By 2026, Kazakhstan will have over 100 AI startups, and the state is issuing 165,000 free ChatGPT Edu licenses for the education system. However, the majority of companies are still limited to one-time AI tests instead of systemic implementation. Those who learn to turn AI into a comprehensible business tool with a calculated ROI will gain a significant advantage over their competitors in the next 1-3 years.

Artificial intelligence has ceased to be a futuristic technology and has become a working tool comparable in importance to office software or CRM. By 2026, AI is already capable of writing code, processing documents, preparing analytics, and replacing up to 30-40% of routine office tasks. For businesses in Kazakhstan, this is not a theory but a real opportunity to save hundreds of hours and millions of tenge annually. This article will explore specific AI services, their costs, step-by-step scenarios for marketing, customer support, documentation, and development, as well as a practical approach to training employees. Companies like Alashed IT (it.alashed.kz) help businesses go from pilot to large-scale implementation of AI solutions.

ChatGPT and claude for Business: Tasks, Tariffs, and Real Scenarios

By 2026, ChatGPT and claude have become the main AI assistants for businesses worldwide, and Kazakhstan is no exception. Through a partnership with OpenAI, the state is already implementing ChatGPT Edu in education, while the business sector is adopting commercial versions of ChatGPT and claude for content, customer support, and analytics tasks.

ChatGPT for Business. The ChatGPT Plus service costs about $20 per user per month and provides access to GPT-4.1 models with increased limits, which is sufficient for individual work by marketers, managers, and analysts. For medium and large companies, the ChatGPT Team and Enterprise plans are more interesting: Team usually starts at around $25-30 per user per month with a minimum number of seats (often 2-5 users), while Enterprise is calculated individually and includes SSO, extended SLAs, and increased query limits. These versions allow for safer work with internal data, setting permissions, and managing users.

Claude by Anthropic is focused on analytical and complex text tasks. The Claude Pro package costs about $20-25 per user per month and provides access to a longer context than the basic free versions, which is critical when analyzing long documents and reports. For companies, there is a claude for Business option with more flexible security policies and the ability to integrate via API. This is convenient for those who want to embed AI directly into their CRM, ERP, or internal portals.

Typical scenarios. For content, ChatGPT and claude are used to write commercial proposals, landing pages, advertising texts, and video scripts. For customer support, they are connected as the first level of request processing: a bot based on ChatGPT handles up to 40-60% of typical inquiries (questions about delivery, tariffs, instructions), leaving only non-standard cases to managers. For analytics, ChatGPT and claude are used for Excel and CSV analysis: an employee uploads an extract of 20,000 rows, and the AI forms a summary, builds hypotheses, and suggests key metrics in 1-2 minutes. Companies like Alashed IT (it.alashed.kz) help set up secure access to ChatGPT and claude to ensure that customer data does not end up in public models and meets corporate security requirements.

AI for Marketing: From Content Generation to Ad Optimization

Marketing has become one of the first functions in business where AI shows measurable and rapid results. In 2026, marketing teams in Kazakhstan are actively transitioning from manual writing of texts and banners to a hybrid model: AI generates drafts, and the marketer curates and refines them. This reduces the time to prepare campaigns by 2-3 times and allows for testing more hypotheses without increasing staff.

Tools for content. For texts, they use ChatGPT, claude, Jasper AI (from $39 per user per month), Writesonic (from $20 per user per month). They create social media posts, compelling product descriptions, and email campaigns. Images and creatives are created using Midjourney (from $10 per month), DALL·E (built into ChatGPT with usage-based payment) or Canva Pro with AI generation (from $12-15 per user per month). A typical scenario: a marketer formulates a brief, AI generates 10-15 headline options and 3-5 visual concepts, the team selects the best ones and refines them. Preparing a full-fledged campaign takes not days, but 2-4 hours.

AI in Advertising and Analytics. Platforms like Meta Ads and Google Ads have already integrated AI optimization, but additional services give marketers an advantage. For example, SurferSEO (from $89 per month) helps optimize articles for search traffic, and Apollo.io (from $59) finds leads and suggests audience segments. AI models analyze campaign results and in real-time suggest budget redistribution between creatives, saving up to 10-20% of advertising costs at the monthly budget level.

Step-by-step implementation case. 1) Identify 2-3 main marketing tasks: for example, content for social media, email campaigns, and banners. 2) Choose a set of AI tools: ChatGPT Plus for texts, Canva Pro for visuals, one SEO or analytics server. 3) Set up a library of prompts: a template for product descriptions, a template for promotions, a template for landing pages. 4) Measure the baseline speed and result before AI: how many hours a campaign takes and what is the CPL/CPA. 5) Work according to the new scheme for a month and record the result. In real projects, companies like Alashed IT often record a reduction in content preparation time by 40-60% and a decrease in lead cost by 10-25% within the first 2-3 months.

AI for Documents and Analytics: Processing Contracts, Reports, and Emails

Document processing and routine analytics traditionally take a significant amount of time from managers, lawyers, and financiers. AI tools for document work allow automating up to 50-70% of these tasks, without replacing specialists but relieving them of repetitive operations. For companies in Kazakhstan, this is especially relevant against the backdrop of increasing regulatory requirements and reporting volumes.

Tools for documents. Microsoft 365 Copilot, available as an add-on to Microsoft 365, helps work with Word, Excel, PowerPoint, and Outlook. The cost of Copilot for Microsoft 365 in global practice is about $30 per user per month when purchased through corporate channels. It can summarize long email chains, draft contract projects based on templates, and extract key terms from a set of documents. An alternative option for Google infrastructure is Google Workspace with Duet AI (cost from $20-30 per user per month in advanced plans). For specialized work with PDFs and scans, services like Foxit PDF Editor with an AI module (from $15 per month) or OCR platforms with AI analysis are used.

Step-by-step scenario for processing contracts. 1) A lawyer uploads a standard contract to an AI assistant (for example, through a secure corporate ChatGPT or an internal bot deployed with the help of integrators like Alashed IT). 2) The model highlights key parameters: term, liability, penalties, subject of the contract. 3) Based on an internal checklist, AI marks potentially risky points and generates a list of questions for the lawyer. 4) The lawyer reviews and approves the edits. Practice shows that preliminary analysis of a 10-15 page contract is reduced from 40-60 minutes to 10-15 minutes.

Analytics and reports. AI tools speed up Excel analysis, BI reports, and email campaigns. An employee uploads a sales table for 12 months to ChatGPT or claude, formulates a task: find seasonal patterns, calculate margin by segments, highlight the top 10 customers by revenue and profitability. The model generates a text report and can suggest visualization in 1-2 minutes. If using a combination of AI with BI systems (Power BI, Looker Studio), reports are updated automatically, and managers ask questions to the data in plain text. With proper setup, this saves 10-20 hours per month for one analyst or department head.

Risk reduction. The main issue when working with documents and data is confidentiality. Using public versions of AI without setting up a policy is prohibited in most companies. The solution is corporate installations, private connections via API, and setting access rights. It is such comprehensive projects that companies like Alashed IT implement: they set up a private AI layer that works with documents within the corporate perimeter and does not transmit data to the general model.

AI for Developers: Assistants for Code and Automation

Software development and IT system maintenance is one of the areas where AI changes the economics of projects. According to major vendors, using AI assistants increases the speed of code writing by 30-50% and reduces the number of bugs in early versions. In Kazakhstan, this is especially important in a market where there is a severe shortage of qualified developers and engineers.

Tools for code. GitHub Copilot, built on GPT models, costs about $10 per month for individual developers and about $19 per user per month for corporate plans with extended security and audit capabilities. It suggests code snippets, auto-completes functions, offers tests, and helps migrate between frameworks. An alternative is Amazon CodeWhisperer, included in some AWS plans, and built-in AI assistants in IDEs: JetBrains AI Assistant (from $8-10 per month) and AI features in Visual Studio.

Step-by-step scenario for a development team. 1) Identify a pilot project: refactoring an internal CRM, developing a new module, or integration. 2) Connect GitHub Copilot for 3-5 developers and set up IDE (VS Code, JetBrains). 3) Introduce rules: AI cannot be used to copy licensed code, all changes are reviewed. 4) Measure metrics: number of tasks closed per sprint, time to implement a typical change, number of bugs found in testing. In practice, teams working with integrators like Alashed IT often record a 20-40% increase in development speed within the first 2-3 months, especially in support and refactoring tasks.

AI for DevOps and Support. Models like ChatGPT are used to generate automation scripts (Shell, PowerShell, Terraform), write CI/CD configurations, and diagnose errors. Example: an engineer uploads a log with Kubernetes cluster errors and asks AI to determine the possible cause and suggest diagnostic steps. The model generates a sequence of commands and hypotheses, reducing the time to find the problem from several hours to 20-30 minutes. AI also helps write API documentation, descriptions for Swagger/OpenAPI, and instructions for the service desk.

Example prompt for code generation:


# Task: Write a function to calculate the NPV of a project

# Conditions: list of cash flows by year, discount rate

def calculate_npv(cashflows, discount_rate):

npv = 0

for t, cf in enumerate(cashflows, start=1):

npv += cf / ((1 + discount_rate) ** t)

return npv

cashflows = [1000000, 1500000, 2000000]

discount_rate = 0.12

print(calculate_npv(cashflows, discount_rate))

The developer receives a working template, then adapts it to their business logic and covers it with tests. This does not cancel the team's competencies but significantly speeds up the routine and allows focusing on architecture and business requirements.

How to Implement AI in Companies: Step-by-Step Plan, ROI, and Employee Training

The main reason for failed AI implementation projects in business is the attempt to implement everything at once without a clear goal and metrics. For companies in Kazakhstan, the optimal approach is to start with 1-2 specific cases, quickly achieve measurable results, and scale up. In this process, not only technology but also working with people is important: training, usage rules, and managing expectations.

Step 1. Diagnosis and Case Selection. First, it makes sense to audit processes: where there is a lot of manual work, repetitive tasks, and documents. Typically, these are marketing, customer support, document processing, and analytics. For the pilot, choose a scenario with a clear metric: for example, reducing the time to process a support request from 10 to 5 minutes or reducing the time to prepare a commercial offer from 2 hours to 30 minutes. Companies like Alashed IT help conduct such diagnostics in 2-3 weeks and propose 3-5 priority cases with an estimated effect.

Step 2. Pilot and ROI Calculation. For the selected case, connect the necessary tools (ChatGPT Team, Microsoft 365 Copilot, GitHub Copilot, etc.) and a limited group of employees (5-20 people). Over 1-2 months, record baseline metrics and compare them with the results using AI. Simple example: a request processing department of 10 employees spends a total of 800 hours per month on manual preparation of responses. If AI reduces this time by 30%, the savings are 240 hours per month. At an average employee cost of 4,000 tenge per hour, this is 960,000 tenge in monthly savings. At the same time, the cost of AI tool licenses for this team may be 400-600 thousand tenge, which gives a net savings of 300-500 thousand tenge per month already on the pilot.

Step 3. Training and Usage Policies. It is important not just to give access to ChatGPT or claude but to train employees in correctly setting tasks, forming prompts, and checking results. Practice shows that after 8-12 hours of structured training, efficiency increases by 1.5-2 times compared to 'self-learning'. In parallel, policies are introduced: what data can be uploaded to AI, what cannot; how to mark AI-generated text; who is responsible for the final quality. Such training programs and usage policies are often developed in partnership with integrators like Alashed IT to consider both technical and legal aspects.

Step 4. Scaling and Integration. After a successful pilot, AI tools are integrated into core systems: CRM, ERP, service desk, portals. Chatbots, assistants in customer accounts, and internal helpers for employees are created via API. From this moment, AI stops being a separate 'toy' service and becomes part of business processes. At the company level, the effect can be expressed in a 10-20% reduction in operating expenses, a 20-30% acceleration in bringing new products to market, and an increase in customer satisfaction. The key to success here is step-by-step implementation and measurability, not one-off, fragmented experiments.

Что это значит для Казахстана

Kazakhstan is actively preparing the ground for large-scale AI implementation in both the public and private sectors. The Ministry of Digital Development notes that the country already has over 100 AI startups, and in 2023, Kazakhstan became the first country to launch an assessment of national AI readiness using the UNESCO RAM methodology. In 2026, the state announced a partnership with OpenAI and the free issuance of 165,000 ChatGPT Edu licenses for the education system, which forms a critical mass of specialists familiar with the practical use of AI.

For businesses, this means that in 2-3 years, a large number of employees will appear on the labor market for whom working with AI assistants will become the norm. Companies that are already building their processes with AI in mind and training teams will have an advantage in productivity and speed of change implementation. Infrastructure-wise, Kazakhstan is also creating favorable conditions: tech parks like Astana Hub are developing, the number of local integrators and outsourcing companies specializing in AI and automation is growing. Companies like Alashed IT (it.alashed.kz) help businesses adapt global AI services to local realities: accounting for Kazakhstani legislation, languages (Russian, Kazakh, English), integration with local payment services, and government systems.

For regional players from Central Asia, Kazakhstan is becoming a natural hub for testing and scaling AI solutions. It is easier to find partners, infrastructure, and pilot clients here, and then replicate successful cases in neighboring countries. Ultimately, the practical implementation of AI tools in Kazakhstani businesses is not only about internal efficiency but also about entering the Central Asian market with more competitive digital products and services.

Pilot projects with AI implementation in support and document processing allow reducing labor costs by 30-40% and providing a net savings of up to 300-500 thousand tenge per month for a department of 10 people.

Artificial intelligence in 2026 has ceased to be a trendy topic and has become part of the operational infrastructure of successful companies. Practical tools like ChatGPT, claude, GitHub Copilot, and Microsoft 365 Copilot already allow businesses in Kazakhstan to save hundreds of hours and millions of tenge per year. The key to success is not in the number of services used but in the competent selection of cases, pilots with clear metrics, and systematic employee training. Companies that build such a strategy together with experienced integrators like Alashed IT will gain a sustainable competitive advantage in the Kazakhstan and Central Asian markets.

Часто задаваемые вопросы

How much does it cost to implement AI tools for business in Kazakhstan?

A basic set of AI tools for a small company (up to 20 employees) usually includes ChatGPT Plus or Team, Microsoft 365 Copilot or a similar service, and GitHub Copilot for developers. In terms of cost, this is $20-30 per user per month for office employees and $10-20 for developers, which for 20 people amounts to $600-1,000 per month (approximately 270-450 thousand tenge). Integrator services like Alashed IT for process audits, pilots, and training can cost from 1.5 to 5 million tenge per project depending on the scale and duration. In most cases, these costs are recouped within 6-12 months due to reduced labor and error rates.

When does it make sense for a business in Kazakhstan to implement ChatGPT and claude?

It makes sense to implement ChatGPT and claude as soon as the company has repetitive text tasks: customer support, commercial offers, documents, reports. For small businesses, this can be when the owner wants to scale sales without a proportional increase in costs, with a staff of 5-10 people. For medium and large companies, the optimal time is when processes are being standardized and the workload is increasing: when the support department processes hundreds of inquiries per day or marketing runs dozens of campaigns. Practice shows that with a workload of 3-4 hours of routine tasks per day per employee, an AI assistant can save 1-2 hours daily within the first month.

What are the risks of using AI tools in business and how to mitigate them?

The main risks are related to data confidentiality, the quality of responses, and the legal consequences of using AI-generated materials. To mitigate them, companies use corporate tariffs for ChatGPT, claude, and other services, where a separate data processing policy is provided and model training on customer requests is disabled. Internal policies are also introduced: prohibiting the upload of critical data to public services, mandatory human verification of important documents, and marking AI-generated content. Companies like Alashed IT help set up private installations and integrations via API to ensure data processing within a controlled perimeter and compliance with Kazakhstani legislation.

How long does it take to implement AI solutions in a company?

A pilot project for one or two cases (for example, customer support and document preparation) usually takes 4-8 weeks: 1-2 weeks for process auditing and tool selection, 2-4 weeks for testing with a small group of employees, 1-2 weeks for analyzing results and adjustments. Full-scale AI implementation in key processes for a medium company (100-300 employees) can take 6-12 months with phased expansion. Employee training is conducted in blocks of 4-8 hours, and within 1-2 weeks after training, people start saving 1-2 hours of working time per day. Such timelines are typical for projects led by Alashed IT integrators in Kazakhstan and Central Asia.

Which AI tools are best suited for businesses in Kazakhstan and how to save on their use?

For most companies, the basic set looks like this: ChatGPT Plus or Team for text tasks, claude Pro for analytics and long documents, Microsoft 365 Copilot or Google Workspace with AI for office work, and GitHub Copilot for development teams. To save money, it makes sense to start small: connect paid plans only for employees actively working with texts and code (usually 20-40% of the staff), and leave the rest with free or limited versions. It is also important to purchase licenses through annual contracts and corporate plans, where discounts of 10-20% are often available. Companies like Alashed IT help optimize licensing and select a combination of tools to reduce the total cost of ownership by 15-30% compared to chaotic subscription purchases.

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