What to Fix Before Bringing AI Into Your Distribution Center

Every distribution center has choke points. Maybe a forklift inspection process slows down every shift. Maybe a conveyor jam takes too long to resolve. Or maybe your backlog of work orders keeps growing because there aren’t enough technicians available when throughput is at its peak.

In the name of improving efficiency, many distribution center leaders make the mistake of trying to “bolt on” automation or AI. 

But if you have broken processes, digitizing them (or sprinkling AI on them) won’t magically make them work. In fact, things like inconsistent SOPs and siloed maintenance records will ultimately undermine AI’s performance.

Before investing in technology, distribution centers need to tackle fundamental workflow issues. Here are the steps to get started.

Step 1: Dig into your data and your technicians’ day-to-day routines 

Dashboards and reports highlight trends, but they may not always capture the full reality of daily maintenance work. To build a foundation for AI, distribution center leaders need a dual approach to rooting out potential process issues: dig into the data and spend time on the floor.

First, review the data: Start by looking for red flags in your CMMS and performance dashboards. Are work orders being closed unusually quickly or taking too long? Do certain shifts consistently log more incomplete tickets? Does one asset type account for a disproportionate share of downtime? These patterns can signal real operational issues or data-quality gaps that will throw off AI models later.

Shadow technicians live: Data only tells part of the story. To go deeper into potential problem areas and uncover the “why” behind the numbers, leaders should consider walking the floor and observing technicians. For example, shadowing may show:

• Skipped inspection steps: The dashboard might say “100% forklift inspections completed,” but shadowing reveals that operators sometimes pencil-whip checklists because forms are long and the team needs equipment immediately.
• Parts access delays: Reports might show work orders closed on time, but being present uncovers the 20-minute trips technicians make to fetch parts from poorly organized stockrooms.
• Unrecorded workarounds: Systems may not flag chronic asset issues that techs have learned to “just work around.”

Action step: Each month, run a “data + floor review.” Start by pulling a simple dashboard (e.g., work order completion time, percentage of preventive maintenance tasks incomplete, or performance by asset type and shift). Then schedule floor walks with technicians in those areas. Close the loop with a short debrief to validate what the data suggested and agree on fixes.

Step 2: Standardize before you digitize

Once the problems are clearer, your next step is standardization. Because without consistency, digitization might just speed up bad habits.

Take conveyor systems. If one technician logs a breakdown as “belt issue” and another calls it “roller jam,” AI won’t see them as related. The same applies to pallet jack inspections, robotic arm service, or AS/RS malfunctions.

CMMS platforms like MaintainX help distribution centers standardize language, steps, and expectations by digitizing checklists and inspections. Teams can create standard operating procedures (SOPs) once and share them across sites, so work gets done the same way everywhere. It also makes data structured and comparable, which is critical for AI. 

Action step: Identify the 10 most frequent maintenance activities (e.g., conveyor belt inspection, forklift PM, dock leveler repair). Build out or refine digital SOPs for each, with standardized codes, steps, and required fields. Roll them out across all shifts and sites, and audit compliance weekly for the first 90 days. This creates a baseline for operations and AI training.

Step 3: Build data quality

In a Plant Engineering study of industrial maintenance, only about half of facilities reported using a CMMS, while many still rely on siloed systems like spreadsheets and paper.

This is a problem because AI is only as effective as the data it’s fed. If information is incomplete or inconsistent, the “intelligence” will be equally flawed. Garbage in, garbage out.

Imagine you want AI to predict equipment downtime or optimize maintenance schedules, but downtime data on your conveyors, sorters, or forklifts is patchy (maybe your team logged some events in spreadsheets, wrote some on paper, and forgot to record several others). Without a record of when failures happen, how long assets are down, which parts are replaced, and what actions resolved the issue, an AI model can’t distinguish between a conveyor that fails once a quarter versus one that fails weekly. 

This leads AI tools to either produce low-confidence predictions or learn the wrong lessons entirely. In a distribution center, that could mean underestimating the risk of a sorter failure right before peak season or overestimating forklift utilization because downtime wasn’t captured properly. 

Some more examples of how common data problems impact AI’s effectiveness:

• Missing downtime logs lead AI to think assets are more reliable than they are
• Wrong timestamps skew MTTR/MTBF, leading to bad staffing and spare parts plans
• Inconsistent asset IDs mean AI can’t connect PLC alarms to work orders or parts usage
• Paper-based inspections prevent critical issues from making it into the system

Before asking AI to optimize, teams should prioritize giving it something trustworthy to optimize against. 

Action Step: Choose one high-value asset category (e.g., conveyors, forklifts, or dock doors) and audit every work order, downtime event, and parts transaction tied to it for a single month. Track things like: percentage of records with missing fields (e.g., failure code, downtime minutes), percentage of duplicate or inconsistent asset IDs, and percentage of work orders closed late. Then, hold a review with technicians to fix gaps. By the end of 30 days, you’ll have a clean dataset for one asset family, and a repeatable process.

The payoff: AI that works

Our 2025 State of Industrial Maintenance report shows that 65% of industrial maintenance organizations expect to use AI by 2026. 

But the ones that are most likely to see success with the technology will prioritize cleaning up their workflows and data first. 

In a distribution center, AI success might look like:

• An AI assistant that creates SOPs for a robotic arm by pulling information directly from OEM manuals, saving hours for maintenance managers
• Predictive triggers that flag a conveyor motor before it fails
• AI tools that analyze downtime across multiple sites to spot recurring patterns and suggest fixes
• Resource planning tools that optimize technician allocation when throughput is at its peak

These are longer term goals, but if you want to roll out AI at your distribution center in a sustainable way, start small. Your first step could be digitizing forklift inspections, shadowing a conveyor maintenance process to learn where you might improve the process, or standardizing just one SOP across sites.

Fix your workflows first, then let AI help you scale.