IONOS has warned that UK small and medium-sized businesses risk falling behind on artificial intelligence adoption unless technology providers address concerns around data security, vendor trust, and digital sovereignty.
The company’s latest YouGov research found that more UK SMBs are considering AI, but adoption remains constrained by fears over data theft, unauthorised access, and reliance on non-European technology providers. IONOS said 51% of UK SMB decision-makers cited fear of data theft or unauthorised access as a major barrier to AI adoption, while 46% said they lacked trust in non-European AI vendors.
The findings add a procurement and governance dimension to the UK’s wider AI adoption push. Ministers have already linked AI deployment to productivity and workforce capability, as explored in Ministers tie AI adoption to workforce skills. Encouragement alone will not resolve hesitation if smaller companies remain uncertain about where their data goes and who controls the infrastructure behind AI services.
AI decisions are different for smaller companies than they are for large enterprises. Many SMBs want practical tools that reduce administration, improve customer response times, help with forecasting, support content creation, or automate routine work. They often lack specialist procurement teams, in-house legal resources, and technical staff able to assess model governance, data-processing terms, hosting arrangements, or security controls.
Digital sovereignty has become a practical operating question. Companies need to know where business data is hosted, which legal regimes may apply, how third-party providers use inputs, and whether commercially sensitive material could be exposed. Those concerns are especially sharp in sectors handling customer data, HR records, financial information, intellectual property, or regulated communications.
The research also exposes a problem for technology vendors. AI product launches have often emphasised features, productivity claims, and speed of deployment. Smaller companies appear to be asking for something more fundamental: security, reliability, transparent data practices, pricing clarity, and support that reflects their operating constraints.
Cost pressure adds to the caution. Many smaller companies are already managing inflation, labour shortages, tax and payroll changes, energy bills, cyber threats, and compliance obligations. AI investment has to compete with immediate operational priorities, and leaders may delay adoption if the risk feels hard to assess or the return is uncertain.
The trust deficit may influence supplier choices. UK or European-hosted AI infrastructure could become more attractive where companies want clearer assurance over data location and regulatory alignment. Non-European providers are unlikely to disappear from procurement discussions, but they may face stronger demands for transparency, contractual safeguards, explainability, and control over data use.
A divide could widen between AI-capable and AI-cautious companies. Larger enterprises may move faster because they already have governance frameworks and specialist teams. Smaller companies may either adopt tools informally without adequate safeguards or avoid adoption altogether. Both routes carry risk: uncontrolled use can create compliance exposure, while delayed adoption can weaken productivity and competitiveness.
The next phase of SMB AI adoption will depend on more than model capability. Trusted infrastructure, clear vendor accountability, practical guidance, and security-led design will shape whether smaller companies use AI confidently or keep it at the edge of their operations.
