New research has found that while a majority of businesses are engaging with artificial intelligence, only a minority feel prepared to scale its use across their organisations.
The report, produced by Kore.ai and titled Practical Insights from AI Leaders, surveyed 1,000 senior business and technology leaders across 10 countries, examining the barriers and strategies related to AI deployment within enterprises.
The findings suggest that 71% of companies are actively using or piloting AI, a figure that indicates widespread engagement with the technology. Despite this, just 30% of organisations reported feeling equipped to move beyond pilot projects to full-scale adoption.
Barriers to scaling
The study identified several obstacles that limit readiness to scale AI. Nearly half of the leaders surveyed (44%) cited the lack of AI-skilled talent as the primary barrier to further growth. This sentiment is particularly pronounced among UK-based organisations, which are focusing on hiring and upskilling initiatives to address these gaps.
Cost uncertainty is another significant issue, with 42% of respondents highlighting the unpredictable costs associated with large language models as a challenge to expanding AI initiatives organisation-wide. Additionally, 41% remain concerned about data privacy and regulatory compliance, which is a notable issue within the UK as standards and frameworks continue to evolve.
Focus on productivity
A major trend outlined in the report is the focus on productivity as a return on investment from AI. Half of the business leaders surveyed stated that they plan to invest in AI tools designed to enhance workforce efficiency, use automation, and support information discovery.
The report also noted a strategic shift in how organisations are deploying AI. Rather than building solutions from scratch, 72% of the respondents preferred to buy and customise AI solutions. Factors such as ease of deployment, regulatory compliance, solution quality, and system integration were cited as motivators behind this preference.
Tech adoption patterns
The survey found that technologies like generative AI, large language models, and conversational AI are now in production or scaling within more mature organisations. Meanwhile, areas such as multi-modal AI, retrieval-augmented generation (RAG), and agentic AI are subjects of ongoing experimentation and pilot programmes.
"AI is no longer experimental, it's foundational. We're seeing businesses reimagine how they operate, innovate, and grow - and AI is at the heart of this transformation. The future enterprise will have AI embedded across every function, with humans and intelligent agents working side by side. To prepare for this future, organisations must prioritise data readiness, build scalable infrastructure, implement responsible governance, and invest in empowering their workforce to thrive alongside AI," said Raj Koneru, Founder and CEO of Kore.ai.
Investment priorities
When asked about key investment areas to support AI scalability, business leaders identified four main priorities: hiring skilled talent both internally and externally (66%), enhancing data quality (51%), strengthening solution security (40%), and upgrading IT infrastructure (37%).
These factors, when combined, were reportedly seen as crucial to ensuring that AI initiatives deliver sustained value across entire organisations. Approaches that prioritise these elements are also viewed as better able to meet regulatory demands and business objectives.
Strategic focus and application
For enterprises intent on scaling AI, the report emphasises the need for a focused strategy with measurable return on investment. Senior leaders listed their top criteria for successful scaling as operational efficiency, output quality, employee productivity, customer satisfaction, and reduced time-to-completion for tasks.
In terms of specific application areas, the most commonly identified priorities were business process orchestration (44%), workforce productivity enhancements (31%), and customer experience improvements (24%). Use cases include automation, risk management, task automation, information discovery, and customer self-service.
While a majority of organisations (71%) state that they are deploying or experimenting with AI across multiple departments and use cases, success is ultimately being determined by the ability to focus on appropriate applications and to establish a company-wide AI strategy from the outset.