Colliers’ latest Hungary research, published by Tóth Kristóf, Colliers Associate Director and Head of Research, examines how artificial intelligence (AI) is shifting from experimental projects to a structural technology shaping corporate operations. The study argues that AI is becoming a general-purpose technology affecting many parts of the corporate value chain.
Adoption rates and growth
Across the European Union in 2025, 20% of enterprises with at least ten employees used at least one AI-based technology, up from 13.5% in 2024. In Hungary the adoption rate was 10.4% in 2025, below the EU average, but the domestic market showed one of the region’s strongest growth rates: corporate AI use expanded by 39.9% in a single year.
Adoption is uneven by company size: roughly 40% of large companies already use AI, while only 8.7% of small firms do.
Why this matters for commercial real estate
AI’s spread affects not only labour requirements or output but also the technical and service expectations placed on buildings. Colliers emphasizes that an asset’s technological readiness and adaptability are becoming independent competitive factors, meaning demand can no longer be judged solely by floor area or headcount metrics.
Offices: smaller footprints but higher quality demand
In offices, AI can both reduce and reshape demand. Automation of document processing, data analysis, reporting and customer support can lower the number of routine tasks and therefore the space needed per employee. At the same time, the role of highly skilled, technology-capable staff may grow, and companies may prioritise workspaces that support productivity, collaboration and employee engagement.
As a result, firms may seek smaller overall footprints but higher quality: buildings with strong digital infrastructure, flexible floorplates, usable communal spaces, good ESG performance and accessibility will be favoured. Rather than disappearing, demand is likely to reallocate toward higher-quality office stock — a trend that may be especially pronounced in Budapest.
Industrial and logistics: rising technical requirements
In the industrial and logistics segment AI adoption is currently lower overall, but its impact shows up directly in technical building requirements. Introducing predictive maintenance, machine vision, automated quality control and production management requires not only advanced software but also greater electrical capacity, reliable data connectivity, modern sensor and control systems and flexible manufacturing space.
The report notes that 22.8% of Hungarian companies already using AI applied it in production processes, exceeding the EU average of 20.8%. This suggests smart manufacturing is already operational at some domestic firms, and the value of industrial assets will increasingly depend on their readiness for the next wave of automation.
For logistics, route and fleet optimisation, demand forecasting, inventory management and automated warehouse processes will change selection criteria: adequate clear height, a strong and even floor, reliable power supply, fast data links and expandability will become more important.
Retail, hotels and construction: different channels of impact
In retail, AI primarily improves operational accuracy and efficiency. Better demand forecasting, analysis of shopper behaviour, personalised offers and inventory optimisation allow retailers to align assortments and logistics more tightly with actual performance. The market effect is more likely to appear as tighter integration of store networks, e-commerce and fulfilment rather than a dramatic change in total area demand. Efficient retailers can generate higher sales from the same space and create more stable tenants for landlords.
In hotels, AI-enabled dynamic pricing, revenue management, occupancy forecasting, personalised guest communication and automated services can improve capacity utilisation and profitability while reducing some labour needs. Technological maturity can thus become a competitive factor for hotels.
In construction, AI can support design, optimise building information models, improve cost estimation and scheduling, identify risks earlier and assist site inspections via image analysis. In Hungary — where labour availability, construction costs and project lead times remain constraints — productivity gains from AI could materially affect the economics of some developments.
Conclusion: qualitative differentiation and adaptation pressure
Colliers concludes that AI’s impact should not be interpreted simply as growth or decline in floor space demand. Instead, AI is likely to accelerate qualitative differentiation in the real estate market: assets that provide strong digital infrastructure, flexible spaces, robust energy and data capacity and readiness for automation will gain value, while hard-to-upgrade buildings may fall behind.
Hungary’s rapidly expanding yet still catching-up AI market therefore represents both growth opportunities and adaptation imperatives for owners and developers. The pressing question is not whether AI will affect commercial real estate, but how quickly individual buildings and their owners can adapt to the changing technological requirements.
