Induced effects: The true value of AI isn’t reflected on the token bill
When we talk about the cost of AI, we often focus on the wrong aspect. The cost of tokens is only the tip of the iceberg. The real value lies elsewhere: in shorter time-to-market, incidents prevented, fraud detected earlier, and the time freed up for employees to focus on higher-value-added activities. To understand the true ROI of agent-based AI, we must look beyond the direct impact of inference and analyze the indirect and ripple effects it has on the entire organization. That’s where transformation happens—and often where the bulk of the value is created.
Direct effect, indirect effect, induced effect: the cost is just the first tier
The first two articles addressed the direct effect—what the token costs, how to govern it, and why volume is a misleading metric. However, the economics of impact distinguishes three tiers. The direct effect is the inference cost—the smallest number on the page. Indirect effects encompass the changes AI brings to processes, the speed of software delivery, avoided production downtime, and intercepted fraud. Induced effects refer to what the transformation unlocks within the organization—reallocated human capacity and the top-line revenue it generates. Most FinOps dashboards measure only the first tier, and a recent trend illustrates this perfectly: some organizations post internal rankings of token consumption to drive adoption, to the point of rewarding gross spending as a performance metric. This is the direct effect elevated to a religion, whereas the top two levels pose the only question that really matters: What has this consumption changed in the company’s real economy?
Software delivery: The benefit is measured in time to market, not in code volume
Agent-assisted development is the first area where the indirect effect becomes measurable, and the documented gains are nuanced—ranging from 35 to 40 percent productivity gains on simple, new tasks but often 10 percent or less on complex legacy systems. AI acts as an amplifier for the existing engineering system; it strengthens organizations with solid foundations and exposes the weaknesses of others. The trajectory follows a J-curve, with an initial dip in productivity while teams build their skills, followed by an increase in value; this dip represents a learning cost to be budgeted for, not a failure to be penalized. The guidance I provide to technical and general management can be summed up in one sentence. Value is not measured in lines of code generated or in raw velocity; it is measured in time to market, in the total cost of a delivered feature, and in the durability of code that withstands production, because code written twice as fast but rewritten three times has not accelerated anything at all.
Predictive Maintenance: Inference Costs Cents, Avoided Downtime Is Worth Millions
Predictive maintenance offers the clearest demonstration of the indirect effect because the asymmetry is greatest in this context. One hour of unplanned downtime costs an average of several hundred thousand euros in industry and can exceed two million on certain automotive assembly lines, whereas mature predictive programs reduce unplanned downtime by 30 to 50 percent and maintenance costs by 18 to 25 percent—since proactive intervention costs four to five times less than an emergency repair. Moreover, models that predict failures are not the most resource-intensive. The real innovation is the agent layer that transforms prediction into action—the agent that makes the diagnosis, triggers the intervention, creates the work order, and coordinates the systems with one another. It is this layer that is metered in tokens, and its cost is negligible compared to these figures; no one asks about the price of the inference when the avoided downtime is worth half a million. The same logic applies across all sectors—from fraud intercepted before a loss occurs to an early detection of a stockout that saves a shelf during peak season, all the way to operational resilience that prevents a service outage. The indirect effect is the loss that never happened—the one that leaves no trace on the invoice yet still changes the bottom line.
Reallocated capacity is the resulting effect that drives top-line growth.
The final level pertains to human capital and deserves to be addressed with rigor because this is where narratives often go off track. The ROI of automation is not measured by the number of positions replaced; the most rigorous studies explicitly advise against workforce reduction as a strategy, since retaining and training existing teams is less costly and preserves the organization’s knowledge. The proper metric is reallocated capacity—the number of hours of expertise shifted from repetitive work to judgment, relationships, and design. A manager who no longer handles simple cases can now adjudicate complex ones, and an engineer freed from routine tasks can design rather than merely maintain systems. This reallocation is the mechanism by which the “token” translates into growth rather than mere cost savings; the time reclaimed for customer relations, consulting, and design fuels top-line growth, whereas automation alone only boosts margins. This induced effect does not appear on any inference statement, even though it is what the executive committee should be looking at first.
An organization that only looks at its token statement is looking at the direct effect—the smallest number on the page. The direct effect can be counted. Indirect and induced effects are built up. A “Frontier firm” is one that manages all three levels, in that order of importance.
Sources :
Siemens, The True Cost of Downtime 2024
McKinsey, Manufacturing: Analytics unleashes productivity and profitability, 2017
Google Cloud DORA, The ROI of AI-Assisted Software Development, 2026
Fortune, Tokenmaxxing is over. That’s because it never measured what really counts to see ROI from AI, 28/05/2026