Tashkent, Uzbekistan (UzDaily.uz) — Companies in Uzbekistan are increasingly moving away from requests for standalone chatbots towards artificial intelligence systems integrated with their accounting software, customer relationship management (CRM) platforms and other business systems, according to Abbos Khamidov, founder and CEO of AISolution, a Tashkent-based AI integrator and OpenAI Select Partner.
Businesses are approaching AI developers with specific operational problems, including slow customer responses, fragmented data and manual document processing, Khamidov said. He added that customer service and lead processing tend to deliver the fastest returns, while unsuccessful pilot projects often lack clear business objectives, use demonstration rather than real data, or have no designated person responsible for results.
According to Khamidov, a ready-made AI assistant for a small business in Uzbekistan costs from US$1,000, plus monthly support. An enterprise platform comprising several modules and integrated with 1C accounting software typically costs between US$10,000 and US$30,000.
Where businesses are using AI
AI is already being used daily in sectors with high volumes of customer interactions, including education, overseas study agencies, retail, distribution and some banks. In these industries, systems answer customer questions and sort incoming enquiries, Khamidov said.
Most companies in manufacturing, construction and the public sector remain at the pilot stage or use a single AI module, he added.
The nature of customer requests has changed over the past year. Previously, businesses approached developers asking for a chatbot. Now, they are more likely to describe a specific problem: enquiries take too long to answer, information in 1C, CRM systems and warehouses does not match, or employees spend too much time processing documents manually.
Khamidov said this shift indicates that business owners are increasingly focused on saving money and time rather than adopting technology for its own sake.
Projects are usually initiated by company owners, which Khamidov considers the most effective approach because it facilitates access to data and speeds up decision-making. Projects driven solely by IT departments often stall when the main obstacle is a business rule that the IT team has no authority to change.
Uzbekistan entered the AI adoption process later than Kazakhstan and Russia but is moving quickly, Khamidov said. One reason, in his assessment, is that many Uzbek companies have less legacy software to replace.
Language remains a major challenge. Customers frequently switch between Uzbek and Russian and use both Cyrillic and Latin scripts, a combination that many global AI tools still struggle to handle effectively.
Where returns come fastest
Customer service and lead processing can produce measurable results within weeks, Khamidov said. These processes are relatively straightforward to evaluate, and businesses already possess the necessary data, including records of questions that customers ask repeatedly.
He cited two examples from AISolution's work.
An overseas education agency previously took two to three hours to respond to enquiries. After a new system was introduced, response times fell to less than one minute, while the number of consultation bookings increased by 40% in the first month.
An IT school implemented an AI system in two weeks and recorded a 35% increase in conversion over the following six weeks, Khamidov said.
"On our market, customers usually go to whoever responds first," he said, explaining that prospective students at the overseas education agency often chose competitors before the agency's managers had replied.
The systems are not intended to replace managers, Khamidov stressed. Instead, AI identifies customers' needs and budgets, filters out casual enquiries and passes qualified prospective buyers to managers along with their full conversation histories.
This allows managers to spend less time repeating standard answers and more time working with customers who are seriously considering a purchase.
Khamidov recommends agreeing on performance indicators before implementation begins. These should include response times, conversion rates, hours saved and error rates. Simply reporting that an AI system has answered 5,000 messages does not demonstrate its business value, he said.
For larger projects, AISolution links the final payment to acceptance criteria for each module.
Why AI pilots fail
Khamidov identified three common reasons why pilot projects fail to progress to full implementation: the absence of a clear business objective, testing on demonstration data instead of real documents, and the lack of an employee responsible for the outcome.
"If even one of these conditions is missing, the pilot remains a presentation," he said.
Data quality and business rules are often greater obstacles than the technology itself.
Khamidov cited an AISolution client that manufactures plumbing products. Its production, warehouse, sales centres, CRM and finance departments maintained separate sets of data.
Under the company's loyalty programme, sales staff manually entered purchase amounts, leading to errors and instances of abuse. The problem was resolved not through new software but by introducing fixed purchase tiers, he said.
Local business systems are only partly ready for AI integration, according to Khamidov. Companies generally have 1C and CRM software, but the same product may appear under three different names, records may be duplicated, and some processes remain in Excel spreadsheets or Telegram chats.
Before introducing AI, businesses need to establish which systems contain authoritative data, he said.
Errors can be particularly costly in areas involving visas, legal matters or financial transactions. AISolution therefore configures its assistants to respond only using an approved knowledge base maintained by designated personnel. Questions the system cannot resolve clearly are referred to a human employee.
"It is better for it to say, 'Let me connect you with a manager,' than to guess," Khamidov said.
Resistance from employees is another common challenge. Staff may fear losing their jobs, while managers may worry about losing control. Khamidov said this resistance can be reduced by demonstrating during the first few weeks that the system removes routine work while leaving final decisions to people.
He also outlined circumstances in which businesses should reconsider launching AI projects. These include adopting AI simply because competitors use it, without identifying a specific process to improve; lacking access to real data; having no employee responsible for the system; and expecting it to operate entirely independently from the first month.
Costs and return on investment
A ready-made AI assistant for a small business in Uzbekistan costs from US$1,000, plus monthly support, Khamidov said. An enterprise platform with several modules and 1C integration typically costs US$10,000 to US$30,000.
Prices rise with each additional integration and depend on the amount of data cleaning required, security requirements and whether the customer wants the system hosted on its own servers.
Additional costs include model usage fees, knowledge-base updates, technical support and employees' working time spent handling complex cases. Companies most often overlook the last of these expenses, Khamidov said, adding that every deployed system needs an internal owner.
The payback period depends on the process being automated. Lead processing tends to generate returns fastest because businesses can identify customers they previously lost due to slow responses. Internal enterprise platforms take longer to pay for themselves, so Khamidov recommends evaluating returns separately for each module.
He also advocates paid pilot projects using real company data instead of free demonstrations. A free demonstration shows what a technology can do in general, while a paid pilot establishes how it performs with a particular company's documents and employees.
"When a client pays, they genuinely provide us with data and people," he said.
The Uzbek language challenge
Global AI models have become better at processing Uzbek text but still lag significantly behind their performance in Russian and English, Khamidov said. Voice applications are particularly challenging, especially speech recognition and synthesis during real telephone conversations involving different accents and background noise.
Mixing alphabets and languages is routine in Uzbekistan. A single customer message may combine Uzbek written in Latin script, Cyrillic characters and Russian words. AISolution therefore tests its systems using actual customer correspondence rather than standardised examples of literary Uzbek.
For banks, call centres and public services, these limitations make voice-based automation particularly difficult. Many services remain Russian-language or text-only, Khamidov said.
As a result, a substantial segment of the population, particularly outside Tashkent, remains underserved by these technologies, while call centre operating costs remain high, he added.
Khamidov expects businesses to continue using global AI models for general-purpose tasks. The main local gaps concern Uzbek speech and specialised terminology in banking, legal and public-sector applications.
Addressing these gaps requires large volumes of real Uzbek-language speech data and cooperation between businesses, universities and the government, he said. AISolution has consequently established Uzbek speech technology as a separate area of work.
Data security and regulatory requirements
Before granting an AI agent access to corporate systems, Khamidov recommends limiting its permissions to those required for specific tasks, requiring human approval for irreversible actions such as payments or document submissions, recording all system activity in audit logs and initially testing the system in an isolated environment.
"An error by the model must under no circumstances lead to actual harm," he said.
When global AI services are used, data is processed on the provider's servers abroad unless the system has been configured differently, Khamidov noted.
Uzbekistan's personal data law requires the personal data of citizens to be stored on servers within the country.
For sensitive information, AISolution either deploys systems on customers' own servers or local servers, or removes personal information before sending data to an external AI model, he said.
Banks and state-owned enterprises tend to take a more cautious approach, which Khamidov considers justified. They generally require local deployment, clearly defined data-storage rules, audit logs and a paid pilot before approving wider implementation.
He said regulators should clarify which types of data foreign AI services may process and under what conditions, establish practical rules for cross-border data transfers, and determine who is responsible when an AI system makes a mistake.
Skills and future prospects
Uzbekistan has a sufficient pool of software developers, but relatively few specialists have experience taking AI systems into full production, Khamidov said. Such work includes integrating AI with 1C or banking systems, ensuring security and monitoring the quality of generated responses.
AISolution trains engineers internally and plans to expand its team to 50 engineers together with partner teams.
Khamidov identified three essential skills for employees working with AI: clearly defining tasks, checking AI-generated results and maintaining knowledge bases. He said every department should have at least one person who understands both the business process and the technology.
Over the next two to three years, Khamidov expects AI to move beyond standalone chatbots towards systems connected to the core operations of businesses, including 1C, CRM platforms, warehouses and document management systems.
He also anticipates significant improvements in Uzbek speech technology and expects security requirements and local data storage to become standard provisions in tenders.
Demand for AI is growing beyond the capital as well. Although most AISolution projects are currently based in Tashkent, the company already has a major project in Gulistan. Khamidov expects the next wave of adoption to extend further into the regions.
He advised company executives to begin by answering three questions: whether there is a specific process with a clearly measurable problem, such as slow customer responses or manual document processing; whether the company has access to real data for that process; and whether someone within the organisation will be responsible for the outcome.
If all three conditions are met, Khamidov recommends starting with a small, paid pilot project. Otherwise, companies should address the gaps before proceeding.
"AI will not solve these problems for you," he said.