Estimated reading time: 6 minutes

Estimated reading time: 6 minutes

• AI Investment vs. Value: Despite significant AI investment and anticipated growth (32% by 2026), 89% of businesses, according to a Red Hat report, are not seeing tangible customer value from their AI efforts.
• UK’s AI Ambition and Challenges: The UK prioritizes AI, with Agentic AI being a top focus. However, a talent pipeline gap, limited funding, and insufficient private sector engagement hinder value realization.
• Barriers to AI Adoption: High costs (34%), data privacy/security (30%), and integration complexities (28%) are major hurdles. The widespread “shadow AI” (83% unauthorised use) also poses significant risks.
• Open Source as a Solution: 84% of UK organizations view enterprise open source as critical for their AI strategy, offering flexibility, control, transparency, and reducing vendor lock-in to accelerate innovation.
• Bridging the Talent & Value Gap: A persistent skills shortage, especially in agentic AI, and a lack of AI literacy across the workforce are critical. Strategic, value-driven use cases and focused training are essential to unlock AI’s potential.

• The UK’s Ambitious AI Landscape: Priorities and Paradoxes
• Navigating the Obstacle Course: Barriers and Open Source Solutions
• Bridging the Talent Gap and Cultivating a Value-Driven Culture
• Actionable Steps for Unlocking AI Value
• Conclusion: Towards Pragmatic AI Adoption
• Frequently Asked Questions (FAQ)

In an era driven by digital transformation, Artificial Intelligence (AI) has emerged as a top-tier investment priority for businesses worldwide. The promise of enhanced efficiency, innovation, and competitive advantage fuels this enthusiasm. Yet, a striking paradox persists: despite significant capital outlay, many organisations are struggling to translate their AI ambitions into tangible customer value.

A new report from Red Hat sheds critical light on this disconnect, revealing a stark reality confronting the enterprise sector. The findings are unequivocal:

“A new report from Red Hat finds that 89 percent of businesses are yet to see any customer value from their AI endeavours. However, organisations anticipate a 32 percent increase in AI investment by 2026.”

This compelling revelation underscores a significant challenge: how to bridge the chasm between experimental AI deployment and the realisation of its full, transformative potential to benefit customers directly. Understanding this gap is crucial for charting a more effective course forward for AI adoption.

The United Kingdom serves as a compelling case study of a nation with grand aspirations for AI. According to the Red Hat survey, AI and security jointly lead the list of IT priorities for UK organisations over the next 18 months, with a substantial 62 percent of respondents citing them as necessary. This dual focus highlights a clear strategic intent to both innovate through advanced technologies and robustly safeguard digital assets. Close behind these are hybrid or multi-cloud strategies and virtualisation, collectively indicating a clear and progressive direction in the technological emphasis of British businesses.

Specific areas of AI are drawing particular attention. Agentic AI, which involves systems capable of operating with a high degree of autonomy, is the top priority for 68 percent of respondents. This emphasis on intelligent automation suggests a desire to leverage AI for more complex and independent tasks, moving beyond simple automation. Furthermore, enabling broad employee adoption and operationalising AI capabilities across the enterprise are also key objectives, reflecting a commitment to integrating AI into the very fabric of business operations.

Despite this clear ambition, the path to AI integration is fraught with difficulties. Joanna Hodgson, UK Country Manager at Red Hat, aptly captures this dynamic, noting the “gap between ambition and reality”: “Organisations are investing substantially in AI but currently only a few are delivering customer value.” Her insight points directly to the critical need for robust integration strategies and a deep organisational understanding of AI’s practical applications to truly pave the road to achieving value.

A strong sense of optimism, however, pervades the UK’s outlook on its global AI standing. A significant 83 percent of respondents believe the UK is either already a global AI powerhouse or has the potential to become one within the next three years. This national confidence, though, is tempered by pragmatic concerns. A notable lack of a talent pipeline, limited public funding, and insufficient private sector engagement are collectively identified as the main factors hindering the UK from fully extracting tangible value from its substantial AI investments, creating a paradoxical blend of aspiration and apprehension.

The journey towards successful AI integration is anything but straightforward, as the Red Hat report clearly illustrates. It illuminates a landscape riddled with significant barriers that impede widespread adoption and value realisation. Foremost among these concerns are the high costs associated with both the initial implementation and the ongoing maintenance of AI systems, cited by 34 percent of respondents. This financial burden often acts as a significant deterrent, particularly for organisations with tighter budgets or those unsure of immediate return on investment (ROI).

Data privacy and security issues emerge as a close second, troubling 30 percent of businesses. As AI systems increasingly process vast quantities of sensitive information, ensuring robust data governance and protection becomes an absolute imperative. The inherent complexities of integrating AI into existing, often legacy, IT infrastructure also pose a considerable hurdle, with 28 percent of organisations struggling to weave new AI capabilities seamlessly into their current operational frameworks. These challenges highlight the acute need for adaptable, scalable, and compatible solutions that can coexist harmoniously with current enterprise systems.

A particularly intriguing, and concerning, finding is the prevalence of “shadow AI.” An alarming 83 percent of organisations report the unauthorised use of AI tools by employees. This phenomenon suggests a significant disconnect between official IT strategies and the day-to-day practices of the workforce. While individual employee initiative can sometimes drive innovation, unmanaged AI tool usage can introduce considerable security risks, data compliance issues, and operational inefficiencies, potentially undermining the very goals AI is meant to serve.

In response to these multifaceted challenges, UK organisations are increasingly recognising the strategic importance of open source software. The survey reveals that a striking 84 percent of respondents consider enterprise open source critical for their AI strategy. Similarly high figures are reported for its importance in virtualisation, hybrid and multi-cloud, and security – all areas that are intricately linked with effective AI deployment. This strong emphasis on open source reflects a pragmatic approach, offering flexibility, transparency, and a collaborative ecosystem to overcome proprietary lock-in and foster rapid innovation.

Hans Roth, SVP and GM for EMEA at Red Hat, powerfully reinforces this perspective. He states: “Organisations want greater operational control and IT resiliency to adapt in a world of constant disruption. The survey results, as well as our daily conversations, show sovereignty prominently on the agenda for enterprise’s ongoing cloud strategies and the budding AI opportunity.” He further adds, “Open-source is central to this shift as it provides businesses with the transparency and flexibility to innovate rapidly without compromise.” This viewpoint firmly positions open source as a foundational element for achieving greater control, agility, and trust in complex AI and cloud environments.

Beyond the technical and financial hurdles, the human element remains a critical determinant of AI success. The skills gap, identified as the most urgent area of concern for the second consecutive year, poses a persistent and significant challenge. Within the rapidly evolving field of AI, the talent shortage is particularly acute in agentic AI—the development, deployment, and management of highly autonomous systems. Furthermore, organisations struggle not only with the ability to efficiently use existing AI capabilities but, crucially, to educate the wider business on how to effectively leverage AI in their daily operations and strategic planning.

This deficit in expertise extends beyond mere technical proficiency to encompass a broader understanding of AI’s strategic applications and ethical implications. Without a workforce adept at both implementing and truly utilising AI, the potential for extracting customer-centric value remains largely untapped. The report also highlights that a persistent lack of a robust talent pipeline, coupled with limited public funding and insufficient private sector engagement, are significant factors collectively preventing the UK from fully capitalising on its substantial AI investments.

To illustrate the power of targeted AI application, consider a real-world scenario: a mid-sized retail chain grappling with unpredictable inventory levels and associated waste. Instead of attempting to deploy a sprawling, complex AI system across all operations simultaneously, they adopted a focused approach. They integrated an open-source AI model specifically for demand forecasting with their existing sales data. This enabled them to develop a precise forecasting tool, which in turn allowed them to optimise stock levels, drastically reduce waste, and significantly improve product availability. This directly impacted customer satisfaction through consistent product access and boosted profitability, serving as a clear instance of delivering tangible customer value from a focused, pragmatic AI endeavour.

To successfully navigate the complexities of AI adoption and truly deliver on its promise of value, organisations can adopt a strategic, multi-pronged approach:

1. Prioritise Value-Driven Use Cases: Begin with clearly defined business problems or customer needs that AI can address. Develop a robust business case that quantifies expected value before investing heavily. Focus on incremental deployments that deliver measurable benefits, enabling iterative learning and adjustment. This value-first mindset ensures AI initiatives are directly tied to strategic outcomes, avoiding the trap of technology adoption for its own sake.

2. Leverage Open Source Ecosystems for Flexibility and Control: Embrace enterprise open source software to mitigate high costs, enhance integration capabilities, and maintain vital operational control. Open source fosters collaboration, reduces vendor lock-in, and offers the transparency crucial for robust security and data governance. Building an AI strategy on an open foundation can accelerate innovation while providing the adaptability required in a rapidly changing technological landscape.

3. Invest in AI Literacy and Skill Development: Proactively address the pervasive skills gap through targeted training programs for both specialised AI engineers and the broader workforce. Foster a culture of AI literacy that encourages employees to understand, engage with, and responsibly use AI tools. Establish clear guidelines for AI tool usage to mitigate the risks associated with “shadow AI,” transforming potential liabilities into managed opportunities for innovation and efficiency.

The Red Hat report underscores a crucial paradox: despite significant investment and ambitious plans, widespread AI value remains largely elusive for many businesses. UK organisations, while optimistic about their national potential and clear in their strategic priorities, confront substantial hurdles—from high costs and complex integration to pervasive skill shortages and the risks introduced by “shadow AI.” Yet, the strong and growing emphasis on open source software points towards a pragmatic and collaborative path forward.

By embracing open platforms, strategically aligning AI initiatives with tangible business and customer value, and proactively nurturing a skilled, AI-literate workforce, businesses can effectively translate their substantial investments into measurable impact. The UK, like many other nations, stands at a pivotal moment, poised to leverage AI’s immense potential, provided it navigates these complexities with thoughtful planning, a value-driven mindset, and an open, adaptive approach.

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What is the main paradox regarding AI investment?

Despite significant capital outlay and anticipated investment growth, a new Red Hat report reveals 89% of businesses are yet to see any customer value from their AI endeavors.

What are the key IT priorities for UK organizations related to AI?

AI and security are jointly the top IT priorities for UK organizations (62%), followed by hybrid/multi-cloud strategies and virtualization. Agentic AI is a specific top priority for 68% of respondents.

What barriers are hindering successful AI integration and value realization?

Major barriers include high implementation and maintenance costs (34%), data privacy and security issues (30%), and the complexities of integrating AI into existing IT infrastructure (28%). The prevalence of “shadow AI” (unauthorised use of tools) is also a significant concern.

How can open source software help overcome AI challenges?

84% of UK organizations consider enterprise open source critical for their AI strategy. It offers flexibility, transparency, operational control, and a collaborative ecosystem, helping to mitigate high costs and proprietary lock-in while fostering rapid innovation.

What role does the skills gap play in unlocking AI value?

The skills gap is identified as the most urgent concern, particularly in agentic AI. A lack of talent, coupled with insufficient public funding and private sector engagement, prevents organisations from effectively using AI capabilities and educating their workforce on leveraging AI for customer-centric value.