AI adoption in Turkey stood at 7.5% of enterprises with ten or more employees in 2025, according to TurkStat, up from 2.7% in 2021. The EU average for the same year was 20%. Use is concentrated in large firms and in marketing and sales, and the most common barrier is a lack of in-house expertise.
AI adoption in Turkey: the TurkStat 2025 figures
The official measure of company-level AI use in Türkiye comes from TurkStat, the national statistics office, which runs an annual ICT usage survey of enterprises. The survey covers firms with ten or more employees, so micro-businesses are not included. That scope matters when you compare the figures with anything else.
According to TurkStat's Artificial Intelligence Statistics 2025 bulletin, the share of enterprises using at least one AI technology rose from 2.7% in 2021 to 7.5% in 2025.
That is close to a threefold increase in four years, but from a low base. The same bulletin puts adoption at 6.6% among firms with 10–49 employees, 9.6% among those with 50–249 and 24.1% among those with 250 or more. Roughly one large company in four uses AI; among small firms it is closer to one in fifteen.
The pattern mirrors the state of the data foundations AI depends on. TurkStat's ICT Usage in Enterprises Survey 2025 shows 28.3% of enterprises with ten or more staff using ERP software and only 6.5% using business intelligence tools. A company whose data sits in scattered spreadsheets will struggle to get value from AI, which is why decisions on ERP for SMEs and BI dashboards and KPIs usually come first.
| Indicator (2025) | Türkiye | EU |
|---|---|---|
| Enterprises (10+ staff) using AI | 7.5% | 20.0% |
| Large enterprises (250+) using AI | 24.1% | 55.03% |
| Lack of expertise cited as a barrier (among firms that considered AI) | 74.2% | 70.89% |
| Unclear legal consequences cited as a barrier (same group) | 62.4% | 52.52% |
The EU figures come from Eurostat's news release "20% of EU enterprises use AI technologies" and Eurostat Statistics Explained, "Use of artificial intelligence in enterprises". Both rest on the same harmonised survey method and ten-employee threshold, so the figures are broadly comparable, although differences in sector mix also shape the picture.
Where Turkish companies use AI
TurkStat reports that 46.5% of enterprises using AI apply it to marketing or sales, 41.1% to production or service processes and 33.7% to accounting, controlling or finance. In other words, AI tends to arrive first in customer-facing, text-heavy work. For function-level detail, see our guides to AI in B2B marketing and AI in sales.
The production and finance shares are far from trivial. We cover vision inspection, scheduling and maintenance on the factory floor in AI in manufacturing: real examples, and invoice and reconciliation use cases in AI in finance and accounting.
E-commerce gives a more granular picture. In a survey of 781 online businesses published in the Turkish Ministry of Trade's Outlook of E-Commerce in Türkiye Report 2025, 25.4% of respondents said they did not yet use AI; among those that did, 83.4% used it to create product copy and images. By contrast, AI chatbot customer support stood at 24%, personalised product recommendations at 19.9% and voice-assistant search or ordering at 16.8%.
The report also notes that 17.3% of AI-using businesses answered "unknown / not tracked" when asked how intensively they use AI, meaning they do not measure or monitor it. Adoption clusters around the easiest task, content generation, while applications that interact with customers live and connect to other systems lag well behind. System-connected applications such as a customer service chatbot are still a genuine differentiator.
Why adoption is low: the barriers in the official data
According to TurkStat, among enterprises that considered AI but did not use it, 74.2% cited a lack of expertise, 67.4% high costs and 62.4% unclear legal consequences. Among AI-using e-commerce businesses in the Ministry of Trade survey, the ranking is slightly different: legal uncertainty (70.3%) and data privacy and security concerns (70.2%) top the list. A lack of secure cloud solutions (51.8%) and of AI suppliers (51.5%) follow.
The expertise gap is structural. TurkStat's ICT Usage in Enterprises Survey 2026 finds that only 15.2% of enterprises employ ICT specialists, falling to 10.8% among firms with 10–49 staff. Of those trying to hire ICT specialists, 31.7% reported difficulties.
Legal uncertainty has concrete roots too. Any AI use involving personal data falls under KVKK, Türkiye's data protection law (Law No. 6698, broadly comparable to the GDPR), and the EU AI Act can come into play for firms that place AI-enabled products or services on the EU market or whose AI output is used in the EU; whether you are in scope depends on the specific use case. We explain both in practical terms in AI and KVKK and the EU AI Act for Turkish companies.
Employees are ahead of their employers
The 7.5% company figure reads differently next to individual use. In TurkStat's first measurement, 19.2% of people aged 16–74 used generative AI in 2025, rising to 39.4% among 16–24-year-olds. A good share of the workforce already knows these tools, and brings them to work.
The Turkish Data Protection Authority addressed this in its notice Use of Generative AI Tools in Workplaces of 5 March 2026. It warns that such tools are often used on the basis of individual preference rather than within a defined corporate policy, which makes them hard to monitor. This unmeasured "shadow" use may be a bigger risk than the adoption the statistics capture; our company AI acceptable use policy guide sets out a framework.
Türkiye versus the EU and beyond: where the gap opens
Eurostat reports that AI use among EU enterprises with ten or more staff rose from 13.5% to 20.0% in a single year, while the Turkish figure for 2025 is 7.5%. For a company exporting to the EU, that gap can mean competing in a market where buyers and local rivals are bringing AI into their processes faster.
Survey evidence also suggests adopters see gains. In the OECD D4SME 2025 Policy Highlights, 91% of SMEs using generative AI reported productivity boosts, while 67% of non-users were unsure how to use it or what the risks were. For firmer evidence from field experiments, read our review of AI productivity research.
Saying "we use AI" is not enough, though. According to the OECD's 2026 D4SME survey report, 61% of the SMEs surveyed across twelve countries (a sample the OECD notes is not nationally representative) reported using AI, but 76% of them were "AI novices" using off-the-shelf tools for isolated tasks. What separates the rest is connecting AI to a business process and measuring the effect, as we explain in measuring AI project ROI.
How to read these numbers for your own company
Statistics earn their keep when they answer "which barrier are we stuck on?" rather than "is everyone doing it?". The steps below turn national data into something you can act on:
- Pick the right peer group. Use TurkStat's size bands; a 60-person firm should compare itself with 9.6%, not 24.1%.
- Surface existing shadow use. A short internal survey will show which teams use which AI tools, and with what data.
- Check your data foundations. A process with no ERP, CRM or document archive behind it needs its data collected first.
- Name your barrier. Expertise, cost, legal risk or data security each call for a different first move.
- Choose one process and measure it. Pilot a repetitive task whose time and error rate you can measure, not just marketing copy.
- Check available support. Turkish manufacturing SMEs should review the programmes in our guide to SME digitalisation grants in Turkey.
The checklist below maps each barrier in the official data to a concrete first step:
| Barrier (TurkStat / Ministry of Trade) | How it shows up in a company | First step |
|---|---|---|
| Lack of expertise | Nobody knows which tasks suit AI | External discovery work plus one internal owner |
| Perceived high cost | The project is scoped like a major software rollout | A limited pilot on one process with a clear success metric |
| Legal uncertainty | No one will sign off on work involving personal data | Data inventory and a KVKK assessment |
| Data privacy and security | Staff paste company data into personal accounts | A usage policy and an internal, permission-aware assistant |
| Shortage of suppliers | No team can integrate AI with your systems | Scoping with a team experienced in integration |
We cover how to choose a first project, and the mistakes to avoid, in AI in business: where to start. This article's job is to ground that decision in the barriers the national data actually reveals.
How we approach this at Digital Bridge
The two biggest barriers in the data, missing expertise and legal uncertainty, usually have to be tackled together. That is why we begin with discovery and needs analysis under our digital transformation consultancy. We map processes, data sources and the tools teams already use, then rank AI candidates by impact and risk.
For the chosen process we run a limited pilot. Our AI integration work connects the model to your ERP, CRM or document archive, and we take a baseline measurement before the pilot starts. For companies that want to stop staff moving data into external tools, a permission-aware enterprise LLM assistant is one of the options.
Wherever personal data is involved, our data protection compliance team reviews the data flows and notice obligations with you. If the data itself is fragmented, we first put basic order in place through data governance and quality work. We do not sell off-the-shelf packages; after the needs analysis you receive a written proposal setting out scope, phases and cost.
Next step
AI adoption in Turkey is still low, and that is also an opening: most of your peer group has not started yet. To pin down which barrier you face and where your first pilot should be, get in touch with our team. For more guides on the subject, browse all our AI articles.