The AI Bill Is Due: Can You Show What Your Firm Got Back?

ROCHESTER, N.Y. — When a business pays for AI to help its team work faster, that investment should lead to better work, more capacity or faster service.

An AI-generated proposal can look polished yet fail to make a compelling case for why a client should choose your firm. If a colleague has to correct inaccuracies, sharpen the value proposition and connect the recommendation to the client’s goals, the time saved drafting it may have simply moved to someone else’s desk.

For businesses investing in AI subscriptions and automated tools, that creates a budgeting problem: more activity does not necessarily mean more useful work.

McKinsey’s 2026 global survey illustrates the distinction. Eighty percent of respondents said AI had improved their personal productivity, while 37 percent reported a positive impact on their organization’s earnings before interest and taxes. McKinsey’s State of AI

As AI becomes a recurring expense, business owners need a way to connect that spending to results. The discipline used to manage advertising budgets offers a practical starting point: track performance, fund what works and reallocate spending when the return falls short.

Three Forms of AI Payoff Drift

AI payoff drift happens when spending on AI tools, or the time employees spend using them, grows without a clear increase in business value.

The first form is shifting work to someone else. An employee may save time using AI to draft a client proposal, only for a colleague to spend that time correcting errors and rewriting unclear recommendations. The sender’s time savings become the recipient’s cleanup assignment.

Researchers at BetterUp Labs and Stanford’s Social Media Lab call this “workslop.” In a September 2025 survey of 1,150 full-time U.S. desk workers, 40 percent said they had received it in the previous month. Respondents reported spending about two hours resolving each incident. These are self-reported estimates, but they illustrate a cost that an AI subscription invoice will never show. BetterUp’s research

A second form is mismatched licensing. A business buys the same subscription for everyone. Occasional users barely touch it, while employees using AI for valuable daily work run into limits. Uniform licensing can become an allocation decision that nobody revisits.

A third is duplicated effort. Several employees share the same operational problem, but each builds a separate AI assistant or agent to handle it. Experimentation can be useful. Without a shared review, however, the business can end up maintaining multiple versions of the same solution, with different instructions and inconsistent results.

All three deserve attention when the budget comes up for renewal.

Accountability Belongs With Leadership

These problems can begin with leaders deploying AI without a plan for supporting employees or measuring outcomes.

Buying access does not give employees the judgment to use it well. Training should demonstrate relevant tasks, show what a useful result looks like, and make review part of completing the work. An employee should be able to recognize an immediate application to their own responsibilities.

KPMG’s Q2 2026 Global AI Pulse found that 7 percent of surveyed organizations placed themselves in its most mature, “established ROI” stage. That share was 14 percent where the CEO was accountable, compared with 4 percent where the CEO was less or not accountable. The relationship is an association, but it supports treating AI performance as a leadership responsibility. KPMG’s Global AI Pulse

Borrow the Discipline of Ad Spending

An advertising budget creates room to test. A campaign gets funding, its performance gets reviewed, and the next spending decision reflects what the business learned.

AI needs a similar review cycle.

The comparison has limits. Marketing attribution is imperfect, and an AI assistant may contribute to several projects at once. Spending dashboards can show usage and costs, but connecting those costs to completed work still requires a business process.

Start with a simple intake form to build a shared database. For each recurring AI use case, record:

• The project and owner: What work does it support, and who reviews the result?
• The measurable value: What improves, compared with the previous process?
• The tools and costs: Which subscriptions or usage charges support it?

Keep the record short enough that employees will maintain it. Review it monthly with the people responsible for the work.

For a proposal workflow, for example, measure the time required to produce a proposal ready to send, including review and corrections. A faster first draft has limited value if the total effort stays the same.

This database will make duplication visible. If several people have built assistants for the same task, compare them and establish a shared approach. If a lightly used license contributes little while another employee’s workflow delivers consistent value, the next allocation decision becomes clearer.

McKinsey found that AI high performers were twice as likely as other respondents to report having defined processes to measure the impact of their initiatives. McKinsey’s findings

Let Results Guide the Next Dollar

This approach may justify a larger AI budget. McKinsey’s high performers were also more likely to devote a substantial share of their technology budgets to AI. The purpose of measurement is to make that decision deliberately.

Preserve room to test new use cases, with a review date and a stated objective. Increase funding where results hold up. Consolidate overlapping tools. Reconsider deployments whose costs keep outrunning their contribution.

In KPMG’s global survey, 49 percent said their organizations had scaled back or delayed AI-agent deployments because expected costs began to outweigh anticipated value. KPMG’s findings

Before the Next Renewal

Ask employees to identify one recurring task they already use AI for and compare the process before and after: how long the work takes, how much correction it needs, and how they use any time saved. Use those examples to assess whether the benefits justify the cost.

For a firm selling expertise, the return may be room for another client, faster service, or fewer evenings spent catching up. Those are outcomes worth funding.

The next AI bill should come with a clearer answer to what value the business is buying.