The recent MAP Connected conference in Southfield, MI, highlighted the ongoing collision between product engineering and field service. When Mike Roberts, a conference leader and mentor, reached out in August 2026 to discuss my transition from an original equipment manufacturer software engineer to an auto repair shop owner, the core topic was clear. We needed to address how the phenomenon of No-Trouble-Found (NTF) followed me from the OEM engineering floor to the service bay, and how modern software-defined vehicles complicate these diagnostic realities.

While my presentation covered a specific portion of the event, the breadth of expertise across automotive, aerospace, insurance, heavy machinery, and software was striking. Warranty and quality challenges are frequently treated as unique to automotive circles, but the conference revealed these exact struggles are mirrored across multiple industries.

Beyond the formal sessions, the primary value lay in engaging with professionals from my former employers and colleagues. Running a small business naturally narrows your focus toward day-to-day survival, leaving fewer opportunities for discourse. Context switching among so many complex technical and economic topics was a welcome challenge. Stepping back into this environment proved that the analytical skills honed in product development adapt readily to the broader industry discussions happening today.

Highlight 1: AI is changing how we Communicate

Like many other industries, AI took center stage, but in a way that rarely makes it to social media feeds. The most notable development is the emergence of a new communication layer where artificial intelligence agents talk directly to other agents. If you spend time in automotive circles online, you have likely seen a popular video format: a warranty administrator arguing with a claims adjuster about the labor time and bill of materials for a vehicle service contract repair. These videos typically end with a frustrated administrator venting about an adjuster refusing to pay a legitimate claim. Soon, those human-to-human arguments will largely disappear.

Claims processing companies, which are contracted by service centers to maximize warranty returns, are now using large language models to construct and submit claims. These models are tuned specifically to navigate complex warranty rules and find valid paths to approval that a human might miss or lack the specialized knowledge to leverage. Memorizing hundreds of pages of warranty stipulations is an impossible task for a person, but it is exactly the type of workload software handles perfectly. Doctors offices and hospitals have done something very similar with claims processing operating entirely as a sub-contracted function.

Conversely, original equipment manufacturers and vehicle service contract providers are deploying their own language models. Their goal is to automatically scrutinize incoming claims and weed out submissions that ride the line between legitimate repairs and fraud. We are entering an era of adversarial AI in the service department, where the shop’s software attempts to maximize the claim while the payer’s software attempts to minimize the payout.

Highlight 2: Warranty Admin will be about beating the Machines

In many service centers, warranty repair is treated as a profit center rather than a simple medium of reimbursement for labor. Dealerships and independent shops profit not only from selling the vehicle service contracts themselves but also from the arbitrage between the labor performed and the reimbursement provided by the contract company or OEM. To maintain this revenue stream, service departments must invest in technologies capable of outpacing the automated systems deployed to monitor and adjudicate claims.

In theory, shops equipped with the most sophisticated language models will see higher claim approval rates. Securing those approvals prevents difficult conversations with anxious customers who expect their expensive extended warranties to cover large repair bills in full. Minimizing that friction directly improves Customer Service Index (CSI) scores. High CSI scores drive positive online reviews, which ultimately elevates a business in local search rankings and crowds out the competition.

Highlight 3: Quality & Warranty Organizations need to Transform their Roles

While AI, telemetry, digital twins, automated claim adjudication, process improvements, and over-the-air updates were the topics du jour, my thoughts returned to an observation I made a few years ago regarding how quality and warranty organizations have become incredibly isolated from product development. Many of the presented solutions focused on the containment of issues and managing costs post-launch. Few speakers discussed the importance of using these solutions to drive fundamental engineering changes in the product itself.

Quality and warranty teams are historically structured around appraisal and containment. Their core metric is minimizing financial exposure and isolating defects quickly, whereas engineering is measured by development timelines and hitting design specifications. These opposing incentives create isolated departments that prioritize their own operational survival over cross-functional defect prevention.

A key roadblock to effective integration is the data and its context. Warranty administration gathers transactional data to pay claims, resulting in right-censored, noisy, and delayed information. Engineering teams require log files and copious amounts of very specific telemetry directly associated with a failure. When quality organizations attempt to use field telemetry to force an engineering change, they frequently present aggregate failure rates rather than validated physical hypotheses. Because the feedback loop does not arrive in the native structural language of design engineers, it is easily dismissed.

Modern technologies provide an incredible opportunity for quality departments to become technical experts in analyzing customer complaints, understanding how the product fails to meet expectations, structuring compelling data-driven narratives, and defining how the engineering must change. Large language models do a credible job summarizing issues across social media, warranty transaction verbatims, customer support calls, and technical support documentation. Correlating telemetry to these complaints can be accomplished with existing technologies, provided it is targeted toward specific patterns of use rather than operating as a data firehose that causes further confusion.

Highlight 4: Role of Government in Rising Warranty Spend & Inflation

Historically, warranty rates for labor and parts were negotiated between franchised dealers, service centers, and OEMs. Because manufacturers act as bulk buyers, these negotiated rates were significantly lower than standard retail rates. However, a growing number of states now legally require OEMs to pay full retail rates for warranty work. This increase in warranty spend is not just hitting corporate bottom lines; it is creating indirect impacts on inflation that often remain obfuscated.

Because warranty repairs are fulfilled at no cost to the consumer, the transaction itself does not trigger an increase in the motor vehicle maintenance and repair category of the Consumer Price Index. The Bureau of Labor Statistics calculates the CPI by measuring actual out-of-pocket expenses paid by consumers for a specific basket of goods and services. Therefore, the immediate cost of these legislative changes is hidden from traditional repair inflation metrics.

Instead, the cost surfaces in the price of new vehicles. When states mandate retail labor rates rather than standard negotiated bulk rates, manufacturers experience a direct increase in operating costs. They account for this by building higher warranty reserves into the initial purchase price of the vehicle. This upward price pressure is eventually captured in the New Vehicles category of both the CPI and the Producer Price Index.

Furthermore, there is a secondary effect on inflation data driven by dealership behavior. Retail warranty reimbursement laws generally mandate that manufacturers pay the dealer based on the shop’s average customer-pay rate. Because franchised dealers know their customer-pay rate dictates their warranty reimbursement from the OEM, they are heavily incentivized to steadily raise their retail labor rates. Regular consumers then pay more out of pocket for non-warranty maintenance, and that subsequent increase in retail repair costs is directly captured in the motor vehicle maintenance and repair metrics of the CPI.

Highlight 5: New Cost Sharing Approaches might materially Increase MSRPs

Regulations are certainly one avenue for driving increased costs, but another is how warranty expenses are shared with the supply base. Each contract with a tier supplier generally outlines that they will share in the costs of warranty, often regardless of fault. These agreements have been standard for at least a decade and function as a baseline pay-to-play cost for doing business with an automaker. The intent behind these cost-sharing terms is to incentivize suppliers throughout the chain to remain invested in building quality products. If the OEM experiences lower costs related to field quality issues, suppliers retain more margin on their components.

However, structural changes to these agreements are currently being implemented across the industry. OEMs are increasingly moving toward guaranteeing a warranty bill for the supplier regardless of actual root-cause fault. When coupled with generally rising raw material costs, the downstream effect is that customers will continue to see a rise in new car pricing. Suppliers will rationally respond to this shifted liability by raising their initial component prices to hedge their own financial positions. Similar to the price effects driven by government regulations, the Producer Price Index will rise as these new risk-mitigation costs are baked directly into the parts and the vehicles themselves.

Highlight 6: We are struggling with Diagnosis & it’s a Big Problem

A consistent theme amongst OEMs, Suppliers, Technical Assistance Centers (TAC) and A consistent theme among OEMs, suppliers, Technical Assistance Centers, and repair shops is that we are replacing parts that are not defective. When functional parts are replaced, they are shipped back to suppliers and OEMs at significant expense, only to undergo costly testing that reveals they meet all engineering requirements and do not correlate with the customer complaint. The industry calls this phenomenon No Trouble Found (NTF).

If JD Power data is accurate, 25% of customers in their Customer Service Index study note the service center did not fix their problem the first time. Meanwhile, OEMs report, and my own analysis confirms, that NTF rates for certain electronic modules consistently exceed 50%. These rates are significantly higher for electronic modules than for mechanical components like control arms. How is it that only 25% of repairs fail on the first attempt, yet NTF rates are so staggeringly high? Several systemic factors drive this discrepancy.

First, Shop Management Software (SMS) and Dealer Management Software (DMS) function as accounting systems that treat customer issues as isolated transactions rather than comprehensive issue management platforms. If a customer presents with an intermittent blank screen and an auto stop-start malfunction, the software logs these as separate events. A loose ground attachment can cause both problems, yet it is notoriously time-consuming to trace. The software fails to capture the relationship if the shop replaces a battery in one transaction, an infotainment module in another, and repairs the ground in a third. If the ground is never fixed, the underlying root cause goes unnoticed as voltage drops trigger electronic control unit (ECU) resets across the network. OEMs and service providers simply cannot identify these hardware relationships when analyzing hundreds of thousands of fragmented accounting transactions.

Second, the data required for diagnosis is highly volatile. By the time parts are bench-tested, the contextual data surrounding the failure is gone. Unless vehicle telemetry was actively monitoring for a specific failure mode, the OEM has little actionable data for a postmortem analysis. Furthermore, software-related failures are largely obscured from the field. ECUs are treated as black boxes and replaced in the absence of an identifiable physical defect. These software failures are often hidden from the OEMs themselves, as engineers are hesitant to log extensive fault data in production modules to avoid memory wear-out and reliability concerns.

Third, the diagnostic procedures themselves create friction. Certain diagnostic trouble trees require technicians to swap in a known good module to test a circuit. However, modern ECUs must be programmed and cryptographically keyed to the vehicle. Once an ECU is installed, it cannot be returned without a financial loss to the dealer or the technician. Shops take the risk of returning a functional module under warranty because it is virtually impossible for the OEM to prove the ECU was not faulty at the time of replacement. Even if they could, states like California make the dealer debit process cost-prohibitive for the manufacturer to pursue.

Ultimately, I am confident the most critical root cause to address is the economic incentive structure. Flat-rate labor times incentivize the most efficient path to replacing a part rather than identifying the root cause. This model made sense when vehicles were primarily mechanical and problem-solving relied on linear, if-then logic. Modern automotive systems operate non-linearly. ECU functions are highly complex, and their behaviors change drastically based on environmental factors or micro-degradations in hardware that evade visual inspection.

A simple thought experiment frames this challenge well. Consider for a moment that you are responsible for diagnosing your own car. A vehicle rolls into your bay with a work order containing five words: screen goes blank every so often. The instrument cluster has displayed unrelated errors for months, which the customer never mentioned. You have roughly fifteen to twenty minutes of diagnostic time on the ticket to understand a distributed electrical architecture and isolate a ghost symptom that is not currently active. If you spend one minute past that threshold without an approved repair, you are working for free.

You connect the scan tool. The topology scan takes several minutes, returning twenty separate communication codes ranging from CAN timeouts to body controller faults. You check the OEM service information for a diagnostic tree covering an intermittent blank screen. Nothing exists. Twenty minutes in, you face a basic economic calculation. You can spend the next three hours mapping pinouts, checking grounds, and monitoring network traffic entirely unpaid, or you can replace the display module, submit the warranty labor operation, and earn the hours you need to pay your rent this week.

You replace the module. The claim clears. Two weeks later, that identical part arrives at an OEM warranty center, passes a bench test, and is cataloged as No Trouble Found. Meanwhile, the car rolls back into your bay with the exact same failure, only now you are tearing into the dashboard on an unpaid comeback. This is not a technician failure. It is an economic and technical architecture designed to manufacture parts-swapping and 50% to 80% NTF rates.

OEMs need to drive a culture change and hold dealers accountable for the poor quality repairs their compensation packages directly incentivize. OEMs may not own the franchise dealers, but they bear responsibility for allowing service centers to exploit the labor time system.

The industry is Broken, but not beyond Repair

There is an inherent irony that an industry built around repair struggles to mend its own operational brokenness. The discussions at the MAP Connected conference made it clear that the challenges we face in the service bay are no longer just mechanical. They are a complex intersection of adversarial artificial intelligence, outdated economic incentives, and a massive disconnect between product engineering and field service.

The technology required to solve these diagnostic and warranty challenges exists today. Large language models and targeted telemetry can bridge the communication gap between what a technician sees in the bay and what an engineer designs. The remaining hurdles are entirely cultural, relying on a willingness from OEMs, suppliers, and service centers to align their incentives toward actual problem-solving rather than isolated financial preservation.

The shift to software-defined vehicles has already occurred. The hardware and software architectures have evolved, but the economic frameworks supporting them remain stuck in the past. We are no longer waiting for an insurmountable technological breakthrough; we are simply waiting for quality assurance and serviceability to finally catch up.