Appendix
Glossary of terms:
Startup: A business founded in the last 2 years which provides a new product/service or innovation and is aiming for rapid growth in terms of employees and turnover.
Large enterprise: A large enterprise (also referred to as a large business or established enterprise) is a business with 500 or more employees, founded 10 years ago or more.
Mid-sized business /Midcap: A business with 250-499 employees.
SME: A business with fewer than 250 employees.
Experienced AI adopters: Businesses that reported having adopted AI 3+ years ago.
Three stages of AI adoption: A framework used in this report to describe the maturity of AI use within organisations, ranging from basic efficiency-driven applications to advanced, transformative integration across core operations.
Basic AI adoption: These businesses are using AI primarily for incremental improvements such as efficiency gains and process streamlining rather than for innovation. They rely on publicly available chatbots for routine tasks or purchasing ready-made AI solutions.
Intermediate state of AI adoption: In these firms, AI is supporting a more innovative customer experience alongside operational efficiency.
Advanced use of AI: These firms are integrating the most advanced uses cases of AI, combining multiple models, developing custom AI solutions, or using agentic and autonomous AI. At this stage, businesses are rapidly innovating with AI and, in turn, disrupting their industry.
Next-generation technologies: Next-generation technology refers to emerging, advanced innovations — such as agentic AI and edge AI — that significantly expand capabilities, autonomy, and performance beyond current mainstream systems.
Two-tier AI economy: A situation in which a small group of advanced AI adopters, especially startups, are surging ahead to capitalise on the opportunity of AI and its most advanced use cases, while the wider economy remains concentrated in basic AI adoption.
Next-generation AI technologies: Emerging AI capabilities such as agentic AI, physical AI, and advanced robotics that extend beyond traditional predictive or generative models.
Agentic AI: AI systems capable of autonomously planning, executing, and optimising tasks or workflows with limited human intervention.
AI readiness: A measure of how prepared a business is to adopt advanced and next-generation AI technologies, considering skills, infrastructure, governance, and financial capacity.
Digital skills gap: The mismatch between the digital and AI-related skills businesses require and the skills currently available within the workforce.
Methodology
The fieldwork for this study was undertaken by Strand Partners’ research team for Amazon Web Services. This research has followed the guidance set forth by the UK Market Research Society and ESOMAR. For the purposes of this study, business leaders are defined as founders, CEOs, or members of the C-suite in organisations.
‘Citizens’ are nationally representative members of the public based on the latest available census.
For inquiries regarding our methodology, please direct your questions to: polling@strandpartners.com.
For each market:
We conducted a survey targeting 1,000 nationally representative members of the public – representative by age, gender, and NUTS 1 region.
Additionally, we surveyed 1,000 business leaders, representative by their business size, sector, and NUTS 1 region. To ensure representativeness, we weighted our sample such that 94% were SMEs, 4% were mid-caps, and 2% were large enterprises.
Sampling:
Our sampling process used a mix of online panels that are recognised for their validity and reliability. These panels are carefully curated to ensure diverse representation across various demographics. For the business leaders, the panels are selected with a consideration for organisational size, sector, and position within the company. Our objective with the sampling strategy is to achieve an optimal mix that mirrors the actual distribution of our target populations in the respective markets.
Survey
This study was designed with the objective of delving deep into the digital landscape:
Usage patterns: This survey gauges the evolving patterns of digital technology usage. We are particularly interested in examining the adoption and implementation levels of technologies, focusing on cloud computing and artificial intelligence.
Perceptions and attitudes: The survey seeks to unearth the prevailing perceptions and attitudes towards digital technologies, understanding the perceived benefits, challenges, and potential ramifications of both present and emerging tech solutions.
Challenges and opportunities: The survey scrutinises the predicted challenges and potential avenues that both businesses and individuals anticipate on their digital trajectory. This involves pinpointing challenges, from skill deficits to regulatory complications, and recognising opportunities for growth, innovation, and market development.
‘Size of the prize’: The survey shed light on the economic repercussions and growth prospects linked with digital transformation. By elucidating the ‘size of the prize’, we aspire to stress the importance of digital transformation and foster further investments and technology adoption.
Weighting techniques:
Post-data collection, we applied iterative proportional weight to correct any discrepancies or over-representations in the sample.
Markets:
The markets surveyed were Belgium, the Czech Republic, Denmark, France, Finland, Germany, Greece, Ireland, Italy, the Netherlands, Norway, Poland, Portugal, Spain, Sweden, Switzerland, and the UK. Across all these markets, we have surveyed a total of 34,000 respondents, consisting of 17,000 consumers and 17,000 business leaders.