RPM’s Auto Transport Deal: The AI Integration Play

RPM Freight’s acquisition of Dealers Choice Auto Transport looks, on the surface, like a classic “add a premium service line” move. But the bigger story is what this kind of deal makes possible next: a wider, richer data footprint that AI can actually learn from—across OEM moves, dealer moves, auctions, and now the high-touch world of enclosed and driveaway.

Finished vehicle logistics is messy. Every move has a different clock, different risk, different customer tolerance for delays, and different damage exposure. When you add white-glove vehicle transport—where a minor scuff becomes a major claim—operational discipline has to tighten. That’s exactly where modern AI in transportation and logistics earns its keep: not with flashy demos, but with fewer surprises.

This acquisition matters because it signals a direction a lot of transportation leaders are already taking: build a broader platform through M&A, then standardize operations and decision-making using AI-driven logistics systems. The winners won’t be the companies that “buy growth.” They’ll be the ones that can integrate it.

Why this acquisition makes strategic sense (beyond scale)

RPM bought margin, specialization, and optionality—three things that are hard to manufacture internally. Dealers Choice brings high-touch services like enclosed transport, driveaway, and storage, with a reputation in luxury/exotic moves. That isn’t just a new offering; it’s a different operating model.

RPM has been building a finished vehicle logistics platform with OEM priorities, and it’s publicly talked about ambitious volume goals (millions of vehicles annually). Dealers Choice, meanwhile, has historically been closer to dealerships, auctions, and individual vehicle moves. Put them together and you get something powerful: coverage across the vehicle lifecycle.

Here’s the important nuance: premium services aren’t just “more expensive.” They’re often more schedule-constrained, more exception-prone, and more sensitive to service quality.

That’s why the deal is interesting for this series (“AI in Transportation & Logistics”). When you mix high-volume OEM flows with high-touch white-glove flows, you create the conditions where AI can reduce complexity—if the integration is done right.

The hidden value: joining two decision systems

Every logistics business has an unspoken “decision system”—how dispatch happens, how carrier selection works, how exceptions get handled, how ETAs are communicated, and how claims are prevented.

M&A forces these systems to collide.

• RPM’s model likely optimizes for throughput, network efficiency, and OEM visibility.
• Dealers Choice optimizes for risk management, service precision, and careful handling.

The combined company now has a choice: keep two parallel operating systems (costly, slow) or build a shared one. AI becomes the practical bridge—a way to standardize decisions while still respecting different service tiers.

White-glove auto transport is an AI problem in disguise

Luxury/exotic vehicle transport amplifies the cost of being wrong. A late delivery is annoying in standard freight. For a dealership awaiting a high-end unit for a buyer appointment, it can mean a lost sale or a relationship hit. And damage risk changes everything: enclosed carrier availability, driver experience, route smoothness, and handoff procedures all matter.

This is where AI-driven logistics can be specific and useful.

Where AI helps immediately

1. Carrier selection and scoring
   White-glove moves benefit from carrier performance models that weigh:

   • damage history and claim frequency
   • on-time performance by lane and season
   • equipment type (enclosed vs open)
   • driver experience on premium SOPs

2. Dynamic ETA and exception prediction
   Predicting exceptions early is more valuable than “perfect tracking.” A good model flags likely issues before they become customer-visible problems:

   • pickup windows at dealers and auctions
   • weather + congestion risk along route
   • driver hours constraints
   • terminal/yard dwell risk

3. Routing that accounts for risk, not just miles
   Finished vehicle transport isn’t always shortest-path routing. AI routing can incorporate:

   • road quality (where available)
   • low-clearance and construction risk
   • fewer handoffs for high-value units

4. Claims prevention workflows
   When margin comes from premium service, the fastest way to lose it is claims. AI can support:

   • anomaly detection on inspection photos
   • identifying “high-risk” lanes or partners
   • enforcing checklists (digital SOP compliance)

The headline: white-glove is a data and process business wearing a service-business mask. The more consistent the data capture, the more predictable the outcomes.

M&A is where AI projects go to die—or finally work

Most companies get AI wrong by treating it like a feature instead of an operating model change. Acquisitions make that problem worse because now you’ve got multiple CRMs/TMSs, multiple carrier databases, and multiple definitions of “on time.”

But consolidation also creates the one thing AI needs to deliver value: enough volume and variation to learn patterns.

The integration tasks that matter (and the ones people skip)

If you want the acquisition to translate into AI-enabled performance, these are the hard parts:

1) Standardize the data model first

You can’t optimize what you can’t describe consistently.

A pragmatic finished vehicle logistics data model includes:

• order milestones (order created, tendered, accepted, picked up, delivered)
• appointment windows and dwell times
• equipment type and service tier (standard, enclosed, driveaway)
• condition reports and inspection artifacts
• exception reason codes (and not 200 of them)

2) Unify identity across the network

Carrier and driver identity resolution is a real problem:

• the same carrier listed under multiple names
• inconsistent insurance records
• different performance histories per system

AI can help match entities, but leadership has to force a single source of truth.

3) Build “tiered optimization,” not one-size-fits-all dispatch

You don’t dispatch a Lamborghini the same way you dispatch a fleet unit.

A sensible approach:

• Tier A (white-glove): maximize reliability and damage avoidance
• Tier B (OEM/dealer standard): balance cost and on-time
• Tier C (backhaul/fill): maximize utilization

AI should optimize within tiers and manage tradeoffs between them.

4) Operationalize the feedback loop

AI models need outcomes.

The best operators treat every shipment like a learning event:

• Was it on time relative to appointment?
• Was there avoidable dwell?
• Was there a claims incident?
• Was communication proactive?

If those outcomes aren’t captured cleanly, AI becomes expensive math with no payoff.

What shippers and OEMs should watch next

The immediate benefit of a deal like this is broader service coverage. The long-term benefit is better decision-making at scale. If RPM executes, OEMs and enterprise shippers should see improvements in a few very specific areas.

1) Capacity resilience during peak periods

Dealers Choice’s specialized carrier network can help when demand spikes (seasonality, model-year pushes, promotional cycles). The AI angle: use forecasting to pre-position capacity and reduce last-minute premium costs.

2) Better visibility that’s actually actionable

“Visibility” often means a map dot. What shippers really want is:

• accurate delivery confidence
• early warning on exceptions
• clear next action

Expect the market to reward providers that can say: “This unit is 87% likely to miss the appointment; here’s why, and here’s the fix.”

3) Premium service as a controlled upsell, not a fire drill

White-glove offerings should be a predictable product with defined SLAs and SOPs. When AI supports carrier selection and risk scoring, premium service becomes:

• easier to quote
• easier to schedule
• easier to deliver consistently

That’s how high-margin services stay high-margin.

A practical AI roadmap after an acquisition like this

If you’re a transportation or logistics leader watching RPM’s move and thinking, “We’re facing the same integration issues,” here’s a sane order of operations I’ve found works.

1. Centralize shipment + milestone data (90 days)
   Don’t chase perfect. Chase consistent.

2. Create carrier performance baselines (120 days)
   Start with on-time, dwell, claims, and tender acceptance.

3. Deploy exception prediction + alerting (180 days)
   Focus on the top 5 exception types that drive customer pain.

4. Introduce tiered optimization for dispatch (6–12 months)
   White-glove and standard moves shouldn’t compete in the same optimization logic.

5. Automate SOP compliance artifacts (ongoing)
   Checklists, inspections, and handoffs are where quality lives.

The reality? AI value shows up first in fewer escalations, fewer rework cycles, and fewer claims—not in a press release.

Where RPM’s acquisition fits in the bigger AI-in-logistics trend

Consolidation is creating larger logistics platforms—and larger platforms are where AI has the most impact. A small operator can run on tribal knowledge. A multi-service, multi-segment platform can’t. It needs systems that make consistent decisions across teams, geographies, and customer types.

RPM’s acquisition of Dealers Choice is a clear step in that direction: wrapping niche, high-value services around a core finished vehicle network. If the company follows through on integration, the next chapter isn’t just “more services.” It’s smarter orchestration—better forecasting, better routing decisions, better carrier matching, and tighter exception management.

If you’re a shipper, OEM, or 3PL leader, the question to ask isn’t whether a provider has an “AI strategy.” Ask this instead: Are they building a unified operating system where AI can improve decisions every day?

Because acquisitions like this don’t just change who owns what. They change who can run a complex vehicle logistics network without relying on heroics.