Finance leaders within companies with heavy-duty transportation assets are asked to defend EBITDA (earnings before interest, taxes, depreciation, and amortization), justify capital allocation decisions, and protect shareholder returns using data that was never designed to answer those questions. Equipment investments represent one of the largest capital commitments on the balance sheet, yet most finance teams are still managing it with historical ledgers rather than forward-looking forecasts. The intent to model risk and plan strategically is there; what is missing is the financial intelligence infrastructure to do it.

The question finance still cannot answer

A board asking about next year's capital budget, residual value exposure, or capital replacement timing is asking a forecasting question. Most finance functions supporting organizations with transportation fleets can only answer it with a rearview mirror. Maintenance invoices, fuel receipts, and lease schedules arrive in disconnected systems, get reconciled weeks after the fact, and land in a spreadsheet built to close the books, not to forecast what happens next quarter. That gap between the question being asked and the tools available to answer it is now a financial weakness, not a minor inconvenience.

Why the data keeps failing finance

The board rarely asks about cost per mile directly, but it is a granular number that eventually rolls up into the EBITDA and capital allocation figures the board does track. According to the American Transportation Research Institute's (ATRI's) 2026 operational cost analysis, the average cost to run a truck reached a record $2.336 per mile in 2025, up 3.4% and the highest figure on record. Every major cost category increased, and maintenance and repair alone climbed 8.6%.

A meaningful share of that increase traces back to government mandates rather than routine wear and tear. Emissions and safety requirements phasing in over the next several years are driving up the cost of new equipment and the parts needed to keep older units on the road, while tariff pressure on components adds to the impact. The CFO needs to know how much of this year's rising operating expenses are structural costs tied to policy changes versus ordinary volatility, since that distinction shapes how the number is explained and how the organization plans around it.

The volatility becomes a financial planning problem quickly. A spike in transmission or engine repairs on a handful of aging units can distort an entire quarter's operating expense line. Without a system that isolates which specific assets are driving the variance and rolls that detail into a clean per-vehicle profit and loss view, the CFO is left explaining the variances after the fact instead of anticipating them before they reach the income statement.

The benchmarking blind spot 

Even fleets with solid internal reporting often lack a reliable external reference point. A 2026 fleet benchmark report aggregating anonymized data from 1.2 million vehicles and roughly $7 billion in service spend found that many organizations still struggle with basic maintenance coordination and capacity planning even as both operating costs and equipment age continue to rise. Without benchmarking against a dataset of that scale, a finance team has no way to know whether its cost per mile, downtime rate, or repair frequency reflects sound operations or simply industry-wide inflation. That distinction matters enormously in a boardroom, where the CFO needs to explain not just that costs rose but whether the organization performed better or worse than its peers.

From ledger to model

Total cost of ownership (TCO) calculations frequently fail for a related reason. Replacement decisions are often made too late because capital expenditure is visible and immediate, while variable costs, including maintenance costs, are scattered across many small invoices that may never get aggregated into a single per-vehicle view. A truck can quietly cross into a cost escalation zone for years before anyone connects the dots because the data needed to see it lives in separate systems, and none of them are designed to model total life cycle economics or support a broader total value of ownership (TVO) strategy. The same per-vehicle view that makes a confusing expense line explainable to the board is what catches this shift while there is still time to act.

Even broader use of AI has not solved the problem. A recent industry survey on the use of AI revealed a critical disconnect in TCO modeling for Class 8 assets. Thirty-two percent of respondents still perform TCO modeling manually, while 29% do not perform TCO modeling at all. Across the remaining responses, adoption of AI-driven TCO modeling averages just 12.1%, signaling that most organizations are not yet using analytics in one of the most impactful areas of asset management. That gap is a real opportunity to unlock cost savings and improve life cycle decisions through more sophisticated, AI-enabled modeling.

This is not so much a data collection problem as a data architecture problem. Organizations today are not short on information; they are short on a layer that turns operational data, telematics, work orders, invoices, and utilization records into a single forward-looking financial model a CFO can present to the board and stand behind.

Building the missing layer

Closing this gap requires treating financial intelligence as infrastructure rather than an afterthought bolted onto operations. Predictive modeling should be built on real per-vehicle cost trends and profit and loss detail instead of fleet-wide averages. Mandate-driven costs need to be separated from ordinary wear so finance can explain them at the level the board tracks. External benchmarking should come from internal reporting, not an annual PDF, and TCO/TVO calculations should update continuously as maintenance and utilization data change.

Finance teams already have the analytical instinct to ask the right strategic questions. What they need is a financial intelligence layer that converts data into forward-looking, boardroom-ready insights, enabling CFOs to answer strategic questions before the next capital cycle, insurance renewal, or asset replacement decision forces the issue.

The organizations that close this gap first will not only report performance more accurately but also make capital allocation decisions with greater confidence, defend those decisions with data that can withstand boardroom scrutiny, and shift finance from explaining yesterday’s results to forecasting tomorrow’s outcomes.