving you with a network that can’t support tomorrow’s needs or adapt to new opportunities.
Focus on business needs first
The smarter (and safer) move is to focus on your warehouse’s business needs before deciding which AI platforms you want to bring on board. In other words: What do you want to improve or change about the way goods are moved, orders are fulfilled or customers are served?
For post/parcel providers and 3PLs, these improvements could look like:
- More accurate forecasting to prepare for demand surges
- Scalable capacity that can align production with volume fluctuations
- Resilient operations that can withstand disruptions like labor shortages or inventory issues
- Greater customer transparency to deliver accurate order status
- Less downtime with predictive maintenance
Only after your business priorities are defined are you ready to chart how and where AI in warehouse management fits into your operational roadmap. Don’t make the leap until you can answer questions like:
- What’s our AI strategy?
- What do we want AI to do?
- How deep do we want to go with AI?
Clarify what you want AI to do first. Then, talk about how you’re going to accomplish it.
The goals you set at this stage help shape the data requirements and network infrastructure you’ll need, defining whether your digital foundation can support and scale with AI in warehouse management.
Data must be usable and actionable for AI
Every modern warehouse is churning out data from a growing mix of systems: smart conveyors, autonomous mobile robots (AMRs), cloud-based order trackers and PLCs. This data forms the basis for essential tasks like operational visibility, exception detection and resource planning. But data alone isn’t enough for AI. To unlock its potential, the data from these sources must be unified and contextualized, which is an extremely challenging task.
Why data context matters so much
In a typical e-commerce fulfillment center, high-speed sorting, fast-moving storage allocation and fulfillment operate simultaneously and interact across multiple systems. Each step of the process generates and requires large amounts of data. Adding context to the data so you can understand what it’s telling you is the only way to unlock fast, accurate, business-critical decisions in the moment and predict disruptions before they happen.
A real-life example of AI in warehouse management
Imagine a conveyor system that tracks parcel movement. Raw data can indicate whether a belt motor is running and packages are present, but that’s where its value ends. Meanwhile, contextualized data brings conveyor status and live-order data together with AMR positioning and expected delivery SLAs. When the system detects a growing backlog on a sorter just before the cutoff for next-day delivery, it can automatically reroute tasks or alert personnel, avoiding shipment delays and keeping service promises.
In contrast, siloed or raw data only triggers a reaction after downtime or a missed window, when the value opportunity is lost.
The more data your warehouse needs to gather, orchestrate and contextualize in real-time, the greater the demands on your network’s bandwidth, latency and scalability.
If your network can’t seamlessly connect every sensor, software platform, system and machine that make up your operations, you won’t be able to gather or contextualize data at the speed and scale AI requires.
The network is your enabler for AI in warehouse management
A resilient, scalable network underpins your operations. Creating a solid digital foundation based on a unified OT and IT network infrastructure allows you to contextualize data in a unified model. It also gives you the flexibility and scalability to easily apply any kind of AI tool so you can implement AI in warehouse management.
The value of a unified network
A unified network makes sure your tech solutions can perform at their best. Consider a warehouse execution system (WES), for example. A WES requires all components and data streams to connect to one central location so it can orchestrate, synchronize and optimize every process across the warehouse in real-time. Once that network foundation is in place, warehouse leaders can tap into continuous visibility, control and the ability to adjust strategies and operations as conditions change.
Is your network ready to handle AI in warehouse management? Start by asking these essential questions.
2. Consistency
Does data flow in a consistent, structured and accessible format, or do you battle integrations and islands?
3. Performance
Does your network have the bandwidth and latency to keep up with real-time decision-making?
4. Security
Can your systems support secure, real-time data sharing across internal operations and external partners?
5. Scalability
Can your network scale and adapt as volumes and digital complexity grow?
Why Belden’s approach works
AI is inevitable for any warehouse or 3PL that wants to stay relevant. But most sites aren’t ready for it yet: They juggle legacy PLCs, digital I/O, old and new protocols, fieldbus and Ethernet, and IT and OT siloes. These disparate technologies must be integrated before AI can deliver business value.
Belden’s complete connection solutions help create AI-ready infrastructure that eliminates technical debt and empowers you to work with any AI providers you choose. Our deep understanding of operational needs helps bring all your platforms and technologies together. And our focus on the network as your foundational layer helps you connect to what’s possible. With the right digital backbone in place, you can ingest, contextualize and analyze what’s happening in real-time with every system and process.
Through digital maturity, network and Wi-Fi assessments, we can help you establish a baseline so you can make improvements that will prepare your warehouse to harness AI. Then, when you’re ready, we can help you create a unified network environment so you can capture and contextualize valuable data to feed AI tools and models.
We build one unified, AI-ready network that contextualizes all your warehouse data—making digital transformation real, resilient, and scalable for post/parcel and 3PL operations.
Start with your assessment
Related Links:
Why building a data foundation matters in AI for manufacturing
How Smart Warehouses Can Automate and Improve Operations
Create Your Own Path to an Automated Warehouse
Know your network before deploying automated warehouse systems