Artificial intelligence adoption in many companies began with enthusiasm. Employees were encouraged to use AI, test new tools, accelerate work, generate more content, write more code, analyze more information and automate more processes. In the first stage, AI spending was often treated as a sign of innovation: if a company used a lot of AI, it meant that it was thinking about the future.
Now a new question is emerging in global business: How much does large-scale AI use actually cost, and who controls that cost?
AI agents – systems that can read, interpret, plan and act – use far more computing power than simple chatbots. They process large amounts of text, documents, data and code. This usage is often priced in tokens, the small units of text processed by AI models. The more tokens are used, the higher the cost becomes.
For Georgia, this issue is especially important. Georgian companies, banks, universities, startups and public institutions often still imagine AI as a tool for “cheap productivity.” But if AI use grows without measurement, rules or budgeting, organizations may quickly discover that automation has not reduced costs, but created a new and unexpected expense.
BTUAI assesses that the next stage for Georgian business should not be simply more AI usage, but AI cost management: which model is needed for which task, how much each outcome costs, what real value is created, where overuse appears and how AI budgets should be planned so that innovation does not become a financial risk.
Main idea
The first stage of AI adoption was experimentation. The second stage will be discipline.
Companies have already seen that AI can write text, generate code, analyze documents, create presentations, answer customers and support employees. But when AI is used at scale, a new problem appears: the cost is often invisible until the bill arrives.
Traditionally, a company knows how much it pays for salaries, office space, software licenses or advertising. With AI, costs can spread across hundreds of employees, dozens of software tools and thousands of small prompts. Each request may look small, but together they can create a large bill.
Therefore, the financial question of the AI era is: Does AI save money, or does it create a new hidden cost?
Why AI costs are rising
AI costs are rising for several reasons.
The first reason is scale. If a company uses AI for only a few employees, the cost may be manageable. But if AI enters every department – marketing, sales, software development, HR, legal, finance and customer service – the cost grows quickly.
The second reason is the rise of AI agents. A simple chatbot gives an answer to one prompt. An agent may divide a task, search for information, create several versions, verify outputs, write code, call other tools and then prepare the final result. This means more computation and more cost.
The third reason is overuse of expensive models. Not every task needs the most powerful model. Sometimes a cheaper model is enough for editing a simple text, summarizing an internal document or drafting a standard message. If a company uses the most expensive model for every task, costs rise unnecessarily.
The fourth reason is lack of internal control. If employees do not have limits, guidance or evaluation systems, they will naturally use AI even where it does not create significant value.
The fifth reason is changing pricing by AI providers. Today, many model providers subsidize usage as they try to attract customers. In the future, when model-makers need to become profitable, prices may rise.
What global experience shows
Global business already shows several important signals.
One analysis of corporate spending suggests that companies’ AI expenditure increased 13-fold in a single year. One large company reported that it had used its annual AI budget in four months. Among the highest-intensity users, average monthly AI spending reached about $7,450 per employee, compared with only about $11 for the median customer.
This difference shows that AI spending does not grow equally in all companies. The problem is most visible where AI is used intensively, especially in software development, agentic systems, automated research and heavy data processing.
Companies are already responding. Some are removing internal leaderboards that rewarded heavy AI use. Others are setting monthly limits. In some cases, companies define a specific monthly budget for each employee using a coding tool. Others are beginning to choose models according to task: expensive models for complex work, cheaper models for standard tasks.
The main lesson from global experience is clear: AI usage should grow by value, not by volume.
Why this matters for Georgia
Many Georgian companies are still in the early stage of AI adoption. They have tested individual tools: text generation, translation, marketing ideas, code assistance, customer replies and data summarization.
This is a good beginning. But the next stage is more difficult: AI must enter the company’s workflow. This is where cost risk appears.
For Georgian businesses, this is especially important for several reasons.
First, many Georgian companies have small or medium-sized budgets. If AI costs grow without control, they may quickly become an excessive operating expense.
Second, labor costs in Georgia differ from those in the United States and Western Europe. In markets where AI replaces or accelerates the work of an expensive developer or analyst, the cost may be easy to justify. In Georgia, the same AI cost may be relatively expensive unless it creates clear productivity.
Third, companies often lack systems for measuring AI results. If a company does not know how much time AI saves, how many errors it reduces, how much revenue it creates or how much service quality improves, it cannot know whether AI spending is an investment or an unnecessary cost.
Fourth, AI adoption in Georgia is often driven by fashion, reputation or fear of falling behind. But “everyone is using AI” is not a business case. Specific value is needed.
Where AI costs may rise in Georgia
- Banks and fintechs
Banks may use AI in customer service, risk analysis, fraud detection, document processing, personalized offers and internal productivity.
But if AI agents begin processing large volumes of data, documents and customer communication automatically, costs may rise quickly. Banks need to know not only the benefits of AI, but also the real cost of each automated process.
- IT companies and software developers
AI is especially strong in writing code. Therefore, AI use grows quickly in IT companies. But code generation, testing, debugging, documentation and agentic programming are often token-heavy.
For Georgian IT companies, the key question will be: does AI reduce development time enough to justify the cost, or does it simply generate more code that still needs human review?
- Universities and education
Universities may use AI in learning materials, student support, research, administration and personalized learning.
But if AI enters every course, every student service and every administrative process, cost management becomes essential. Universities will need not only an AI policy, but also an AI budget.
- Public services
In the public sector, AI can support citizen service, document processing, question answering, analysis and administrative workflows.
But in public services, cost is not the only issue. Responsibility also matters. AI must be transparent, secure and justified from a budget perspective.
- Marketing and media
AI is easy to use in text creation, video scripts, advertising ideas and social-media content. This is exactly where overuse may appear: many versions, many experiments, many automated generations – but limited real impact.
What AI budgeting means
AI budgeting means that a company defines in advance:
how much it spends on AI;
who uses AI;
which department uses it and for what purpose;
which model is used for which task;
what the monthly limit is;
how outcomes are measured;
when AI spending is justified;
when a tool should be stopped or changed.
This is now part of financial management. AI should not be an unlimited digital resource that everyone spends differently. It should become manageable, measurable and tied to outcomes.
The most expensive model is not always the best choice
One of the main ways to reduce AI cost is choosing the right model.
The most powerful model is needed when the task is complex: legal analysis, high-risk decision-making, difficult code, strategic documents, multi-source research or critical business processes.
But many everyday tasks do not need such a model. Simple summaries, first drafts of internal messages, standard translations, idea lists or basic classification can often be done by cheaper models.
This is especially important for Georgian companies because cost efficiency must be assessed more strictly. Proper AI use does not mean always choosing “the strongest” model. It means using a sufficiently good model for the right task.
Outcome-based pricing
AI product pricing is also changing. Some companies are trying to offer not only token-based pricing, but outcome-based models.
For example, a customer may pay only when an AI agent actually resolves a support request, closes a case or produces a measurable result.
For Georgia, this approach is interesting. Georgian companies often struggle to justify abstract AI spending. Outcome-based pricing is easier to understand: how many questions were resolved, how much time was saved, how many cases were closed, how much sales increased or how many errors were reduced.
What Georgian businesses should do
For Georgian businesses, AI cost management should begin with several practical steps.
- Make AI costs visible
A company should know how much it spends on AI overall, by department and by employee. If the cost is not visible, it cannot be managed.
- Classify tasks
Not all AI tasks are equal. Companies should classify tasks into:
simple low-risk tasks;
medium-complexity analytical tasks;
high-risk decisions;
critical business processes.
The model and budget limit should be selected accordingly.
- Choose models by value
Using expensive models for every task is wrong. A company should define which model is used for which task.
- Set monthly limits
Each department or employee can have a monthly AI budget. This does not stop innovation. It creates responsible usage.
- Measure outcomes
AI spending should be linked to outcomes: time saved, sales growth, service improvement, error reduction, faster document processing or better code quality.
- Protect data
AI budgeting should come together with data-protection rules. A cheap or free tool can become expensive if it creates a data risk.
What the state should do
AI cost matters especially in the public sector because technology spending is ultimately financed by citizens.
Several steps are needed:
a transparent framework for AI procurement;
cost-benefit evaluation of technology projects;
usage limits for AI services;
clear data-protection standards;
AI budgeting rules in the public sector;
consideration of outcome-based contracts;
evaluation of pilot projects before scaling;
analysis of the cost and value of Georgian-language AI resources.
In the public sector, AI should not be adopted only because it is new. It should be adopted where it improves citizen service and where the cost is justified.
What universities should do
Universities should teach AI cost as part of AI education. Students should understand not only how to use a model, but also what its use costs.
Education should include:
what a token is;
how AI cost is created;
how to choose a model by task;
how to measure AI productivity;
how to write an AI budget;
how to evaluate the business impact of AI projects;
how AI cost connects with data security;
how AI is managed inside organizations.
For BTU, this topic is especially important because the economics of AI should be taught not only technically, but also financially, organizationally and ethically.
Key risks for Georgia
The first risk is fashionable and unmeasured AI use. If a company simply gives employees access to all tools without measuring results, costs may rise quickly.
The second risk is overuse of the most expensive models. Not every task requires the strongest AI.
The third risk is ignoring data security. A cheap tool may become expensive if it exposes confidential data.
The fourth risk is unexpected budget growth, especially when AI agents generate additional requests or work through many steps.
The fifth risk is measuring AI value incorrectly. If a company measures only usage volume and not outcomes, many tokens may be spent on little value.
Opportunities for Georgia
The first opportunity is responsible AI adoption. Georgian companies can build cost-management discipline early and avoid mistakes made by global firms.
The second opportunity is a new competence in AI financial management. Demand will grow for specialists who can analyze AI costs, model selection, productivity and business impact.
The third opportunity is protection for small and medium-sized businesses. Properly selected AI tools can genuinely help Georgian SMEs if costs remain controlled.
The fourth opportunity is development of the Georgian AI ecosystem. Knowledge of local language, data and business processes can support AI solutions that are more efficient for the Georgian market.
BTUAI assessment
BTUAI assesses that rising AI costs are one of the most practical and timely issues for Georgia. After the first stage of enthusiasm, Georgian businesses should move to the second stage: financial management of AI.
AI usage is not success by itself. Success comes when AI reduces time, improves quality, strengthens service, creates revenue or reduces errors at a cost that is justified.
In Georgia, where many companies are small or medium-sized, the economics of AI must be calculated carefully. What is a small expense for a large American company may be a major budget risk for a Georgian company.
The main conclusion is that in the AI era, companies need not only an AI strategy, but an AI budget. Smart use of artificial intelligence does not mean maximum use. It means using the right model for the right task, at the right price, with measurable results.
Key findings
- Large-scale AI use creates a new financial risk for companies.
- AI agents are especially token-heavy and can increase costs quickly.
- Global companies are already limiting excessive AI use and introducing spending caps.
- For Georgia, AI cost is especially important because local labor costs and company budgets differ from large Western markets.
- Not every task needs the most expensive model. Model choice should depend on task complexity and risk.
- AI budgeting should become part of business financial management.
- The success of an AI project should be measured by outcomes, not by the number of tokens used.
- Universities should teach the economics of AI cost as part of new technology education.
Data and evidence base
International business practice already shows a sharp rise in AI costs.
One analysis of corporate spending suggests that companies’ AI expenditure increased 13-fold in one year.
One large technology company spent its annual AI budget in four months.
Among the highest-intensity AI users, average monthly spending reached about $7,450 per employee, while the median customer spent around $11.
Some companies are setting monthly limits for employees – for example, a $1,500 limit for specific coding tools.
Price differences between models can be very large. In some cases, a mid-level model is far cheaper than a leading model, while open or lower-cost models can reduce cost further.
These signals show that AI cost is already a separate management issue, not only a technical detail.
Methodology
This report was prepared as part of BTUAI Research. The analysis is based on international business and technology trends related to AI costs, token-based pricing, AI agents, corporate budgeting, model selection and measurement of AI productivity.
The materials are processed using analytical methods applied by BTU researchers, with the support of BTUAI.
The purpose of the research is not to recommend a specific AI tool or model, but to explain a financial and organizational trend that may affect Georgian businesses, universities, startups and the public sector.
Limitations
AI service prices, model capabilities and corporate usage practices are changing quickly.
This material does not recommend any specific AI product, provider, model, software license or technology purchase.
The implications described for Georgia are analytical scenarios and require additional research into local companies’ AI costs and productivity.
This material is analytical and educational in nature. It does not constitute financial, investment, legal, tax or technology-procurement advice. Before making specific decisions, consultation with a relevant specialist is required.
Sources
International business and technology analysis of AI costs, token-based pricing and corporate AI usage.
Global practice in AI budgeting, model selection, spending caps and outcome-based pricing.
BTUAI analytical interpretation based on Georgia’s business, educational, technological and public-sector context.
Frequently asked questions
What is a token?
A token is a small unit of text processed by an AI model. Many AI services are priced according to the number of tokens used.
Why can AI costs rise quickly?
Because AI agents often work through many steps, process large amounts of text and data, use expensive models and may spend far more resources on one task than a simple chatbot.
What should a Georgian company do first?
It should make AI spending visible: who uses AI, for what purpose, how much is spent, what result is achieved and which model is used for each task.
Does cost control stop innovation?
No. Cost control means AI is used more intelligently – where it creates real value.
What is the main conclusion for Georgia?
For Georgian businesses, AI success should not be measured by how much AI is used. It should be measured by how much real value AI creates compared with its cost.
Keywords
AI costs; tokens; AI budget; AI agents; artificial intelligence in business; AI productivity; AI in Georgia; model selection; AI cost management; Georgian business; BTUAI; Business and Technology University.
Citation format
BTUAI Research Team. “AI Costs Are Rising: What Georgian Businesses Should Consider Before Artificial Intelligence Becomes a Budget Risk.” Business and Technology University, BTUAI.ge, 2026.
Prepared by the academic team of Business and Technology University and the BTUAI Research Team.
Tbilisi, Georgia
BTUAI is an analytical platform of Business and Technology University that studies the impact of artificial intelligence, digital transformation, innovation, startup ecosystems, data analytics and emerging technologies on business, the economy, education and society. BTUAI materials are designed to explain complex technological and economic changes in a clear, reliable and Georgia-focused way.