Right to repair. Interoperability. AI. Data ownership. We have been hearing these phrases for years, and they have become background noise to the daily challenges dealers and OEMs actually face. That is a mistake. Because the real disruption is not any one of these things. It is the collision between all of them.

Nobody is talking about that collision. So let me try.

Right to Repair Changed the Terrain. It Didn't End the Debate.

Colorado passed first-in-the-nation agricultural right-to-repair legislation in 2023, effective January 1, 2024. Manufacturers must now provide parts, embedded software, firmware, tools and documentation to owners and independent repair providers. In January 2025, the FTC and multiple state attorneys general filed suit against a major equipment manufacturer over repair restrictions. The case is contested. It was filed in the final days of the outgoing administration with the incoming FTC chair dissenting, but several states have committed to continue regardless of what happens federally.

You do not have to pick a side on any of this to see what it means. Repair access is now a contested market boundary, not a customer service issue. And that matters enormously for what comes next.

Interoperability is customer freedom. It is also the beginning of a different problem.

When repair access opens up, switching costs fall. Interoperability accelerates that further. ISOBUS was designed to enable cross-manufacturer communication between tractors, implements and terminals. Anyone who has tried to make it work in the field knows a standard on paper is not the same as plug and play in practice. That gap is why the Agricultural Industry Electronics Foundation exists.

Now the same dynamic is moving into the cloud. The AEF's Agricultural Interoperability Network, AgIN, is a standardized gateway connecting equipment manufacturers, data hubs, farm management systems, and service providers for brand-agnostic data sharing. It's scheduled for initial release in 2026. That is not a future concept. It is infrastructure being built right now.

For customers, this is freedom. More choice, more flexibility, less lock-in. I understand why the industry frames this as progress and largely gets on with it.

But here is what the customer-wins framing misses entirely.

When Data Becomes Interoperable, AI Turns It Into Something Nobody Anticipated.

Most conversations about AI in this industry still treat it as a better search tool. That framing is already a generation behind. Gartner forecasts that by 2028, 33% of enterprise software applications will include agentic AI, enabling at least 15% of day-to-day work decisions to be made autonomously. These are not systems that answer questions. They plan, coordinate, and act, across workflows, with minimal human involvement, using whatever data they can legally access.

Now think about what a farmer's data world looks like today. Machine telemetry in one portal. Agronomic records in another. Service history in a dealer system. Finance, warranty, and parts records somewhere else. Individually, each of those streams has limits. Combined and run through AI, they become a prediction engine.

This is what privacy professionals call the mosaic effect. Information that looks unremarkable in isolation becomes commercially powerful when aggregated with other sources. The insight that emerges was not anticipated in any contract, and it was not explicitly consented to by anyone.

An AI system with access to cross-brand telemetry, service history, and agronomic context can calculate expected wear and failure probability. It can model likely downtime windows and cost-to run profiles by machine and operation. It can generate perfectly timed offers based on all of it. Before the dealer even knows there is a need.

That is the threat. Not that someone steals data. Not that a regulation goes the wrong way. It is that a third party, or a direct competitor, assembles a picture of your customer's operation from legally accessible, interoperable data streams, and uses it to make a better argument than you can.

Think about what that means in competitive terms. If someone can model a farmer's uptime, operating costs, and efficiency across a mixed fleet, they can build a brand-switch argument using the farmer's own data. The farmer hears it framed as optimization advice. It lands because it is specific, accurate, and perfectly timed.

This is not theoretical. In 2025, a court ruling arising from right-to-repair litigation granted one equipment manufacturer access to confidential pricing, financial, and sales data belonging to several of its direct competitors. The companies described that information as their most commercially sensitive intelligence. That happened through legal process. Not a data breach. Not a hack. A legal process that the interoperability and right-to-repair wave is actively expanding. AI will not need a court order to achieve the same outcome at scale.

This Isn't Someday. It's Already in the Building.

AI helpdesk and triage tools are already being deployed that sit between the customer and the dealership. CNH's AI Tech Assistant, now in use across hundreds of dealer groups, gives technicians instant access to a library of 1.5 million pieces of technical documentation. Purpose-built third-party platforms go further, ingesting dealer work orders, CRM data, and service histories to answer questions and recommend next steps.

The pitch makes sense. Reduce low-value calls. Free up capacity. Improve customer experience at peak times. I am not arguing against the tools themselves.

But most dealers who adopt them have not asked the question that matters most: when an external layer ingests your operational history, who owns the derived insight? Not the raw data. The insight. And what other products can be built from it over time?

Service contracts and support bundles are the current retention play. They work, for now. But in an interoperable, AI-mediated world, lock-in gets recreated by whoever controls the insight layer. You do not have to own the machine or the relationship today to own the decision tomorrow. OEMs are not immune from this either. If AI can model performance and total cost across brands, comparative claims get sharper, purchase decisions get more spreadsheet and less legacy, and brand loyalty becomes a weaker defense than it used to be. 

So What's the Answer?

The stable door is wide open and the horse has long gone. These forces are not reversing. More restrictions on data access are not the answer, and frankly the regulatory direction makes them harder to sustain anyway.

The move is to compete at the level that is actually shifting.

Treat data partnerships like strategic alliances, not software purchases. Get explicit about derived insights in contracts, not just ownership of the raw records. Build the governance that makes openness sustainable rather than exploitable. And compete to be the customer's most trusted interpreter of their own operation, because that is the role that is actually up for grabs.

Dealers who become the execution layer while someone else becomes the decision layer will feel it first in workshop volume. Not in a headline. Not in a contract cancellation. Just a gradual drift in where customers go when they are ready to act.

The next decade will not kill the dealer. Trust, proximity, and someone who picks up the phone still matter enormously in this industry. But trust alone will not protect a model that stops being the place where decisions get shaped.

The winners will be the organizations that stay central to how decisions get made, not just to how problems get fixed.