AI has increasingly become a focal point in board-level discussions within UK businesses, particularly regarding growth, competitiveness, and resilience. However, a significant challenge persists in how these companies approach AI technology. Despite investing nearly £16 million on average this year, a recent study by SAP and Oxford Economics reveals that only 7% of firms have a strategic, enterprise-wide plan governing their AI investments. This fragmented and ad-hoc spending model suggests a short-term focus that may undermine long-term goals.
As a result, while some AI pilots and implementations yield benefits, many organizations struggle to scale these improvements across their operations. This disjointed approach leaves 70% of UK businesses uncertain about whether their AI initiatives are reaching their full potential. “At the moment, most AI projects are tech-led and focused on one business process or department, so they’re not necessarily aligned to the company’s strategic ambitions,” explains Sonia Nash, head of business AI at SAP UK & Ireland.
To overcome these obstacles, companies must develop a cohesive strategy that integrates data management, human resources, and governance. This will allow isolated experiments and tools to be connected and scaled effectively, leading to transformative outcomes such as enhanced customer experiences, expedited innovation, and unique product offerings. Establishing cross-functional AI governance, supported by C-level sponsorship, is critical for this integration, Nash asserts.
Organizations need to clarify their core business objectives and identify how AI can assist in achieving them. Rather than permitting teams to advance with siloed proof of concept (PoC) experiments, companies should first determine the specific business problems AI can address as part of an overarching strategy. Nash points out that while “agentic AI” is trendy, it is not always the solution; sometimes, traditional machine learning may be more appropriate.
To facilitate scaling and avoid wasted investments, effective KPIs must be established around employee adoption, cost savings, and revenue growth, among other crucial metrics. Nash emphasizes the importance of monitoring not just AI’s technical performance but its tangible business impacts. When PoCs lack clear KPIs, teams often struggle to present a compelling business case, making it harder to secure enterprise-wide investments.
Employee readiness is another significant concern, as many wish to utilize AI but often lack the necessary training and support. The SAP and Oxford Economics report indicates that 68% of organizations have staff using unapproved AI tools, and 44% have experienced data leaks related to this “shadow AI.” Nash warns, “If someone is trying to experiment with a tool for creating AI agents and plugging that into your data without having undertaken a thorough compliance check, they could be putting the company at great risk.”
Over half of the organizations surveyed acknowledge that their employees have not completed comprehensive AI training, which Nash considers essential for safe and effective experimentation. “Mandatory training is going to address the two sides of the coin,” she explains. Training not only helps employees understand the risks associated with unapproved tools but also showcases the approved tools available for their use.
Organizations must also create a culture that encourages transparent communication about AI’s benefits, ensuring that employees understand how AI will reshape business processes. Nash stresses that workers should feel comfortable raising questions or reporting issues without fear. Concerns regarding job security must also be openly addressed, as many employees worry about AI rendering their tasks obsolete. Leaders must clarify that the AI strategy is designed to “augment and help humans, not replace them.”
Fostering grassroots AI communities can enhance enthusiasm for new tools by enabling collaboration among employees, promoting knowledge sharing, and facilitating feedback. “The message that AI is here to help needs to come from both ends of the spectrum to ensure you have safe, compliant use of AI,” Nash states. “If it’s not utilized by your workforce, your project has failed.”
A critical aspect of AI deployment hinges on having accurate and connected data. Businesses are working to build robust data foundations to support their AI initiatives, yet many still face challenges in breaking down information silos. While AI pilots may succeed in controlled environments, broader organizational issues can hinder scaling. “Poor data quality and accessibility is one of the big barriers to bringing PoCs into a production environment,” Nash notes.
However, solutions may be closer than many companies anticipate. Nash highlights available tools that help assess data quality and address deficiencies, enabling businesses to establish a solid foundation. Transitioning disconnected legacy systems and data into the cloud can also mitigate data challenges and facilitate end-to-end AI usage. “AI is now putting the move to the cloud at the forefront of people’s minds,” she says, as cloud infrastructure promotes faster innovation and dismantles data silos.
Contrary to a common misconception, Nash advises against simply aggregating data into a lake, as this can lead to outdated and contextually weak datasets. “Having a unified data fabric is key to get the best out of agentic AI,” she asserts, noting that AI tools require a comprehensive understanding of data relationships to deliver impactful results. SAP’s Business Data Cloud aims to connect various data sources into a single, curated layer, providing a reliable foundation for AI.
UK businesses anticipate that their return on AI investments will nearly double over the next two years, from 17% in 2025 to 32% in 2027. However, this ambitious goal will necessitate a shift from fragmented attempts to a unified, strategic approach to AI deployment. In conclusion, UK firms must not only increase their AI spending but also ensure that investments are made wisely, integrating scattered experiments, training employees, and connecting fragmented data to derive genuinely transformative results.
For more information, visit sap.com/uk
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