Artificial intelligence is becoming more than a market for models and chatbots. Capital is moving into data centres, compute, software platforms and specialised services, while companies increasingly judge AI by measurable business outcomes rather than novelty.
Key Takeaway
Gartner forecasts worldwide AI spending to reach $2.7 trillion in 2026, up 49.5% year over year. The largest opportunity is not concentrated in a single product category: infrastructure provides the physical capacity, models and platforms provide intelligence, software embeds it into workflows, and service providers turn it into business outcomes.
Where the money is moving
AI infrastructure is the largest spending layer. Gartner estimates 2026 AI infrastructure spending at about $1.48 trillion. Data-centre systems spending is forecast to rise from $506 billion in 2025 to $822 billion in 2026. According to calculations by BTU researchers, that is growth of about 62.5%, far above the 14.2% growth forecast for overall IT spending.
This matters because the most visible AI applications are not necessarily where most capital is being deployed. Cloud providers and technology companies are building the compute capacity that will support a much broader set of applications over time.
A model is no longer enough
For enterprises, model capability is increasingly only one purchasing criterion. Integration, data protection, reliability, latency, operating cost and measurable outcomes are becoming equally important.
Gartner forecasts end-user spending on AI models and platforms at $64 billion in 2026, up 63.4% from $39 billion in 2025. The market is increasingly rewarding providers that can demonstrate cost transparency, usage control and reliable performance.
From experimentation to workflow
McKinsey’s 2026 research suggests that many organisations remain early in the transition from individual AI use to enterprise-wide value creation. Giving employees AI tools is not the same as redesigning a workflow around them.
That gap is creating a service market. Companies need help preparing data, integrating systems, setting access rules, evaluating outputs, training employees and measuring business results. A provider does not need to build a frontier model to create value; it can connect existing models to a specific operational problem.
The economics of AI products
AI businesses can monetise through subscriptions, usage-based pricing, premium features embedded in existing software, or outcome-oriented services. The strongest model depends on how closely revenue tracks the value created for the customer.
At the same time, compute is becoming a core unit-economics issue. Gartner forecasts AI-optimised infrastructure-as-a-service spending to reach $42 billion in 2026, with 96% growth. As usage scales, model selection and compute efficiency become business decisions, not only engineering choices.
Georgia’s context
Georgia does not yet have comprehensive official statistics measuring the AI market as a standalone sector. The broader information and communication sector can therefore provide context, but it should not be treated as a proxy for AI itself.
Georgia’s National Statistics Office, Geostat, reports information and communication turnover of GEL 2.7 billion in Q1 2026 versus GEL 2.6 billion a year earlier. According to calculations by BTU researchers, nominal growth was about 3.8%. Employment in the sector reached 53,000 in Q2 2026, compared with 49,800 a year earlier, an increase of about 6.4%.
These changes cannot be attributed to AI. The sector includes telecommunications, software and other information services. But they describe the broader digital economy in which AI products and services can develop.
A concrete Georgian adoption example is Geostat itself. Since 2026, the agency has expanded AI use in data processing, statistical analysis, methodology, IT and user services, including an AI-powered chatbot on its website.
Georgia’s opportunity
For a small economy, competing directly in frontier-model development is exceptionally capital-intensive. The application layer offers a different opportunity. Georgian companies can use existing models to solve problems in local-language services, banking, tourism, retail, logistics, education and document-heavy business processes.
The Georgian language can be both a constraint and an advantage. Global tools may have less local data and context. Companies that understand Georgian terminology, regulation and workflows can create value through localisation and domain knowledge.
BTU Researchers’ Assessment
According to an assessment by BTU researchers, the central shift in the AI business is from technological capability to economic outcome. The question is increasingly not what a model can demonstrate, but what it can save, earn, accelerate or improve – and at what operating cost.
This creates two very different competitive environments. At the infrastructure and frontier-model layer, scale and capital dominate. At the application layer, specialised knowledge of an industry, language or workflow can become a meaningful advantage.
Conclusion
The new AI business is much larger than the chatbot market. Global spending approaching $2.7 trillion in 2026 reflects an ecosystem of infrastructure, models, software and services.
For Georgia, the most realistic opportunity may not be to compete at every layer. It is to convert global AI capability into local and exportable business value through language, industry expertise, integration and measurable productivity gains.
Data and Main Sources
Gartner – Worldwide AI Spending Forecast, 16 September 2026
URL: https://www.gartner.com/en/newsroom/press-releases/2026-09-16-gartner-forecasts-worldwide-ai-spending-to-grow-49-point-5-percent-in-2026
Dataset: No
Gartner – Worldwide IT Spending Forecast, 27 July 2026
URL: https://www.gartner.com/en/newsroom/press-releases/2026-07-27-gartner-forecasts-worldwide-it-spending-to-grow-14-point-2-percent-in-2026-totaling-6-point-37-trillion
Dataset: No
McKinsey & Company – From adoption to impact: Three horizons of AI transformation, 8 July 2026
URL: https://www.mckinsey.com/capabilities/people-and-organization/our-insights/from-adoption-to-impact-three-horizons-of-ai-transformation
Dataset: No
Georgia’s National Statistics Office, Geostat – Information and Communication, 2026
URL: https://www.geostat.ge/en/modules/categories/410/information-and-communication2465
Dataset: Yes
Prepared by the academic team of Business and Technology University and the BTUAI Research Team, Tbilisi, Georgia.