A new report by Economist Impact and Databricks has highlighted the widespread use and testing of generative AI (GenAI) among UK enterprises, with 90% of them already engaged with the technology.
The "Unlocking Enterprise AI: Opportunities and Strategies" report reveals that despite high engagement, only 40% of UK enterprises believe their GenAI applications are ready for production. Moreover, 60% have acknowledged that their GenAI use cases have yet to be deployed internally. Cost, skills, quality, and governance are identified as key obstacles, with respective challenges cited by 41%, 40%, 37%, and 33% of respondents.
Databricks' Vice President of Northern Europe, Michael Green, commented, "Whilst it's encouraging to see so many UK enterprises already using or testing GenAI, the fact that so few feel their architectures are ready for this technology is notable. There is undoubtedly enthusiasm for everything that GenAI can help enterprises achieve, but still a myriad of barriers to overcome first." He further emphasised the importance of data intelligence in overcoming these barriers, stating, "This report from Economist Impact showcases why data intelligence is essential, particularly as a solution to GenAI adoption challenges - enabling organisations to take a holistic approach to data management and governance, creating the perfect environment for GenAI use cases to flourish."
The report also highlights that by 2027, 96% of UK enterprises are expected to develop custom models based on proprietary data, underscoring a strategic shift towards integrating specific organisational data with GenAI systems.
Globally, 85% of enterprises are using or testing GenAI, but confidence in IT architecture to support AI applications is notably low, with just 22% of global enterprises feeling assured of their current systems' capabilities.
Senthil Ramani, Global Lead, Data and AI at Accenture, shared insights on the strategic value of AI: "AI can lead to gains in productivity across the workforce. And for businesses just starting out on their AI journeys, it's a logical way to measure initial progress. However, organisations aiming to become the AI leaders of tomorrow will need to capitalise on the use of the technology to drive growth, enhance customer experience, manage risk and unleash enterprise knowledge. This holistic approach will not only boost efficiency but also open new business opportunities and can attract and retain talent."
Despite the optimism around AI's capabilities, confidence in AI talent pools and governance frameworks remains tepid, with only half of UK respondents confident in securing enough AI talent and 33% citing insufficient data and AI governance. Additionally, broader adoption challenges include producing high-quality outputs and effectively connecting AI solutions to the workforce.
Tamzin Booth, Editorial Director of Economist Impact, provided commentary on these findings: "From classic machine learning to generative AI, the business world's obsession with AI isn't letting up. But our findings show that, for many organisations, the real value comes when the technology is unleashed on their own proprietary data to develop data intelligence. That data intelligence is even more valuable in an increasingly unpredictable world. To drive the algorithm advantage they're seeking, it's clear enterprises must address significant challenges with producing high-quality outputs, identify ways to evaluate performance and governance with large AI models, and work out how to effectively connect AI to the workforce."
The report includes insights from a broad survey of 1,100 technical executives and technologists across 19 countries, alongside inputs from 28 C-suite executives from a diverse range of industries. It also notes that globally, nearly half of data scientists are still utilising general-purpose large language models (LLM) without enterprise-specific data, prompting a move towards augmenting LLMs with proprietary data to enhance quality and contextuality.
