What ARC 2026 Signals for AI in Logistics Teams

February 9–12, 2026, a few thousand operations and supply chain leaders will land in Orlando for the 30th Annual ARC Industry Leadership Forum. The headline theme—how AI is driving the future of industrial operations and supply chain—sounds familiar. What’s changed is the tone: the industry has moved from “pilot projects” to “prove it at scale.”

If you’re responsible for transportation, warehousing, procurement, or supply chain planning, this matters because your next set of AI decisions won’t be about experimentation. They’ll be about operational risk, data readiness, and who owns the outcomes when automation starts making (and explaining) recommendations.

This post is part of our AI in Supply Chain & Procurement series, where we focus on practical adoption: forecasting, supplier risk, decision intelligence, and the systems that keep global networks moving. The ARC forum is a useful case study because it reflects what leaders are prioritizing right now: cognitive analytics, digital twins, predictive technologies, and energy transition—all topics that directly affect transportation and logistics performance.

Why industry forums matter for AI adoption in logistics

AI in logistics isn’t blocked by ideas. It’s blocked by execution. Industry forums work because they compress the learning curve: you see patterns across companies, compare architectures, and learn what broke in production.

Here’s the blunt truth I’ve found after watching AI programs succeed and stall: most organizations don’t fail on model accuracy; they fail on operating model. Who maintains features? Who validates outputs? What happens when data changes? A forum that brings together operations, IT, vendors, and analysts accelerates alignment on those questions.

The “peer proof” effect is real

When you hear a peer explain how they reduced detention costs with earlier exception detection—or how they cleaned shipment milestone data to make ETA predictions usable—your team gets permission to stop chasing shiny demos and start fixing fundamentals.

A well-run conference also surfaces what’s emerging as standard practice across transportation and logistics AI:

• Exception-first workflows (humans manage the 5–10% of shipments that cause 80% of pain)
• Decision intelligence (models paired with business rules and guardrails)
• Digital thread thinking (connecting procurement → production → fulfillment → returns)

What ARC’s 2026 theme reveals: AI is becoming the operating system

The event framing—AI plus digital twins plus predictive technologies—signals a shift: AI isn’t a layer you “add on” to a TMS, WMS, or ERP. It’s increasingly the logic that sits across them.

In transportation and logistics, that shows up as a move from single-point optimizations (like better route planning) to network-level orchestration:

• Predict demand variability and upstream constraints
• Translate risk into inventory and transportation decisions
• Replan continuously as capacity, labor, weather, and supplier signals change

AI + digital twins: where the value actually lands

A digital twin gets thrown around too loosely. In logistics, a useful digital twin is not a 3D warehouse model. It’s a living simulation of constraints:

• Dock schedules, labor availability, pick/pack rates
• Carrier lead times, tender acceptance, appointment windows
• Supplier OTIF behavior, material availability, production cadence

Pair that with AI and you get something practical: “If we change X, what happens to Y?”

Example: if procurement switches a lane’s supplier to save 3% on unit cost, the twin can model downstream effects—expedites, missed appointments, higher damage rates, or additional safety stock. That’s how AI becomes a supply chain decision tool, not a dashboard.

Predictive technologies: the underrated win is earlier decisions

Many teams chase perfect predictions. The better target is earlier, good-enough predictions that allow cheaper actions.

• Knowing a shipment will be late 48 hours earlier is more valuable than knowing it with 95% confidence 6 hours before delivery.
• Predicting which suppliers will miss lead time in the next month is more valuable than explaining last month’s misses.

ARC’s focus on predictive tech lines up with the strongest logistics ROI pattern: reduce variability by acting sooner, not by forecasting perfectly.

The four AI use cases logistics leaders should benchmark at ARC 2026

If you’re attending an industry event like this (or even just tracking what comes out of it), these are the areas I’d measure your program against. They’re mature enough for real impact, and they connect directly to transportation and logistics cost drivers.

1) Demand forecasting that operations can actually use

Demand forecasting is old news—until you tie it to decisions. The best forecasting programs in AI in supply chain planning share two traits:

1. They forecast at the right decision grain (SKU-location-week, not “regional monthly”)
2. They connect the forecast to downstream actions (inventory, labor, transportation bookings)

What to ask vendors or peers about:

• How they handle promotions, substitutions, and new-item introductions
• How forecast error is translated into buffer stock and service targets
• Whether planners can override, and how overrides are learned

2) Inventory + transportation trade-offs (where procurement often loses the plot)

Procurement savings don’t count if they create freight and inventory penalties.

AI can quantify trade-offs that teams usually argue about with spreadsheets:

• Mode shifts (ocean vs air vs intermodal) based on risk
• Placement strategies (forward stocking vs centralized)
• Order policies (fewer, larger orders vs frequent replenishment)

A practical metric to monitor: cost-to-serve by customer segment. If AI can’t improve that, it’s probably not connected to real decisions.

3) Supplier risk analytics that’s tied to execution

Supplier risk tools fail when they live in quarterly scorecards. The point is to drive actions week-to-week:

• alternate sourcing triggers
• pre-booked capacity
• revised safety stock
• expedited approval workflows

The best supplier risk analytics combine structured and semi-structured signals:

• lead time variability, ASN quality, OTIF
• logistics disruptions (port congestion, lane capacity)
• quality holds and returns

This is where AI in supply chain & procurement becomes tangible: it’s not “risk visibility,” it’s risk-controlled planning.

4) Warehouse labor and automation orchestration

Warehouse automation is no longer just robotics. It’s how you orchestrate humans, automation, and work waves together.

AI applications that are producing consistent results:

• labor forecasting by zone and shift
• dynamic slotting recommendations
• predictive maintenance for material handling equipment
• exception detection (shorts, damages, mis-picks) before shipping

If ARC 2026 is discussing “industrial operations,” expect a lot of attention here—because labor availability and throughput variability are still two of the hardest constraints in logistics.

What to listen for in Orlando: signals that a solution is production-ready

Conferences can be noisy. Here’s a filter I use: if a speaker can’t explain governance and data operations, they’re describing a prototype.

The production checklist (use this in conversations)

When you hear about an AI program—whether it’s cognitive analytics, digital twins, or forecasting—ask these questions:

1. What decision does it change? (tendering, safety stock, labor scheduling, supplier allocation)
2. How often does it run? (weekly planning vs intraday replanning)
3. What data breaks it? (missing milestones, wrong unit of measure, inconsistent item master)
4. Who approves actions? (planner, transportation manager, autonomous thresholds)
5. How do you measure outcomes? (service, cost, working capital, emissions)

A snippet-worthy rule: If there’s no owner for the last mile of the decision, there’s no AI program—only analytics.

Don’t ignore the energy transition angle

ARC’s mention of energy transition and sustainability isn’t a side topic. Transportation and logistics leaders are being pushed to report and reduce emissions while still hitting service and cost targets.

AI helps when it’s used for:

• emissions-aware transport planning (mode and consolidation choices)
• idle time reduction (yard and appointment optimization)
• network redesign (distance, service zones, inventory placement)

The trap is treating sustainability as a reporting project. The real win is when AI makes lower-emission choices the default because it’s optimized alongside cost and service.

A practical 90-day plan if you want AI impact before mid-2026

You don’t need to wait for a conference to act. If you want measurable progress before the ARC forum (or shortly after it), here’s a plan that works for most transportation and logistics teams.

Days 1–30: Pick one decision and define the metric

Choose a decision with frequent repetitions and clear economics:

• carrier tendering and re-tendering rules
• appointment scheduling and dock prioritization
• safety stock parameters for volatile SKUs

Define one primary metric and two supporting metrics. Example:

• Primary: on-time delivery %
• Supporting: premium freight spend, detention hours

Days 31–60: Fix the data that hurts you every week

Most AI ROI comes from boring work:

• milestone event quality (pickup, arrival, departure, POD)
• item and location master consistency
• carrier and supplier identifiers

If you’re using AI forecasting or ETA prediction, make sure you can answer: What percentage of records are missing the fields the model needs? Track it weekly.

Days 61–90: Put AI into a workflow, not a slide deck

A model that emails a report is easy to ignore. A model that creates tasks in the system people already use gets adopted.

Start with an exception workflow:

• trigger when risk crosses a threshold
• show the recommended action
• require a disposition (accept, modify, reject)
• learn from the disposition

That loop—recommendation → action → feedback—is where AI becomes operational.

Where ARC 2026 fits in the bigger AI in Supply Chain & Procurement story

Our series focuses on a simple idea: AI earns trust by improving decisions that move money—inventory, freight, labor, and supplier performance.

The ARC Industry Leadership Forum is a timely checkpoint because it brings together the people who are turning AI into daily operations: planners, plant and warehouse operators, procurement leaders, and the tech ecosystem supporting them. If the agenda is heavy on cognitive analytics, digital twins, and predictive technologies, read that as a signal that the industry is standardizing around AI-assisted operations, not one-off optimizations.

If you’re planning your 2026 roadmap, your next step is to write down one question you want answered by February:

“Which AI use case will we operationalize next—and what system will our teams use to act on it?”

Get that question right, and the conversations you have around events like ARC won’t be inspirational. They’ll be useful.