How to Improve the Automotive Industry: 4 Moves First

In 2026, a practical priority order is to automate bottleneck plants, reduce supplier risk, harden software and cybersecurity, and then build workforce skills.

Skip that order and you invite downtime, warranty claims, cyber exposure, and launch slips. This guide lays out the 2026 roadmap, sequencing, trade-offs, and where leaders should spend first.

What should the automotive industry improve first in 2026?

Start with the projects that cut defects, downtime, and launch risk fastest. For most companies, that means plant automation at the bottleneck, supplier visibility on critical parts, tighter software controls, and a training plan that supports those changes. A useful planning horizon is roughly 90 days for quick wins and about 12 months for structural fixes.

Set the first 90 days around high-payback fixes

In the next 90 days, leaders should focus on projects that affect day-to-day performance, not just slide decks. The best early wins are line-side error proofing, predictive maintenance on the most failure-prone assets, supplier scorecards for critical components, and release governance for software updates.

A common first-timer mistake is spending the first quarter on broad digital plans before cleaning up data ownership. If a plant cannot trust its downtime codes, or a dealer group cannot trust service-status data, the automation program will produce reports faster than it produces savings.

Use a 12-month program for structural change

Over about a year, the workstream should cover robotics integration, battery-material sourcing contracts, cybersecurity architecture, and technician capability building. KPMG frames the industry shift around modern, intelligent, and resilient businesses, and it also names AI as a strategic theme. That fits the 2026 playbook, but only if AI is tied to measurable plant and service outcomes.

Automotive News is tracking a 2026 top 150 dealership groups list in the U.S., a reminder that dealer-group execution matters as much as factory execution. The same goes for inventory discipline. Automotive News says new-vehicle inventory reached 3 million units, while full-size and big-ticket truck supplies are climbing.

What to fix first by company type

Company type	First priority	Why it ranks first	90-day metric	12-month goal
OEM	Production-line automation at bottlenecks	Lower rework, faster throughput, steadier launches	Defect rate, downtime hours	Fewer line stops, shorter cycle time
Tier 1 supplier	Supplier mapping and quality containment	Reduces cascading shortages and warranty risk	Supplier OTIF, escape rate	Dual-sourced critical parts
Dealer group	Digital vehicle inspections and service workflow	Speeds approvals and improves trust	RO cycle time, estimate acceptance	Higher fixed-ops throughput
Aftermarket operator	Technician diagnostics and parts visibility	Shortens diagnosis and reduces bay waste	First-time fix rate	Higher labor efficiency

Why AI-driven manufacturing belongs near the top of the list

AI-driven manufacturing belongs near the top because it can reduce inspection misses, predict equipment failure, and improve scheduling before it touches customer-facing products. In 2026, the best use cases stay narrow: vision systems for defect detection, predictive maintenance for high-downtime assets, and planning tools that reduce line starvation.

AI pilots fail when plants treat them as software projects instead of process changes. They stall if camera placement is poor, if operators are not trained to respond to alerts, or if the maintenance team has no authority to act on predictive signals. If the model finds a fault but the work order sits idle, nothing improves.

📊 Automotive News says new-vehicle inventory reached 3 million units. Source: Autonews.

How can automakers strengthen supply chains in 2026?

Automakers strengthen supply chains in 2026 by mapping risk at the chemistry, region, and supplier levels, then building backup paths for the parts that can stop production. Lithium, cobalt, and nickel need separate strategies because their failure modes differ. Inventory buffers help, but visibility and dual sourcing matter more.

Lithium supply chain exposure and regional sourcing risk

Lithium supply chain risk is about continuity, not just cost. Battery raw material security depends on regional sourcing, processing concentration, and transport reliability. If a plant depends on a single corridor for cells or precursor materials, EV output can be interrupted even when final assembly appears healthy on paper.

Leaders should track days of cover for cathode inputs, supplier concentration by region, and the share of volume tied to one refiner. A practical target is to reduce single-region dependency for critical battery inputs before launch ramps, not after shortages show up on the assembly schedule.

Cobalt supply chain, ethics, and chemistry dependency

Cobalt supply chain planning needs a separate lens because chemistry choice and ethics are both in play. Some battery chemistries depend on cobalt more than others, which makes material substitution a product decision, not just a purchasing decision. Ethical sourcing and geopolitical concentration also shape continuity risk.

Recycling is part of the answer, but it is not a short-term cure. It helps reduce long-run exposure and can support responsible sourcing claims, yet near-term continuity still depends on supplier audits, chemistry mix, and contingency contracts.

Nickel supply chain and cost stability

Nickel supply chain strategy should focus on procurement resilience and price stability. Nickel is a battery input with meaningful cost volatility, so buyers need diversified suppliers, indexed contracts where suitable, and clear substitution rules across programs. That matters most when EV volumes rise and chemistry choices shift.

Suppliers should track not only unit price, but also delivery reliability and country concentration. If the commercial team wins a lower quote but adds a weak logistics node, the program may save cents and lose weeks.

Inventory, dual sourcing, and supplier visibility metrics

Use a small set of operating metrics: supplier on-time-in-full, inventory turns, days of supply for critical parts, and open corrective actions by tier. For OEMs and Tier 1s, the biggest gain usually comes from dual-sourcing the parts that stop a launch, not the long tail of low-risk items.

How should the industry adapt to software-defined vehicles?

The industry should adapt by treating software-defined vehicles as a governance problem, not only an engineering one. Over-the-air updates, software monetization, and feature rollouts create new revenue options, but they also increase validation burden, cross-team coordination, and customer trust risk. Release discipline is now part of vehicle quality.

Over-the-air updates and release governance

OTA updates generally need signed packages, staged rollouts, rollback plans, and clear ownership between engineering and service. If release governance is weak, one faulty calibration can create warranty cost across a large installed base. Software-defined vehicles magnify small mistakes because the fleet keeps growing after sale.

Software monetization without damaging customer trust

Monetization works only when customers understand what they are buying. Connected features should be priced and explained clearly, with feature availability tied to the vehicle’s hardware path and service support. Hidden subscriptions or surprise paywalls damage trust faster than they lift recurring revenue.

Cross-functional development and validation bottlenecks

Software-defined vehicles require hardware, software, quality, cybersecurity, and service teams to work from one release calendar. That creates coordination burden, especially when validation labs are already full. If the architecture is overly complex, integration queues lengthen and launch timing slips.

Picture a mid-size OEM pushing a new infotainment release, a brake-control calibration, and a dealer diagnostic update in the same quarter. If those teams do not share requirements early, they can each pass internal checks and still fail at fleet level.

Which manufacturing changes improve quality and efficiency fastest?

The fastest quality and efficiency gains usually come from automation at repeatable bottlenecks, not from blanket factory redesign. Robotics, machine vision, and digital work instructions cut variation where the task is stable and high-volume. Material changes can help too, but they pay off on a longer cycle.

Automation and robotics in assembly lines

Automation and robotics improve throughput and reduce operator error when the task is repetitive, hazardous, or highly sensitive to variation. The best first targets are welding, material handling, adhesive application, and inspection stations that trigger rework. Capital intensity is high, so payback should be measured by labor relief, scrap reduction, and line uptime.

The wrong move is automating a messy process before standardizing it. If the work cell changes every week, robots will reflect that instability instead of fixing it.

Material innovation with ultra-high-strength steels

Material innovation matters because lighter structures can improve efficiency without giving up safety. Ultra-high-strength steels are especially relevant in body-in-white and chassis applications, where stiffness, crash performance, and mass all matter at once. The trade-off is that higher-strength materials can raise forming complexity and tooling demands.

Use them where the structural benefit is clear, not everywhere. An indiscriminate swap can add cost, complicate repair, and create joining challenges if the plant is not ready.

Body-in-white and chassis trade-offs

Body-in-white programs should weigh weight reduction against manufacturability, repairability, and capital cost. Chassis parts may justify stronger or lighter materials when they improve range, handling, or load-bearing performance. The right choice depends on volume, platform life, and whether the supplier base can support the new material stack.

What role will AI play in automotive manufacturing in 2026?

AI will play a practical role in inspection, predictive maintenance, scheduling, and root-cause analysis. It will not replace process discipline. The companies that benefit most in 2026 will be the ones that use AI to reduce rework, prevent unplanned downtime, and speed decisions at the plant edge.

Where AI helps most in plants

AI is most useful where large amounts of repeatable data already exist. That includes camera-based quality control, anomaly detection on machinery, and scheduling models that balance labor, parts, and line constraints. KPMG’s emphasis on AI fits here, but only if the use case is tied to a plant KPI, not a generic innovation goal.

Where AI programs commonly break down

AI efforts fail when data labeling is inconsistent, plant systems do not integrate, or frontline users do not trust the output. They also fail when leaders expect a single model to fix poor maintenance or bad process design. AI can point to the problem. It cannot create standard work by itself.

How do Chinese automakers pressure global competitors in 2026?

Chinese automakers pressure global competitors in 2026 through faster cost scaling, tighter product cadence, and lower-friction EV packaging. Automotive News reports Chinese automakers are closer to selling in Canada, and it also says tariffs will not close the cost gap between Chinese EVs and European rivals. That keeps pressure on pricing and speed.

What that means for OEMs and suppliers

Global OEMs need shorter engineering cycles, simpler variants, and more disciplined platform reuse. Suppliers face more price pressure and shorter timing windows. Dealers feel it in product mix and in customer comparisons, especially when fuel-efficient vehicles are flying off lots as gas prices rise and shoppers compare total ownership costs.

How to respond without chasing low margin volume

The answer is not to copy every feature at every price point. It is to remove avoidable complexity, protect margin on core models, and speed launches on vehicles that meet local demand. Automotive News says EVs still cost more to own, but the gap is closing, which makes disciplined positioning more important, not less.

What cybersecurity steps should the automotive industry take in 2026?

Cybersecurity in 2026 should center on secure development, supplier access control, OTA update security, and incident response. Vehicle software protection is now a product-quality issue, a compliance issue, and a service issue. A weak link in one supplier account can become a fleetwide problem.

Vehicle software protection and secure development practices

Build security into the development lifecycle with code review, dependency tracking, penetration testing, and hardware root-of-trust where appropriate. Software teams should treat security defects like functional defects. If a vulnerability reaches production, the cost of patching rises fast across engineering, service, and customer support.

Supplier access controls and third-party risk

Limit supplier access to only what each party needs. Use segmented credentials, logging, and periodic review of dormant accounts. Third-party risk matters because modern vehicle programs involve many partners, and one exposed portal can open access to release tools, diagnostics, or calibration data.

OTA update security and incident response

OTA update security needs signing discipline, validation gates, and clear rollback authority. Leaders should also run incident-response drills that include engineering, legal, service, and communications. The goal is simple: know who can pause a release, who investigates, and how to restore fleet confidence if an update goes wrong.

How can the automotive industry improve in 2026?

The industry can improve in 2026 by sequencing the work around highest-return operating fixes, not broad slogans. That means plant automation, supply-chain de-risking, software hardening, and workforce capability, in that order. The roadmap changes by company type, and the funding mix should too.

2026 automotive improvement priority matrix

Initiative	Impact	Cost	Time to implement	Risk reduction	Best fit
Bottleneck automation and robotics	5	5	3	4	OEM, large Tier 1
Machine-vision quality control	5	3	4	4	OEM, Tier 1, aftermarket
Dual sourcing for battery inputs	5	3	2	5	OEM, Tier 1
OTA security and release governance	4	3	3	5	OEM, dealer groups
Digital vehicle inspections	4	2	5	3	Dealer, aftermarket
Continuous technician training	4	2	4	4	Dealer, aftermarket
Ultra-high-strength steel redesign	3	4	2	3	OEM

Use the matrix this way: fund the 5s first, staff the 4s next, and push the 3s into the longer platform cycle. A 5 on impact and risk reduction deserves attention even if the cost is moderate. A 3 with long lead time should not block near-term savings.

Continuous technician training and service capability

Training should be continuous, not annual and generic. Technicians need EV service knowledge, diagnostics competency, and access to updated procedures as software and powertrains change. KPMG’s Learning Academy is one example of the kind of skills infrastructure that companies will need, but the important point is cadence, not branding.

Partnerships with schools and vocational centers matter because the talent pipeline is not large enough to solve itself. A dealer group that trains for EV diagnostics, high-voltage safety, and software-based troubleshooting will spend less time with stalled bays and repeat visits.

Digital vehicle inspections for dealer and service operations

Digital vehicle inspections improve customer communication by adding photo and video documentation to the repair order. They also make it easier to show defects, prioritize work, and shorten approval cycles. For dealer groups, that can tighten workflow and raise trust at the same time.

Suppose a service lane has a 20-car backlog and every estimate needs a visual confirmation. A photo-based inspection package can move the decision from a phone call to a documented screen review, which cuts back-and-forth and reduces missed upsell opportunities.

What new automotive technology will matter most in 2026?

The most important technologies in 2026 are the ones that improve manufacturing control, software governance, and battery continuity. AI-driven inspection, OTA management, digital service tools, and battery-material traceability are likely to matter more than flashy features that do not change operating performance.

Frequently asked questions

How should automakers adapt to EV and hybrid strategy shifts in 2026?

They should align powertrain plans with battery supply, ownership-cost trends, and regional demand. Automotive News says EVs still cost more to own, but the gap is closing, and fuel-efficient vehicles are flying off lots as gas prices rise. That points to flexible portfolios, not rigid bets.

What workforce skills are needed to improve the auto industry in 2026?

The most needed skills are diagnostics, software literacy, high-voltage safety, data interpretation, and process discipline. Technicians, engineers, and managers all need faster feedback loops. Companies that pair training with real equipment, real software, and real service workflows will improve faster than those that stop at coursework.