UK organisations boost GenAI investment

More than 9 in 10 (92 per cent) UK organisations do not reach mature production in GenAI, despite boosts in investment.

Research from Enterprise Strategy Group (ESG) reveals that while 70 per cent of global organisations, including those in the UK, have increased investment in generative AI (GenAI) over the past year, only 8 per cent have deployed the technology in mature production environments.

The study, which surveyed 832 IT decision-makers worldwide, highlights that many organisations are still in early stages of GenAI adoption, with less than a third (27 per cent) reporting early or mature production deployment.

The number of firms running pilot and proof-of-concept GenAI projects has surged by 22 per cent compared to 2023. GenAI is being used in 3.5 application areas on average, with software development (41 per cent), IT operations, and cybersecurity seeing the highest adoption.

Libero Raspa, Managing Director of adesso UK, commented: “GenAI is showing no signs of slowing down, so it’s important that enterprises embrace AI within their operations by deploying tools and systems that can boost overall productivity. When adopting GenAI, it’s crucial to include sandboxing and piloting for models and tools to minimise risks and better understand the impact of data. While the volume of public data is vast, its quality can be inconsistent, which directly impacts the outputs of GenAI models. This highlights the importance of using internal data, particularly for ensuring accuracy, achieving meaningful outcomes, gaining competitive differentiation, and, most importantly, ensuring data security and compliance. Collaborating with trusted business partners who have already navigated the GenAI path is an effective way to instil confidence in AI adoption and address the multitude of questions surrounding new technology solutions.”

In the UK, IT departments are leading GenAI deployment efforts, focusing on boosting productivity and operational efficiency. Many organisations are beginning to recognise the importance of training custom large language models (LLMs) with proprietary data to maintain a competitive edge in the evolving AI landscape.

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