The corporate leasing and mobility market is entering a new phase in 2026. The pilot stage is over and scaling has begun.

This article sets out why classic TCO calculators have reached their ceiling, how total cost of mobility (TCM) works, where the new centre of gravity lies in fleet policy and which data sets are worth including in the decision model now for genuine fleet electrification instead of just a target announcement.

For a decade now, a fleet has been a procurement and tax problem. Stable fuel prices, predictable residual values and well understood servicing parameters has meant that the TCO was basically rental, fuel, service, insurance and tax. However, electrification has broken that model, not by adding a new cost category, but by making the basis for decision dependent on factors that fleet managers have never had to monitor before, in other words, real-time energy prices, access to home-charging, public infrastructure, country-specific tax regimes, battery price trajectories and emissions caps.

The first signal is cost itself. Over the past five years, the total cost of running a corporate fleet has climbed steadily — but the dominant force has been macroeconomic inflation, not electrification. Rising vehicle prices, higher labour and energy costs and more expensive financing have done most of the work. What electrification has changed is the structure of that cost base — where the money goes — while inflation has lifted the overall level.

The next signal is consumer sentiment. The EY Mobility Consumer Index 2025, which covers around 21,000 respondents across 32 markets, shows BEV purchase intent falling to 14% and 50% of buyers now planning an ICE vehicle as their next purchase. This represents a 13-point jump, year on year^[2]. The reasons are very familiar, encompassing purchase price, range, charging infrastructure and uncertainty around residual values. This is not electrification going into reverse, though. Instead, it is a sign that the easy part of the market has already electrified, while the harder part needs significantly stronger economic arguments.

The final signal concerns batteries. According to BloombergNEF, the average lithium-ion pack price fell by around 20% in 2024 and is expected to continue falling, approaching the 100 USD/kWh threshold widely regarded as the technical point of cost parity between BEV and ICE in major segments^[3]. Without subsidies, BEVs are no cheaper than ICE vehicles are in any segment or market yet. Nonetheless, for a two- to three-year fleet horizon, the power of the ‘electric is simply too expensive’ argument is fading.

Classic TCO treats the vehicle as a unit of cost and totals fixed line items. This worked for homogeneous fleets with uniform driver profiles, stable infrastructure and predictable regulation. None of those conditions still holds true. Five variables in particular are now shattering the static model:

•    unstable energy prices: there can be a several-fold difference between the costs of charging by way of an off-peak tariff, a workplace charger and public DC. Applying a static average misrepresents the real cost;

•    BEV residual values are under pressure: used BEVs fell in value on major European markets faster than their ICE equivalents during 2024 and 2025. In mid-2025, three-year-old BEVs retained roughly 37% of their list price in Germany, as opposed to around 50% for petrol vehicles, with similar differences occurring in other countries^[4]. The figure in each case depends on subsidy policy, the pace of battery technology development and how battery health is priced on the used vehicle market;

•    service and repair cut both ways: fleet data show SMR costs for BEVs at typically 20-37% lower than their petrol or diesel equivalents over a cycle of thirty-six to forty-eight months^[5], yet accident claims for BEVs remain significantly higher, at around 25–35% more. They also take longer to resolve on account of the high-voltage pack, although that gap is narrowing as repair networks gain experience^[6];

•    regulatory changes can alter the model mid-contract: new or amended BiK rates, excise, low-emission zones and mandatory BEV registration quotas on various EU markets may all come into play during the duration of a contract;

•    driver profile matters more than vehicle profile: a BEV running on home charging and doing 25,000 km a year delivers a TCO that differs tremendously from the same BEV with a driver dependent on public DC.

All of these factors are pushing the industry towards total cost of mobility (TCM). TCM extends the model by way of the several dynamic variables, namely, energy, taxes and carbon prices. It takes battery degradation into account^[7], models residual value across scenarios and, in its corporate form, it also covers alternatives to the vehicle, such as charging cards, mobility budgets, public transport and carsharing. The question ceases to be ‘ICE or BEV?’. Instead, it becomes ‘Which combination of vehicle, driver, infrastructure and contract closes the cost and emissions gap for this usage profile?’.

The international benchmarks for corporate car and mobility policies are consistently pointing in one direction; the foundations of the ‘one policy fits all’ model are crumbling’^[8]. The share of organisations that provide a cash allowance as a fully fledged alternative to a company car is rising, as is the share of those offering a mobility budget, salary sacrifice and public transport support. The company car is becoming one of several routes rather than the default.

The motive is not purely cost-driven. Fleet policy is increasingly being leveraged as an HR instrument designed to attract talent, support wellbeing, facilitate hybrid working and include staff without necessitating the use of a car every day ^[8].

PHEVs carry the most policy risk. A growing number of companies are allowing them only under the condition of demonstrable charging behaviour; an unplugged PHEV in combustion mode produces higher emissions than a well-chosen HEV or ICE. The introduction of Euro 6e-bis procedures is driving the official WLTP CO₂ figures for numerous PHEV models materially higher and this can take a model beyond internal emissions limits or EU green taxonomy thresholds^[8]. To all intents and purposes, enforcing genuine plug-in behaviour without vehicle data is impossible. In practice, a policy that can’t be measured is a policy that doesn’t exist.

Three or four years ago, telematics was an optional extra. In modern vehicle and mobility policies, it is now standard for both risk management and emissions reporting^[8]. Telematics is no longer an add-on; it’s now the data layer for every fleet decision, including the real-time refresh of the TCM model. It enters real-world usage patterns, predictable battery health curves^[7], driving-style coaching and, crucially, auditable Scope 1 emissions at vehicle and driver level, providing a CSRD-grade input, rather than a fuel-based estimate.

One of the most underestimated variables for BEVs is whether the driver has home-charging access. International policy benchmarks consistently show a rising share of organisations offering their staff charging cards, reimbursing electricity costs and providing financial support for a home wallbox^[8]. This is straightforward arithmetic; a driver charging at home shifts the energy mix towards cheaper kilowatt-hours and reduces their dependence on the more costly DC. For PHEVs, the common industry guidance is to cover electricity costs fully, while capping the fuel budget at a realistic WLTP-based figure. Otherwise, the PHEV stops being a plug-in hybrid and becomes an expensive ICE vehicle carrying extra weight^[8]. The emerging EU tax compliance standard for reimbursement is a smart cable or a metered wall-mounted charger.

A decision engine is simply a decision model that lives not in a spreadsheet, but in a continuously updated system. A classic TCO calculator computes the assumptions once. A decision engine recomputes them continuously, pulling in energy prices, the subsidy calendar, RV forecasts and telematics, and then surfaces a specific recommendation. It integrates four layers. The vehicle layer involves residual-value scenarios, battery costs and telematics-informed servicing; the driver layer covers charging access, mileage and route structure; the infrastructure layer encompasses CPO availability, real-time prices and home tariffs; and the policy layer deals with tax rates, subsidies, BEV quotas and carbon price pathway.

Poland entered 2026 at an inflection point. The country’s NaszEauto programme, which is financed from EU Recovery Plan resources and administered by the National Fund for Environmental Protection and Water Management (NFEPWM), offered a subsidy of up to PLN 40 000 for the purchase, lease or long-term hire of BEVs. At the end of January 2026, the NFEPWM announced that the entire PLN 1.18 billion budget had been allocated^[9]. At the same time, the EU Recovery Plan funding rules make no provision for a straightforward extension. Manufacturers are therefore now responding with discount schemes designed to replace the subsidy, while the logic behind fleet decisions is shifting from grant seeking to genuine multi-year TCO and TCM calculations. It is at precisely this point that a static calculator starts to mislead and a decision engine starts to make business sense.

•    Treating BEV and ICE as interchangeable items on a spreadsheet, when they represent two cost regimes, two risk profiles and two regulatory calendars. Using the same formula for both produces an output that is formally correct, but operationally useless.
•    Running TCO on averages and ignoring home-charging access, when these assumptions will cause the model to fall apart in the first quarter of real-world use. Home-charging access is arguably the single most powerful variable in the BEV operating-cost equation.
•    Allowing PHEVs without enforcing charging behaviour, an unplugged PHEV running on its combustion engine produces a higher TCO and worse emissions than a well-chosen ICE vehicle — and without vehicle data, the fleet cannot verify or enforce whether drivers actually charge.
•    Running models rooted in a subsidy that no longer exists, when models built in 2024–2025 on the basis of the active NaszEauto programme actually need updating immediately[9]. Otherwise, the decisions they recommend make no economic sense whatsoever for the market halfway through 2026.

At MakoLab, we work with leasing, fleet and mobility companies as they move their decision-making processes from spreadsheets to a decision engine. We never treat TCO and TCM as separate worlds. They are one decision architecture at different stages of maturity and building that architecture involves leveraging several of our service lines to attain that outcome. Those services are:

•    our Data Services, including data architecture, integration, big data and streaming pipelines that bring together vehicle telematics, energy prices, residual value forecasts and regulatory data into a single, governed and auditable model, where data assessment and governance ensure that the resulting fleet data are CSRD-ready;
•    our AI Services, providing the skill sets to build machine learning (ML) models for residual value, battery health and driver profiling, along with the ontologies and knowledge graphs that structure how vehicles, contracts, charging infrastructure and regulations relate to each other. All of this is the formal backbone of a decision engine that can reason, rather than just calculate. LLM and generative AI are also part of the service, making the engine accessible to fleet managers and corporate clients through natural-language interfaces.
•    our Application Services, covering both custom software development and DevSecOps for the decision interface itself. This is a securely deployed recommendation system that is integrated with existing leasing, ERP and HR platforms and gives fleet managers a concrete answer rather than three scenarios in a file;
•    our Business Services, encompassing business strategy and transformation work that translates the technology into commercial outcomes such as fleet policy redesign, customer experience for corporate clients and the operational change required for the new model to embed.

Technology, data, finance, driver policy and ESG do not operate in silos. They all work towards the same goal of repeatable, transparent and measurable fleet decisions that survive regulatory audit and board-level scrutiny.

If we were to condense this entire topic into a single sentence, what would it say? This: in terms of fleet decisions in 2026, the winners won’t be the organisations that boast the best TCO calculations, but those that leverage TCM for an optimal understanding of their drivers, their data and their regulatory calendar and connect it all in a single, iterative decision engine.

Don’t get caught up in any more TCO spreadsheets. Instead, start looking into whether your decision model is actually keeping pace with the 2026 market.

As your partner in transformation, MakoLab will provide you with end-to-end support, complete with all the crucial skill sets and experience. We combine data, AI, application and business services into one delivery framework that takes leasing, fleet and mobility companies from static TCO to dynamic TCM. 

Why wait? Talk to our experts to launch the process without delay!