AI-Driven Auto Transport: What RPM’s Deal Signals

A finished-vehicle move isn’t “just freight.” When the cargo is a $180,000 performance SUV or a limited-run supercar, a missed pickup window, an unclear handoff, or a scuffed bumper turns into a reputational problem fast—sometimes a legal one.

That’s why RPM Freight Systems’ acquisition of Dealers Choice Auto Transport matters beyond the usual M&A headline. RPM is building a broader finished-vehicle logistics platform, and Dealers Choice adds the high-touch layer: enclosed shipping, driveaway, storage, and the kind of customer communication that luxury dealers and collectors expect.

For this AI in Transportation & Logistics series, the interesting part is what this kind of acquisition enables: a richer data environment (more specialized capacity, more exception handling, more service-level variation) that makes AI-driven logistics more valuable—and more necessary.

Why this acquisition fits the “AI logistics platform” pattern

Answer first: This deal strengthens RPM’s ability to standardize, measure, and optimize complex auto transport workflows—the exact conditions where AI performs best.

RPM Freight Systems has been clear about its focus on finished vehicles and OEM customers, with leadership citing an ambitious long-term goal of moving five million vehicles annually. That kind of scale only works if the operation behaves like a platform: consistent processes, predictable carrier performance, and tight visibility.

Dealers Choice brings something platform builders often lack: a premium service line with different operational physics.

• Enclosed moves have different capacity constraints than open-deck.
• “White-glove” deliveries introduce more checkpoints (inspection photos, signature flows, appointment scheduling).
• Driveaway adds driver qualification, insurance nuance, and route compliance.

Those differences are exactly why acquisitions like this happen. A broad 3PL network can move a lot of volume. A specialist can protect margin with high standards. Put them together and you can offer tiered service levels while still running one integrated operating system.

The December reality: peak demand punishes manual planning

Late December is when logistics teams feel every crack in their processes: holiday staffing, year-end pushes, weather disruptions, and shippers trying to close budgets. Finished-vehicle logistics is no exception. If you’re dispatching specialty moves from spreadsheets or juggling updates across phone calls and inboxes, peak season doesn’t just slow you down—it breaks service.

A combined RPM + Dealers Choice footprint creates the conditions to use AI for what it’s good at:

• forecasting capacity needs by lane and service level
• predicting which moves will become exceptions
• automating customer updates without losing the “human” feel

White-glove auto transport is a data problem (not just a service problem)

Answer first: Premium vehicle transport succeeds when you reduce uncertainty—AI helps by turning messy operational signals into decisions.

Dealers Choice is known for “white-glove” handling of luxury and exotic vehicles, including enclosed transport and driveaway. RPM’s president described it bluntly: if RPM “moves metal,” Dealers Choice “moves art.” That line is more than marketing. It’s operational truth.

In high-touch auto hauling, the customer doesn’t just want delivery. They want confidence.

That confidence comes from visibility and control:

• precise pickup scheduling (and re-scheduling)
• inspection documentation at multiple handoffs
• temperature/weather considerations for high-value vehicles
• secure storage options when timing doesn’t line up
• proactive communication when anything changes

Here’s the thing I’ve seen repeatedly: companies try to “add white-glove” by bolting on customer service scripts. It rarely works. White-glove is a systems discipline.

Where AI actually helps in high-value vehicle moves

AI adds value when it improves decisions under constraints. In this segment, constraints are everywhere.

1. Appointment-based routing: AI-supported route planning can account for delivery windows, driver hours, and real-time congestion.
2. Exception prediction: models can flag moves at risk (weather, tight handoffs, historically unreliable carriers, high dwell locations).
3. Computer vision for inspections: image comparison can speed damage detection and standardize condition reports.
4. Dynamic ETA and comms: AI-generated updates can be accurate, consistent, and personalized—without burying the ops team.

None of this replaces experienced dispatchers. It gives them early warning and faster playbooks.

Integration is the hard part—and the opportunity

Answer first: The post-acquisition win comes from unifying workflows, data, and service tiers; otherwise you just bought revenue, not capability.

The press release language points to combining RPM’s network and technology platform with Dealers Choice’s expertise and customer service. That’s the right intent. But execution is where acquisitions in logistics either pay off—or drag down the core business.

You can’t get AI-optimized auto transport without standardized operational data. That means the combined company will need to align basics that often differ across organizations:

• load lifecycle definitions (what counts as “picked up,” “in transit,” “delivered”)
• inspection and photo requirements
• exception codes (delay, reschedule, accessorials, storage holds)
• carrier scorecards and qualification rules
• customer communication standards by segment (OEM vs dealer vs consumer)

The hidden KPI that matters: exception cycle time

Most teams track on-time pickup and on-time delivery. Useful, but incomplete.

For white-glove logistics, the differentiator is often how fast you resolve exceptions:

• A rescheduled delivery that’s confirmed in 10 minutes feels premium.
• The same reschedule that takes 24 hours feels like chaos.

AI can compress exception cycle time by:

• auto-classifying inbound emails/calls into exception types
• recommending next actions (reroute, swap carrier, move to storage)
• generating customer-facing messages that match policy and tone

This is where integration becomes a growth engine: the more consistently you capture exception data, the better the models get.

What this means for OEMs, dealers, auctions, and collectors

Answer first: Buyers of auto transport should expect more “tiered logistics”—and they should demand measurable service levels, not just premium branding.

RPM gains a higher-margin specialty line and another service offering to sell into OEM relationships. Dealers Choice gains access to OEM business it didn’t primarily serve before. That complementary fit is straightforward.

But for customers, the big shift is what becomes possible when a provider can offer multiple service levels inside one platform:

• Standard open-deck for high-volume moves
• Enclosed for high-value models, new launches, or high-scrutiny dealer deliveries
• Driveaway for specific regional or last-mile needs
• Storage + sequencing for inventory timing and release control

Practical questions buyers should ask now

If you’re an OEM logistics manager, dealer group, auction, or specialty retailer, use this acquisition news as a prompt to tighten your procurement checklist.

Ask providers these questions:

1. Can you show lane-level performance by service tier? Enclosed and open-deck shouldn’t share the same KPIs.
2. What’s your exception playbook—and can you measure cycle time? If they can’t, they’re guessing.
3. Do you have inspection standards that don’t depend on “who’s working today”? Consistency matters more than charm.
4. How do you match loads to carriers—rule-based, dispatcher intuition, or model-assisted? The answer tells you how scalable quality is.
5. What data do I get back? You want structured status events, inspection artifacts, and reason codes.

A provider that answers clearly is a provider that can improve.

A realistic AI roadmap for finished vehicle logistics (post-M&A)

Answer first: Start with visibility and exception handling, then move into optimization and automation once the data is clean.

If I were advising a combined RPM + Dealers Choice operation, I’d sequence AI work like this:

Phase 1: Data foundation (0–90 days)

• unify milestone events and exception codes
• standardize inspection/photo capture requirements
• establish carrier identity resolution (avoid duplicates across systems)

Phase 2: Visibility and service assurance (3–6 months)

• predictive ETA with confidence ranges (not just a single timestamp)
• automated customer notifications by segment
• carrier scorecards tuned by service tier (enclosed vs open vs driveaway)

Phase 3: Optimization (6–12 months)

• AI-assisted dispatch and load-carrier matching
• capacity forecasting by lane and seasonality
• “next-best-action” recommendations for exception resolution

Phase 4: Premium differentiation (12+ months)

• computer vision assisted condition reporting
• dynamic pricing models for specialty capacity
• proactive risk alerts (weather, theft hotspots, dwell-risk locations)

This progression avoids a common failure mode: trying to jump to optimization when status data is inconsistent.

The bigger signal: logistics M&A is turning into “data M&A”

Answer first: Acquisitions like this are about building a dataset and operating model that supports AI at scale.

RPM’s recent activity (including another acquisition earlier this month) suggests a clear strategy: wrap niche, value-added services around a core finished-vehicle transport capability to pursue higher-margin opportunities and deeper customer relationships.

If you zoom out, this is what’s happening across transportation and logistics:

• Platforms want end-to-end coverage across the asset lifecycle.
• Specialties (like white-glove enclosed transport) protect margin and customer loyalty.
• AI needs consistent, cross-workflow data—so companies buy capabilities they can integrate into one system.

Most companies get this wrong by treating AI as a software purchase. It isn’t. AI performance is the output of operational discipline.

RPM buying Dealers Choice is a bet that disciplined premium operations plus scale plus technology equals a stronger platform.

What to do next if you’re building AI-enabled transportation operations

If you’re on the shipper side, tighten your requirements around structured visibility, exception metrics, and inspection artifacts. If you’re on the logistics provider side, treat every acquisition or new service line as a data integration project first.

If you want one practical next step: start measuring exception cycle time by cause code across your auto transport network. Do that for 60 days and you’ll immediately see where AI and automation will pay off.

Where do you think finished-vehicle logistics is headed next: more premium service tiers, or more standardized “one-size-fits-most” networks as AI improves planning and visibility?