fter-sales demand for HVBs could become an unprecedented issue for the automotive industry.
The EV battery is an auto part like any other and thus has a natural after-sales demand. Degradation over time below a certain level of SOH (state of health) – typically around 70% – would require replacements, on warranty or otherwise, depending on its mileage and the number of years driven. Recalls, accidents, and unexpected failures all add to the after-sales demand, similar to other parts in a vehicle.
However, the high prices of EV batteries and the massive investment required to produce them bring unprecedented challenges to the industry. These are further compounded by the rapid evolution of battery technology, which makes a battery outdated within a decade, as well as the difficulty in transporting and storing them, making conventional after-sales procedures unsuitable.
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urope's and North America's automotive sectors are under increasing pressure as Chinese OEMs rapidly produce high-quality vehicles at significantly lower costs, especially in markets where price sensitivity is rising. How to leverage AI for Automotive Manufacturing?
In manufacturing alone, cost gaps can reach 60–75%, with direct labor being a major driver. Labor costs in Europe are eight to ten times higher than in Asia, yet the output per worker is falling behind. While China recorded a 4% increase in labor productivity between 2023 and 2024, Germany saw a 4.3% decline from 2024 to 2025. However, a significant portion of the cost gap is controllable through automation, tooling, facilities, and equipment choices.
Figure 1 – Labor productivity China (2023-2024) and Germany (2024-2025)
Source: Berylls by AlixPartners
Furthermore, the shift to EVs has eroded traditional advantages of established OEMs. EV designs are typically less complex than ICE designs, enabling more streamlined assembly and higher levels of automation. However, many legacy OEMs operate out of brownfield plants and rely on production systems originally designed for ICE vehicles. While these legacy setups once represented operational excellence, they now often come with rigid layouts, outdated tooling and equipment, and limited digital integration. These structural constraints limit attempts to modernize brownfield environments because they typically cause high costs and operational disruption. Implementing automation or reconfiguring lines for EV-specific purposes is not only technically challenging but also financially demanding. Consequently, legacy OEMs are less efficient than purpose-built greenfield plants, which further widens the productivity and cost gap. These dynamics also extend to automotive suppliers, who face similar challenges.
Advanced manufacturing, including the use of AI, can help close the gap. However, these solutions are not plug-and-play. Value depends heavily on solid foundations, such as clean data, redesigned processes, and personnel who have been upskilled. A useful rule of thumb is that in AI programs, roughly 10% of the benefit stems from the algorithm, 20% from technology and data, and 70% from people applying insights to day-to-day decisions. This ratio underscores a critical truth: the applicability and success of AI depend heavily on organizational capabilities and employee AI literacy. Although AI has evolved from a buzzword to a necessity due to its increasing application, upskilling the workforce remains limited. The question is no longer whether AI is helpful, but rather, how to quickly deliver pragmatic, measurable impact.
Figure 2 – Where AI value really comes from
Algorithm
Technology & data
People
Source: Berylls by AlixPartners
In automotive plants, the largest portion of controllable losses occurs during the “make” phase of the supply chain due to process instability, scrap, micro-stops, and cycle time variance. These issues directly impact profit and loss. AI improvements directly translate into cost savings and increased throughput per vehicle without requiring new platforms or supplier renegotiations. The impact is tangible: reductions in scrap of 10-30%, improvements in first-pass yield of 5-10%, and reductions in unplanned downtime of up to 50% – often within a single quarter. By reducing breakdowns and rework, AI maximizes output from existing assets, defers capital expenditures, and unlocks capacity before new equipment is needed. These improvements have a fast time to value because the necessary data – from MES, PLC, vision systems, and quality records – is already available in most plants. AI solutions are designed to support existing operations rather than disrupt them.
Instead of invasive line rebuilds, manufacturers benefit from tools like AI-powered set-point guidance, which are systems that continuously analyze production data to recommend the optimal machine settings for quality, speed, and efficiency. These tools support human operators and automated systems, helping them make better, faster decisions and improve performance without interrupting workflows. Predictive maintenance builds on this foundation to take operational efficiency a step further. Leveraging AI and Internet of Things (IoT) technologies, manufacturers can anticipate equipment failures before they occur. Machine learning algorithms detect patterns in historical and real-time data that indicate early signs of wear or malfunction, and anomaly detection models flag unusual behavior. This approach is particularly valuable for rate-limiting assets, helping manufacturers avoid unplanned downtime, maintain consistent throughput, and extend equipment life. Another predictive use case is AI-driven material ordering, which forecasts subcomponent shortages by analyzing production schedules and supplier lead times. This helps minimize bottlenecks and maintain a steady flow of materials for uninterrupted assembly.
These manufacturing advancements improve not just plant-level performance but also have a ripple effect across the entire supply chain. More stable production enables better forecasting, more reliable delivery schedules, and fewer returns. Thus, AI-driven optimization of manufacturing processes becomes a strategic lever for driving systemic improvements, transforming not just operations, but also the broader ecosystem they support.
The transition from concept to impact is difficult, not because AI models are inadequate, but because most automotive plants are designed for launches, firefighting, and cost reductions, rather than data-driven operations. Systems, processes, and structures were never designed with AI in mind. To identify these gaps, we apply two sequential lenses:
The Operations Maturity Assessment identifies structural bottlenecks in current performance, such as chronic bottlenecks, scrap hotspots, and launch instability, and prioritizes areas where AI can have the greatest impact. The second is the AI Readiness Assessment, which evaluates seven capabilities across more than 100 criteria to determine whether a plant can reliably scale those use cases beyond a single line or program.
Figure 3 – AI Readiness Assessment
Source: Berylls by AlixPartners
1. Data readiness is limited by fragmented legacy systems. Shop-floor data is static and is only collected at the start and end of processes. Resource analysis focuses on past states and requires significant manual effort. Poor quality, limited accessibility, and minimal external integration further hinder data readiness, creating a major barrier in AI projects.
2. Automotive plants typically run on a patchwork of technical infrastructure, including outdated PLCs and aging MES releases locked into vendor contracts. Any change to a line recipe, interface, or dashboard requires weekend downtime and long validation cycles. Cloud adoption is often blocked by corporate security or cost concerns. Consequently, promising AI pilots reside on “shadow IT” laptops and local servers, while the official production stack cannot absorb or operate them at scale.
3. Process integration is rigid and fragmented. Decision-making is rarely digitized, interoperability is weak, and KPI tracking inconsistent. Scaling AI from pilot to production is slow due to the lack of templates and limited flexibility.
4. Operational technology and machine connectivity in manufacturing are often fragmented, especially in brownfield environments. Production equipment is rarely fully integrated, and inconsistent machine data limits visibility into cycle times or process parameters. Quality systems are siloed, making end-to-end traceability and automated documentation difficult. Sensor and IoT coverage is often incomplete, and real-time data transmission rarely in place. Furthermore, logistics data integration is minimal, leaving gaps in material movement tracking and predictive analytics for inventory optimization. These shortcomings create a serious bottleneck to achieving seamless connectivity and AI-driven efficiency.
5. Core IT capabilities are often geared toward transactional stability, not analytics and AI. Even when business cases are strong, IT lacks the tools and skills to operate AI models like any other critical service – with monitoring, versioning, rollback, and standardized deployment patterns.
6. AI in manufacturing is not just a technical line upgrade but a cultural shift within the organization. Many firms lack a clear AI strategy, defined roles, and sufficient budgets. Workforce upskilling and change management remain major gaps.
7. Cost optimization in brownfield plants faces structural challenges. Retrofitting legacy systems often drives high upfront costs and consumes scarce engineering capacity. These investments compete for capital with pressing operational priorities – such as new tooling for OEM launches, mandatory safety upgrades, and overdue equipment replacements. Adding sensors and connectivity to older equipment is expensive and requires significant capital investment. The expense is hard to justify without a clear business case that demonstrates measurable production benefits – fewer line stops, lower scrap rates on critical parts, faster ramp-up for new launches, or reduced warranty risks. Without these outcomes, projects are often viewed as discretionary “IT spend” and become easy targets for budget cuts.
The challenges are real – fragmented data, legacy systems, unclear strategy – but so are the opportunities. Many plants already have valuable data sources, experienced teams and proven technologies in place. Often, it is a matter of connecting the dots and aligning efforts. Our structured approach combines the Operations Maturity Assessment and AI Readiness Assessment. While the Operations Maturity Assessment identifies gaps and prioritizes high-value areas, the AI Readiness Assessment evaluates each plant and its processes across seven key capabilities using a 1-5 scale. Together, these frameworks provide transparency on both current maturity and future opportunities. The result is a clear path forward:
If you want to understand where you stand today, which levers will improve performance the fastest, and how AI can make that happen, let’s get in touch.
Managing after-sales EV batteries – unlike any other spare part in the traditional automotive ecosystem Munich, December 2025 A fter-sales
Wir sprechen über die Überschneidungen und Möglichkeiten der Automobilindustrie im Bereich stetig wachsender Verteidigungsausgaben.
Gäste sind Stefan Schneeberger (Associate Partner) und Christian Leber (Partner)
Europe’s and North America’s automotive sectors are under increasing cost pressure. Advanced manufacturing, including the use of AI, can help close the gap.
hina kontrolliert nicht nur Magnetmaterialien, sondern auch das Nadelöhr der Batteriewertschöpfungskette, was die europäische Produktion von Automobilkomponenten ins Wanken bringt.
Seit Monaten drosselt China den Export seltener Erden, was die europäische Produktion von Automobilkomponenten ins Wanken bringt. Bereits im Frühjahr führten Exportbeschränkungen zu Produktionsausfällen bei der E‑Motoren-Herstellung einiger OEMs, nun gibt es erneut Ausfälle. Doch das eigentliche Risiko liegt tiefer: China kontrolliert nicht nur Magnetmaterialien, sondern auch das Nadelöhr der Batteriewertschöpfungskette. Wer glaubt, die aktuelle Krise sei auf seltene Erden begrenzt, irrt – die nächste Stufe der Eskalation betrifft Lithium, Kobalt und Co.
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Managing after-sales EV batteries – unlike any other spare part in the traditional automotive ecosystem Munich, December 2025 A fter-sales
Wir sprechen über die Überschneidungen und Möglichkeiten der Automobilindustrie im Bereich stetig wachsender Verteidigungsausgaben.
Gäste sind Stefan Schneeberger (Associate Partner) und Christian Leber (Partner)
Europe’s and North America’s automotive sectors are under increasing cost pressure. Advanced manufacturing, including the use of AI, can help close the gap.
Raw Materials • Rare Earths • Chips. They’ve all disrupted production before. None are secured for the future. The risk of recurrence? Higher than ever.
From lithium for batteries to palladium for catalysts – raw materials define the limits of future mobility.
“Rare earths remain the automotive Achilles’ heel – Europe thinks it has a pathway to reduce dependency, but China’s choke points are structural, not cyclical”
How the headline(s) really impact the price:
Racing Toward a Chip Crunch: SDVs and AI Set the Stage for
2027 Shortage
Supply Chain Risks – Automotive Left Behind: AI Chips Win the Capacity Race
This Podcastepisode was recorded and published in September 2025 by Insights On Air, a Podcast by Berylls by Alix Partners
he automotive industry is at a structural turning point. For decades, growth masked inefficiencies: after the financial crisis, OEMs rebuilt their sales and marketing organizations, adding layers of regional and national headcount to capture rising volumes.
The transformation imperative is being fueled by four powerful forces:
Together, these forces mean that traditional sales structures—often centralized, overlapping, and headcount-heavy—are no longer fit for purpose.
Deep Dive Declining Sales:
The end of growth through growing competition drives the necessity to reorganize Sales Organization across the entire sales chain
¹ Top-12 OEM established OEMs including Toyota, VW Group, Hyundai, Renault-Nissan-Mitsubishi, Stellantis, GM, Ford, Honda, Suzuki, BMW, Mercedes, Mazda
Source: AlixPartners / Berylls by AlixPartners analysis; S&P(IHS)
“Streamlining retail standards across corporate groups and maintaining tight budget control are becoming essential for operational efficiency and profitability.”
- CSO, Volume OEMThe Sales & Marketing organizations of the Top-12 established OEMs employ around 95,000 people today. But the landscape is shifting fast. With declining sales volumes, overcapacities are becoming visible – an estimated 7% of roles are at risk. In parallel, efficiency gains of about 8% can be unlocked through new operating models, automation, and the increased use of digitalization and AI.
However, the picture is not only about reductions. Roughly 2% of future demand will be created by the need for new capabilities – in areas such as AI, data analytics, and digital expertise. This signals a clear direction: while traditional roles will decline, demand for tech-driven skills is growing rapidly.
In total, the expected future demand is around 84,000 FTEs, reflecting a net decrease of 12%. The implications are clear – OEMs will need to actively manage this transition, balancing efficiency gains with investments in reskilling and capability building. At the same time, further job losses are likely to occur at service providers, as OEMs continue to optimize their own headcount by outsourcing.
Estimated own Sales & Marketing FTE of Top-12 established OEMs1 and future demand (in k FTE)
¹ Top-12 OEM established OEMs including Toyota, VW Group, Hyundai, Renault-Nissan-Mitsubishi, Stellantis, GM, Ford, Honda, Suzuki, BMW, Mercedes, Mazda
Source: AlixPartners / Berylls by AlixPartners
At the same time, market dynamics are diverging. Regional regulatory frameworks, shifting customer expectations, and competitive pressures demand more localized, agile approaches. OEMs face a dual challenge: reduce structural overcapacity while equipping organizations with the digital and analytical skills needed to compete in a more fragmented, data-driven environment.
To respond, OEMs are experimenting with new organizational blueprints:
1. Market-led – full local ownership and accountability, giving national sales companies (NSCs) end-to-end responsibility.
2. Region-led – consolidated regional hubs with lean field structures, balancing efficiency with local responsiveness.
3. HQ-led – centralized control with AI-enabled processes, shifting many functions directly to HQ for scale and cost advantages.
Source: AlixPartners / Berylls by AlixPartners
Each archetype comes with trade-offs. The right choice will depend on market exposure, product portfolio, and the degree of control OEMs want to retain over pricing and customer data. But across all models, the trend is clear: leaner structures, fewer overlaps, and stronger digital enablement.
Rightsizing should not be equated with simple headcount reduction. While efficiency gains will reduce structural costs—AI-driven automation alone could replace up to 8% of current sales roles—the true opportunity lies in capability building. OEMs must reinvest savings into future-critical skills:
In this context, rightsizing is less about cutting fat and more about building muscle: creating lean, capability-driven sales organizations that can outperform in a digital-first, regionally fragmented market.
The future sales operating model will combine the best of both worlds—dealers, direct-to-consumer, and agency approaches—tailored by market. It will decouple regional development, allowing greater autonomy where needed, while leveraging AI, automation, and data-driven processes at scale.
OEMs that succeed will not only reduce costs but also unlock efficiency gains, improve customer retention, and ensure they remain competitive in a world where new entrants and tech-driven players are redefining the rules of automotive sales.
The end of growth as usual means OEMs can no longer afford bloated, overlapping structures. Rightsizing is inevitable—but its true value comes when it is combined with capability building. By aligning organizational size with market realities and reinvesting in digital skills, OEMs can transform sales organizations from cost centers into competitive weapons.
The winners of tomorrow will be those who move fastest today: slimming down, smartening up, and preparing their salesforce not just for survival, but for sustained advantage in a transformed industry.
or years, automotive OEMs have looked to new (digital) profit pools—subscriptions, in-car services, and data monetization— beyond the traditional vehicle sales as the next frontier of growth.
“Most experiments have failed or remained marginal, especially in the U.S. as a conservative market – car buyers still want things like XM satellite radio.”
- CEO USA, Premium OEMThe core insight is simple: owning the vehicle beyond the first sale generates up to 1.5x more profit per unit than the traditional one-time transaction model. Multi-cycle vehicle allocation (e.g., Vehicle-as-a-Service (VAAS)) allows OEMs to keep cars in their portfolio longer, extend their revenue opportunities across multiple owners, and continuously re-engage customers through recurring contracts.
By retaining assets in their portfolio, OEMs and Captives can expand their customer base while generating up to 1.5x more profit per vehicle
Source: AlixPartners / Berylls by AlixPartners
Each cycle—be it a subscription, a used car lease, or a second-life credit program—adds incremental margin from aftersales, finance, and services. While individual VAAS contracts may be less profitable than a new sale, the aggregated profit across three or more cycles surpasses traditional models significantly. In one case example, a single vehicle generated over €6,000 of cumulative profit across three lifecycle stages, compared to less than €4,200 in a one-off new car sale.
“Loyalty programs are the most powerful driver of incremental revenue and customer retention –ensuring clients stay within the brand ecosystem even after a change of vehicle ownership.”
- CSO, Volume OEMThe benefits extend beyond direct financial gain only. Retaining control of vehicles creates recurring customer touchpoints, lowering acquisition costs and strengthening brand loyalty. Research shows that keeping existing customers loyal is 5–10 times less costly than acquiring new ones. Multi-cycle models also create natural upsell opportunities: a driver who leases a used EV today may be primed for a new model subscription tomorrow.
The indirect benefits compound over time. Satisfied customers generate word-of-mouth recommendations, lower churn, and reduce the need for costly marketing campaigns. They also prove less price-sensitive, enabling more dynamic pricing with fewer discounts. For OEMs, this translates into higher returns on marketing spend, more efficient customer service, and stronger employee engagement.
Financial services sit at the heart of lifecycle control. Leasing, credit, insurance, and bundled service contracts not only generate stable profit streams but also tie customers into the OEM ecosystem. For BEVs, financing innovation is particularly critical:
Such offerings directly address some of the biggest barriers to BEV adoption — price, residual uncertainty, and maintenance anxiety—while keeping OEMs connected to vehicles across successive owners.
Aftersales remains the backbone of OEM profitability, often accounting for around half of industry profits. However, the transition to EVs threatens this foundation, as BEVs require fewer traditional service interventions. OEMs must respond by redefining aftersales: predictive maintenance, software upgrades, battery diagnostics, and connected services can replace declining mechanical revenue streams.
By embedding these services into financing contracts or subscription packages, OEMs can transform aftersales into a future-proof, digitally enabled profit pool.
The message is clear: the future of automotive profitability lies less in speculative new revenue pools and more in mastering the full lifecycle value of each vehicle. OEMs that retain ownership and customer connection across multiple usage cycles can protect margins, stabilize BEV adoption, and build resilience in an increasingly competitive market.
The winners will be those who treat vehicles not as one-off transactions but as recurring platforms for value creation—from first registration to reuse and recycling. By shifting to multi-cycle models, OEMs can unlock stronger profits, deeper customer loyalty, and a sustainable path to long-term growth.
Managing after-sales EV batteries – unlike any other spare part in the traditional automotive ecosystem Munich, December 2025 A fter-sales demand for HVBs could become
Wir sprechen über die Überschneidungen und Möglichkeiten der Automobilindustrie im Bereich stetig wachsender Verteidigungsausgaben.
Gäste sind Stefan Schneeberger (Associate Partner) und Christian Leber (Partner)
Europe’s and North America’s automotive sectors are under increasing cost pressure. Advanced manufacturing, including the use of AI, can help close the gap.
Featured Insights
he automotive sustainability narrative currently seems to stand at a critical crossroad as progress on decarbonization and fleet emission reductions slows down.
In this discussion paper, we highlight the influencing factors that lead to the recent cooldown of sustainability efforts. While economic pressures increasingly impair financial headroom of industry players, we argue – though certainly an inconvenient truth – that a departure from sustainability actions would not only put an end to progress achieved so far, but also harm OEM and supplier sustained competitiveness.
1. The automotive industry demonstrates measurable progress in sustainability.
OEMs have significantly reduced their direct emissions (Scope 1: –18%, Scope 2: –58% since 2018) and lowered average EU fleet emissions by 8.3%. These achievements underscore that sustainability has become a core performance indicator across the industry.
2. Sustainability ambitions are losing momentum.
Despite notable progress, around 50% of OEMs have recently scaled back, postponed, or abandoned their electrification targets. This trend risks undermining achieved gains and delaying the realization of scale effects in the transformation process.
3. Four global forces are slowing down the transformation.
Heterogeneous global customer demand (especially China vs. Rest of World), divergent regulations, rising geopolitical tensions (tariffs, wars, supply bottlenecks), and the ascent of Chinese competitors are putting pressure on margins and complicating investment decisions. OEMs are thus challenged to advance sustainability in an environment of declining planning certainty.
4. An inconvenient truth: Sustainability remains a strategic imperative.
In the face of global headwinds, OEMs cannot afford to scale back their sustainability efforts. Instead, they must respond with regionally differentiated strategies and flexible investment approaches. Those who ease off now risk competitive disadvantages, reputational damage, and limited access to capital—particularly when competing with consolidated, scale-driven Chinese players.
5. Sustainability as a management tool and value driver.
Integrating environmental and ESG metrics into core processes, governance, and product development is becoming a prerequisite for investor confidence, resilience, and growth. Companies that embed sustainability as a fundamental element of their management systems will not only ensure compliance but also unlock long-term economic value.
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Featured Insights
treamlining the Operating Model for the Software-Defined Vehicle is necessary for OEMs who want to unlock their R&D Performance for the future
WHY now is the time to act to unlock R&D Performance
The rise of software-defined vehicles (SDVs) is reshaping the economics of the automotive industry. Software has become the primary driver of differentiation and profit, while digital-native OEMs, particularly from China, set the benchmark with weekly OTA releases, vertically integrated organizations, and cost structures that are up to 40% lower than those of their legacy peers. An example of this can be seen in the quadrupling of the market share of Chinese OEMs in Europe since 2020. Without a fundamental redesign of their R&D operating model, incumbents risk not only falling behind but also becoming irrelevant.
WHAT it takes is a dual transformation of the R&D operating model to unlock performance
Winning in the SDV era depends on two essential shifts: First, a strategic transformation that aligns the operating model with SDV-driven product strategy, and second, an operational transformation that delivers speed and efficiency by simplifying structures, accelerating workflows, and optimizing workforce setup. The SDV.OM (Operating Model) Framework, developed by Berylls by AlixPartners and the Institute of Technology Management at the University of St. Gallen, builds on insights from leading industry executives and provides practical guidance for assessing and transforming R&D operating models. Structured around six key dimensions and corresponding 18 action fields, it focuses on the KPIs that truly matter. This enables fact-based R&D performance baselining and direct benchmarking against SDV front-runners.
HOW to turn strategy into measurable performance when it comes to R&D performance
The SDV.OM Framework provides a clear path to steer the dual transformation of R&D organizations. The process begins with preparation, involving the alignment of key decision-makers and the establishment of a validated fact base across the six operating model dimensions. It continues with analysis, benchmarking against SDV front-runners to expose the execution gaps that matter most. Finally, it drives transformation by closing these gaps with targeted measures, clear ownership, and continuous steering via the SDV.OM Framework. Applied with discipline, the playbook enables CTOs to turn strategy into measurable R&D performance.
Managing after-sales EV batteries – unlike any other spare part in the traditional automotive ecosystem Munich, December 2025 A fter-sales
Wir sprechen über die Überschneidungen und Möglichkeiten der Automobilindustrie im Bereich stetig wachsender Verteidigungsausgaben.
Gäste sind Stefan Schneeberger (Associate Partner) und Christian Leber (Partner)
Europe’s and North America’s automotive sectors are under increasing cost pressure. Advanced manufacturing, including the use of AI, can help close the gap.
attery electric vehicle (BEV) adoption is growing, but the momentum is uneven. While digital interest in BEVs is high—often three times higher than for internal combustion vehicles—this enthusiasm does not yet translate into retail BEV sales.
In most major European markets, it is fleet customers, not private buyers, who are driving Battery electric vehicle adoption. In Germany and the UK, more than 70 percent of new BEV registrations already come from fleet channels. For OEMs, fleets are therefore not just a sales engine, but a critical gateway to private demand.
Despite rising visibility, private adoption of BEVs remains constrained by a set of recurring barriers. Purchase prices (MSRP) are still up to 30 percent higher than comparable ICE models. Residual values lag as much as 32 percent behind ICE equivalents, undermining consumer confidence in resale. Charging infrastructure continues to be insufficient or unreliable, particularly outside urban centers, fueling range anxiety. On top of this, customers often face limited retail guidance—dealers are frequently better trained (and/or incentivized) to sell ICE products than to reassure hesitant EV buyers.
These obstacles are not just perception issues; they have tangible financial impact. In China, for example, an intensifying price war has pushed average BEV prices down by 30 percent in just twelve months, leaving consumers wary of resale stability. BYD in China offers the model Seagull for less than 7,000 € (vs. 22,990 in EU). In Europe, cross-market comparisons show BEVs carry a visible MSRP premium, leading many private buyers to abandon them early in the consideration funnel—even if leasing rates are sometimes more favorable – up to +50% less compared to equivalent ICE models.
(See also BEV Study 2025)
¹ Basic entry price for the vehicle class without consideration of comparable equipment features ; Excluded VAT and additional fees;
² 0€ deposit/ 10k km p.a.; 36month leasing period
Source: AlixPartners / Berylls by AlixPartners analysis; Leasingmarkt.de; Leasing.uk, truecar.com
By contrast, fleet adoption tells a different and more encouraging story. In Germany, more than two-thirds of BEVs registered belong to corporate fleets. In the UK, the figure is closer to 85 percent. Chinese OEMs are leveraging aggressive fleet strategies to establish a foothold in Europe, with the top five Chinese BEVs in Germany achieving a B2B market share growth of over 113 percent CAGR within a year.
Fleet channels matter because they lower the adoption barrier for individuals. Every corporate BEV has both a driver—who experiences e-mobility risk-free, often with strong tax benefits—and a future private buyer when the vehicle enters the used market.
“Fleet sales are a key driver of BEV penetration, supported by extended test-drive programs (four weeks) that can convert up to 50% of customers.”
- CCO EU, Volume OEMOff-lease BEVs thus create a pipeline of more affordable vehicles, critical to reaching price-sensitive mass-market customers.
“Intelligent management of total cost of ownership, residual values, and the used car market is essential to overcoming BEV adoption barriers”
- CSO, Premium OEMUnlocking private BEV demand requires tackling three interconnected issues: pricing, residual values, and customer trust. OEMs must narrow the MSRP gap with ICE models and communicate the advantages of lower operating costs more effectively. Leasing strategies should directly address residual value concerns, for example by introducing “battery health guarantees” that protect resale prices. Innovative financing, such as subscriptions or EV-specific leasing bundles with charging services, can also make EV ownership more predictable and attractive.
At the same time, used BEV markets need targeted support. Frequent model updates and rapid technological cycles depress residuals, creating skepticism among second-hand buyers. Proactive remarketing, certified pre-owned EV programs, and partnerships with auto banks can stabilize this market segment and build confidence.
The path to mass adoption will not be automatic. OEMs must actively convert fleet momentum into private sales by addressing the structural barriers that hold back retail customers. This means ensuring the digital-to-retail funnel does not break down—turning high online interest into real-world registrations through stronger test drive programs and better-trained retail staff. It also means adapting strategies by market: in Europe, leveraging corporate fleets as a launchpad; in China, navigating price volatility while defending brand equity; and in the US, ensuring competitive leasing and federal incentive alignment.
Fleets have adopted BEVs much faster – can we use that to unlock mass BEV acceptance?
Fleets are the launchpad, but not the endgame.
To secure lasting BEV adoption, OEMs must move beyond relying on corporate customers and unlock private demand through pricing discipline, residual value management, and innovative leasing solutions. Done right, this dual strategy will ensure that today’s corporate drivers become tomorrow’s private EV buyers—scaling electrification far beyond fleets and into the mass market.
Managing after-sales EV batteries – unlike any other spare part in the traditional automotive ecosystem Munich, December 2025 A fter-sales demand for HVBs could become
Wir sprechen über die Überschneidungen und Möglichkeiten der Automobilindustrie im Bereich stetig wachsender Verteidigungsausgaben.
Gäste sind Stefan Schneeberger (Associate Partner) und Christian Leber (Partner)
Europe’s and North America’s automotive sectors are under increasing cost pressure. Advanced manufacturing, including the use of AI, can help close the gap.
Copyright Berylls by AlixPartners
