Summary

• Google Cloud says UK SMB use of its AI platforms has nearly doubled year on year.
• Use cases include customer support, payroll and accounting automation, data analysis, marketing design, research, and employee built agents.
• The story highlights the gap between AI adoption and measurable productivity, especially outside London and larger enterprises.

Google Cloud says UK small and midsize businesses are moving more rapidly into AI adoption, with use of its AI platforms nearly doubling over the past year.

The company set out the figures around its Google Cloud London Summit, highlighting UK SMB customers using Gemini models, Gemini Enterprise, AI Studio, NotebookLM, Looker, and BigQuery. Google Cloud says AI is being used to improve customer support, automate repetitive payroll and accounting tasks, help employees work with data, create marketing designs, build employee agents, and conduct research faster.

Examples cited by Google include sustainability fintech Neural Alpha using Gemini models to process unstructured environmental and corporate sustainability reports, digital security provider Sep 2 using Gemini Enterprise for autonomous AI agents in threat monitoring, design agency Sunhouse using Gemini Enterprise to find archived design work, and B2B events company Terrapinn using Google tools to automate manual workflows.

The company has also introduced training resources for smaller businesses, including an SMB Learning Path, a Gemini Enterprise Agent Ready programme, and access to AI courses through Google Skills for Organisations.

The adoption message fits a broader UK pattern. AI is moving beyond experimentation in some organisations, although the scale of value remains uneven. Smaller companies are often attracted to AI because they have limited capacity in customer support, finance, marketing, research, and data analysis, where routine work can absorb a large share of staff time.

SME AI moves into workflows

The strongest part of the development is the nature of the tasks. Customer service, finance administration, data analysis, marketing production, research, and internal agents are practical functions where smaller companies often lack specialist teams. If AI tools reduce manual work without creating new risk, they could affect productivity more directly than broad AI awareness programmes.

Adoption figures alone still need care. AI tools are easy to trial, and experimentation can spread quickly through familiar software. Sustainable value is harder. SMEs need to know whether AI saves time, improves customer outcomes, reduces costs, or helps staff do work that previously required unavailable expertise.

Security, data handling, accuracy, and supplier dependence remain live constraints. Smaller businesses may not have dedicated governance teams, procurement specialists, or security staff to evaluate how AI tools handle sensitive operational, financial, or customer data. The easier AI becomes to deploy, the more important those checks become.

Regional imbalance is another issue. Previous UK market findings have suggested that AI adoption is much stronger among London based SMEs than in some regions. National productivity gains will be limited if adoption concentrates among businesses that already have better access to capital, digital skills, and advice.

Training initiatives can help, although skills are only one part of adoption. Smaller companies need clear use cases, trusted suppliers, practical implementation support, and confidence that the tools will not create new operational exposure. The most useful programmes will connect training to specific workflows rather than general AI enthusiasm.

Google’s position is commercially powerful because many SMEs already use Google Workspace. That gives Google Cloud a route from productivity tools into data platforms, AI models, and agent building services. The next phase should be judged by measurable operational gains, not simply by the number of small businesses experimenting with AI.