Georgia’s central AI challenge is no longer access to tools alone. The harder question is whether an organization can redesign workflows, decision rights, responsibilities, and its operating model so that personal productivity gains become measurable enterprise value.
Using BTUAI, BTU researchers adapted McKinsey’s 2026 research on the three horizons of AI transformation to Georgia’s business and institutional context. The conclusion is straightforward: AI use may spread quickly across Georgian workplaces, but sustainable value appears only when the organization changes with the technology. Competitive advantage will therefore belong not to the company with the largest number of AI users, but to the company that rewires value-creating work most effectively.
What the McKinsey research shows
McKinsey’s article From adoption to impact Three horizons of AI transformation draws on a global survey conducted between February and April 2026 among 750 employees and leaders across industries and regions. It examines personal and organizational readiness for AI-enabled transformation.
Seventy percent of respondents say they personally feel prepared to adopt and use AI, while only 27 percent of leaders believe their organizations are ready to make the shifts required for an agentic future. Employees are often adapting faster than the institutions around them. The challenge is therefore less about initial use and more about changing workflows, structures, behaviors, and leadership practices.
Organizational readiness accounts for 48 percent of the difference between leaders who report capturing value and those who do not, compared with 25 percent for personal readiness. The implication is not that individual skills are unimportant. It is that training and access cannot compensate for an operating model that remains unchanged.
Why the readiness gap matters in Georgia
Technology access is becoming a smaller barrier for Georgian enterprises. According to Geostat, 94.9 percent of enterprises had internet access in 2025, and 35.5 percent of employees used an internet-enabled portable computer or smartphone provided by their employer for business purposes. These figures demonstrate a digital foundation, but they do not indicate that workflows have been redesigned around AI.
BTU researchers assess that Georgia may face a particularly visible organizational readiness gap. The country’s compact market and relatively small organizations can enable rapid change. At the same time, many businesses depend on informal knowledge, a few key employees, undocumented processes, fragmented systems, and centralized decisions. In this environment, AI may accelerate individuals without improving the organization as a whole.
The relevant question is not how many employees use AI. Leaders should ask which process became faster or less expensive, where error rates fell, whether customer outcomes improved, which decisions now use better information, and whether saved time has been redirected toward higher-value work.
The three horizons for Georgian organizations
| Horizon | Organizational change | Example in Georgia | Primary measure |
|---|---|---|---|
| 1. Enablement | Employees use AI to accelerate parts of existing jobs | Drafting content, summarizing reports, reviewing documents, preparing analysis | Time saved, sustained use, quality |
| 2. Automation | AI supports an end-to-end workflow across functions | A request is classified, processed, escalated when needed, and recorded | Cycle time, unit cost, errors, escalation |
| 3. Reinvention | Roles, decisions, workflows, and the operating model are redesigned | Human-agent teams manage a complete service or decision flow | Revenue, quality, speed, new value |
Horizon one – enablement
At the first horizon, employees use general-purpose AI to complete parts of existing work. This is where many Georgian organizations begin: drafting, translation, meeting summaries, preliminary data analysis, presentation preparation, or customer-response support.
In McKinsey’s research, leaders in this horizon are 5.3 times more likely to report enterprise value when workflows are redesigned than when they remain unchanged, a comparison of 32 percent and 6 percent. For Georgian organizations, the lesson is that distributing AI licenses should immediately be followed by a decision on what the employee will do differently and where the released capacity will go.
If an employee drafts a document twice as fast but approval still takes several days, enterprise performance barely changes. Success at this stage therefore requires a new work routine, not simply a faster individual task.
Horizon two – automation
At the second horizon, AI moves from isolated tasks into end-to-end processes that cross functional boundaries. A sales inquiry, for example, can be classified, enriched with data, routed to the right employee, and followed through to the next action. This requires a process owner, shared data standards, escalation rules, and clear human accountability.
McKinsey reports that leaders with highly AI-fluent leadership teams are 3.9 times more likely to report value capture than those with low-fluency teams, 35 percent versus 9 percent. Leaders who receive the training and support needed to develop new skills are 3.3 times more likely to report value capture, 30 percent versus 9 percent.
BTU researchers assess that the biggest risk at this stage in Georgia is automating a poorly designed process. If the request is ambiguous, data is incomplete, and accountability is unclear, AI will scale the existing problem. Before automation, the workflow, exceptions, decision thresholds, and boundaries of human intervention need to be explicit.
Horizon three – reinvention
At the third horizon, the organization no longer asks how to accelerate the existing process. It asks how the work would be designed if AI had been part of the organization from the beginning. Roles, authority, team composition, decision flows, and the value model all change.
Only 11 percent of leaders in McKinsey’s survey place their organizations in the reinvention horizon. Yet 48 percent of leaders in that group report meaningful enterprise value, compared with 24 percent in automation and 13 percent in enablement. These figures are not estimates for Georgia, but they clearly indicate the direction of travel: value rises when AI changes organizational design rather than remaining an individual tool.
For Georgia, reinvention could mean smaller but clearly accountable teams, integrated human-agent workflows, faster data-informed decisions, and a shift in managerial work from distributing tasks to governing systems, exceptions, and outcomes.
Trust underpins all three horizons
McKinsey identifies trust in the organization as a critical readiness factor in every horizon. Respondents with low trust in organizational support are 1.5 times more likely to report anxiety about AI-related workplace change. Anxiety is particularly visible among middle managers, affecting roughly one in four, compared with about one in five individual contributors.
This is an important warning for Georgian organizations. If employees see AI mainly as a tool for job cuts or surveillance, they will withhold knowledge, hide process weaknesses, and comply only formally. Trust is not created by promising that roles will never change. It grows when leaders explain what they know and do not know, define how decisions will be made, invest in reskilling, and distribute the costs and benefits of change fairly.
Four organizational barriers in Georgia
- Undocumented processes. In small and medium-sized firms, work often depends on individual experience. AI requires at least a clear description of objectives, inputs, outputs, exceptions, and accountability.
- Insufficient AI fluency among leaders. Executives do not need to become model engineers, but they must understand capabilities, limitations, data risk, and which workflows are worth transforming.
- Fragmented data and systems. Scaling automation is difficult when one process is distributed across email, spreadsheets, accounting software, and employee memory.
- An unclear role for middle management. Managers may feel most threatened by change, yet they are essential for converting strategy into daily operating practice.
A practical path for Georgian organizations
BTU researchers recommend a staged transformation anchored in a specific business outcome. Launching dozens of experiments across every department creates visible activity, but not necessarily value. A stronger approach is to select one or two high-value processes and redesign them end to end.
| Period | Priority action | Expected output |
|---|---|---|
| First 30 days | Select two or three processes and document baseline time, cost, errors, and customer pain | A measurable baseline and accountable owner |
| Days 31–90 | Design a human-AI workflow with testing and escalation rules | One operating pilot with comparable results |
| Months 3–6 | Connect data and functions; update roles and performance measures | Controlled scaling of the redesigned process |
| Months 6–12 | Reallocate resources, remove redundant steps, and update the operating model | Visible impact on cost, revenue, quality, or speed |
Where early value can emerge
The fastest gains in Georgia are likely where large volumes of repeatable information, service requests, and partially standardizable decisions come together.
- Financial services: request processing, document review, compliance support, and customer service with human oversight.
- Retail: inventory planning, personalized offers, supplier coordination, and customer-feedback analysis.
- Tourism and hospitality: multilingual service, booking management, demand planning, and operational coordination.
- Education: student services, learning support, program management, and administrative redesign with clear academic accountability.
- Public services: application classification, information retrieval, case-flow transparency, and citizen support while humans retain responsibility for final decisions.
What leaders should measure
The number of AI users is an activity metric. To determine value, leaders should track business outcomes and whether the new way of working is sustainable.
| Measure | Management question |
|---|---|
| End-to-end cycle time | How quickly does work move from request to completion |
| Unit cost | How many resources are required for one transaction or service |
| Errors and rework | Has quality and reliability improved |
| Human escalation | Where and why can AI not act safely on its own |
| Customer outcome | Have speed, satisfaction, or accessibility improved |
| Use of released capacity | Has employee time moved to higher-value work |
| Trust and capability | Do people understand their new roles and have the skills to perform them |
Georgia’s opportunity
Georgia’s small scale can become an advantage. Compared with large enterprises, Georgian firms may have fewer legacy systems and shorter decision paths, allowing them to test, redesign, and spread new practices faster. The same scale, however, can increase dependence on key people and constrain investment capacity.
BTU researchers conclude that the Georgian model should not aim to be the most expensive or widest AI program. It should be selective: deep transformation of a few high-value processes, explicit accountability, shared data, human control, and transparent measurement. This is how small and medium-sized organizations can offset limited scale with speed and adaptability.
Conclusion
McKinsey’s research shows that enterprise value depends less on personal AI use than on an organization’s readiness to change how work gets done. For Georgia, the path runs through three connected horizons: enabling employees, automating workflows, and eventually reinventing the organization.
When AI enters an unchanged organization as one more tool, its effects are likely to remain local and temporary. When a company also changes workflows, decision rights, capabilities, incentives, and accountability, AI can move from isolated productivity gains to sustainable competitive advantage. The central choice for Georgian business is therefore not whether to use AI, but whether to stop at adoption or proceed to organizational transformation.