AI skills gap in logistics: how to train teams fast

47% of workers say they’d leave if their employer doesn’t offer AI-related training. That single number should make every logistics and transport leader sit up—because replacing experienced planners, dispatchers, warehouse supervisors, and analysts is already hard, and it’s about to get harder.

Here’s what I’m seeing across the industry: companies are buying AI tools for route optimization, warehouse slotting, demand forecasting, and customer service—but they’re underinvesting in the people who must run them day-to-day. The result isn’t “AI replacing jobs.” It’s AI widening a skills gap that slows down adoption, increases errors, and drives turnover.

This article is part of our „Изкуствен интелект в логистиката и транспорта“ series, where the focus isn’t hype—it’s practical ways AI improves routes, warehouse operations, fleet management, and supply chain efficiency. This time, the theme is simple: your AI roadmap is a talent roadmap.

Snippet-worthy truth: The limiting factor for AI in logistics in 2026 won’t be software—it’ll be the number of people who can use AI responsibly and profitably.

What the Randstad numbers mean for logistics leaders

The core message from recent workforce research is direct: people are ready to adopt AI, but they don’t trust employers to train them. Randstad reports 78% of workers are ready to adopt AI, while 46% aren’t convinced their employer will invest in AI/tech learning. Most alarming: 47% would leave their job if no AI-related training is offered, up 22 percentage points in one year.

In logistics and transport, this hits harder than in many office-heavy industries because the “skills gap” isn’t only about prompt-writing or data science. It’s about mixing operational judgment with AI outputs under time pressure:

• A dispatcher deciding when to override an AI-recommended route due to weather, driver hours, or customer constraints
• A warehouse supervisor using AI-driven labor planning without burning out teams during peak weeks
• A transport manager interpreting ETA prediction confidence levels and setting customer expectations

AI doesn’t remove work—it changes the work

Most companies get this wrong: they assume AI “automates tasks,” therefore the work disappears. In reality, AI shifts work:

• From manual planning to exception management
• From “knowing every lane by heart” to validating model recommendations
• From chasing data to asking better questions of the data

If you don’t train people for that shift, your operation becomes dependent on a small number of “AI whisperers.” That’s fragile, and it doesn’t scale.

The real 2026 skill: human judgment + AI in operations

Randstad’s stance is one I agree with: the most valuable talent combines strategic judgment with AI capability. For logistics, that combination becomes a competitive advantage because the domain is full of trade-offs AI can’t fully own:

• Service level vs. transport cost
• Throughput vs. accuracy
• Safety vs. speed
• Inventory buffers vs. cash flow

AI can compute options; humans must choose the policy. That’s the collaboration model that actually works in transport management systems (TMS), warehouse management systems (WMS), and planning towers.

“Blue-collar is the new digital” applies directly to warehouses and fleets

One of the strongest ideas in the workforce outlook is that operational roles are becoming “digital.” Logistics is already living that reality:

• Warehouse associates use scanners, voice picking, computer vision checks, and robot/AMR interfaces
• Yard and dock teams coordinate via real-time visibility tools
• Drivers interact with telematics, safety coaching, dynamic routing, and digital proof of delivery

So the training goal isn’t to turn everyone into an engineer. It’s to build digital confidence:

• Understanding what the system optimizes for
• Knowing what inputs drive recommendations
• Recognizing when outputs don’t match operational reality

Where AI can close the workforce gap (if you implement it right)

AI can absolutely reduce pressure caused by labor shortages—but only when it’s deployed to reduce cognitive load and standardize decisions, not when it’s bolted on as another dashboard.

Route optimization and dispatch: fewer “heroics,” more consistency

AI route optimization and real-time re-routing help teams manage more variables than a human can juggle at once: traffic, road restrictions, delivery windows, vehicle capacity, and driver hours.

What it changes in practice:

• Dispatchers spend less time building routes from scratch
• More time goes into handling exceptions (late pickups, customer changes)
• Service becomes less dependent on one “legendary” dispatcher

Training focus: teach dispatch to interpret recommendations, constraints, and confidence—not just click “accept.”

Warehouse labor planning: forecasting workload and smoothing peaks

AI models can forecast inbound/outbound volumes and translate them into staffing needs by shift, zone, or process (picking, packing, replenishment).

Done well, it reduces:

• Overtime spikes
• Last-minute agency labor reliance
• Bottlenecks caused by poor slotting or mis-timed replenishment

Training focus: supervisors need to understand drivers (orders, wave profiles, SKU velocity) and how to adjust plans without breaking the model.

Customer service and control towers: AI as a first responder

During the December peak (and every “small crisis” week after), customer service teams drown in status requests. AI assistants can triage:

• “Where is my shipment?” responses using ETA prediction
• Exception classification (weather delay vs. capacity issue vs. paperwork)
• Suggested resolutions (rebook, partial ship, alternate hub)

Training focus: agents should learn escalation rules and how to avoid confident-sounding wrong answers.

Snippet-worthy rule: If AI talks to your customers, humans must own the escalation playbook.

A practical AI training plan for logistics and transport (90 days)

If your goal is leads, retention, and measurable operational impact, skip the generic “AI awareness seminar.” Build a plan tied to real workflows.

Step 1 (Weeks 1–2): Map the “human-AI handoffs”

Start by documenting where decisions happen:

• Dispatch: routing, tendering, appointment changes
• Warehouse: wave release, labor allocation, slotting changes
• Fleet: maintenance scheduling, safety interventions
• Planning: inventory targets, forecast overrides

For each decision, define:

• What the AI recommends
• What data it uses
• When humans may override
• What must be logged when overriding (reason codes)

This creates operational governance without slowing teams down.

Step 2 (Weeks 3–6): Train by role, not by tool

Most training fails because it’s “here’s the software.” Better: “here’s how your job changes.”

Build three tracks:

1. Frontline (warehouse, drivers, yard/dock): digital workflows, exception signals, safety implications
2. Operators (dispatch, CS, supervisors): interpreting recommendations, handling edge cases, feedback loops
3. Leads (ops managers, planners): KPI design, guardrails, adoption metrics, risk management

Keep sessions short (45–60 minutes), repeated, and scenario-based.

Step 3 (Weeks 7–10): Create an AI champions bench

Pick 1–2 people per site/shift who are respected operators (not necessarily the most technical). Give them:

• Extra practice time in sandboxes
• A direct channel to the product/process owner
• Ownership of a small improvement backlog

This is how you avoid the “one central AI person” bottleneck.

Step 4 (Weeks 11–13): Measure adoption like an operational KPI

If you can’t measure adoption, you can’t manage it.

Track:

• % of decisions using AI recommendations (by process)
• Override rate and top override reasons
• Time-to-resolution for exceptions
• Training completion + competency checks (lightweight quizzes + observed tasks)

Strong signal: override rates should initially be high (healthy skepticism), then stabilize as data quality improves and guardrails tighten.

The hidden risk: fewer entry-level roles and less experience in the pipeline

Another trend highlighted in the labor outlook is uncomfortable: entry-level roles are shrinking (Randstad cites a 29% global drop in entry-level roles), while experienced talent becomes scarcer.

Logistics feels this in two ways:

• Fewer junior planners learn the basics because “the system does it”
• Senior people become overloaded as the only ones trusted to handle complex exceptions

How to protect your experience pipeline

I’m opinionated here: if you automate without designing learning pathways, you’ll pay for it later.

Do this instead:

• Rotate juniors through “exception review” meetings weekly
• Maintain a “manual mode” simulation: once per quarter, teams solve a scenario without automation to learn fundamentals
• Require post-mortems on major disruptions (missed cutoffs, misroutes, inventory misallocations)

This keeps operational intuition alive while still benefiting from AI optimization.

People also ask: “Will AI replace jobs in logistics?”

Direct answer: AI will replace some tasks, but it will mostly replace fragile processes—the ones dependent on tribal knowledge, spreadsheets, and heroics.

In transport and warehousing, the winners aren’t the companies with the fanciest AI demo. They’re the ones that:

• Standardize decisions
• Reduce cognitive load
• Train teams to collaborate with AI
• Treat data quality as a frontline responsibility

If you want a stable operation in 2026, invest in the human side with the same seriousness as the software budget.

What to do next (and why it matters for Q1 planning)

Budget season is basically over, and January is when AI projects collide with operational reality. The fastest way to lose trust is to deploy AI tools and tell teams, “You’ll figure it out.” The fastest way to earn trust is to say, “We’ll train you, we’ll measure it, and your feedback will change the system.”

If your organization is rolling out AI in logistics—route optimization, warehouse automation, AI forecasting, or fleet analytics—treat training as a first-class deliverable. The 47% attrition risk isn’t theoretical. It’s a warning.

The forward-looking question I’d leave you with: when your AI systems make a recommendation that conflicts with a veteran operator’s instinct, do you have a clear, trained way to resolve it—or do you just hope the loudest voice wins?