2026 Logistics Conference Calendar for AI-First Leaders

December is when a lot of supply chain teams quietly set the tone for the next 12 months. Travel gets approved (or denied). Project backlogs get reshuffled. Vendor shortlists appear out of nowhere. And the events you choose—starting as early as mid-January—end up shaping which AI and automation bets you make in 2026.

Here’s my stance: most logistics leaders pick conferences like they’re picking continuing education credits. The smarter move is to treat the 2026 conference calendar as an operating tool—one that helps you pressure-test your AI strategy, validate your roadmap with peers, and accelerate implementation by learning what’s actually working in transportation and logistics networks.

This post reframes the 2026 global event calendar into a practical plan for the AI in Transportation & Logistics agenda: forecasting, routing, warehouse automation, real-time visibility, and last-mile optimization.

Use the 2026 calendar as an AI strategy map (not a travel list)

Answer first: The fastest way to sharpen your AI roadmap is to align each major event with a specific business decision you need to make in 2026—then show up with a targeted set of questions, datasets, and stakeholders.

A conference isn’t “worth it” because it has big speakers. It’s worth it because it helps you make one of these decisions with more confidence and less rework:

• Which operational problems are stable enough to automate (and which aren’t)?
• What data do we need to make AI outputs dependable in production?
• Which vendors can integrate into our current TMS/WMS/ERP stack without turning into a 9‑month IT project?
• How do we govern AI in planning and execution so operators trust it?

The four outcomes you should plan for

If you go to an event without a desired outcome, you’ll come home with notes and no momentum. A useful 2026 event plan aims for these outcomes:

1. Roadmap validation: confirm your AI use cases match where the market is actually investing.
2. Reference discovery: find peers running similar networks who’ve deployed models in real operations.
3. Vendor narrowing: move from “interesting demos” to a shortlist based on integration, data, and proof.
4. Execution acceleration: bring back playbooks your team can apply in 30 days.

A simple trick I’ve found effective: assign every event a single “decision it must improve.” If it can’t improve a decision, skip it.

Which 2026 events match your AI priorities in transportation & logistics?

Answer first: Different conferences cluster around different AI adoption zones—retail and demand signals, execution and automation, ocean and capacity risk, or enterprise platforms and governance.

The RSS calendar lays out the major 2026 gatherings. Below is the same calendar, reorganized by what it’s best for—specifically through an AI and automation lens.

Demand + fulfillment pressure: start the year with NRF and retail execution

Events like NRF (January) and RILA LINK (February) are where retail supply chain leaders compare notes on:

• inventory accuracy and allocation logic
• fulfillment network design
• returns and reverse logistics
• labor planning and store-to-door execution

If you’re building AI forecasting or replenishment improvements in 2026, these events are useful because they expose a hard truth: forecasting accuracy is rarely the bottleneck by itself. The real bottleneck is connecting forecast outputs to decisions—purchase orders, allocations, labor plans, and transport capacity.

What to listen for on-stage and in side conversations:

• how teams measure forecast value (service level, fill rate, expedite reduction)
• whether AI outputs are explainable enough for merchants and planners
• how companies handle promotion spikes, substitutions, and shifting demand channels

Execution and automation: warehouse, yard, and transportation reality checks

If 2026 is about making AI real in operations, MODEX (April) and WERC (May) are the most “rubber meets the road” environments in the calendar.

• MODEX is where warehouse automation, robotics, and orchestration software show up at full scale.
• WERC is where you hear how DC leaders are managing labor variability, safety, slotting discipline, and continuous improvement.

AI in warehousing tends to deliver value when it’s paired with constraints you can’t ignore:

• travel paths
• pick sequencing
• induction rates
• dock schedules
• labor availability

A practical planning point: if you’re considering AI-driven labor planning, slotting optimization, or robotic picking support, go into these events ready to ask about exception handling. When a model is wrong—or a pallet shows up late—what happens next? The teams who’ve solved that are the ones you want to learn from.

Transportation visibility + network coordination: where collaboration gets specific

Events like Manifest (February) and Home Delivery World (May) are strong for teams focused on:

• real-time visibility
• carrier collaboration and appointment management
• dynamic routing and last-mile orchestration
• cost-to-serve analytics

This is where the collaboration theme from the calendar becomes concrete. “Collaboration” isn’t a workshop topic; it’s a system behavior:

• shippers share accurate forecasts and tender patterns
• carriers share capacity and status
• DCs and yards share appointment and unload reality
• customer promise dates update based on live constraints

AI helps when it reduces coordination friction. For example, predictive ETA models matter because they trigger actions: re-slot docks, adjust labor, resequence picking, or switch delivery windows.

Ocean contracting and risk: where planning meets volatility

TPM (March) is the obvious anchor for ocean contracting and reliability. It’s also one of the best places to understand a recurring AI challenge: models struggle when the world changes faster than the training data.

For ocean and port-to-inland flows, your 2026 AI advantage often comes from mixing:

• scenario planning (capacity, price, lead time)
• multi-carrier allocation rules
• real-time visibility signals
• exception triage automation

If your team is still doing disruption response through spreadsheets and late-night calls, TPM season is when you can find practical approaches to modernize that—without betting everything on a “fully autonomous” planning vision.

What to ask vendors in 2026 to avoid “demo-driven” AI decisions

Answer first: The best vendor conversations in 2026 will focus on integration, data requirements, and operational governance—not model types or flashy UX.

A lot of AI logistics demos look good because they’re built on clean, curated datasets. Your network is not clean. Your master data is not perfect. Your carrier timestamps are inconsistent. That’s normal.

So here are the questions that separate an implementation partner from a slide deck.

Vendor vetting questions that actually matter

Bring these to vendor and platform user conferences (SAP, Oracle, Blue Yonder, Manhattan, Coupa) and also to broader expos.

1. What data is required, and what data is optional? If it requires perfect event timestamps, be cautious.
2. How does the system behave when data is missing or late? Ask to see degraded-mode behavior.
3. Can we override or constrain the AI? Operators need guardrails, not magic.
4. What’s the deployment pattern? Pilot in one DC, one lane, one region—then scale.
5. How do you measure value? Look for metrics tied to cost-to-serve, service, labor hours, detention, or expedite reduction.

A reliable AI system isn’t the one that’s right 100% of the time. It’s the one that fails safely and visibly.

Red flags you should treat as deal-breakers

• “Our model is proprietary” as a substitute for explainability
• No clear answer on who monitors drift and performance monthly
• Integration hand-waving (“we have APIs”) without implementation specifics
• Value claims without a measurement plan you can reproduce

Build a 2026 event plan that creates leads, not just learning

Answer first: If your goal is faster execution and better vendor selection, go to fewer events—but go with a cross-functional agenda, pre-booked meetings, and a 30‑day post-event plan.

This campaign is about AI in transportation and logistics, but the real-world constraint is the same everywhere: AI projects die when only one team owns them. Planning buys software. Ops doesn’t trust it. IT can’t support it. Procurement negotiates the wrong scope. Then everyone blames the model.

A simple event operating cadence (that works)

Before the event (2–3 weeks):

• pick 2–3 use cases (example: dynamic routing, DC labor planning, predictive ETA)
• write the current baseline metrics (even if they’re ugly)
• define your “implementation constraints” (systems, data, labor, union rules, customer promise)
• schedule 10–15 short meetings, including peer conversations (not just vendors)

During the event:

• attend fewer sessions; spend more time in targeted conversations
• capture specifics: timelines, integration patterns, staffing, what broke, what surprised them
• collect names of customers willing to share deployment lessons

After the event (within 30 days):

• run one internal workshop: “What we learned + what we’ll test next”
• pick one pilot and assign an owner, timeline, and success metrics
• kill one idea that doesn’t fit your data reality (this is a win)

That last point matters. A good 2026 calendar doesn’t just add projects; it helps you subtract the wrong ones early.

Where the 2026 conference themes are heading (and how AI fits)

Answer first: The recurring 2026 themes—cost pressure, reliability, labor, compliance, and governance—are pushing AI toward practical automation and explainable decision support.

From the event mix in the calendar, you can see what’s getting budget attention:

• Cost-to-serve and service simultaneously: AI routing and network optimization tied to customer promise.
• Labor constraints: warehouse automation plus AI-assisted labor planning and task prioritization.
• Governance and compliance: more scrutiny on data security, auditability, and responsible AI use.
• Collaboration: real-time coordination across shippers, carriers, and nodes—powered by visibility and predictive analytics.

If you’re building your 2026 plan right now, here’s the cleanest way to frame it:

• Use AI to improve decisions (planning) where humans need speed and scenario coverage.
• Use AI to trigger actions (execution) where exceptions and variability create waste.
• Use governance to keep trust (adoption) so operators don’t revert to manual workarounds.

The 2026 calendar isn’t just a list of dates. It’s a preview of what your peers will be buying, testing, and scaling—and a chance to show up prepared.

If you could only justify one AI initiative in transportation and logistics for 2026, would you pick forecast-to-fulfillment decisioning, real-time transportation coordination, or warehouse execution automation—and what metric would you use to prove it worked?