AI cost governance: policies to control AI spend

The share of employees with access to sanctioned AI tools rose 50% in a single year, reaching roughly 60% of workers, according to Deloitte’s State of AI in the Enterprise 2026. For many companies, usage has moved beyond the experimental: 25% of survey respondents said they have moved 40% or more of their AI into production, and 54% expect the same in three to six months.

That capability comes with big bills attached. As noted by the Wall Street Journal, AI costs are expected to climb sharply over the next few years as more organizations move from on-demand chatbots and response engines to always-on agentic tools.

The result is a paradox. Companies can’t afford to ignore the growing impact of AI, but may also struggle to afford AI itself. Solving the problem requires balance: AI use governed by robust policies designed to control spend.

Here’s what that looks like in practice.

The state of AI spending

U.S. private AI investment reached $285.9 billion in 2025, with 1,953 newly funded AI companies, more than 10 times the next closest country.

Despite advancements in machine learning (ML) and data analysis, however, research from Stanford found that AI doesn’t always live up to the hype. For example, while intelligent models can win gold medals at competitions like the International Mathematical Olympiad, they struggle to reliably tell time on analog clocks. When faced with these ticking timepieces, AI accuracy was barely better than half at 50.1%.

OSWorld task success shows the same unevenness. Average agent accuracy reached 66.3%, up from roughly 12% in 2024, still short of the 72.4% human baseline. The frontier has already crossed it, though: GPT-5.4 posted 75.0% on OSWorld-Verified.

The result is an AI market that’s big on investment but short on outcomes. In many cases, companies are spending on intelligent tools because they need ways to capture, track, and analyze large data sets, but they’re not sure how to integrate and apply these tools effectively. This leads to investment without return; while AI has promise, it seems to always be just out of reach.

Improvements in large language models (LLMs), natural language processing (NLP), and sentiment analysis, however, mean that these investments will pay off sooner rather than later. Until then, businesses need strategies to invest in AI that don’t break the bank.

What drives AI costs?

Several components contribute to rising AI costs.

First is token usage. Tokens are the smallest units of data processed by AI models. One token typically equates to four characters, meaning the word “word” is worth one token. Spaces and punctuation also count toward token usage, which means that even relatively short sentences can have significant token costs.

If companies use web-based AI interfaces, they typically pay per-seat rather than per-token, but don’t have the ability to connect AI with local applications and services. API-based plans, meanwhile, are billed on a million-token basis. For example, companies using Google Gemini 3.5 Flash pay $1.50 for one million input tokens and $9.00 for one million output tokens.

Next are the skyrocketing prices of RAM. According to data from Counterpoint, memory prices rose 80% to 90% between Q4 2025 and Q1 2026. DRAM, NAND, and HBM all hit record-high prices.

AI is one key driver of this memory cost increase. To perform complex calculations quickly and reliably, intelligent tools require a massive amount of memory. This compounds. More AI usage demands more memory, which drives up memory prices, which raises the floor on every subsequent deployment. The teams getting real returns from AI are the ones expanding fastest, which means the successful projects are the ones most exposed to the next price increase.

Rounding out the list is the growing efficiency and efficacy of AI. While this looks like a paradox at first glance, it’s actually the natural course of technology advancement. Consider a company using chatbots. They’re simple and efficient, but they lack depth. Customers can get quick answers to common questions, but AI models aren’t capable of more complex reasoning.

After doing some research, the business makes the move to agentic AI solutions. These agents are capable of going beyond basic ask-and-answer scenarios with more in-depth research and can assess and understand common emotional cues. The result is a more satisfying experience for customers and improved ROI for companies.

These improved experiences, however, come with a cost. Agentic tools need far more compute power and token volumes than their chatbot counterparts. Still, in most cases, the improved experience is worth the additional spend. Over time, more agents are added, more tokens are used, and total costs go up, not down.

What policies control AI spend?

So how do companies control AI spend?

Policies lay the groundwork for consistent cost control. While suggestions and recommendations make staff aware of rising AI costs, they’re not enough to stem the spending tide. To ensure costs remain reasonable, companies need clear policies that outline expectations, actions, and consequences.

Here are four policies worth implementing.

Budget caps and token quotas

Don’t wait until the bill comes due to set budget caps and token quotas. Instead, C-suites should consult with IT teams to create caps and quotas before deployment occurs.

This reduces the risk of large and unexpected AI bills. While it’s possible to lower budgets after a big bill sidetracks the budget, it’s much easier (and more cost-effective) to ramp up usage than clamp down on operations.

Prompt caching

Every prompt and every output costs tokens. Prompt caching stores and reuses portions of previous prompts for similar queries. Many AI providers offer large discounts on cached rather than new tokens, allowing companies to significantly reduce costs.

Limited steps or tool calls

If organizations don’t have full transparency into agentic tools or AI applications, they could end up with massive token bills. By implementing policies that limit the total number of reasoning steps or tool calls made by AI, teams can avoid issues tied to runaway token usage.

Unified AI allocation

While it’s possible to implement these policies individually across operations, businesses are often better served by AI cost governance platforms. With access to AI outcome attribution, multi-dimensional allocation, and real-time spend data in one place, teams can shift from tracking raw spend to finding real answers.

These answers allow companies to discover what’s driving costs and present actionable plans to C-suites that balance cost control with strategic AI initiatives.

Cost, control, and consistency: finding the balance

How much does AI cost? The answer depends on the type of model used, the number of tokens required, and the nature of functions performed.

Finding a balance requires a recognition of common cost sources paired with clear policies that prioritize comprehensive governance and oversight. Companies can’t control what they can’t see. AI cost governance tied to enforceable policy is what turns AI spend into an answerable question: what did this model, this feature, this agent actually return?

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Frequently Asked Questions about AI cost governance

Author Spotlight

Doug Bonderud

For more than a decade, Doug has worked with companies to create compelling and engaging content. His content has helped multinational enterprises, midsize businesses, and SMB startups better connect with their target audiences and highlight what makes them unique. Doug is passionate about the intersection of technology and humanity, especially the rising impact of AI on our daily lives. He is also a passionate soccer fan and a minimally skilled (but equally passionate) golfer.