There is a significant disconnect between AI's promises and the measurable impact on UK business productivity, new research reveals.
Based on insights from 250 businesses and UK IT leaders, QLIK's research shows that despite aggressive investments in AI, many organizations struggle to translate pilot projects into productivity gains, and even fewer profits can clear financial KPIs.
Everywhere, but inconsistent
Despite widespread deployment, AI productivity continues to be inconsistent. Over half of business and IT leaders report that under 50% of AI projects have brought measurable improvements.
More concerning, only 11% of respondents said that the majority of the initiative (over 75%) had a concrete benefit.
This is exacerbated by only 51% of organizations using KPIs and only 51% of organizations using KPIs to assess AI initiatives. 44% acknowledge that teams' perceptions of increased AI productivity do not match actual results.
“AI adoptions are high, but their impact is prominent,” said James Fisher, Chief Strategy Officer at Qlik. “This gap between hype and reality is wake-up calls. Companies need to focus on measuring, alignment and building the data infrastructure that enables AI to deliver at scale.”
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If AI is marking it, it is primarily in the technical domain. The IT and cybersecurity sector are outstanding beneficiaries, with 81% of leaders reporting improvements in these areas. However, other business functions do not see the same uplift.
However, HR and finance continue to be at the margins of AI success, with 37% and 30% respectively saying that these sectors see the least specific profits. This shows that the full potential of AI is not being realized across the business, and innovation and investment is still focused on silos.
Operational gaps are the biggest barriers
Only a quarter of respondents cited the budget as the highest barrier to AI success. Instead, the biggest challenge is operational, with nearby businesses saying they lack internal skills to effectively integrate AI with existing analytics and business intelligence systems.
Other biggest concerns include incompatible tools and platforms (36%) and lack of real-time data integration (37%). These infrastructure and capabilities gaps limit the returns on AI investments, even for funded organizations.
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Projects stacked in pilot mode
The study also suggests that companies still operate with a proof-of-concept idea. Almost a quarter (23%) said that the majority of AI use cases (over 75%) are still in the experimental stage. Additionally, 11% reported that almost all initiatives are still in early-stage pilots.
This shows a major disconnect between perception and reality. Many companies think they are ready to scale, but when it comes to operating large AI, they remain stuck at the starting line.
Need clearer KPIs and better tools
Many companies struggle to effectively measure AI success. 89% agree that a unified data strategy is key to assessing ROI, but only half say there are tools to connect AI output with business performance.
There is also a strong demand for better tools and transparency. 57% say that improving data integration and analytics will help communicate AI's business value to stakeholders, while 55% want more visibility into the way AI models make decisions. Stronger collaboration between IT and other business units (49%) and KPIs focusing on clearer outcomes (46%) is also considered important to move from experiment to shock.
“To fully realize the potential of AI, companies need to go beyond experiments and focus on execution,” Fisher said. “It means scalable tools, integration strategies and collaboration across all features.”
