Finding Hidden Capacity in Your 2027 AI Budget

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Boston Consulting Group found that companies expected to roughly double their AI spending in 2026, from about 0.8% of revenue to 1.7%, and that 65% of CEOs ranked accelerating AI among their top three priorities (Apotheker et al., 2026). As budgets for 2027 are finalized, most leadership teams have moved past asking whether to invest and are focused on getting the most work from every dollar they commit.

That focus matters because AI is billed differently from almost anything else a company buys. Much of the cost now arrives as tokens, the small units of text a model reads and writes, metered on every request. Unlike a fixed license fee, token spend grows quietly with each prompt, each document pasted in, and each new team that finds a use for the tools. The companies that pull ahead in 2027 will be the ones that manage tokens with the same care they give to cash, inventory, and their people's time.

Where the Waste Hides

In most organizations, AI spending is spread across departments, vendor accounts, and individual credit cards. Larridin's State of Enterprise AI 2026 research found that the average enterprise runs 23 different AI tools, yet only 38% keep a complete inventory of what is in use (Larridin, 2026). Gartner's 2027 CEO Survey points in the same direction, finding that the main obstacle to CEOs' AI ambitions is operational readiness rather than a shortage of new ideas (Gartner, 2026).

This is the central argument of TokenOps: From Token Waste to Competitive Advantage, a book I wrote earlier this year. Token waste usually comes from habits that made sense during early experiments, such as using the most powerful model for every task or sending the same long instructions with every request, and that were never revisited once usage grew. Each habit is small on its own, but across thousands of requests a day they can consume a meaningful share of the budget without adding value.

The encouraging news is that most of this waste can be recovered without reducing what teams accomplish. In many cases, the same changes that lower cost also make the tools faster and easier to use.

Five Places to Find Capacity

The following areas are where leaders most often find room in their AI budgets. Each comes with a question worth bringing to your next leadership or technology review.

Match the Model to the Task

AI providers offer families of models at very different prices. The most capable models earn their cost on complex analysis, strategy work, and difficult writing. Routine tasks such as sorting emails, summarizing meeting notes, or pulling figures from invoices can usually be handled by smaller models that cost a fraction as much and respond faster. Many organizations default to the top model everywhere simply because it was the one used in the pilot.

Question to ask: Which of our high-volume tasks are running on our most expensive model, and do they need to be?

Trim What Every Prompt Carries

Every request pays for everything sent along with it, including standing instructions, background documents, and the full history of a long conversation. Teams tend to add context over time and seldom remove it, so prompts grow heavier month after month. Shorter, clearer instructions and only the documents a task actually needs reduce cost on every call and often improve the quality of the answer.

Question to ask: What are we sending to the model on every request that it does not need?

Reuse What Repeats

When the same instructions or reference material go out with request after request, most major providers offer prompt caching, which stores that repeated content so it is not billed at full price each time. On OpenAI's current flagship models, cached input costs 90-95% less than standard input (OpenAI, n.d.). Anthropic charges a modest premium to store content in the cache but bills each reuse at a tenth of the standard rate or less, so with the standard five-minute cache, the savings begin after a single reuse (Anthropic, n.d.). Shared templates and prompt libraries extend the same idea to people, so teams stop rebuilding the same prompts on their own.

Question to ask: Are our most-used workflows set up to take advantage of caching and shared templates?

Batch the Work That Can Wait

Many tasks do not need an instant answer. Overnight reports, bulk document reviews, and large data-tagging jobs can be submitted in batches and processed in the background at a lower price. Anthropic, for example, offers a 50% discount on both input and output tokens for batch requests and allows that discount to be combined with caching, so the savings on suitable work can be greater still (Anthropic, n.d.).

Question to ask: Which of our AI workloads are truly time-sensitive, and which could run overnight at half the cost?

Consolidate Overlapping Tools

As adoption spreads, different teams often buy similar products to solve the same problem, and individual subscriptions pile up on expense reports outside any central budget. Bringing these under a smaller set of approved tools reduces duplicate fees, strengthens the company's position in vendor negotiations, and lowers security risk, a concern that has grown sharply after this fall's wave of AI-assisted cyberattacks.

Question to ask: How many tools are we paying for that do the same job?

Manage Tokens Like Any Other Operating Cost

A one-time cleanup helps, but the gains last only when leaders can see how AI is being used. Leading companies are beginning to track AI usage the way they track cloud computing or payroll, by team, by use case, and by the business result it supports. Once leaders can see which workflows consume the most tokens and what each one produces, budget conversations become far more productive, spending can follow results, and the teams getting the most value can be given more room to grow.

This does not require a large new function. For most mid-sized companies, it starts with three simple habits: naming an owner for AI spending, reviewing each provider's usage reports every month, and asking every team that uses AI to describe what it is getting in return. Those habits are often enough to surface the largest opportunities within a single quarter.

Turning Savings Into Advantage

The purpose of token optimization is to expand what a company can do with AI. Every dollar recovered from an oversized model, a bloated prompt, or a forgotten subscription can fund the next customer-facing tool, the next automation, or broader training for employees. Over time, an organization that runs its AI efficiently can afford to experiment more often and scale its best ideas faster than competitors spending the same amount with less discipline.

As you finalize your 2027 plans, set aside an hour with your finance and technology leaders to work through the five questions above. The answers will likely reveal capacity you are already paying for. For a fuller framework on measuring token use and building it into planning, explore TokenOps: From Token Waste to Competitive Advantage, or reach out to discuss AI strategy and training for your leadership team.

References

Anthropic. (n.d.). Pricing. Claude Platform Docs. Retrieved November 12, 2026, from https://platform.claude.com/docs/en/about-claude/pricing

Apotheker, J., Duranton, S., Lukic, V., de Bellefonds, N., & Schweizer, C. (2026, January 15). As AI investments surge, CEOs take the lead. Boston Consulting Group. https://www.bcg.com/publications/2026/as-ai-investments-surge-ceos-take-the-lead

Gartner. (2026, September 18). CEO watch 4Q26: AI investments reveal a dangerous readiness gap. https://www.gartner.com/en/documents/8397281

Larridin. (2026, July 2). How AI tool sprawl is draining your tech budget. https://larridin.com/blog/ai-tool-sprawl-enterprise

OpenAI. (n.d.). Pricing. OpenAI API. Retrieved November 12, 2026, from https://developers.openai.com/api/docs/pricing