Key Takeaway

AI Has Already Become an Everyday Business Tool in Georgia

The Chamber survey shows that artificial intelligence is moving beyond experimentation in Georgian business. Sixty-two percent of respondents use AI tools, most often for content creation, marketing, and information search. These are activities in which an employee can obtain a visible result immediately: a draft, an idea, a plan, or a summary can be produced within seconds and inserted into daily work.

Speed is AI’s most attractive feature, but it also creates a management problem. The same platform can produce completely different outcomes for two employees. An experienced marketer can use it to segment audiences, develop campaign alternatives, and refine a message. An inexperienced colleague may publish the first generated text, overlook a factual error, lose the brand voice, or use content that does not reflect the realities of the Georgian market.

Growth in use does not automatically mean that knowledge grows at the same pace. AI lowers the technical barrier to completing a task, but it does not remove the need for professional judgment. As text, analysis, and recommendations become faster to produce, employees must make more frequent decisions about what is correct, what only sounds convincing, which data should be used, and where the model’s competence ends.

This is especially important in Georgian SMEs, where one employee often performs several functions. A marketer may create content, research the market, prepare a sales proposal, and answer customers. AI gives that employee scale. But without adequate knowledge and validation habits, one mistake can spread through several business functions at once.

Why Strong Employees Gain More from AI

An international experiment with small-business owners in Kenya illustrates the difference. Participants received access to a GPT-4-based AI adviser for six months. Stronger entrepreneurs adapted recommendations to local conditions and increased profits by an average of 15%. They could distinguish generic advice from an actionable opportunity—for example, buying a generator during power disruptions or expanding a service for which demand already existed.

Lower-performing entrepreneurs often did the opposite. They implemented generic advice directly, without testing it against local conditions or the economics of their company. A recommendation to lower prices became a reduction in revenue and margin, and income in this group fell by 10%. Access did not create the difference; participants had the same type of adviser. The difference came from the user’s ability to evaluate the recommendation and translate it into a context-specific decision.

A strong employee does more than ask AI a question. The employee creates context, defines constraints, requests alternatives, tests assumptions, and rejects an answer when necessary. Such users do not give the model final authority. They use it as a fast analytical partner that increases the number of options and reduces routine workload.

An unprepared user often treats AI as a vending machine for answers. Speed and fluent language become the main quality criteria. Because the model writes confidently, the employee may experience an illusion of complete knowledge. AI then stops complementing human thought and begins to replace it, allowing errors to move quickly into an email, presentation, proposal, or management decision.

When a Fast Answer Replaces Critical Thinking

In a Dickinson College experiment, 97% of participants copied a deliberately incorrect ChatGPT answer to a simple task. The availability of AI reduced attention, and the group working without the model performed better. Most importantly, one short reminder to analyze the answer before submission doubled accuracy. Part of the problem is therefore a work habit: employees must remember that a fast answer is still material requiring review.

An analysis of 1.4 million work sessions based on KPMG data reveals a similar divide. About 95% of users rely on AI mainly for routine drafts, summaries, and first answers, often accepting the output with little revision. Only about 5% of advanced users work with AI as a thinking partner: they provide context, direct the reasoning, ask for counterarguments, and challenge the response.

This changes the meaning of employee value. Drafting a quick text, finding information, or building a presentation was once valuable in itself. AI makes the technical component of these activities cheaper and faster. The scarce capabilities become problem definition, source selection, fact checking, contradiction detection, organizational knowledge, and responsibility for the final decision.

AI can therefore raise average productivity while widening differences between employees. A strong specialist performs more work, learns faster, and makes better decisions. A weaker employee may appear more productive-more texts, slides, and answers-while producing more errors, superficial content, and “workslop” that colleagues must later correct.

Georgia’s Central Challenge Is Unequal Capability

In the Chamber survey, 45% rate their AI knowledge as basic, 27% as intermediate, and 8% as advanced or expert. Georgia has already developed a broad population of AI users, but the group with advanced practice remains small. Giving everyone the same technology can therefore generate unequal results because employees meet the tool with different levels of professional and critical readiness.

Seventy-two percent identify insufficient knowledge as the main barrier to AI adoption and implementation. This helps explain why use can be broad while quality remains uneven. An employee may know how to open a platform and write a prompt, yet not know how to validate the output, protect confidential information, adapt an answer to Georgian customers, or determine whether AI created genuine business value.

Limited finance and a shortage of qualified personnel deepen the challenge. SMEs rarely have separate data, AI, and process teams. Success depends on whether existing employees can embed a new tool into their professional role. In that environment, the strongest employees improve quickly, while colleagues without support risk falling further behind.

According to BTU researchers, the central task of AI transformation in Georgian business is not merely to spread the technology but to improve the quality of use across the workforce. If companies measure licenses, logins, or generated text, they may achieve high formal adoption and low real impact. The relevant question is whether decisions, service, speed, accuracy, and employee capability improved.

Illustrative Example: One Tool, Two Different Outcomes

Imagine two marketers at a Georgian retail company using the same AI platform to prepare a campaign for a new product. This is an illustrative scenario, not a real company case.

The first employee provides information about the audience, price, seasonality, previous campaigns, and brand voice. The employee requests three alternative concepts, the risk of each one, the likely customer reaction, and an explanation of why the idea should work in Georgia. The output is compared with sales data, facts are checked, strong elements are combined, and a small test is launched.

The second employee writes only: “Create a campaign for our new product.” The platform produces polished but generic copy. A few words are changed and the campaign is published. The task is completed quickly, but the campaign ignores customer behavior, price sensitivity, and the Georgian communication context. Both employees use AI; for one it amplifies professional thought, while for the other it substitutes for it.

The gap can be reduced through operating rules. A company can require a minimum context package, multiple alternatives, fact validation, approval by an accountable person, and a review of results. This process helps a weaker user pause and evaluate rather than accept the first answer, while giving a stronger user a disciplined path to scale.

How Companies Should Manage the AI Performance Gap

Seventy-eight percent of respondents report a need for specialized training, while 51% are interested in consulting and mentoring. These demands belong together. Training provides general knowledge; mentoring helps employees transfer it into their own workflow. A course limited to platform features and prompt examples may produce faster users, but not necessarily better decision-makers.

Effective learning should start with a professional task. Marketing teams need audience analysis, brand consistency, and fact checking. Finance teams need data accuracy, assumption control, and risk escalation. Customer-service teams need answer quality, personal-data protection, and clear handoff to a person. General AI literacy is essential, but function-specific learning produces operational value.

Companies also need differentiated autonomy. Experienced specialists who can evaluate output and accept responsibility should have more room to experiment. Beginners need clear boundaries: which tool to use, which information cannot be entered, what must be checked, where to escalate uncertainty, and which actions always require human approval.

Management should not evaluate AI success by frequency of use. Better measures include reduced handling time without lower quality, error rates, repeat customer contacts, sales conversion, decision speed, and the time required for validation. If an employee produces large volumes of AI content that colleagues must repair, usage is high but productivity is not.

Seventy-two percent of respondents consider AI development very important for Georgia, and almost half expect it to become decisive or necessary for competitiveness in their sector within one or two years. The time for action is now. Companies need to create not merely AI users, but employees who can direct the technology, evaluate its output, and make accountable decisions within a business context.

Key Findings

• According to the Chamber survey, 62% of respondents already use AI tools, mainly for content creation, marketing, and information search.
• AI produces the largest gains for employees who already possess professional knowledge, context, and the ability to challenge an answer.
• In the Kenya small-business experiment, stronger entrepreneurs increased profit by 15%, while income fell by 10% among lower-performing users who followed generic advice without adaptation.
• Insufficient knowledge is the main barrier for 72% of respondents, while only 8% rate their capability as advanced or expert.
• Seventy-eight percent need specialized training and 51% want consulting or mentoring, pointing to a need for learning plus workflow implementation.
• AI performance should be measured through quality, accuracy, and business outcomes—not only logins or output volume.
• In the next one or two years, advantage will go to companies that develop employee judgment together with technological access.

Why This Matters for Georgia

A large share of Georgian companies are small or medium-sized, and employees often combine several functions. AI can give a strong specialist the scale that once required an additional team: faster research, multilingual communication, content production, customer responses, and decision alternatives. This is a major productivity opportunity for smaller businesses.

Unequal capability means that the benefits will also be distributed unequally. If advanced employees accelerate while basic users remain unsupported, a new internal divide emerges—not only between experience levels and job titles, but between different qualities of AI use. That divide will influence pay, promotion, team design, and demand for talent.

AI education is therefore simultaneously a productivity, employment, and competitiveness issue for Georgia. Businesses, educational institutions, and support organizations can turn widespread use into broad opportunity only when learning includes critical thinking, professional context, data responsibility, and measurement of real outcomes.

Conclusion

AI does not create a strong or weak employee by itself. It amplifies the knowledge and work habits a person already possesses: professional judgment becomes a faster, scalable decision, while superficial practice becomes a faster mistake. That is why the same tool can strengthen one employee and reduce another employee’s independent thinking.

For Georgian business, the choice is no longer between adopting and rejecting AI; adoption is already under way. The choice concerns the kind of use that becomes normal: automatic acceptance of a first answer or critical collaboration with AI. The high demand for training and mentoring in the Chamber survey shows that businesses want this transition. The next step is a learning, governance, and measurement system that turns AI from a force widening the employee gap into an instrument of shared professional growth.

Data and Main Sources

• საქართველოს სავაჭრო-სამრეწველო პალატის კვლევა ბიზნესსექტორში AI-ის გამოყენებაზე
• Quartz — AI Advice Is Only as Good as Employee Judgment

Prepared by the academic team of Business and Technology University and the BTUAI Research Team, Tbilisi, Georgia.