In the AI era, competitive advantage is no longer determined simply by which model or software a company uses. Technology is broadly available. The real difference is how quickly an organization can select a business problem, redesign the workflow, combine data and technology into one system, and scale the result.
Using BTUAI, BTU researchers adapted McKinsey’s 2026 management research to Georgia. The central conclusion is that a Georgian company does not need the budget of a global corporation, but it does need the same management discipline: focus on a few high-value economic domains, appoint an accountable business leader, form a cross-functional team, build reusable technology and data foundations, and design for scale from the start.
The economics in McKinsey’s research
McKinsey’s article “The new management playbook for AI” analyzes 20 large companies that created significant economic value through AI-enabled business transformation. Their performance stands out against a broader market in which meaningful enterprise-level value remains concentrated among a small group of organizations.
Across the 20 companies, steady-state EBITDA after three years improved by 20 percent on average. Two-thirds concentrated on three business domains or fewer. They became cash positive in one to two years on average, while full benefits often took three to four years. For every $1 of one-time investment, they generated an average of $3 in annual incremental EBITDA.
These figures are not financial benchmarks for an average Georgian firm. The companies often invested between $50 million and more than $200 million and operated at a much larger scale. What transfers to Georgia is the logic: invest where small operational gains create material economic impact, sequence delivery so early wins help fund the journey, and ensure that each pilot becomes part of a larger system.
Georgia’s problem is value creation, not basic access
According to Geostat, 94.9 percent of Georgian enterprises had internet access in 2025, but only 15.3 percent used a website and 25.8 percent used any social-media tool. These figures do not measure AI adoption, but they reveal an important contrast: basic connectivity is widespread, while deep use of digital capabilities in business processes remains uneven.
BTU researchers assess that the main AI risk for Georgian companies is a large number of small experiments without a shared business logic. Individuals produce content, analysis, or visuals faster, but profit does not rise, end-to-end service time does not fall, and organizational knowledge does not accumulate. That is technology activity without an economic system.
Six capabilities behind successful AI transformation
| Capability | Meaning | Practical requirement for Georgia |
|---|---|---|
| An AI-fluent C-suite | The top team selects value domains and owns investment choices | The CEO and functional leaders agree on one to three priority business domains |
| Tech-capable business leaders | A domain owner integrates business, technology, data, and change | Each initiative has a senior business leader accountable for outcomes |
| An operating model built for speed | Cross-functional teams own a full journey or domain | Business, technology, operations, and risk work as one team |
| Technology as a platform | APIs, data, and services are built for reuse | A new solution does not become another isolated system |
| Easy-to-consume data | Data has an owner, meaning, quality, and secure access | High-value data becomes managed data products |
| Scale by design | Architecture and process anticipate new units and channels | The pilot separates a reusable core from local adaptation |
The top team must own the AI agenda
McKinsey identifies an AI-fluent C-suite as the strongest driver of success. The work cannot be delegated to the CIO or an innovation unit alone. The leadership team must decide where AI can create differentiated value, what must change in the business model, and which multiyear capabilities require investment.
This is even more important in Georgia because resources are constrained. Transforming one complete domain can be more valuable than running ten average pilots. A bank might redesign the journey from onboarding to loan servicing; a retailer might connect demand forecasting, inventory, suppliers, and personalized offers.
Business leaders need technology and AI muscle
Successful transformations consistently have a senior business leader accountable for the outcome and able to integrate technology, process, data, and people change. The leader is not measured by the number of models deployed. The measure is revenue, cost, throughput, service quality, or risk reduction.
BTU researchers assess that the most strategic capability investment for Georgian companies is not only teaching employees to use AI tools. It is developing business leaders who can manage people, data, and AI agents as one operating system.
The operating model must be built for speed
In a traditional model, a business unit sends a request to IT, the project moves to a vendor, then to security and other functions. Every handoff adds waiting and weakens accountability. The new model creates a persistent cross-functional team around an end-to-end business domain or customer journey.
The team does not disband after the first release. It continuously improves the product, workflow, and AI system. McKinsey reports that a version of this distributed operating model appears in every documented success story, yet only about 10 percent of companies have adopted one.
Platforms and consumable data compound value
An isolated AI solution becomes expensive when it must be rebuilt for every channel. A platform creates common APIs, data assets, identity, rules, and services that can be reused across products. This lowers the marginal cost and time of every subsequent initiative.
Data must also be discoverable, understandable, reliable, and securely accessible. McKinsey’s DBS example shows the compounding effect: AI model deployment took 15 to 18 months in 2018. After building unified data and AI platforms, the cycle fell to two to three months by 2023. A foundation built once accelerated every later use case.
Scale needs to be designed from the beginning
A successful pilot may not work unchanged in another branch, product, or customer segment. Organizations should separate the common core from the local layer. In McKinsey’s Freeport example, roughly 60 percent of the AI system could be reused across plants while 40 percent required local adaptation.
This principle fits Georgian banks, retailers, hotel groups, clinics, and public services. A central team should manage shared data, architecture, security, and monitoring, while local units adapt the workflow to actual operating conditions.
Three stages for a Georgian company
| Dimension | Stage 1 First wins | Stage 2 Scaling value | Stage 3 Agentic enterprise |
|---|---|---|---|
| Business road map | Point problems | End-to-end domain transformation | Cross-domain real-time AI systems |
| Talent | Software and data engineering | Tech-capable business leaders and stronger IT | Teams that build and run agentic systems |
| Operating model | Agile projects | Persistent domain and platform teams | Smaller flatter human-agent teams |
| Technology | Cloud and modern development | Decoupled architecture and enterprise platforms | AI-enabled software delivery life cycle |
| Data | Data lake or warehouse | Unified productized consumable data | Context-rich data moats |
| Adoption and scale | User-experience design | Reconfigured scalable workflows | Orchestration layers and automated guardrails |
A company cannot skip stage two and move directly to stage three. An agentic enterprise requires reliable data, modular architecture, explicit decision rights, monitoring, and leaders who have already learned to turn technology into business value.
An investment model suited to Georgia
The $50 million to $200 million range in the international cases is not a direct guide for Georgia. BTU researchers recommend managing sequence and value gates rather than copying absolute spending levels.
- Select one or two economic leverage points where a five to ten percent operational improvement would create material value.
- Record the baseline: revenue, cycle time, unit cost, error, loss, and customer outcome.
- Build a minimum AI system rather than an isolated demo: data, workflow, technology, human control, and an accountable owner.
- Release the next investment only when technical, adoption, and economic gates have been met.
- Use early value to fund shared data, platform, and leadership capabilities that will accelerate later initiatives.
An 18-month practical path
| Period | Primary decision | Measurable output |
|---|---|---|
| 0–60 days | Choose the leverage point, domain, accountable leader, and baseline | One priority journey and an agreed value hypothesis |
| Months 2–6 | Build a cross-functional team, redesign the process, and launch the first system | Verified change in cost, time, quality, or revenue |
| Months 6–12 | Strengthen shared data products, APIs, monitoring, and controls | Repeatability across another channel or unit |
| Months 12–18 | Expand to a second domain and introduce a human-agent operating model | Compounding value and a shorter innovation cycle |
Where Georgia can start
- Banking and insurance: onboarding, credit journeys, claims, compliance, and service as integrated domains.
- Retail: forecasting, inventory, pricing, suppliers, and personalized offers in one system.
- Tourism and hospitality: multilingual sales, reservations, pricing, and guest service on a common platform.
- Manufacturing and logistics: throughput, quality, maintenance, routing, and asset use.
- Education: admissions, the student journey, learning support, and administration with human academic accountability.
- Public services: end-to-end applications, document checks, case flow, and citizen communication with humans retaining final authority.
What management should measure
| Metric | Management test |
|---|---|
| Incremental EBITDA or savings | Is the program creating economic value? |
| Time to cash positivity | Is investment sequencing working? |
| End-to-end cycle time | Did the whole process improve rather than one task? |
| Reuse rate | How much code, data, or service accelerated the next initiative? |
| Adoption and active use | Has the system entered daily work? |
| Quality, errors, and escalation | Is the organization controlling reliability and risk? |
| Speed of the next release | Is organizational capability compounding over time? |
Conclusion
McKinsey’s research shows that major economic value from AI is possible, but it is rare and concentrated in companies that have built a management system around the technology. They did not pursue thousands of disconnected ideas. They selected economic leverage points, built AI systems, developed leaders, created platforms and consumable data, and turned individual successes into repeatable capability.
Georgia’s advantage may come from speed and selectivity rather than investment scale. In a small market, a company can identify the most valuable workflow, concentrate accountability in one leader, and spread a successful core to other domains. When this is done well, AI stops being a standalone tool. It becomes a management and operating capability that increases the value of every subsequent innovation.