Red Hat has published new survey results highlighting key drivers for cloud strategies over the next 18 months. Preparing for AI adoption was cited as a priority for 88% of UK respondents, almost level with cloud-native application development and DevOps (89%) and evolving cloud strategy in line with business objectives (89%.)
98% of UK IT managers see advantages in adopting enterprise open source solutions for AI, including predictive and generative. Accelerating innovation is seen as the top benefit of enterprise open source AI in the UK (53%), with cost-efficiency coming in second place (50%).
The research surveyed 609 IT managers from large businesses (500+ employees) across six countries: France, Germany, Italy, Spain, the UAE and the UK. It explores the priorities and challenges for IT managers as they navigate cloud complexity and the opportunities and barriers they face in executing AI strategies.
Key UK-specific findings include:
• Almost all UK IT managers surveyed (98%) consider cloud technology investment a priority for 2025. 28% are planning a strong focus on innovation and new technologies, while 61% expect balanced growth between new technologies and enhancements to existing systems. Of this subset of respondents, over half (53%) plan to increase their investment by 21-50%.
• 81% of UK respondents agree that there is an urgent skills gap in the area of AI – including data science, large language model (LLM) and generative AI – making it the top ranked skills gap.This number is up from 72% in 2023. Other urgent gaps include cybersecurity (75%), and strategic thinking and ability to tackle business-level issues (68%).
• When asked about the advantages in adopting enterprise open source solutions for AI – including predictive and generative – more than half of UK IT managers surveyed (53%) cited accelerated innovation, alongside cost-efficiency (50%), and trust and transparency (43%).
• When determining their trust in an enterprise model for generative AI, UK IT managers deem having transparent, modifiable models with explainable sources the most important factor (95%).
Cloud is an investment priority
IT managers surveyed across the six countries describe their organisation’s planned approach to investing in cloud technology by 2025:
• Half (50%) are taking a balanced approach, focusing equally on new technologies and enhancements to existing systems
• 26% have a strong focus on innovation and new technologies
• 14% are focused on essential services only
Across the six countries, respondents were asked about priority areas for their organisation's cloud strategy for the next 18 months. The following came top:
• Centralising cloud management (80%)
• Security, compliance and sovereignty regulatory requirements (78%)
• Preparing for AI adoption (77%)
• Evolving cloud strategy in line with business objectives (76%)
Siloed teams are slowing cloud adoption
96% of IT managers surveyed said siloed teams pose challenges when adopting cloud technologies, with 53% encountering this issue frequently.
Among those affected, the most common impacts on cloud strategy are:
• Inconsistent security and compliance across different providers (cited by 54%)
• Increased costs (47%)
• Limited control and visibility over cloud resources (42%)
Readiness for AI
When asked about their ability to take advantage of the growing AI opportunity, 40% of IT managers surveyed state that their organisation has scalable, flexible and accessible IT platforms but lacks the right skill sets to fully harness AI’s potential. This compares to 25% that have the right platforms and feel well-positioned to get the best value from them, and 35% who are in need of new platforms (22% of which are on a path to acquire these).
With generative AI increasingly being explored by enterprises looking to solve existing problems or seize new opportunities, the survey looked into the importance of a range of factors in determining trust in an enterprise model for generative AI. It found that the following all placed similarly among respondents:
• Transparent, modifiable models with explainable sources (cited by 89%)
• Proven performance and reliability in similar use cases (85%)
• Protection with model indemnification (84%)
• Compliance with data privacy and security standards (83%)
• Accessible for use across teams, not just data scientists (83%)
• Cost-effectiveness (82%)
• Domain-specific models (as opposed to generic LLMs) (79%)
The survey asked for the main obstacles preventing CTOs or equivalent decision makers from advancing generative AI initiatives. The top listed were:
• Concerns about data privacy and security (43%)
• Energy consumption / sustainability concerns (39%)
• Insufficient infrastructure or resources (32%)
• Lack of transparency in AI models (31%)
