- HR and Finance don’t see good results from AI, Report Fund
- Only 11% see concrete gains from most of their AI initiatives
- A unified data strategy with improved integration and analysis is needed
New research has claimed that AI investments in British companies are still not translated into consistent or measurable returns, suggesting that many companies should not yet develop from their experimentation to implementation phases as they are struggling to prepare effective use cases.
This is coming as many sectors are still struggling to see real results from AI tools, with 37% of HR and 30% of the financing companies examined by QLIK that say they see the least tangible benefits.
This is compared to the four out of five (81%) IT and cyber security departments that have seen improvements.
AI -Investments are not translated directly into results
Qlik also found that most companies are still stuck in pilot phases, lacking tools and skills to scale AI impact.
Only one in 10 (11%) companies report that most (75%+) of their AI-Initiatives have delivered concrete gains, with about a quarter (23%) that acknowledge that most of their AI-use case is still in the experimental phase.
Almost half (44%) also admitted that there is an interruption between perceived and actual productivity gains from AI, with a similar number (51%) that evaluate AI using KPIs bound directly to business results, rather than developing their measurements for the changing tech landscape.
“This gap between hype and reality is a wake-up call. Companies have to focus on measurement, adaptation and construction of the data infrastructure that allows AI to deliver on scale,” explained Qlik Chief Strategy Officer is James Fisher.
Lack of internal skills almost affects one in two (49%) companies with technical problems such as incompatible tools and platforms (36%) and a lack of real -time data integration (37%) that also prove to be worrying. Obviously, architecture and data fundament still hold back many companies while the budget becomes less of a problem.
Looking ahead, 89% agree that a total data strategy is important to assess ROI. Many also agreed that improved data integration and analysis (57%), greater visibility in how AI models make decisions (55%), strong cooperation across departments (49%) and performance-focused KPIs (46%) are impotent to deliver real AI impact.
“It means scalable tools, integrated strategies and collaboration across any function,” Fisher concluded.
