Speak at ARC 2026: AI Proof for Resilient Supply Chains

Disruption isn’t a “black swan” anymore. For most transportation and logistics teams, it’s a recurring calendar invite: port congestion one month, carrier capacity crunch the next, then a tariff or policy shift that reroutes an entire sourcing plan.

That’s why the ARC Industry Forum 2026 call for speakers matters—and not just for people who like conference badges. If you’ve built something that actually works (an AI forecasting approach that holds up in chaos, a control tower that predicts exceptions early enough to act, a procurement model that stops surprises before they hit margins), this is one of the few stages where operators and executives show up expecting real lessons, not product theater.

This post is part of our AI in Supply Chain & Procurement series, where we focus on practical AI: demand sensing, supplier risk, inventory strategy, transportation optimization, and the operational plumbing needed to make it all run in the real world.

Why ARC 2026 is a big deal for AI in logistics

ARC is valuable because the audience is outcome-driven. Operations, supply chain, and technology leaders don’t come to hear “we used AI.” They come to hear what changed in lead times, service levels, expedite spend, dock-to-stock, forecast accuracy, and on-time delivery.

The Forum’s theme—intelligent operations and resilient supply chains—maps cleanly to where AI is earning its keep in transportation and logistics:

• Intelligent operations: AI optimization for routing, labor planning, yard flow, slotting, network design, and automation decisions.
• Resilience: AI-based forecasting, risk sensing, exception prediction, supplier performance analytics, and scenario planning.
• Connected intelligence: turning TMS/WMS/ERP data into decisions that frontline teams trust and actually use.

Here’s my stance: 2026 will reward teams that operationalize “decision velocity.” Not just visibility. Not dashboards. The ability to decide and act faster than the disruption spreads.

What “intelligent operations” should mean in 2026

Intelligent operations isn’t a vision statement—it’s a closed-loop system. Data comes in, decisions are recommended (or automated), actions are executed, and outcomes feed back into the model.

If you’re thinking about a talk proposal for ARC 2026, anchor it in one of these operator-friendly definitions.

A useful definition: decision loops, not AI features

An operations AI capability is only “intelligent” if it does three things:

1. Predicts what will happen (late trailer, missed cutoff, stockout risk, detention risk)
2. Recommends what to do (reroute, pre-pick, re-slot, rebid, adjust safety stock)
3. Learns from the result (did the recommendation work, and why?)

That framing helps you avoid the most common speaker mistake: presenting an AI architecture diagram instead of an operational story. Executives care about architecture only after the business case is obvious.

Strong talk angles (with supply chain + transportation specifics)

If you want a session that lands with both supply chain and logistics leaders, consider:

• AI routing optimization with service guarantees: how you balanced cost, delivery windows, driver hours, and customer promises.
• Exception prediction in freight execution: how you reduced “surprises” by flagging high-risk loads before they went late.
• Warehouse labor + transportation synchronization: how you aligned wave planning, dock scheduling, and carrier appointments to cut dwell.
• Procurement with AI-guided risk controls: how supplier scoring influenced sourcing decisions, not just quarterly reports.

If you can attach even one hard number—like “expedite spend down 18%” or “forecast bias cut in half”—you’ll instantly separate yourself from the noise.

Resilient supply chains: the practical playbook leaders want

Resilience is the ability to keep customer commitments when the plan is wrong. That’s it. Not redundancy for its own sake. Not expensive buffers that destroy working capital.

In the ARC call for speakers, one theme stands out: resilience in shifting geopolitics. Whether you’re managing tariffs, trade policy changes, regional conflicts, or new compliance requirements, the winning pattern is the same:

• Detect changes early
• Quantify impact fast
• Execute alternatives with minimal chaos

Where AI actually helps (and where it doesn’t)

AI is excellent at:

• Demand forecasting across noisy, multi-channel signals
• Risk sensing (supplier performance drift, lane volatility, lead-time instability)
• Scenario planning at speed (if X port closes, if Y tariff hits, if Z supplier fails)
• Automated exception triage (which 20 shipments matter most right now)

AI is not a substitute for:

• Master data discipline
• Contract and incoterms clarity
• Clear escalation paths
• Ownership of decisions

A speaker session that tells the truth about those limits—without turning it into a rant—will get attention. Most companies get this wrong by buying “resilience software” and hoping it magically creates resilience behaviors.

A concrete example structure that works on stage

If you’re building your ARC 2026 proposal, use a case-study format people can copy:

• Trigger: what disruption hit (capacity drop, border delay, commodity swing)
• Signal: what data indicated the issue early
• Model: what was predicted (impact window + confidence)
• Decision: what changed (inventory policy, routing guide, supplier allocation)
• Outcome: service, cost, lead time, and what you’d do differently

That’s vendor-neutral, executive-friendly, and operationally credible.

Knowledge transfer is the quiet make-or-break issue

The best AI models fail when the organization can’t transfer knowledge. People leave, tribal workarounds vanish, and the “why” behind decisions disappears.

ARC’s session theme on knowledge transfer in enterprise systems is more timely than it sounds. As more teams adopt AI copilots and automated planning, knowledge transfer becomes a risk category—right next to cybersecurity and single-source suppliers.

What leaders want: decisions that are explainable enough to trust

In logistics and supply chain execution, explainability doesn’t need academic purity. It needs operational usefulness:

• Why did the model recommend rerouting this load?
• Which constraints were binding (cost cap, delivery promise, driver hours, capacity)?
• What’s the impact if we override it?

A strong ARC session can show how you built human-in-the-loop controls:

• Guardrails (service-level floors, compliance rules, cost ceilings)
• Override workflows (who can override, when, and how it’s audited)
• Postmortems (what overrides taught the model and the team)

That’s the difference between “AI pilots” and “AI operations.”

What a winning ARC 2026 speaker proposal looks like

ARC explicitly asks for vendor-neutral, educational, real-world sessions. Treat that as a gift: it pushes you into the stories that generate trust—and trust is what creates leads.

Use this proposal checklist (practical and ARC-aligned)

Include these elements in your submission:

• A title that names the outcome (not the tool)
• A 150–250 word abstract that follows problem → approach → result
• 3–5 takeaways that are specific enough to implement
• Your role and scope (budget, facilities, regions, shipment volume—whatever’s relevant)

Here are example takeaways that read like an operator wrote them:

• “How we reduced late deliveries by prioritizing exceptions 8 hours earlier.”
• “A simple method for combining carrier ETA data with warehouse capacity to cut dwell.”
• “How we built a forecasting workflow that planners didn’t ignore after week three.”

The best topics for lead generation (without sounding salesy)

If your goal is leads, don’t pitch your product. Teach the hard parts people struggle with:

• Data readiness for AI in transportation management systems
• Modeling lane volatility and accessorial risk
• AI-based demand forecasting that accounts for promotions and stockouts
• Supplier risk scoring that actually changes procurement decisions
• GenAI copilots for planners (with governance and audit trails)

A good rule: if the audience could repeat your method without buying anything, they’ll trust you enough to want a follow-up.

Snippet-worthy line: “Visibility tells you what happened. Resilience depends on how early you can decide what to do next.”

People also ask: speaker FAQs for operations leaders

What if my story isn’t “big enough” for ARC?

If you can show a repeatable approach and a measurable improvement, it’s big enough. A single site, a single region, or a single mode can be compelling when the lesson generalizes.

How technical should an AI logistics talk be?

Technical details matter only when they change decisions. Spend more time on how you operationalized the model—workflow, ownership, metrics, guardrails—than on model types.

What metrics resonate most with this audience?

Stick to outcomes leaders track weekly:

• On-time delivery / service level
• Expedite spend and premium freight
• Detention/dwell time
• Inventory turns and stockout rates
• Forecast accuracy (and bias)
• Perfect order rate

If you’re serious about AI resilience, ARC 2026 is the right room

The ARC Industry Forum 2026 call for speakers is a chance to shape how the industry talks about AI in supply chain and procurement—not as hype, but as operational advantage. If you’ve built an intelligent operations loop that makes your network faster, steadier, and easier to run, other leaders want to hear it.

A strong submission doesn’t need buzzwords. It needs a clear problem, a clear approach, and proof that the approach survived real constraints.

If you’re considering a proposal, write it like you’re explaining it to the person who’s on-call when a major customer shipment goes sideways. Would they be able to use your playbook next week? If yes, you’ve got the kind of session ARC 2026 was built for.

What’s the AI decision in your supply chain that still takes too long—and what would it be worth to cut that time in half?