Still a long way to improve digital technology adoption in the UK
A new survey by Silvia Massini and Mabel Sanchez Barrioluengo provides an analysis of the adoption and impact of advanced digital technologies (ADTs), including AI, Big Data, Cloud Computing, 3D Printing, Internet of Things (IoT), and Robotics, as well as digital platforms in the UK.
By Professor Silvia Massini and Dr Mabel Sanchez-Barrioluengo
In December 2024, the Department for Science, Innovation and Technology launched a Technology Adoption Review. This review seeks evidence across eight strategic growth sectors identified in the Industrial Strategy green paper: advanced manufacturing, clean energy industries, creative industries, defence, digital and technologies, financial services, life sciences and professional and business services. The primary goal of the review is to identify the most significant barriers to technology adoption and to develop recommendations that could help the government in advancing the UK’s growth mission.
But, how are UK businesses navigating the digital transformation? In a new study, Adoption of Advanced Digital Technologies and Platforms: Insights from a UK national survey, we provide evidence on the varying rates of adoption of advanced digital technologies (ADTs). The main results suggest that adoption rates and utilisation intensity vary significantly across technology, regions, sectors, and company sizes. These findings provide valuable insights for managers and policymakers seeking to understand the current landscape of digital technology adoption and identify strategic opportunities to enhance the effectiveness and reach of these innovations.
Current state of diffusion of ADTs in the UK
Our survey, the “National Adoption of Digital Technologies and Skills (N-ADiTS) survey 2024”, shows that 90% of the firms in the UK have adopted at least one of the following ADTs: AI, Big Data, Cloud Computing, 3D Printing, Internet of Things (IoT), or Robots. While ADTs are widely diffused across the UK, the diffusion stages of different ADTs vary.
The figure below shows that Cloud Computing is the most widely adopted, with four out of five companies utilising it. Its wide reach stems from its flexibility and broad applicability, benefitting areas such as storage to collaboration and data analytics. High Cloud usage covers all regions and sectors, including both SMEs and large companies. AI ranks second in terms of usage, with nearly half of the companies adopting it. Big Data ranks third, with a 37% adoption rate. Similar patterns of usage are observed for IoT and Robotics, though their overall adoption rates are lower.
The survey also shows that ADTs are not adopted in isolation. Almost one out of four companies have adopted at least two digital technologies. The most common combinations are Cloud Computing and AI, and Cloud Computing and Big Data, reinforcing the importance of data and signalling a strong move toward digital-first business processes.
Figure 1. Adoption of ADTs per type of technology
Note: Weighted sample (n = 3,175).
The spatial patterns and diffusion processes of these ADTs are heterogeneous. While Cloud Computing is adopted by at least three-quarters of companies in every region, London outpaces all other regions for the other technologies. For example, London shows an adoption rate for AI at 67%, followed by the South East (51%), the West Midlands (48%), and the North West (47%). In other regions, AI adoption ranges from 32% (North East) to 44% (East of England). Big Data is primarily adopted in London (57%) and the North West (40%), followed closely by Scotland (36%), the East Midlands (35%), and the South East (35%). IoT adoption is also highest in London (40%), followed by Northern Ireland (32%), and the North West (25%), with lower adoption rates in other regions. Robotics and 3D Printing have lower adoption rates, with the highest levels in London (28% and 30% respectively).
‘Larger firms adopt and use these technologies more intensively, while adoption rates and usage vary significantly across regions and sectors, with the adoption rate notably higher in regions with established business hubs, such as London.’
Sector-wise, the utilities and extractive sectors lead in ADTs adoption, with Cloud Computing standing out, followed by Big Data (72%), AI (64%), and IoT (57%). Nearly half of companies in this sector also adopt 3D Printing and Robotics. In manufacturing, ADTs adoption is more evenly distributed, with AI adopted by about 50% and IoT by one-third of companies. Robotics is adopted by almost half of manufacturing companies. In contrast, ADTs adoption in services is more polarized, with high adoption of Cloud Computing (81%), followed by AI (47%) and Big Data (35%). In construction, Cloud Computing presents the highest adoption rate compared to other sectors (83%). When examining the intensity of use (low, moderate, high) of the different technologies, there is a consistent pattern of moderate use across all sectors and ADTs, with AI showing particularly low intensity levels. This suggests that there is still room for increasing the use of ADTs within the different sectors.
In terms of company size, larger businesses generally show higher ADTs adoption rates. Smaller firms adopt AI less (one in three) than larger firms (almost two out of three). Usage intensity also increases with company size, with larger firms using ADTs at higher intensity.
What are the barriers for ADTs adoption?
Some of the most significant barriers to ADTs adoption include the immaturity of the technology, high costs, security concerns, and a lack of access to skilled personnel. Despite the widespread adoption of Cloud Computing, some businesses refrain from adopting it primarily because of safety and security concerns (20%). Among non-adopters, AI is often considered insufficiently mature (24%), followed by concerns over safety/security (17%) and a lack of human capital or skills (15%). Big Data adoption is more often hindered by a lack of human capital (11%) than by cost concerns (9%). Meanwhile, the costs associated with 3D Printing and Robotics are cited as barriers (6% for 3D Printing, 7% for Robotics) more often than skill shortages (3% for 3D Printing, 4% for Robotics). IoT adoption is somewhat hampered by safety/security concerns (5%) slightly more than by other factors. The concerns around safety and security of ADTs call for further data regulations, as suggested by the OECD (2019), urging governments to prioritise the development of policies addressing data access and strengthen the responsiveness and agility of policies in view of rapidly changing contexts, to include security as well as ethical considerations around AI.
‘High cost and maturity of the technology, lack of access to workers with the relevant skills and concerns over security are significant barriers to adopting ADTs.’
Figure 2. Barriers to adoption across all Advanced Digital Technologies
Note: Weighted sample (barriers: n = 2,773)
Impact of adoption of ADTs on productivity and other outcomes
Companies that adopt ADTs also report their effect on productivity-related and other outcomes. The data show that nearly 50% of adopters report improved service quality, and just above one-third report enhanced product quality. Companies utilising Cloud Computing, Big Data, and 3D Printing particularly benefit in terms of increased product and service diversification and higher production volumes. This indicates that the adoption of ADTs allows companies to expand their offerings and reach a broader customer base. While many ADTs are introduced to improve operational efficiencies, some ADTs, like AI and Robotics, also introduce cost and time pressures, with a noticeable percentage of adopters reporting increased production costs and longer delivery times. As our current data cannot tell, future analysis could explore whether these challenges are temporary or indicative of a more systematic or deeper failure in the technologies. At the same time, there can be substantial increases in production costs and the cost of processes associated to the adoption or technologies like Cloud Computing and 3D Printing. This, in turn, may result in increasing the selling price of the goods and services.
‘ADTs adoption generates skill enhancement and positively affects productivity-related and other outcomes, although there is mixed impact depending on the specific technology.’
There are two potential explanations for the observed increase in production and process costs. First, they could reflect the initial challenges associated with adopting complex technologies. Companies often face learning curves, adaptation hurdles, and the need to restructure business processes to integrate new technologies effectively. Additionally, firms may need to continuously refine these processes to strike a balance between cost and efficiency. Second, the rising costs could be a result of companies adopting a technology without a clear strategic plan. Some firms may take a trial-and-error approach, implementing various solutions without fully understanding their long-term impact—essentially throwing everything to the wall to see what sticks. Others might hesitate to fully commit due to uncertainty about the outcomes, leading to cautious or fragmented adoption rather than a streamlined and intentional integration strategy.
Table 1. Impact of ADTs on productivity-related and other outcomes among adopters
| Any ADT | AI | Big Data | Cloud Computing | 3D Printing | IoT | Robotics | ||||||||
| ⇑ | ⇓ | ⇑ | ⇓ | ⇑ | ⇓ | ⇑ | ⇓ | ⇑ | ⇓ | ⇑ | ⇓ | ⇑ | ⇓ | |
| Production costs/cost of processes | 21% | 26% | 32% | 23% | 37% | 19% | 28% | 20% | 52% | 17% | 42% | 16% | 41% | 25% |
| Selling price of goods and/or service | 22% | 6% | 35% | 4% | 44% | 5% | 27% | 4% | 57% | 6% | 51% | 5% | 46% | 6% |
| Volume of production | 31% | 3% | 48% | 2% | 50% | 3% | 38% | 2% | 62% | 4% | 53% | 3% | 66% | 3% |
| Product diversification | 27% | 3% | 44% | 2% | 51% | 3% | 32% | 2% | 67% | 4% | 54% | 3% | 52% | 2% |
| Quality of product | 36% | 2% | 53% | 3% | 63% | 2% | 43% | 2% | 69% | 5% | 58% | 2% | 59% | 4% |
| Time to deliver product | 21% | 27% | 35% | 23% | 36% | 22% | 25% | 22% | 53% | 18% | 39% | 22% | 43% | 26% |
| Service diversification | 27% | 3% | 45% | 3% | 52% | 2% | 34% | 1% | 64% | 5% | 57% | 2% | 54% | 2% |
| Quality of service | 46% | 3% | 58% | 3% | 67% | 2% | 57% | 2% | 69% | 4% | 64% | 2% | 65% | 3% |
| Time to deliver services | 23% | 28% | 41% | 25% | 37% | 22% | 28% | 25% | 53% | 15% | 44% | 17% | 46% | 23% |
| Number of customers | 28% | 3% | 42% | 3% | 52% | 3% | 34% | 2% | 60% | 4% | 51% | 3% | 49% | 3% |
| Types of customers | 21% | 2% | 34% | 2% | 47% | 2% | 27% | 2% | 59% | 3% | 51% | 2% | 48% | 3% |
| n (weighted) | 2,858 | 1,516 | 1,174 | 2,559 | 513 | 7,88 | 570 | |||||||
Note: This table presents the percentages of firms that declared increases or decreases in the factors in employment and productivity-related and other outcomes. Firms could select from five categories to describe the impact of adoption of ADTs: 5 – “increased considerably”, 4 – “increased”, 3 – “did not change”, 2 – “decreased” and 1 – “decreased considerably”, for each particular statement. To present the effect of adopting a specific type of ADTs we classify 1 and 2 as “decrease” (⇓), and 4 and 5 as “increase” (⇑). The percentages for “any ADT” are based on the average impact of all the technologies adopted by a respondent: a factor of employment or productivity-related and other outcomes is considered “decreased” for the adopter if the average impact is between 1 and 3, and is considered “increased” if the average is between 4 and 5. Those that selected “did not change” are not reported in the table. Weighted sample (Any DT: n = 2,858; AI: n = 1,516; Big Data: n = 1,174; Cloud Computing: n = 2,559; 3D printing: n = 513; IoT: n = 788; Robotics: n = 570).
Leveraging strengths, specialising regionally, and focusing on skills
Our research offers valuable insights that can inform the aforementioned Technology Adoption Review. First, a national Digital Technology Strategy should stress the importance of leveraging current technology strengths, recognise different stages of technology diffusion, and support the adoption of multiple technologies to benefit from their complementarities. Second, the Strategy should incentivise regional specialisation in specific ADTs building on available skills and technological capabilities. For this, it is important to design policy instruments that are technology specific, recognising that barriers to adoption vary by technology, place, industrial sector and company size. Finally, the adoption of ADTs should follow a human-centric approach by ensuring that skills play a key role in the adoption of ADTs. This requires coordination with education-related policies and training schemes to develop specific skills associated with particular ADTs, enhancing their potential for adoption and increased productivity.
