AI ROI for Small Business: What the Enterprise Reckoning Reveals

Sometime in early 2025, Uber’s finance team realized the company had already consumed its entire annual AI software budget. By February. The culprit had a name inside AI circles: tokenmaxxing.

Tokenmaxxing described a corporate behavior pattern as simple as it sounds. Companies told developers to use AI tools liberally, without friction, without spending limits. The theory was that productivity gains would cover the cost. In most cases, they did not.

Now that calculus is reversing. Reporting from CNBC this week reveals that enterprises are pulling back from unlimited AI spending, demanding genuine returns, and in some cases switching to cheaper alternatives. For the small and mid-sized businesses (SMBs) watching from the sideline, this is not a spectator sport. There are real lessons here, available right now, at no cost.

The Short Version: AI ROI for Small Business Is Now Non-Negotiable

Enterprises that flooded AI tools with unlimited budgets burned cash without proportionate returns. Uber burned its annual AI budget in four months. A CEO switched from Claude to DeepSeek and said costs “crashed.” Now businesses at every scale are being asked to justify AI spend with data. SMBs that build that discipline now, before a crisis forces it, are in a far better position than their enterprise counterparts were.

What Is the Tokenmaxxing Problem and Why Did It Happen?

Tokenmaxxing was not irrational at the time. In 2023 and 2024, AI tools were novel, the productivity upside was real, and measuring it precisely was genuinely hard. Companies told teams to experiment. They bought licenses broadly. They did not require ROI justification because early signals were promising enough to justify optimism.

The problem: optimism is not a budget strategy. According to research compiled this year, 80 to 85 percent of enterprises missed their AI infrastructure forecasts by more than 25 percent. That is not rounding error. That is structural. And the returns were thinner than expected: less than 1 percent of companies achieved “significant ROI” of 20 percent or more, while 53 percent report only 1 to 5 percent returns.

There is a less-discussed reason behind that gap: what researchers call the “rework tax.” Nearly 40 percent of the time savings from AI tools disappear when you account for the human hours spent reviewing, correcting, and verifying AI output. The headline productivity number looks clean. The actual number, once a human checks the work, is considerably smaller.

How Did the Reckoning Actually Unfold?

Two specific examples from the past month are worth understanding.

Uber implemented spending tiers on AI tools after realizing the company had burned through its full annual AI budget in roughly four months. The solution: hard spending caps per tier, starting at $1,500 per month, requiring justification to unlock higher tiers.

Lindy’s CEO announced that after switching the company off Anthropic’s Claude models to DeepSeek, costs “crashed.” No additional context needed. The same outputs, at a dramatically lower price point, were available from a less-premium provider.

Meanwhile, OpenAI and Anthropic both filed confidentially for IPOs in early June 2026, precisely as enterprise customers are beginning to scrutinize their spend. The premium AI provider model, which was built on the assumption that companies would pay almost anything for the best model, is now under real pressure.

What Does This Mean for SMBs Specifically?

Small businesses are not Uber. They are not burning $10 million on AI tools annually. But the structural error is the same at every scale: spending on AI without a clear framework for measuring whether it works.

The good news is that the SMB data is actually encouraging when AI is used deliberately. Small businesses using AI effectively report cost savings of $500 to $2,000 per month and time savings of 20 or more hours per month. The key word is “effectively.” That means knowing what you are paying, what you are getting, and cutting what is not working.

You may already be paying for more AI than you realize. If you recently upgraded to Microsoft 365 Business Standard with Copilot, which became a permanent SKU at $23.50 per user starting July 1, you are paying for AI tools whether you use them or not. The same is true of Google Workspace’s Gemini features, Adobe Firefly in Creative Cloud, and any number of SaaS tools that bundled AI into their pricing this year. That bundling is not a problem, but it does require intentional use to justify the cost.

How Should SMBs Actually Measure AI ROI in 2026?

ROI measurement does not need to be complicated. Three practical steps apply at any business size.

First, audit what you are paying. List every AI-enabled subscription, including tools where AI is bundled rather than priced separately. Tools like Microsoft 365 Copilot, which uses consumption-based billing for agentic features, can produce surprise costs if left unmonitored.

Second, assign each tool to a specific task. “We use AI for writing” is not specific enough. “We use AI to draft initial client proposal emails, which we then review and send” is. The specificity lets you measure time saved per task and compare against cost per month.

Third, account for the rework tax. Track not just the time AI saves on generation, but also the time your team spends reviewing and correcting AI output. If a tool generates a report in 3 minutes that takes 20 minutes to fact-check, the net time saving is modest. If it does the same and takes 2 minutes to review, you have a real winner.

The enterprise world is learning this lesson through budget crises and forced policy changes. SMBs that build the habit now are ahead of the curve, not behind it.

Are There Cheaper AI Alternatives Worth Considering?

Yes, but with caveats. The Lindy CEO’s switch to DeepSeek is instructive, but context matters. DeepSeek’s models are open source and considerably cheaper per token than Claude or GPT-4o. For many repetitive business tasks, like summarizing documents, drafting standard emails, or categorizing data, a less-premium model performs comparably at a fraction of the cost.

Where premium models still earn their price: complex reasoning, legal or financial document review, sensitive data requiring US-hosted infrastructure, and nuanced customer communication. Microsoft’s new MAI models and Google’s Gemini 2.5 Pro are also eroding the gap between premium and mid-tier AI at scale, which means prices across the board are likely to fall further over the next year.

The practical recommendation for SMBs: use the cheapest model that delivers the quality you need for a given task. That does not mean defaulting to cheap models for everything. It means matching model quality to task complexity.

Frequently Asked Questions

What is “tokenmaxxing” and does it affect small businesses?

Tokenmaxxing refers to the practice of encouraging unlimited AI tool use without spending controls or ROI requirements. It affected large enterprises most visibly, but the underlying habit of paying for AI without tracking its value applies at any business size. SMBs with multiple AI subscriptions and no usage audit are doing a small-scale version of the same thing.

How much should a small business budget for AI tools in 2026?

There is no universal answer, but a reasonable starting framework is: start with one or two tools tied to specific workflows, measure their impact for 60 days, then expand based on evidence. Many SMBs find $100 to $500 per month covers meaningful AI capacity across writing, customer service, and workflow automation tools, with room to grow as ROI is confirmed.

Is switching from Claude or GPT-4o to cheaper AI models worth it for my business?

It depends on the use case. For structured, repetitive tasks like email drafting or data summarization, cheaper open-source models like DeepSeek often perform comparably. For nuanced, high-stakes tasks like legal review, complex customer service, or sales copy, premium models typically justify the cost. The right approach is task-specific selection, not a blanket switch.

Why are OpenAI and Anthropic filing for IPOs now, and should that affect my AI tool choices?

Both companies filed confidentially for IPOs in early June 2026, partly because their near-$1 trillion valuations need public market capital to sustain. This adds commercial pressure to their pricing and feature roadmaps. It does not make them less capable, but it is a reasonable signal to review your contract terms and consider whether their pricing structures align with your long-term costs.

Are you actively tracking the ROI of your AI subscriptions, or are you still in the phase of trusting that it is all worth it somewhere? The enterprises that learned the hard lesson are hoping you figure it out the easy way first.