Spending on artificial intelligence now stands at 3.3% of revenue, against about 1% in early 2025, according to the Boston Consulting Group’s Applied AI Index 2026, and more than 80% of it sits outside the central IT budget. AI is thus becoming a matter of capital allocation, management control and performance governance, while the link between this spending and margin, costs, growth or cash often remains poorly defined.

The same report indicates that companies that directly track the impact of AI in their income statement report about three times more value than those with no formal financial measurement, at 3.6% against 1.2%. This finding does not prove that measurement by itself produces performance: these companies may also have a better organization, greater maturity or a more rigorous selection of their investments. It does suggest, however, that the ability to measure economic value and the ability to capture it are closely linked.

For finance leaders, this development amounts to separating the technological and operational progress of AI from its actual contribution to company performance. A company can deploy high-performing solutions, record strong adoption and observe productivity gains without any improvement in its operating result or its cash, for lack of explicit mechanisms that convert these benefits into financial results. The assessment must therefore cover the whole economic chain, from the initial investment to the value actually captured.

A Significant Expense Whose Visibility Remains Fragmented

In many companies, AI spending does not correspond to any identifiable budget line. It is spread across software subscriptions, features embedded in existing applications, in-house development, external services, infrastructure, data, training and process transformation.

Each initiative may be relevant at its own level, but consolidating them remains difficult as long as spending is classified by functional budget and not by the capability developed and used.

That a large share of this spending falls outside the central IT budget does not mean it escapes financial control, but its steering can no longer be that of a centralized IT program.

The scope of costs raises a second difficulty. The cost of a solution includes, beyond the subscription, the development or the consumption of resources, the data required, integration with existing systems, process changes, training, controls, maintenance and successive adaptations. Some of these costs are directly attributable to an initiative, while others are shared across projects or absorbed by existing teams, which leads to underestimating the real economic cost. The gap widens in the move from experimentation to industrialization: a pilot runs with a small team, manually prepared data and close supervision, whereas large-scale deployment demands availability, security, quality, control and continuity.

The finance function therefore needs a consolidated view of the company’s AI commitment, without seeking to centralize technology decisions. This view shows the amount actually invested, the economic objectives targeted and the results expected, and it conditions a coherent allocation of resources across initiatives.

Productivity Produced and Productivity Captured

Initiatives are still often assessed on activity indicators: licenses deployed, adoption rates, number of queries, volumes of documents processed, time saved, share of tasks automated. They provide information on usage and deployment, but they are not enough to conclude that an initiative is profitable. The number of active users measures adoption; the volume of automated operations measures processing capacity without showing that the full cost of the process has fallen; and a shortened task improves the availability of teams without necessarily producing a financial saving.

Productivity produced is the improvement observed in carrying out an activity, for example a task that went from thirty to twenty minutes. Productivity captured depends on what the company does with the capacity released: handling more cases with constant resources, avoiding hires that would otherwise have been needed, reducing expenses or developing margin-generating activities. When the gain remains scattered across many tasks and changes neither the resources deployed, nor the volumes produced, nor the results, its financial translation remains limited.

These gains retain their value, since they can improve quality, shorten lead times and strengthen team resilience or customer satisfaction, but they must be presented for what they are, without automatic conversion into accounting savings. An organization that theoretically saves several thousand hours cannot value those hours at the average hourly cost: it must verify the share that can actually be mobilized, the constraints of work organization and the decisions that convert this capacity into benefit.

Time saved measures productivity; it measures profitability only once its use and its economic contribution have been demonstrated. This discipline limits the overestimation of business cases, in particular in support functions and information-processing activities.

A Bridge Between Operational Results and the P&L

The contribution of AI to earnings must be explainable through an identifiable economic chain. It starts from the baseline (current costs, volumes processed, service quality, error rate, productivity, margins or losses before the intervention), continues with the improvement measured on a defined scope and with the operational changes that convert it into value, and then compares this value with the full costs of the solution. An AI Value Bridge can structure this analysis by distinguishing savings actually realized, additional margin, costs or losses avoided, productive capacity redeployed, and recurring and non-recurring costs.

By way of illustration, take an initiative whose verified annual effects amount to €300,000 of savings actually captured and €200,000 of additional contribution margin. If its recurring costs are €120,000 and the transformation charges attributable to the year are €80,000, its net contribution to the year’s earnings is €300,000, subject to the absence of double counting and to appropriate accounting treatment.

In practice, one must distinguish recurring savings from one-off gains, costs avoided from expenses actually reduced, and additional revenue from its net contribution to margin. A productivity improvement already translated into a cost reduction cannot be counted a second time as hours saved, and an increase in revenue creates value only after deducting the costs needed to achieve it.

Effects on working capital call for particular attention: a reduction in inventory or an acceleration of collections releases cash without producing an equivalent gain in the income statement, whereas a margin improvement increases earnings without immediately generating the same amount of cash.

The contribution to the P&L, the economic profitability of a project (based on its future cash flows, its investment horizon and its risks) and the effect on cash are therefore measured separately, before being brought together in an overall assessment. The finance function sets consistent measurement conventions for this purpose, verifies assumptions and reconciles the reported results with the changes actually observed in the company.

Measuring the Cost per Result Obtained

Technical costs, in particular the consumption of models, are used to compare architectures or optimize a solution, but they are not enough to judge its economic effectiveness. A system that is cheap to run can produce many inaccurate results, require heavy human checks or need frequent corrections, whereas a technically more expensive solution may be preferable if it resolves more situations with fewer interventions and errors. The relevant indicator is the full cost per correctly obtained result.

In a customer collections process, the number of automated reminders does not measure the value created. The economic objective is to improve the recovery of receivables, reduce payment delays and control the associated costs and risks. The solution’s effectiveness is assessed against the cases correctly handled, the total cost of the actions taken, the cash collected and the effects on the customer relationship. One must also distinguish a collection that is merely accelerated, which improves the cash profile and working capital, from a collection that probably would not have occurred without intervention, which can also reduce an economic loss depending on the treatment of credit risk.

The comparison is made with the previous method or with a simpler alternative, taking into account the nature of the cases and the other factors likely to influence results. A rule-based system or conventional automation can deliver a lower cost per result than the most advanced model when the situations to be handled are sufficiently standardized.

The Role of the Finance Function

According to MIT’s AI Strategy and Leadership program, value creation does not fall to a single function: technology teams select, integrate and maintain the solutions, the business units know which decisions to improve, general management arbitrates resources, and the finance function makes commitments and their results economically legible.

Its role is to establish an investment discipline suited to the nature of the projects, to verify the value-creation assumptions and to organize the tracking of expected benefits, without approving every model choice or intervening in architecture decisions. This discipline is proportionate to the stage of maturity. An exploratory experiment is not meant to demonstrate a financial return immediately, but its budget, duration and learning criteria are explicit. An operational pilot compares the solution with a baseline, measures its impact on a real process and specifies the conditions for capturing value. A large-scale deployment calls for a full justification: total costs, capturable benefits, responsibilities, risks and conditions for follow-up after go-live. This graduated approach avoids both funding experiments indefinitely with no clear economic prospect and imposing profitability requirements too early, which would block exploration.

Steering continues after launch. Gains below expectations lead to an examination of the causes (flawed initial estimate, insufficient adoption, operational constraints, underestimated costs or inadequate technical performance), and positive results are verified before any extension of scope. Each significant initiative requires a clearly assigned economic owner and a tracking arrangement involving finance, the business units and the technology teams.

From Investment to Captured Value

Four levels of performance can be distinguished.

Technical performance shows that a solution works; operational performance establishes that it improves a process or a decision; economic value appears when this improvement produces a benefit greater than the costs and risks it entails; and financial value captured is the share of that benefit actually converted into margin, savings, cash or a measurable reduction in risk exposure.

None of these levels automatically guarantees the next.

Companies that build this continuity gain better visibility on their investments: they identify the initiatives to develop, those to adapt and those whose benefits do not justify continuing, and they compare AI with other transformation projects on consistent economic criteria. The value creation from AI is also not limited to cost reduction, since it can support growth, decision quality and execution capacity; financial measurement then distinguishes what is demonstrated, what can reasonably be estimated and what is still a hypothesis. It is thus part of the AI strategy from the outset, and not a check carried out after deployment.

For a finance function, the useful measure concerns the share of the investment that actually improves the company’s results, the mechanisms that produce it and the associated degree of certainty, more than the amount committed.

Sources

• Boston Consulting Group, The Formula for Agentic AI Value: The Applied AI Index 2026
• People Matters, Nearly half of companies are now generating value from AI: BCG