A survey conducted in May by the London School of Economic Performance Centre and the Confederation of British Industry asked companies about their use of artificial intelligence and going green, and the results shed new light on how businesses in the UK are using AI and its impact on them. Juliana Oliveira Cunha and Bruno Serra Lorenzo and Anna Valero I will explain the results.
The crises and changes of the 2020s have brought many challenges for UK businesses, but these have come against a backdrop of stagnant productivity since the financial crisis. But crises can also force businesses to make changes that increase productivity. For example, our previous research, conducted in collaboration with the Confederation of British Industry (CBI) in 2020 and 2021, found that businesses have innovated more as a result of COVID-19. Since our last survey, the UK and other economies have experienced an energy crisis that has hit businesses and consumers hard. And in response to the ongoing climate crisis and net-zero targets, many businesses are accelerating their sustainability programs.
In May 2024, we ran a new survey. We asked companies about the lasting impact of their digital technology adoption during the COVID pandemic. We also asked a number of more detailed questions, especially about their use of artificial intelligence (AI) and the steps they are taking to transition to green. We had a sample of 400 companies across a range of size distributions, industries and geographies.
In this blog post (the first of a two-part series), we share some of our initial findings on digital adoption, and AI in particular, and provide a new perspective on how businesses are leveraging AI in the UK, their motivations, and the current and expected impact. We report simple unweighted aggregate figures here and will explore differences between businesses and drivers of adoption in future research.
Digital Adoption Through Crisis
As in our previous survey, around two-thirds of companies have adopted new digital technologies since the pandemic, and 70% of these adopters say that the pandemic has triggered or accelerated their adoption. In fact, around a quarter of adopters say that the pandemic has prompted an ongoing adoption process, significantly higher than the share who consider it a one-off change (see Figure 1). In contrast, and as expected, given the different nature of the shock, the energy crisis did not generally affect digital adoption, but it is interesting to note that more companies said that the crisis had promoted or accelerated their digital adoption (17%) than those who said it had slowed it down (6%).
Figure 1. Impact of shocks on digitalisation
Notes: Unweighted aggregates based on the sample of adopters who answered this question (269 and 264 for each shock, respectively).
Types of digital technology
We found that the most commonly adopted digital technologies in the 2020s were around online sales and marketing, remote working, cloud computing and cybersecurity, with 65-70% of businesses having invested in them (see Figure 2). AI was less prevalent, with only a quarter of sampled businesses saying they had invested in AI technologies in the 2020s, and 23% saying they had not yet invested but planned to. These figures seem broadly in line with the DCMS analysis, although as our survey is more up-to-date, AI adoption is unsurprisingly a little higher.
Figure 2. Investments in specific technologies
Note: Unweighted aggregates, based on a sample of 396 companies that responded to this question (excluding a small number of gaps that apply to some sub-technologies). “AR,VR” = Augmented Reality and Virtual Reality, IoT = “Internet of Things”, DLT = Distributed Ledger Technology (including blockchain). Note that the survey questions present a broad view of AI technologies, including chatbots, document analysis using machine learning, and generative AI.
How are businesses using AI?
AI has made rapid advances in recent years, driven by the popularity of generative AI models such as Chat GPT. However, there is still widespread debate about the economic impact of AI. Some argue that AI will usher in a new era of productivity gains, while others argue that it will primarily displace workers and increase inequality. Others are skeptical, believing that the impact on overall productivity and the labor market will take a long time to materialize.
To shed light on these issues in our research, we first sought to understand the extent to which AI use is embedded in different business functions, beyond the “broad scope” that tends to be the focus in many standard business surveys (i.e., whether a company is using AI). Figure 3 shows that AI penetration is highest in IT and marketing and sales functions, with around 30% of companies using or piloting AI and 18% planning to use it. Around 20% of companies say they are using or piloting AI in core business functions such as production, administrative, operations, and engineering activities. AI appears less prevalent so far in transportation, logistics, and facilities maintenance.
Figure 3. Use of AI by business function
Note: Unweighted aggregate values, based on a sample of 381 companies that responded to this question (excluding a small number of gaps that apply to some business functions).
Why businesses are using AI
A widespread concern in the AI debate is that it will lead to mass layoffs as companies replace workers with cheaper AI tools. However, others argue that AI will increase worker productivity by reducing the time workers spend on procedural tasks, allowing them to focus on more conceptual tasks and augmenting the workforce.
When asked why they were adopting AI, roughly 20 percent said replacing tasks previously performed by humans was a consideration (either “somewhat” or “a lot”), but twice as many said creating new or improved processes, products or services was a key consideration (Figure 4). This suggests that for our sample of companies, AI may have a more positive view when it comes to the workforce.
Figure 4. Considerations influencing AI adoption decisions
Note: Unweighted aggregate figures based on a sample of 374 companies that responded to this question (excluding a small number of gaps in responses).
Impact on businesses reported
We asked companies how they think AI will impact revenue, profitability, employee numbers, training, and the overall resilience of their business. Figure 5 shows that in all these areas, the largest number of companies say they have no impact yet. Interestingly, more companies report that they will have an impact. positive The impact across these areas, such as workforce size (9%) and training (14%), was smaller than that of companies that reported Negative The impact on these results is 3% and 4%, respectively.
Figure 5. The impact of AI on various aspects of business performance
Note: Unweighted aggregate values based on a sample of 378 companies that responded to this question (excluding a small number of gaps in some categories).
As the use of AI is still in a relatively early stage across the economy, we also asked about the expected impact. It appears that all sectors are expecting a more positive impact in the coming years. Over 40% of companies expect a positive impact on sales, profits, training and resilience (Figure 6). As noted above, increased profitability may come at the expense of worker attrition. About 22% of all companies expect a positive impact on employment. This is lower than profitability, sales, resilience and training, but still more companies expect a positive impact on employment than a negative impact. “Employment pessimists” were 13%, 9 percentage points less than employment optimists. It is noteworthy that most companies are either unsure about the impact of AI on employment or expect no impact at all.
Figure 6. Projected impact of AI on various aspects of business performance over the next 5-10 years
Note: Unweighted aggregate values based on a sample of 381 companies that responded to this question (excluding a small number of gaps in some categories).
Overall, these results indicate that companies expect the impact of AI across sectors to be more positive in the future and to be applied across different areas of business performance. In some companies there may be labor-substituting activities, while in others there will be positive impacts. Taking together the results on profits, workforce size, and training, the overall picture seems consistent with a relatively large number of companies expecting AI to increase labor productivity.
What are the barriers?
A key question for researchers and policymakers is why businesses are not adopting technologies and practices that promise to improve productivity. As in our previous survey, we asked businesses what they perceive as barriers to the adoption of digital technologies in general, and AI technologies in particular. The results are presented in Figure 7.
When it comes to digital technologies, financial constraints are the biggest issue, cited as a barrier by 55% of companies, followed by lack of information, skills constraints, and policy/technical uncertainty. These constraints also seem to be important for AI, but with AI, lack of information seems to be the bigger issue and financial constraints seem to be less of an issue. This perhaps reflects how easily AI-enabled applications have become available in recent years.
Figure 7. Barriers to adoption: AI and digital technologies overall
Note: Unweighted aggregate figures, based on a sample of 394 companies that responded to questions on digital barriers and 381 that responded to questions on AI.
I'm looking forward to
Overall, the results seem to reflect a positive view of how AI is being used and expectations for the future. The barriers to digital and AI adoption are consistent with previous research, but it is also clear that information constraints are an issue for AI as the technology is developing rapidly and its application areas are not yet fully understood.
An upcoming discussion paper will analyse the survey findings in more detail, studying differences across business types, the complementarities between AI and other digital technologies, and broader changes for businesses in the transition to net zero.
This research was funded by the Economic and Social Research Council, with additional funding from Google.
- This blog post It expresses the views of the author, and not the position of LSE Business Review or the London School of Economics and Political Science.
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