The Economics of Enterprise AI: Why the Benefits Far Outweigh the Costs

Perhaps the most significant promise of artificial intelligence is the productivity it can unlock. Public companies have already begun citing the time their teams save through AI agents on earnings calls.

Capturing those gains, however, is not free. Organizations must train employees, restructure their data, and pay for the AI services themselves, among other expenses.

This raises a central question: what is the actual cost-benefit equation for companies deploying AI? If the technology returns a few hours a week to each worker, as our model indicates, how much of that recovered time is consumed by the cost of making it possible?

The answer determines whether a company should invest in AI in the first place.

Fortunately, enough surveys and reports now exist to model both the costs and the benefits. Taken together, they show that expenses absorb only about a quarter of the total benefit, suggesting that investing in AI should be a straightforward decision.

Mapping the costs and benefits of AI for businesses

Any investment requires a plan for covering its cost. With AI, companies can pursue several paths: produce more with their existing workforce, slow the pace of hiring, or reduce headcount.

To date, research indicates that most companies are maintaining their current staffing levels.

That points to the goal of generating more output with the same people.

Given this, the most sensible way to value the time AI saves is to base it on the worth of a worker’s time, measured through salary and benefits. That figure can then be weighed against the cost of the AI required to produce the benefit.

To make this comparison, we drew on data and estimates from multiple sources and built a model to quantify both the costs and the savings generated by AI.

To estimate the benefits, we calculate the value of salary and benefits saved using:

• Median salary and benefits costs for an S&P 500 company (from FactSet).
• The share of work exposed to AI (from academic research).
• The productivity gain delivered by AI (based on a Goldman Sachs estimate).
• The annual AI adoption rate (which builds over time).

Together, these yield the value of time saved by AI, expressed essentially as: salary multiplied by the share of time spent using AI multiplied by the productivity benefit.

To estimate the costs, we rely on setup and ongoing expenses drawn from a Forrester study.

We then apply a standard net present value (NPV) formula, which accounts for the gradual adoption ramp-up and discounts future net cash flows using the median weighted average cost of capital (WACC) for S&P 500 companies from FactSet.

Table 1: Factors used in our baseline cost-benefit calculations

Our baseline estimate delivers a strong cost-benefit profile.

Weighing the value of the time AI saves (a positive cash flow) against the cost of the AI itself (a negative cash flow), and applying the assumptions above, produces a clear net benefit.

AI returns roughly 9% of a worker’s time. On a 40-hour week, that amounts to about 3¾ hours. Expressed as a cost, the technology consumes less than one hour per week, leaving a net gain of roughly 2¾ hours per week, or more than 130 hours per year for each worker.

With a cost-benefit ratio of 1:4, investing in AI should be an easy call for most companies.

Chart 1: Under baseline assumptions, just one of every 3¾ hours saved is needed to cover AI costs

Stress-testing the model: What if the estimates are off?

What happens if our core assumptions prove inaccurate, or shift over time?

The same analysis can be run with different inputs to see how the results hold up.

Our baseline, for instance, assumes that 47% of work is exposed to AI, the midpoint of the research we cited. That figure will not apply to every role. The same research places the low end at 17% (for chat-based language models alone) and the high end at 77% (including tasks that require broader software integration).

Substituting those figures produces the following:

• At 17% exposure, nearly 70% of the time saved is consumed by costs, a far steeper hurdle (Chart 2, column 2).
• At 77% exposure, only 15% goes to costs (Chart 2, column 3).

Chart 2 also explores changes to:

• Productivity gains: Goldman Sachs identifies a 20% median gain from academic studies but 30% from company anecdotes. Applying 30% alongside our baseline exposure lifts the cost-benefit ratio to roughly 1:6 from 1:4 (column 4).
• AI optimism: If both exposure and productivity assumptions are cut in half, costs absorb 98% of the benefit, at which point the financial case for AI effectively disappears (column 5).
• Slower adoption: A slower rate of employee adoption erodes the cost-benefit, though less sharply than the other inputs (column 6).

We also tested a higher WACC (a 10.5% hurdle rate) to better reflect mid- and small-cap companies. Notably, the results were nearly identical.

Chart 2: Comparing different assumptions and scenarios

Rising AI token volumes signal growing adoption

Recent trends in token consumption suggest that a growing number of companies view the cost-benefit of AI as tilted in their favor.

Volumes have climbed sharply, particularly over the past year.

Chart 3: AI token volumes have surged this year as adoption and implementation accelerate

This trend may also reflect, in part, Jevons paradox, the principle that consumption of a resource rises as technology makes it more efficient to use. That dynamic may well apply to AI, given the strong evidence that model costs are falling rapidly.

As companies discover more ways to apply AI, however, they must also account for the AI “verification tax,” the portion of time saved that a human then spends checking the output and reducing hallucinations. One study found that more than a third of the time saved was diverted to verification, representing another cost that businesses need to factor in.

Two variables drive the entire equation: exposure and productivity

Ultimately, our research shows that the cost-benefit hinges largely on two inputs: how much work is exposed to AI and what productivity gain one assumes.

The math indicates that AI is an easy investment as long as productivity gains are around 20% and roughly half of work is exposed to the technology. Halve those figures, though, and the cost-benefit becomes a wash.

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